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In recent years, the growing pervasiveness of wearable technology has created new opportunities for medical and emergency rescue operations to protect users' health and safety, such as cost-effective medical solutions, more convenient healthcare and quick hospital treatments, which make it easier for the Internet of Medical Things (IoMT) to evolve. The study first presents an overview of the IoMT before introducing the IoMT architecture. Later, it portrays an overview of the core technologies of the IoMT, including cloud computing, big data and artificial intelligence, and it elucidates their utilization within the healthcare system. Further, several emerging challenges, such as cost-effectiveness, security, privacy, accuracy and power consumption, are discussed, and potential solutions for these challenges are also suggested.

In recent years, the growing pervasiveness of wearable technology has created new opportunities for medical and emergency rescue operations to protect users' health and safety, such as cost-effective medical solutions, more convenient healthcare and quick hospital treatments, which make it easier for the Internet of Medical Things (IoMT) to evolve. The study first presents an overview of the IoMT before introducing the IoMT architecture. Later, it portrays an overview of the core technologies of the IoMT, including cloud computing, big data and artificial intelligence, and it elucidates their utilization within the healthcare system. Further, several emerging challenges, such as cost-effectiveness, security, privacy, accuracy and power consumption, are discussed, and potential solutions for these challenges are also suggested.

This thesis presents a critical examination of the positive impact of Machine Learning (ML) and the Internet of Medical Things (IoMT) for advancing the Clinical Decision Support System (CDSS) in the context of COVID-19 and early sepsis detection. It emphasizes the transition towards patient-centric healthcare systems, which necessitate personalized and participatory care—a transition that could be facilitated by these emerging fields. The thesis accentuates how IoMT could serve as a robust platform for data aggregation, analysis, and transmission, which could empower healthcare providers to deliver more effective care. The COVID-19 pandemic has particularly stressed the importance of such patient-centric systems for remote patient monitoring and disease management. The integration of ML-driven CDSSs with IoMT is viewed as an extremely important step in healthcare systems that could offer real-time decision-making support and enhance patient health outcomes. The thesis investigates ML's capability to analyze complex medical datasets, identify patterns and correlations, and adapt to changing conditions, thereby enhancing its predictive capabilities. It specifically focuses on the development of IoMT-based CDSSs for COVID-19 and early sepsis detection, using advanced ML methods and medical data. Key issues addressed cover data annotation scarcity, data sparsity, and data heterogeneity, along with the aspects of security, privacy, and accessibility. The thesis also intends to enhance the interpretability of ML prediction model-based CDSSs. Ethical considerations are prioritized to ensure adherence to the highest standards. The thesis demonstrates the potential and efficacy of combining ML with IoMT to enhance CDSSs by emphasizing the importance of model interpretability, system compatibility, and the integration of multimodal medical data for an effective CDSS. Overall, this thesis makes a significant contribution to the fields of ML and IoMT in healthcare, featuring th, Denna avhandling presenterar en kritisk granskning av den positiva effekten av maskininlärning (ML) och Internet of Medical Things (IoMT) för att främja det kliniska beslutsstödsystemet (CDSS) kopplat till covid-19 och tidig upptäckt av sepsis. Avhandlingen betonar övergången mot patientcentrerade vårdsystem som kräver personlig och deltagande vård – en övergång som skulle kunna underlättas av dessa framväxande områden. Studien visar hur IoMT kan fungera som en robust plattform för dataaggregering, analys och överföring, vilket kan ge vårdgivare möjlighet att erbjuda mer effektiv vård. Covid-19-pandemin har särskilt betonat vikten av sådana patientcentrerade system för fjärrövervakning av patienter och sjukdomshantering. Integreringen av ML-drivna CDSS med IoMT ses som ett extremt viktigt steg i vårdsystemen som kan erbjuda stöd för beslutsfattande i realtid och förbättra patienternas hälsoutfall. Avhandlingen undersöker maskininlärningens förmåga att analysera komplexa medicinska dataset, identifiera mönster och korrelationer, samt göra anpassningar till föränderliga förhållanden, vilket därmed förbättrar dess prediktiva förmågor. Den fokuserar specifikt på utvecklingen av IoMT-baserade CDSS för covid-19 och tidig upptäckt av sepsis, med användning av avancerade ML-metoder och medicinska data. Nyckelfrågor som adresseras täcker bristen på dataannotering, dataspridning och dataheterogenitet, tillsammans med aspekter av säkerhet, integritet och tillgänglighet. Avhandlingen avser också att förbättra tolkbarheten av ML-prediktionsmodellbaserade CDSS. Etiska överväganden prioriteras för att säkerställa efterlevnad av de högsta standarderna. Avhandlingen demonstrerar potentialen och effektiviteten i att kombinera ML med IoMT för att förbättra CDSS genom att betona vikten av modelltolkbarhet, systemkompatibilitet och integrering av multimodala medicinska data för ett effektivt CDSS. Sammantaget bidrar denna avhandling till områdena ML och IoMT inom hälsovården, med deras

In recent years, the growing pervasiveness of wearable technology has created new opportunities for medical and emergency rescue operations to protect users' health and safety, such as cost-effective medical solutions, more convenient healthcare and quick hospital treatments, which make it easier for the Internet of Medical Things (IoMT) to evolve. The study first presents an overview of the IoMT before introducing the IoMT architecture. Later, it portrays an overview of the core technologies of the IoMT, including cloud computing, big data and artificial intelligence, and it elucidates their utilization within the healthcare system. Further, several emerging challenges, such as cost-effectiveness, security, privacy, accuracy and power consumption, are discussed, and potential solutions for these challenges are also suggested.

In recent years, the growing pervasiveness of wearable technology has created new opportunities for medical and emergency rescue operations to protect users' health and safety, such as cost-effective medical solutions, more convenient healthcare and quick hospital treatments, which make it easier for the Internet of Medical Things (IoMT) to evolve. The study first presents an overview of the IoMT before introducing the IoMT architecture. Later, it portrays an overview of the core technologies of the IoMT, including cloud computing, big data and artificial intelligence, and it elucidates their utilization within the healthcare system. Further, several emerging challenges, such as cost-effectiveness, security, privacy, accuracy and power consumption, are discussed, and potential solutions for these challenges are also suggested.

As the IoMT rapidly expands, severe security risks shadow its profound benefits inpatient monitoring and data management. These devices, integral to critical care like pace-maker shocks and insulin dosing, often sacrifice robust security for functionality due totheir limited capabilities. This critical vulnerability exposes them to exploits that couldhave fatal consequences. This thesis addresses these urgent security gaps by exploring in-novative protection strategies through systematic reviews and simulated penetration testingon a mimicked IoMT environment. Our findings expose pronounced deficiencies withinexisting security frameworks, focusing on Bluetooth LE and Wi-Fi threats, especially theinadequate mechanisms to secure Bluetooth LE connections, commonly used in IoMTdevices and DOS attacks targeted directly to the IoMT devices. In response, two novelsecurity algorithms were designed to enhance the resilience of IoMT systems against cy-ber threats. This algorithm integrates dynamic whitelisting and blacklisting, MAC addressverification, UDID verification, and NFC-based device authentication to curtail unautho-rized access and uphold data integrity. The adopted strategy not only addresses specificsecurity loopholes identified during penetration testing but also establishes a frameworkcapable of adapting to evolving threats. Through this research, we aim to contribute to theongoing discourse on IoMT security, underscoring the critical need for continuous adapta-tion of security measures to protect against emerging vulnerabilities in the rapidly evolvinglandscape of IoT devices. This work aspires to lay the groundwork for future research anddevelopment in IoMT security strategies, fostering a deeper understanding and implemen-tation of adequate security measures within medical technology.

This thesis presents a critical examination of the positive impact of Machine Learning (ML) and the Internet of Medical Things (IoMT) for advancing the Clinical Decision Support System (CDSS) in the context of COVID-19 and early sepsis detection. It emphasizes the transition towards patient-centric healthcare systems, which necessitate personalized and participatory care—a transition that could be facilitated by these emerging fields. The thesis accentuates how IoMT could serve as a robust platform for data aggregation, analysis, and transmission, which could empower healthcare providers to deliver more effective care. The COVID-19 pandemic has particularly stressed the importance of such patient-centric systems for remote patient monitoring and disease management. The integration of ML-driven CDSSs with IoMT is viewed as an extremely important step in healthcare systems that could offer real-time decision-making support and enhance patient health outcomes. The thesis investigates ML's capability to analyze complex medical datasets, identify patterns and correlations, and adapt to changing conditions, thereby enhancing its predictive capabilities. It specifically focuses on the development of IoMT-based CDSSs for COVID-19 and early sepsis detection, using advanced ML methods and medical data. Key issues addressed cover data annotation scarcity, data sparsity, and data heterogeneity, along with the aspects of security, privacy, and accessibility. The thesis also intends to enhance the interpretability of ML prediction model-based CDSSs. Ethical considerations are prioritized to ensure adherence to the highest standards. The thesis demonstrates the potential and efficacy of combining ML with IoMT to enhance CDSSs by emphasizing the importance of model interpretability, system compatibility, and the integration of multimodal medical data for an effective CDSS. Overall, this thesis makes a significant contribution to the fields of ML and IoMT in healthcare, featuring th, Denna avhandling presenterar en kritisk granskning av den positiva effekten av maskininlärning (ML) och Internet of Medical Things (IoMT) för att främja det kliniska beslutsstödsystemet (CDSS) kopplat till covid-19 och tidig upptäckt av sepsis. Avhandlingen betonar övergången mot patientcentrerade vårdsystem som kräver personlig och deltagande vård – en övergång som skulle kunna underlättas av dessa framväxande områden. Studien visar hur IoMT kan fungera som en robust plattform för dataaggregering, analys och överföring, vilket kan ge vårdgivare möjlighet att erbjuda mer effektiv vård. Covid-19-pandemin har särskilt betonat vikten av sådana patientcentrerade system för fjärrövervakning av patienter och sjukdomshantering. Integreringen av ML-drivna CDSS med IoMT ses som ett extremt viktigt steg i vårdsystemen som kan erbjuda stöd för beslutsfattande i realtid och förbättra patienternas hälsoutfall. Avhandlingen undersöker maskininlärningens förmåga att analysera komplexa medicinska dataset, identifiera mönster och korrelationer, samt göra anpassningar till föränderliga förhållanden, vilket därmed förbättrar dess prediktiva förmågor. Den fokuserar specifikt på utvecklingen av IoMT-baserade CDSS för covid-19 och tidig upptäckt av sepsis, med användning av avancerade ML-metoder och medicinska data. Nyckelfrågor som adresseras täcker bristen på dataannotering, dataspridning och dataheterogenitet, tillsammans med aspekter av säkerhet, integritet och tillgänglighet. Avhandlingen avser också att förbättra tolkbarheten av ML-prediktionsmodellbaserade CDSS. Etiska överväganden prioriteras för att säkerställa efterlevnad av de högsta standarderna. Avhandlingen demonstrerar potentialen och effektiviteten i att kombinera ML med IoMT för att förbättra CDSS genom att betona vikten av modelltolkbarhet, systemkompatibilitet och integrering av multimodala medicinska data för ett effektivt CDSS. Sammantaget bidrar denna avhandling till områdena ML och IoMT inom hälsovården, med deras

This thesis presents a critical examination of the positive impact of Machine Learning (ML) and the Internet of Medical Things (IoMT) for advancing the Clinical Decision Support System (CDSS) in the context of COVID-19 and early sepsis detection. It emphasizes the transition towards patient-centric healthcare systems, which necessitate personalized and participatory care—a transition that could be facilitated by these emerging fields. The thesis accentuates how IoMT could serve as a robust platform for data aggregation, analysis, and transmission, which could empower healthcare providers to deliver more effective care. The COVID-19 pandemic has particularly stressed the importance of such patient-centric systems for remote patient monitoring and disease management. The integration of ML-driven CDSSs with IoMT is viewed as an extremely important step in healthcare systems that could offer real-time decision-making support and enhance patient health outcomes. The thesis investigates ML's capability to analyze complex medical datasets, identify patterns and correlations, and adapt to changing conditions, thereby enhancing its predictive capabilities. It specifically focuses on the development of IoMT-based CDSSs for COVID-19 and early sepsis detection, using advanced ML methods and medical data. Key issues addressed cover data annotation scarcity, data sparsity, and data heterogeneity, along with the aspects of security, privacy, and accessibility. The thesis also intends to enhance the interpretability of ML prediction model-based CDSSs. Ethical considerations are prioritized to ensure adherence to the highest standards. The thesis demonstrates the potential and efficacy of combining ML with IoMT to enhance CDSSs by emphasizing the importance of model interpretability, system compatibility, and the integration of multimodal medical data for an effective CDSS. Overall, this thesis makes a significant contribution to the fields of ML and IoMT in healthcare, featuring th, Denna avhandling presenterar en kritisk granskning av den positiva effekten av maskininlärning (ML) och Internet of Medical Things (IoMT) för att främja det kliniska beslutsstödsystemet (CDSS) kopplat till covid-19 och tidig upptäckt av sepsis. Avhandlingen betonar övergången mot patientcentrerade vårdsystem som kräver personlig och deltagande vård – en övergång som skulle kunna underlättas av dessa framväxande områden. Studien visar hur IoMT kan fungera som en robust plattform för dataaggregering, analys och överföring, vilket kan ge vårdgivare möjlighet att erbjuda mer effektiv vård. Covid-19-pandemin har särskilt betonat vikten av sådana patientcentrerade system för fjärrövervakning av patienter och sjukdomshantering. Integreringen av ML-drivna CDSS med IoMT ses som ett extremt viktigt steg i vårdsystemen som kan erbjuda stöd för beslutsfattande i realtid och förbättra patienternas hälsoutfall. Avhandlingen undersöker maskininlärningens förmåga att analysera komplexa medicinska dataset, identifiera mönster och korrelationer, samt göra anpassningar till föränderliga förhållanden, vilket därmed förbättrar dess prediktiva förmågor. Den fokuserar specifikt på utvecklingen av IoMT-baserade CDSS för covid-19 och tidig upptäckt av sepsis, med användning av avancerade ML-metoder och medicinska data. Nyckelfrågor som adresseras täcker bristen på dataannotering, dataspridning och dataheterogenitet, tillsammans med aspekter av säkerhet, integritet och tillgänglighet. Avhandlingen avser också att förbättra tolkbarheten av ML-prediktionsmodellbaserade CDSS. Etiska överväganden prioriteras för att säkerställa efterlevnad av de högsta standarderna. Avhandlingen demonstrerar potentialen och effektiviteten i att kombinera ML med IoMT för att förbättra CDSS genom att betona vikten av modelltolkbarhet, systemkompatibilitet och integrering av multimodala medicinska data för ett effektivt CDSS. Sammantaget bidrar denna avhandling till områdena ML och IoMT inom hälsovården, med deras

Over the last decade, with the increasing popularity and usage of the internet of things worldwide, Internet of Medical Things (IoMT) has emerged as a key technology of the modern era. IoMT uses Artificial Intelligence, 5G, big data, edge computing, and blockchain to provide users with electronic medical services. However, it may face several security threats and attacks over an insecure public network. Therefore, to protect sensitive medical data in IoMT, it is necessary to design a secure and efficient authentication protocol. In this study, we propose a privacy-preserving authentication management protocol based on blockchain. The protocol uses a blockchain to store identities and related parameters to assist communication entities in authentication. In addition, the protocol adopts a three-factor authentication method and introduces Chebyshev chaotic map to ensure the security of user login and authentication. Formal security proof and analysis using the random oracle model and Burrows-Abadi-Needham logic show that the proposed protocol is secure. Moreover, we use informal security analysis to demonstrate that the protocol can resist various security attacks. The functional comparison shows that the protocol has high security. Through performance analysis and comparison with other protocols, the proposed protocol can increase computation overhead, communication overhead, and storage overhead by up to 39.8%, 93.6%, and 86.7%,respectively. © 2023 Elsevier Ltd

In recent years, the growing pervasiveness of wearable technology has created new opportunities for medical and emergency rescue operations to protect users' health and safety, such as cost-effective medical solutions, more convenient healthcare and quick hospital treatments, which make it easier for the Internet of Medical Things (IoMT) to evolve. The study first presents an overview of the IoMT before introducing the IoMT architecture. Later, it portrays an overview of the core technologies of the IoMT, including cloud computing, big data and artificial intelligence, and it elucidates their utilization within the healthcare system. Further, several emerging challenges, such as cost-effectiveness, security, privacy, accuracy and power consumption, are discussed, and potential solutions for these challenges are also suggested.

Electrocardiogram (ECG) is the main criterion for arrhythmia detection. As a means of identification, ECG leakage seems to be a common occurrence due to the development of the Internet of Medical Things (IoMT). The advent of the quantum era makes it difficult for classical blockchain technology to provide security for ECG data storage. Therefore, from the perspective of safety and practicality, this article proposes a quantum arrhythmia detection system named QADS, which achieves secure storage and sharing of ECG data based on quantum blockchain technology. Furthermore, a quantum neural network is used in QADS to recognize abnormal ECG data, which contributes to further cardiovascular disease diagnosis. Each quantum block stores the hash of the current and previous block to construct a quantum block network. The new quantum blockchain algorithm introduces a controlled quantum walk hash function and a quantum authentication protocol to guarantee legitimacy and security while creating new blocks. In addition, this article constructs a hybrid quantum convolutional neural network nameded HQCNN to extract the temporal features of ECG to detect abnormal heartbeats. The simulation experimental results show that HQCNN achieves an average training and testing accuracy of 94.7% and 93.6%. And the detection stability is much higher than classical CNN with the same structure. HQCNN also has certain robustness under the perturbation of quantum noise. Besides, this article demonstrates through mathematical analysis that the proposed quantum blockchain algorithm has strong security and can effectively resist various quantum attacks, such as external attacks, Entanglement-Measure attack and Interception-Measurement-Repeat attack. © IEEE, Funding: National Natural Science Foundation of China (Grant Number: 61373131 and 62071240)

As the IoMT rapidly expands, severe security risks shadow its profound benefits inpatient monitoring and data management. These devices, integral to critical care like pace-maker shocks and insulin dosing, often sacrifice robust security for functionality due totheir limited capabilities. This critical vulnerability exposes them to exploits that couldhave fatal consequences. This thesis addresses these urgent security gaps by exploring in-novative protection strategies through systematic reviews and simulated penetration testingon a mimicked IoMT environment. Our findings expose pronounced deficiencies withinexisting security frameworks, focusing on Bluetooth LE and Wi-Fi threats, especially theinadequate mechanisms to secure Bluetooth LE connections, commonly used in IoMTdevices and DOS attacks targeted directly to the IoMT devices. In response, two novelsecurity algorithms were designed to enhance the resilience of IoMT systems against cy-ber threats. This algorithm integrates dynamic whitelisting and blacklisting, MAC addressverification, UDID verification, and NFC-based device authentication to curtail unautho-rized access and uphold data integrity. The adopted strategy not only addresses specificsecurity loopholes identified during penetration testing but also establishes a frameworkcapable of adapting to evolving threats. Through this research, we aim to contribute to theongoing discourse on IoMT security, underscoring the critical need for continuous adapta-tion of security measures to protect against emerging vulnerabilities in the rapidly evolvinglandscape of IoT devices. This work aspires to lay the groundwork for future research anddevelopment in IoMT security strategies, fostering a deeper understanding and implemen-tation of adequate security measures within medical technology.

This thesis presents a critical examination of the positive impact of Machine Learning (ML) and the Internet of Medical Things (IoMT) for advancing the Clinical Decision Support System (CDSS) in the context of COVID-19 and early sepsis detection. It emphasizes the transition towards patient-centric healthcare systems, which necessitate personalized and participatory care—a transition that could be facilitated by these emerging fields. The thesis accentuates how IoMT could serve as a robust platform for data aggregation, analysis, and transmission, which could empower healthcare providers to deliver more effective care. The COVID-19 pandemic has particularly stressed the importance of such patient-centric systems for remote patient monitoring and disease management. The integration of ML-driven CDSSs with IoMT is viewed as an extremely important step in healthcare systems that could offer real-time decision-making support and enhance patient health outcomes. The thesis investigates ML's capability to analyze complex medical datasets, identify patterns and correlations, and adapt to changing conditions, thereby enhancing its predictive capabilities. It specifically focuses on the development of IoMT-based CDSSs for COVID-19 and early sepsis detection, using advanced ML methods and medical data. Key issues addressed cover data annotation scarcity, data sparsity, and data heterogeneity, along with the aspects of security, privacy, and accessibility. The thesis also intends to enhance the interpretability of ML prediction model-based CDSSs. Ethical considerations are prioritized to ensure adherence to the highest standards. The thesis demonstrates the potential and efficacy of combining ML with IoMT to enhance CDSSs by emphasizing the importance of model interpretability, system compatibility, and the integration of multimodal medical data for an effective CDSS. Overall, this thesis makes a significant contribution to the fields of ML and IoMT in healthcare, featuring th, Denna avhandling presenterar en kritisk granskning av den positiva effekten av maskininlärning (ML) och Internet of Medical Things (IoMT) för att främja det kliniska beslutsstödsystemet (CDSS) kopplat till covid-19 och tidig upptäckt av sepsis. Avhandlingen betonar övergången mot patientcentrerade vårdsystem som kräver personlig och deltagande vård – en övergång som skulle kunna underlättas av dessa framväxande områden. Studien visar hur IoMT kan fungera som en robust plattform för dataaggregering, analys och överföring, vilket kan ge vårdgivare möjlighet att erbjuda mer effektiv vård. Covid-19-pandemin har särskilt betonat vikten av sådana patientcentrerade system för fjärrövervakning av patienter och sjukdomshantering. Integreringen av ML-drivna CDSS med IoMT ses som ett extremt viktigt steg i vårdsystemen som kan erbjuda stöd för beslutsfattande i realtid och förbättra patienternas hälsoutfall. Avhandlingen undersöker maskininlärningens förmåga att analysera komplexa medicinska dataset, identifiera mönster och korrelationer, samt göra anpassningar till föränderliga förhållanden, vilket därmed förbättrar dess prediktiva förmågor. Den fokuserar specifikt på utvecklingen av IoMT-baserade CDSS för covid-19 och tidig upptäckt av sepsis, med användning av avancerade ML-metoder och medicinska data. Nyckelfrågor som adresseras täcker bristen på dataannotering, dataspridning och dataheterogenitet, tillsammans med aspekter av säkerhet, integritet och tillgänglighet. Avhandlingen avser också att förbättra tolkbarheten av ML-prediktionsmodellbaserade CDSS. Etiska överväganden prioriteras för att säkerställa efterlevnad av de högsta standarderna. Avhandlingen demonstrerar potentialen och effektiviteten i att kombinera ML med IoMT för att förbättra CDSS genom att betona vikten av modelltolkbarhet, systemkompatibilitet och integrering av multimodala medicinska data för ett effektivt CDSS. Sammantaget bidrar denna avhandling till områdena ML och IoMT inom hälsovården, med deras

The concept of the Internet of Medical Things brings a promising option to utilize various electronic health records stored in different medical devices and servers to create practical but secure clinical decision support systems. To achieve such a system, we need to focus on several aspects, most notably the usability aspect of deploying it using low-end devices. This study introduces one such application, namely FedSepsis, for the early detection of sepsis using electronic health records. We incorporate several cutting-edge deep learning techniques for the prediction and natural-language processing tasks. We also explore the multimodality aspect for the better use of electronic health records. A secure distributed machine learning mechanism is essential to building such a practical internet of medical things application. To address this, we analyze two federated learning techniques. Moreover, we use two different kinds of low-computational edge devices, namely Raspberry Pi and Jetson Nano, to address the challenges of using such a system in a practical setting and report the comparisons. We report several critical system-level information about the devices, namely CPU utilization, disk utilization, process CPU threads in use, process memory in use (non-swap), process memory available (non-swap), system memory utilization, temperature, and network traffic. We publish the prediction results with the evaluation metrics area under the receiver operating characteristic curve, the area under the precision–recall curve, and the earliness to predict sepsis in hours. Our results show that the performance is satisfactory, and with a moderate amount of devices, the federated learning setting results are similar to the single server-centric setting. Multimodality provides the best results compared to any single modality in the input features obtained from the electronic health records. Generative adversarial neural networks provide a clear superiority in handling the sparsity

The concept of the Internet of Medical Things brings a promising option to utilize various electronic health records stored in different medical devices and servers to create practical but secure clinical decision support systems. To achieve such a system, we need to focus on several aspects, most notably the usability aspect of deploying it using low-end devices. This study introduces one such application, namely FedSepsis, for the early detection of sepsis using electronic health records. We incorporate several cutting-edge deep learning techniques for the prediction and natural-language processing tasks. We also explore the multimodality aspect for the better use of electronic health records. A secure distributed machine learning mechanism is essential to building such a practical internet of medical things application. To address this, we analyze two federated learning techniques. Moreover, we use two different kinds of low-computational edge devices, namely Raspberry Pi and Jetson Nano, to address the challenges of using such a system in a practical setting and report the comparisons. We report several critical system-level information about the devices, namely CPU utilization, disk utilization, process CPU threads in use, process memory in use (non-swap), process memory available (non-swap), system memory utilization, temperature, and network traffic. We publish the prediction results with the evaluation metrics area under the receiver operating characteristic curve, the area under the precision–recall curve, and the earliness to predict sepsis in hours. Our results show that the performance is satisfactory, and with a moderate amount of devices, the federated learning setting results are similar to the single server-centric setting. Multimodality provides the best results compared to any single modality in the input features obtained from the electronic health records. Generative adversarial neural networks provide a clear superiority in handling the sparsity

The concept of the Internet of Medical Things brings a promising option to utilize various electronic health records stored in different medical devices and servers to create practical but secure clinical decision support systems. To achieve such a system, we need to focus on several aspects, most notably the usability aspect of deploying it using low-end devices. This study introduces one such application, namely FedSepsis, for the early detection of sepsis using electronic health records. We incorporate several cutting-edge deep learning techniques for the prediction and natural-language processing tasks. We also explore the multimodality aspect for the better use of electronic health records. A secure distributed machine learning mechanism is essential to building such a practical internet of medical things application. To address this, we analyze two federated learning techniques. Moreover, we use two different kinds of low-computational edge devices, namely Raspberry Pi and Jetson Nano, to address the challenges of using such a system in a practical setting and report the comparisons. We report several critical system-level information about the devices, namely CPU utilization, disk utilization, process CPU threads in use, process memory in use (non-swap), process memory available (non-swap), system memory utilization, temperature, and network traffic. We publish the prediction results with the evaluation metrics area under the receiver operating characteristic curve, the area under the precision–recall curve, and the earliness to predict sepsis in hours. Our results show that the performance is satisfactory, and with a moderate amount of devices, the federated learning setting results are similar to the single server-centric setting. Multimodality provides the best results compared to any single modality in the input features obtained from the electronic health records. Generative adversarial neural networks provide a clear superiority in handling the sparsity

The increasing life expectancy of an aging population has caused a higher demand for healthcare services, highlighting a shortage within the healthcare system due to insufficient projected growth of healthcare personnel. One solution to this issue is the utilization of the Internet of Medical Things (IoMT). This study aims to describe the general acceptance level amongst healthcare recipients of adopting IoMT in Swedish remote healthcare and how the acceptance level differs amongst different age groups depending on the application of IoMT. Acceptance and implementation of IoMT in remote healthcare are discussed, with an emphasis on factors such as user-friendliness and practical hardware design. In this study, a digital survey was conducted with 103 respondents, which demonstrated that the majority were positive about using IoMT for monitoring and treating health conditions remotely. Although awareness of security risks exists, respondents generally expressed a low number of concerns regarding unauthorized access to information and modification of IoMT devices in general. The study's conclusion highlights the notable acceptance of IoMT in remote healthcare in Sweden. The results indicate network-connected devices are integrated into people's daily lives, and user-friendliness and cybersecurity influence the adoption of IoMT. Older age groups demonstrated less concern about cybersecurity than younger age groups. When analysing specific applications, the results show some age-related differences in acceptance, which also varied depending on the application, particularly when comparing more life-critical devices such as IoMT pacemakers with IoMT fall detectors., Den ökande livslängden hos en ökande befolkning har skapat ett ökat vårdbehov och detta har påvisat en brist inom sjukvården då den förväntade utvecklingen av sjukvårdspersonal är otillräcklig. En lösning på detta är användandet av Internet of Medical Things (IoMT) och denna studie har som syfte att beskriva den allmänna acceptansnivån hos vårdtagare för användandet av IoMT inom den svenska distansvården samt hur acceptansnivån skiljer sig för olika åldrar beroende på tillämpning av IoMT. Acceptans och implementering av IoMT i distanssjukvården diskuteras, och faktorer som användarvänlighet och praktisk hårdvarudesign betonas. I denna studie utfördes en digital enkätundersökning innehållande 103 respondenter som visade att majoriteten var positiva till att använda IoMT för att övervaka och behandla hälsotillstånd på distans. Även om medvetenheten om säkerhetsrisker finns, var respondenternas oro för obehörig åtkomst till information samt modifiering av IoMT-utrustning generellt låg. Studiens slutsats framhäver att distansvård med IoMT har en hög acceptans i Sverige. Resultaten indikerar att nätverksanslutna enheter är integrerade i människors vardag och att användarvänlighet samt cybersäkerhet påverkar användningen av IoMT. Äldre åldersgrupper visade mindre oro för cybersäkerheten än yngre åldersgrupper. Vid analys av specifika tillämpningar visar resultaten att det finns vissa åldersrelaterade skillnader i acceptans, vilka också varierade beroende på tillämpning, särskilt i jämförelse med mer livskritiska enheter såsom IoMT-pacemakers i jämförelse med IoMT-falldetektorer.

COVID-19 is a viral infectious disease that has created a global pandemic, resulting in millions of deaths and disrupting the world order. Different machine learning and deep learning approaches were considered to detect it utilizing different medical data. Thermal imaging is a promising option for detecting COVID-19 as it is low-cost, non-invasive, and can be maintained remotely. This work explores the COVID-19 detection issue using the thermal image and associated tabular medical data obtained from a publicly available dataset. We incorporate a multi-modal machine learning approach where we investigate the different combinations of medical and data type modalities to get an improved result. We use different machine learning and deep learning methods, namely random forests, Extreme Gradient Boosting (XGBoost), Multilayer Perceptron (MLP), and Convolutional Neural Network (CNN). Overall multi-modal results outperform any single modalities, and it is observed that the thermal image is a crucial factor in achieving it. XGBoost provided the best result with the area under the receiver operating characteristic curve (AUROC) score of 0.91 and the area under the precision-recall curve (AUPRC) score of 0.81. We also report the average of leave-one-positive-instance-out cross- validation evaluation scores. This average score is consistent with the test evaluation score for random forests and XGBoost methods. Our results suggest that utilizing thermal image with associated tabular medical data could be a viable option to detect COVID-19, and it should be explored further to create and test a real-time, secure, private, and remote COVID-19 detection application in the future.

Internet of Medical Thing (IoMT) is an emerging technology in healthcare that can be used to realize a wide variety of medical applications. It improves people’s quality of life and makes it easier to care for the sick individuals in an efficient and safe manner. To do this, IoMT leverages the capabilities of some new technologies including IoT, Artificial Intelligence, cloud computing, computer networks and medicine. Combining these technologies to monitor the patient’s health conditions in real-time or semi-real-time is a critical challenge in IoMT. In this regard, one of the most crucial components of IoMT are network communication protocols that should provide a fast and reliable communication path between a connected biosensor to a patient and cloud computing environment. In this paper, we propose EQRSRL as an efficient routing mechanism for different types of IoMT applications. The aim of EQRSRL is to provide a reasonable level of Quality of Service (QoS) for IoMT traffics. To achieve this goal, it categorizes the network traffic into three classes and treats them differently concerning their QoS requirements. Moreover, EQRSRL divides the network environment into multiple zones to decrease the number of message exchange between the nodes. In order to compute optimal paths between the nodes, it considers QoS and energy metrics, and makes use of a reinforcement learning approach in path computation process. Simulation results show that the implementation of EQRSRL in IoMT is practical and leads to improvement of 82% in average energy consumption, 25% in end-to-end delay and 7% packet delivery ration in compared to the state-of-the-art routing techniques., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Cyber Security

Remote patient monitoring is a healthcare delivery model that uses technology to collect and transmit patient data from a remote location to healthcare providers for analysis and treatment. Remote patient monitoring systems rely on a network infrastructure to gather and transmit data from patients to healthcare providers through a network. While these systems become more prevalent, they may also become targets for cyberattacks. This paper deals with the development of a domain ontology to facilitate partial automation to improve the security posture of IoT networks used in remote patient monitoring. For this purpose, it captures the semantics of the concepts and properties of the main security aspects of IoT medical devices. This is complemented by a comprehensive ruleset, evaluated by using SPARQL queries, and automated reasoning over the aggregated knowledge.

The Internet of Things (IoT) aims to network everything near and far in our ambient environment. Although the functional innovations for IoT are going full steam ahead, newly-emerging scenarios such as the Internet of Ocean and Implantable Things often come with limited power budgets, challenging deployment scenarios, and demanding computational resources, which fundamentally stress conventional IoT architecture, communications primitives, and sensing capabilities. The goal of this Dagstuhl Seminar was to bring together researchers from both academia and industry globally to i) review the capacity of existing IoT research from radical perspectives; ii) summarize fundamental challenges in modern IoT application scenarios that may then be investigated in joint research projects; and iii) discuss new types of hardware architecture, network stack, and communication primitives for these emerging IoT scenarios.

The increasing life expectancy of an aging population has caused a higher demand for healthcare services, highlighting a shortage within the healthcare system due to insufficient projected growth of healthcare personnel. One solution to this issue is the utilization of the Internet of Medical Things (IoMT). This study aims to describe the general acceptance level amongst healthcare recipients of adopting IoMT in Swedish remote healthcare and how the acceptance level differs amongst different age groups depending on the application of IoMT. Acceptance and implementation of IoMT in remote healthcare are discussed, with an emphasis on factors such as user-friendliness and practical hardware design. In this study, a digital survey was conducted with 103 respondents, which demonstrated that the majority were positive about using IoMT for monitoring and treating health conditions remotely. Although awareness of security risks exists, respondents generally expressed a low number of concerns regarding unauthorized access to information and modification of IoMT devices in general. The study's conclusion highlights the notable acceptance of IoMT in remote healthcare in Sweden. The results indicate network-connected devices are integrated into people's daily lives, and user-friendliness and cybersecurity influence the adoption of IoMT. Older age groups demonstrated less concern about cybersecurity than younger age groups. When analysing specific applications, the results show some age-related differences in acceptance, which also varied depending on the application, particularly when comparing more life-critical devices such as IoMT pacemakers with IoMT fall detectors., Den ökande livslängden hos en ökande befolkning har skapat ett ökat vårdbehov och detta har påvisat en brist inom sjukvården då den förväntade utvecklingen av sjukvårdspersonal är otillräcklig. En lösning på detta är användandet av Internet of Medical Things (IoMT) och denna studie har som syfte att beskriva den allmänna acceptansnivån hos vårdtagare för användandet av IoMT inom den svenska distansvården samt hur acceptansnivån skiljer sig för olika åldrar beroende på tillämpning av IoMT. Acceptans och implementering av IoMT i distanssjukvården diskuteras, och faktorer som användarvänlighet och praktisk hårdvarudesign betonas. I denna studie utfördes en digital enkätundersökning innehållande 103 respondenter som visade att majoriteten var positiva till att använda IoMT för att övervaka och behandla hälsotillstånd på distans. Även om medvetenheten om säkerhetsrisker finns, var respondenternas oro för obehörig åtkomst till information samt modifiering av IoMT-utrustning generellt låg. Studiens slutsats framhäver att distansvård med IoMT har en hög acceptans i Sverige. Resultaten indikerar att nätverksanslutna enheter är integrerade i människors vardag och att användarvänlighet samt cybersäkerhet påverkar användningen av IoMT. Äldre åldersgrupper visade mindre oro för cybersäkerheten än yngre åldersgrupper. Vid analys av specifika tillämpningar visar resultaten att det finns vissa åldersrelaterade skillnader i acceptans, vilka också varierade beroende på tillämpning, särskilt i jämförelse med mer livskritiska enheter såsom IoMT-pacemakers i jämförelse med IoMT-falldetektorer.

The Internet of Medical Things (IoMT) has emerged as a significant research area in the medical field, enabling the transmission of various types of data to the cloud for analysis and diagnosis. Fusing data from multiple modalities can enhance accuracy but requires substantial computing power. Theoretically, quantum computers can rapidly process large volumes of high-dimensional medical data. Despite accelerated developments in quantum computing, research on quantum machine learning (QML) for multimodal data processing remains limited. Considering these factors, this paper presents a quantum neural network-based multimodal fusion system for intelligent diagnosis (QNMF) that can process multimodal medical data transmitted by IoMT devices, fuse data from different modalities, and improve the performance of intelligent diagnosis. This system employs a quantum convolutional neural network (QCNN) to efficiently extract features from medical images. These QCNN-based features are then fused with other modality features (such as blood test results or breast cell slices), and used to train an effective variational quantum classifier (VQC) for intelligent diagnosis. The experimental results demonstrate that a QCNN can effectively extract image data features. Furthermore, QNMF achieved an accuracy of 97.07% and 97.61% on breast cancer diagnosis and Covid-19 diagnosis experiments, respectively. In addition, the QNMF exhibits strong quantum noise robustness. © 2023 Elsevier B.V., Funding: The National Natural Science Foundation of China (No. 61373131, 62071240), PAPD and CICAEET funds.

The concept of the Internet of Medical Things brings a promising option to utilize various electronic health records stored in different medical devices and servers to create practical but secure clinical decision support systems. To achieve such a system, we need to focus on several aspects, most notably the usability aspect of deploying it using low-end devices. This study introduces one such application, namely FedSepsis, for the early detection of sepsis using electronic health records. We incorporate several cutting-edge deep learning techniques for the prediction and natural-language processing tasks. We also explore the multimodality aspect for the better use of electronic health records. A secure distributed machine learning mechanism is essential to building such a practical internet of medical things application. To address this, we analyze two federated learning techniques. Moreover, we use two different kinds of low-computational edge devices, namely Raspberry Pi and Jetson Nano, to address the challenges of using such a system in a practical setting and report the comparisons. We report several critical system-level information about the devices, namely CPU utilization, disk utilization, process CPU threads in use, process memory in use (non-swap), process memory available (non-swap), system memory utilization, temperature, and network traffic. We publish the prediction results with the evaluation metrics area under the receiver operating characteristic curve, the area under the precision–recall curve, and the earliness to predict sepsis in hours. Our results show that the performance is satisfactory, and with a moderate amount of devices, the federated learning setting results are similar to the single server-centric setting. Multimodality provides the best results compared to any single modality in the input features obtained from the electronic health records. Generative adversarial neural networks provide a clear superiority in handling the sparsity

The concept of the Internet of Medical Things brings a promising option to utilize various electronic health records stored in different medical devices and servers to create practical but secure clinical decision support systems. To achieve such a system, we need to focus on several aspects, most notably the usability aspect of deploying it using low-end devices. This study introduces one such application, namely FedSepsis, for the early detection of sepsis using electronic health records. We incorporate several cutting-edge deep learning techniques for the prediction and natural-language processing tasks. We also explore the multimodality aspect for the better use of electronic health records. A secure distributed machine learning mechanism is essential to building such a practical internet of medical things application. To address this, we analyze two federated learning techniques. Moreover, we use two different kinds of low-computational edge devices, namely Raspberry Pi and Jetson Nano, to address the challenges of using such a system in a practical setting and report the comparisons. We report several critical system-level information about the devices, namely CPU utilization, disk utilization, process CPU threads in use, process memory in use (non-swap), process memory available (non-swap), system memory utilization, temperature, and network traffic. We publish the prediction results with the evaluation metrics area under the receiver operating characteristic curve, the area under the precision–recall curve, and the earliness to predict sepsis in hours. Our results show that the performance is satisfactory, and with a moderate amount of devices, the federated learning setting results are similar to the single server-centric setting. Multimodality provides the best results compared to any single modality in the input features obtained from the electronic health records. Generative adversarial neural networks provide a clear superiority in handling the sparsity

The Internet of Things (IoT) paradigm promises to make “things” include a more generic set of entities such as smart devices, sensors, human beings, and any other IoT objects to be accessible at anytime and anywhere. IoT varies widely in its applications, and one of its most beneficial uses is in the medical field. However, the large attack surface and vulnerabilities of IoT systems needs to be secured and protected. Security is a requirement for IoT systems in the medical field where the Health Insurance Portability and Accountability Act (HIPAA) applies. This work investigates various applications of IoT in healthcare and focuses on the security aspects of the two internet of medical things (IoMT) devices: the LifeWatch Mobile Cardiac Telemetry 3 Lead (MCT3L), and the remote patient monitoring system of the telehealth provider Vivify Health, as well as their implementations.

Masterarbeit Universität Innsbruck 2022

Background: Population growth and aging have highlighted the need for more effective home and prehospital care. Interconnected medical devices and applications, which comprise an infrastructure referred to as the Internet of Medical Things (IoMT), have enabled remote patient monitoring and can be important tools to cope with these demographic changes. However, developing IoMT platforms requires profound knowledge of clinical needs and challenges related to interoperability and how these can be managed with suitable technologies. Objective: The purpose of this scoping review is to summarize the best practices and technologies to overcome interoperability concerns in IoMT platform development for medical emergencies in home and prehospital care. Methods: This scoping review will be conducted in accordance with Arksey and O’Malley’s 5-stage framework and adhere to the PRISMA-P (Preferred Reporting Items for Systematic Reviews and Meta-analyses Protocols) guidelines. Only peer-reviewed articles published in English will be considered. The databases/web search engines that will be used are IEEE Xplore, PubMed, Scopus, Google Scholar, National Center for Biotechnology Information, SAGE Journals, and ScienceDirect. The search process for relevant literature will be divided into 4 different steps. This will ensure that a suitable approach is followed in terms of search terms, limitations, and eligibility criteria. Relevant articles that meet the inclusion criteria will be screened in 2 stages: abstract and title screening and full-text screening. To reduce selection bias, the screening process will be performed by 2 reviewers. Results: The results of the preliminary search indicate that there is sufficient literature to form a good foundation for the scoping review. The search was performed in April 2022, and a total of 4579 articles were found. The main clinical focus is the prevention and management of falls, but other medical emergencies, such as heart disease and stroke

von Chiaramaria Bortolamedi, Masterarbeit Universität Innsbruck 2022

Background: Population growth and aging have highlighted the need for more effective home and prehospital care. Interconnected medical devices and applications, which comprise an infrastructure referred to as the Internet of Medical Things (IoMT), have enabled remote patient monitoring and can be important tools to cope with these demographic changes. However, developing IoMT platforms requires profound knowledge of clinical needs and challenges related to interoperability and how these can be managed with suitable technologies. Objective: The purpose of this scoping review is to summarize the best practices and technologies to overcome interoperability concerns in IoMT platform development for medical emergencies in home and prehospital care. Methods: This scoping review will be conducted in accordance with Arksey and O’Malley’s 5-stage framework and adhere to the PRISMA-P (Preferred Reporting Items for Systematic Reviews and Meta-analyses Protocols) guidelines. Only peer-reviewed articles published in English will be considered. The databases/web search engines that will be used are IEEE Xplore, PubMed, Scopus, Google Scholar, National Center for Biotechnology Information, SAGE Journals, and ScienceDirect. The search process for relevant literature will be divided into 4 different steps. This will ensure that a suitable approach is followed in terms of search terms, limitations, and eligibility criteria. Relevant articles that meet the inclusion criteria will be screened in 2 stages: abstract and title screening and full-text screening. To reduce selection bias, the screening process will be performed by 2 reviewers. Results: The results of the preliminary search indicate that there is sufficient literature to form a good foundation for the scoping review. The search was performed in April 2022, and a total of 4579 articles were found. The main clinical focus is the prevention and management of falls, but other medical emergencies, such as heart disease and stroke

The Internet of Things (IoT) paradigm promises to make “things” include a more generic set of entities such as smart devices, sensors, human beings, and any other IoT objects to be accessible at anytime and anywhere. IoT varies widely in its applications, and one of its most beneficial uses is in the medical field. However, the large attack surface and vulnerabilities of IoT systems needs to be secured and protected. Security is a requirement for IoT systems in the medical field where the Health Insurance Portability and Accountability Act (HIPAA) applies. This work investigates various applications of IoT in healthcare and focuses on the security aspects of the two internet of medical things (IoMT) devices: the LifeWatch Mobile Cardiac Telemetry 3 Lead (MCT3L), and the remote patient monitoring system of the telehealth provider Vivify Health, as well as their implementations.

Internet of Medical Things (IoMT) provides an excellent opportunity to investigate better automatic medical decision support tools with the effective integration of various medical equipment and associated data. This study explores two such medical decision-making tasks, namely COVID-19 detection and lung area segmentation detection, using chest radiography images. We also explore different cutting-edge machine learning techniques, such as federated learning, semi-supervised learning, transfer learning, and multi-task learning to explore the issue. To analyze the applicability of computationally less capable edge devices in the IoMT system, we report the results using Raspberry Pi devices as accuracy, precision, recall, Fscore for COVID-19 detection, and average dice score for lung segmentation detection tasks. We also publish the results obtained through server-centric simulation for comparison. The results show that Raspberry Pi-centric devices provide better performance in lung segmentation detection, and server-centric experiments provide better results in COVID-19 detection. We also discuss the IoMT application-centric settings, utilizing medical data and decision support systems, and posit that such a system could benefit all the stakeholders in the IoMT domain.

An intelligent mosquito net employing deep learning has been one of the hotspots in the field of Internet of Things as it can reduce significantly the spread of pathogens carried by mosquitoes, and help people live well in mosquito-infested areas. In this study, we propose an intelligent mosquito net that can produce and transmit data through the Internet of Medical Things. In our method, decision-making is controlled by a deep learning model, and the proposed method uses infrared sensors and an array of pressure sensors to collect data.Moreover the ZigBee protocol is used to transmit the pressure map which is formed by pressure sensors with the deep learning perception model, determining automatically the intention of the user to open or close the mosquito net.We used optical flow to extract pressure map features, and they were fed to a 3-dimensional convolutional neural network (3D-CNN) classification model subsequently. We achieved the expected results using a nested cross-validation method to evaluate our model. Deep learning has better adaptability than the traditionalmethods and also has better anti-interference by the different bodies of users. This research has the potential to be used in intelligent medical protection and large-scale sensor array perception of the environment.

Internet of Medical Things (IoMT) provides an excellent opportunity to investigate better automatic medical decision support tools with the effective integration of various medical equipment and associated data. This study explores two such medical decision-making tasks, namely COVID-19 detection and lung area segmentation detection, using chest radiography images. We also explore different cutting-edge machine learning techniques, such as federated learning, semi-supervised learning, transfer learning, and multi-task learning to explore the issue. To analyze the applicability of computationally less capable edge devices in the IoMT system, we report the results using Raspberry Pi devices as accuracy, precision, recall, Fscore for COVID-19 detection, and average dice score for lung segmentation detection tasks. We also publish the results obtained through server-centric simulation for comparison. The results show that Raspberry Pi-centric devices provide better performance in lung segmentation detection, and server-centric experiments provide better results in COVID-19 detection. We also discuss the IoMT application-centric settings, utilizing medical data and decision support systems, and posit that such a system could benefit all the stakeholders in the IoMT domain.

Internet of Medical Things (IoMT) provides an excellent opportunity to investigate better automatic medical decision support tools with the effective integration of various medical equipment and associated data. This study explores two such medical decision-making tasks, namely COVID-19 detection and lung area segmentation detection, using chest radiography images. We also explore different cutting-edge machine learning techniques, such as federated learning, semi-supervised learning, transfer learning, and multi-task learning to explore the issue. To analyze the applicability of computationally less capable edge devices in the IoMT system, we report the results using Raspberry Pi devices as accuracy, precision, recall, Fscore for COVID-19 detection, and average dice score for lung segmentation detection tasks. We also publish the results obtained through server-centric simulation for comparison. The results show that Raspberry Pi-centric devices provide better performance in lung segmentation detection, and server-centric experiments provide better results in COVID-19 detection. We also discuss the IoMT application-centric settings, utilizing medical data and decision support systems, and posit that such a system could benefit all the stakeholders in the IoMT domain.

Internet of Medical Things (IoMT) provides an excellent opportunity to investigate better automatic medical decision support tools with the effective integration of various medical equipment and associated data. This study explores two such medical decision-making tasks, namely COVID-19 detection and lung area segmentation detection, using chest radiography images. We also explore different cutting-edge machine learning techniques, such as federated learning, semi-supervised learning, transfer learning, and multi-task learning to explore the issue. To analyze the applicability of computationally less capable edge devices in the IoMT system, we report the results using Raspberry Pi devices as accuracy, precision, recall, Fscore for COVID-19 detection, and average dice score for lung segmentation detection tasks. We also publish the results obtained through server-centric simulation for comparison. The results show that Raspberry Pi-centric devices provide better performance in lung segmentation detection, and server-centric experiments provide better results in COVID-19 detection. We also discuss the IoMT application-centric settings, utilizing medical data and decision support systems, and posit that such a system could benefit all the stakeholders in the IoMT domain.

The internet of medical things (IoMT) is a relatively new territory for the internet of things (IoT) platforms where we can obtain a significant amount of potential benefits with the combination of cognitive computing. Effective utilization of the healthcare data is the critical factor in achieving such potential, which can be a significant challenge as the medical data is extraordinarily heterogeneous and spread across different devices with different degrees of importance and authority. To address this issue, in this paper, we introduce a cognitive internet of medical things architecture with a use case of early sepsis detection using electronic health records. We discuss the various aspects of IoMT architecture. Based on the discussion, we posit that the proposed architecture could improve the overall performance and usability in the IoMT platforms in particular for different IoMT based services and applications. The use of an RNN-LSTM network for early prediction of sepsis according to Sepsis-3 criteria is evaluated with the empirical investigation using six different time window sizes. The best result is obtained from a model using a four-hour window with the assumption that data is missing-not-at-random. It is observed that when learning from heterogeneous sequences of sparse medical data for early prediction of sepsis, the size of the time window has a considerable impact on predictive performance.

Internet of Medical Things (IoMT) provides an excellent opportunity to investigate better automatic medical decision support tools with the effective integration of various medical equipment and associated data. This study explores two such medical decision-making tasks, namely COVID-19 detection and lung area segmentation detection, using chest radiography images. We also explore different cutting-edge machine learning techniques, such as federated learning, semi-supervised learning, transfer learning, and multi-task learning to explore the issue. To analyze the applicability of computationally less capable edge devices in the IoMT system, we report the results using Raspberry Pi devices as accuracy, precision, recall, Fscore for COVID-19 detection, and average dice score for lung segmentation detection tasks. We also publish the results obtained through server-centric simulation for comparison. The results show that Raspberry Pi-centric devices provide better performance in lung segmentation detection, and server-centric experiments provide better results in COVID-19 detection. We also discuss the IoMT application-centric settings, utilizing medical data and decision support systems, and posit that such a system could benefit all the stakeholders in the IoMT domain.

The growing world population is facing challengessuch as increased chronic diseases and medical expenses.Integrate the latest modern technology into healthcare systemcan diminish these issues. Internet of medical things (IoMT) isthe vision to provide the better healthcare system. The IoMTcomprises of different sensor nodes connected together. TheIoMT system incorporated with medical devices (sensors) forgiven the healthcare facilities to the patient and physician canhave capability to monitor the patients very efficiently. Themain challenge for IoMT is the energy consumption, batterycharge consumption and limited battery lifetime in sensor basedmedical devices. During charging the charges that are stored inbattery and these charges are not fully utilized due to nonlinearity of discharging process. The short time period neededto restore these unused charges is referred as recovery effect. Analgorithm exploiting recovery effect to extend the batterylifetime that leads to low consumption of energy. This paperprovides the proposed adaptive Energy efficient (EEA)algorithm that adopts this effect for enhancing energyefficiency, battery lifetime and throughput. The results havebeen simulated on MATLAB by considering the Li-ion battery.The proposed adaptive Energy efficient (EEA) algorithm is alsocompared with other state of the art existing method named,BRLE. The Proposed algorithm increased the lifetime ofbattery, energy consumption and provides the improvedperformance as compared to BRLE algorithm. It consumes lowenergy and supports continuous connectivity of devices withoutany loss/ interruptions.

This position paper gives better insight about the role and importance of Internet of Medical Things (IoMT) for independent living and re-learning for older adults. Sensing Technologies are the paradigm shift for transforming conventional healthcare practices into the smart, and self-assisted activities, which are envisioned for today's medical world. Internet of Things (IoT) and IoMT are the interrelated technologies for promoting independent living and re-learning practices. In this paper, re-learning is defined as the process for adults to recover useful instrumental activities of daily living skills that have been lost after an impairment.

Internet of Medical Things (IoMT) provides an excellent opportunity to investigate better automatic medical decision support tools with the effective integration of various medical equipment and associated data. This study explores two such medical decision-making tasks, namely COVID-19 detection and lung area segmentation detection, using chest radiography images. We also explore different cutting-edge machine learning techniques, such as federated learning, semi-supervised learning, transfer learning, and multi-task learning to explore the issue. To analyze the applicability of computationally less capable edge devices in the IoMT system, we report the results using Raspberry Pi devices as accuracy, precision, recall, Fscore for COVID-19 detection, and average dice score for lung segmentation detection tasks. We also publish the results obtained through server-centric simulation for comparison. The results show that Raspberry Pi-centric devices provide better performance in lung segmentation detection, and server-centric experiments provide better results in COVID-19 detection. We also discuss the IoMT application-centric settings, utilizing medical data and decision support systems, and posit that such a system could benefit all the stakeholders in the IoMT domain.

The advances in the miniaturisation of electronic devices and the deployment of cheaper and faster data networks have propelled environments augmented with contextual and real-time information, such as smart homes and smart cities. These context-aware environments have opened the door to numerous opportunities for providing added-value, accurate and personalised services to citizens. In particular, smart healthcare, regarded as the natural evolution of electronic health and mobile health, contributes to enhance medical services and people’s welfare, while shortening waiting times and decreasing healthcare expenditure. However, the large number, variety and complexity of devices and systems involved in smart health systems involve a number of challenging considerations to be considered, particularly from security and privacy perspectives. To this aim, this article provides a thorough technical review on the deployment of secure smart health services, ranging from the very collection of sensors data (either related to the medical conditions of individuals or to their immediate context), the transmission of these data through wireless communication networks, to the final storage and analysis of such information in the appropriate health information systems. As a result, we provide practitioners with a comprehensive overview of the existing vulnerabilities and solutions in the technical side of smart healthcare.

The internet of medical things (IoMT) is relatively new territory for the internet of things (IoT) platforms where we can obtain a significant amount of potential benefits in terms of smart future network computing and intelligent health-care systems. Effective utilization of the health-care data is the key factor here in achieving such potential, which can be a significant challenge as the data is extraordinarily heterogeneous and spread across different devices with different degrees of importance and authority to access it. To address this issue, in this paper, we introduce an intelligent context-based metadata aggregator in the decentralized and distributed edge-based IoMT platform with a use case of early sepsis detection using clinical data. We thoroughly discuss the various aspects of the metadata aggregator and the overall IoMT architecture. Based on the discussion, we posit that the proposed architecture could improve the overall performance and usability in the IoMT platforms in particular for different IoMT based services and applications.

The internet of medical things (IoMT) is relatively new territory for the internet of things (IoT) platforms where we can obtain a significant amount of potential benefits in terms of smart future network computing and intelligent health-care systems. Effective utilization of the health-care data is the key factor here in achieving such potential, which can be a significant challenge as the data is extraordinarily heterogeneous and spread across different devices with different degrees of importance and authority to access it. To address this issue, in this paper, we introduce an intelligent context-based metadata aggregator in the decentralized and distributed edge-based IoMT platform with a use case of early sepsis detection using clinical data. We thoroughly discuss the various aspects of the metadata aggregator and the overall IoMT architecture. Based on the discussion, we posit that the proposed architecture could improve the overall performance and usability in the IoMT platforms in particular for different IoMT based services and applications.

The internet of medical things (IoMT) is relatively new territory for the internet of things (IoT) platforms where we can obtain a significant amount of potential benefits in terms of smart future network computing and intelligent health-care systems. Effective utilization of the health-care data is the key factor here in achieving such potential, which can be a significant challenge as the data is extraordinarily heterogeneous and spread across different devices with different degrees of importance and authority to access it. To address this issue, in this paper, we introduce an intelligent context-based metadata aggregator in the decentralized and distributed edge-based IoMT platform with a use case of early sepsis detection using clinical data. We thoroughly discuss the various aspects of the metadata aggregator and the overall IoMT architecture. Based on the discussion, we posit that the proposed architecture could improve the overall performance and usability in the IoMT platforms in particular for different IoMT based services and applications.

The internet of medical things (IoMT) is relatively new territory for the internet of things (IoT) platforms where we can obtain a significant amount of potential benefits in terms of smart future network computing and intelligent health-care systems. Effective utilization of the health-care data is the key factor here in achieving such potential, which can be a significant challenge as the data is extraordinarily heterogeneous and spread across different devices with different degrees of importance and authority to access it. To address this issue, in this paper, we introduce an intelligent context-based metadata aggregator in the decentralized and distributed edge-based IoMT platform with a use case of early sepsis detection using clinical data. We thoroughly discuss the various aspects of the metadata aggregator and the overall IoMT architecture. Based on the discussion, we posit that the proposed architecture could improve the overall performance and usability in the IoMT platforms in particular for different IoMT based services and applications.

The internet of medical things (IoMT) is relatively new territory for the internet of things (IoT) platforms where we can obtain a significant amount of potential benefits in terms of smart future network computing and intelligent health-care systems. Effective utilization of the health-care data is the key factor here in achieving such potential, which can be a significant challenge as the data is extraordinarily heterogeneous and spread across different devices with different degrees of importance and authority to access it. To address this issue, in this paper, we introduce an intelligent context-based metadata aggregator in the decentralized and distributed edge-based IoMT platform with a use case of early sepsis detection using clinical data. We thoroughly discuss the various aspects of the metadata aggregator and the overall IoMT architecture. Based on the discussion, we posit that the proposed architecture could improve the overall performance and usability in the IoMT platforms in particular for different IoMT based services and applications.

The internet of medical things (IoMT) is relatively new territory for the internet of things (IoT) platforms where we can obtain a significant amount of potential benefits in terms of smart future network computing and intelligent health-care systems. Effective utilization of the health-care data is the key factor here in achieving such potential, which can be a significant challenge as the data is extraordinarily heterogeneous and spread across different devices with different degrees of importance and authority to access it. To address this issue, in this paper, we introduce an intelligent context-based metadata aggregator in the decentralized and distributed edge-based IoMT platform with a use case of early sepsis detection using clinical data. We thoroughly discuss the various aspects of the metadata aggregator and the overall IoMT architecture. Based on the discussion, we posit that the proposed architecture could improve the overall performance and usability in the IoMT platforms in particular for different IoMT based services and applications.

This study aims to explore the impact of healthcare digitalization on smart hospital project financing (PF) fostered by pay-for-performance (P4P) incentives. Digital platforms are a technology-enabled business model that facilitates exchanges between interacting agents. They represent a bridging link among disconnected nodes, improving the scalable value of networks. Application to healthcare public–private partnerships (PPPs) is significant due to the consistency of digital platforms with health issues and the complexity of the stakeholder’s interaction. In infrastructural PPPs, public and private players cooperate, usually following PF patterns. This relationship is complemented by digitized supply chains and is increasingly patient-centric. This paper reviews the literature, analyzes some supply chain bottlenecks, addresses solutions concerning the networking eects of platforms to improve PPP interactions, and investigates the cost–benefit analysis of digital health with an empirical case. Whereas diagnostic or infrastructural technology is an expensive investment with long-term payback, leapfrogging digital applications reduce contingent costs. “Digital” savings can be shared by key stakeholders with P4P schemes, incentivizing value co-creation patterns. Ecient sharing may apply network theory to a comprehensive PPP ecosystem where stakeholding nodes are digitally connected. This innovative approach improves stakeholder relationships, which are re-engineered around digital platforms that enhance patient-centered satisfaction and sustainability. Digital technologies are useful even for infectious disease surveillance, like that of the coronavirus pandemic, for supporting massive healthcare intervention, decongesting hospitals, and providing timely big data.

En el desarrollo de este trabajo se le propone a la empresa una metodología que brinde la posibilidad de abrir nuevos horizontes, aumentar la demanda de sus productos, penetrar mercados con mayor poder adquisitivo, mejorar sus indicadores de rentabilidad y atomizar el riesgo asociado con la salida de clientes. De modo adicional, un plan exportador le brinda a la empresa la posibilidad de innovar en temas productivos e, incluso, en la creación de nuevas líneas de producto que puedan ser complementarios o sustitutivos para los actuales y que en los mercados diferentes al nacional sean un ancla para sobrevivir y crecer de manera sostenible. El trabajo se basa en un esquema de paso a paso, en el que se analizan diferentes mercados y se profundiza en los más relevantes para la compañía. Se propuso un plan de ejecución en un tiempo determinado, que facilita el desarrollo y la implementación del mismo.