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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

In Bangladesh, cities produce huge volumes of solid waste, sewage, and greywater with limited resources to manage it. Waste and sanitation workers, key players in managing waste, are continuously exposed to different health hazards in their work due to lack of occupational safety and basic protections. This vulnerability has been exacerbated by the COVID-19 Pandemic. Focusing on workers’ experiences and everyday working realities, this study sought to assess the organizational capacity, gaps, and challenges of local waste management authorities during COVID-19. It also sought to deepen understanding of job arrangements, occupational safety, hygiene knowledge, and practices of these waste workers at their workplace. A qualitative study was conducted consisting of 61 key informant interviews with seven categories of local officials and 50 in-depth interviews with five categories of waste and sanitation workers in 10 cities of Bangladesh. An inductive content analysis approach was adopted. The results showed that COVID-19 had aggravated the existing waste management challenges and imposed severe health risks on waste workers. Respective conservancy departments lagged behind due to lack of safe cleaning methods and equipment, and limited funds for waste worker training, which ultimately led to poor occupational safety for workers. This vulnerable worker group lacked basic job facilities, received poor payment and insufficient protective equipment, and rarely had any health support from their employers. Also, they were not provided with an adequate understanding of occupational safety and health hygiene; and no appropriate handwashing facilities at their workplace to tackle infectious diseases like COVID-19. The study investigated this countercomplaint and discussed the current arrangements from the prespective of both city authorities and waste workers. The study recommended more automated waste collectors and compositors for the conservancy departments, increased activ

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.

The area of interpretable deep neural networks has received increased attention in recent years due to the need for transparency in various fields, including medicine, healthcare, stock market analysis, compliance with legislation, and law. Layer-wise Relevance Propagation (LRP) and Gradient-weighted Class Activation Mapping (Grad-CAM) are two widely used algorithms to interpret deep neural networks. In this work, we investigated the applicability of these two algorithms in the sensitive application area of interpreting chest radiography images. In order to get a more nuanced and balanced outcome, we use a multi-label classification-based dataset and analyze the model prediction by visualizing the outcome of LRP and Grad-CAM on the chest radiography images. The results show that LRP provides more granular heatmaps than Grad-CAM when applied to the CheXpert dataset classification model. We posit that this is due to the inherent construction difference of these algorithms (LRP is layer-wise accumulation, whereas Grad-CAM focuses primarily on the final sections in the model's architecture). Both can be useful for understanding the classification from a micro or macro level to get a superior and interpretable clinical decision support system., The paper won the ‘best student paper award’ provided by the IEEE Technical Committee on Computational Life Science (TCCLS). For details please visit https://2022.cbms-conference.org/awards

Many natural language processing applications rely on the availability of domain-specific terminologies containing synonyms. To that end, semi-automatic methods for extracting additional synonyms of a given concept from corpora are useful, especially in low-resource domains and noisy genres such as clinical text, where nonstandard language use and misspellings are prevalent. In this study, prototype embeddings based on seed words were used to create representations for (i) specific urinary tract infection (UTI) symptoms and (ii) UTI symptoms in general. Four word embedding methods and two phrase detection methods were evaluated using clinical data from Karolinska University Hospital. It is shown that prototype embeddings can effectively capture semantic information related to UTI symptoms. Using prototype embeddings for specific UTI symptoms led to the extraction of more symptom terms compared to using prototype embeddings for UTI symptoms in general. Overall, 142 additional UTI symp tom terms were identified, yielding a more than 100% increment compared to the initial seed set. The mean average precision across all UTI symptoms was 0.51, and as high as 0.86 for one specific UTI symptom. This study provides an effective and cost-effective solution to terminology expansion with small amounts of labeled data.

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.

Background: Surveillance for healthcare-associated infections such as healthcareassociated urinary tract infections (HA-UTI) is important for directing resources and evaluating interventions. However, traditional surveillance methods are resourceintensive and subject to bias. Aim: To develop and validate a fully automated surveillance algorithm for HA-UTI using electronic health record (EHR) data. Methods: Five algorithms were developed using EHR data from 2979 admissions at Karolinska University Hospital from 2010 to 2011: (1) positive urine culture (UCx); (2) positive UCx + UTI codes (International Statistical Classification of Diseases and Related Health Problems, 10th revision); (3) positive UCx + UTI-specific antibiotics; (4) positive UCx + fever and/or UTI symptoms; (5) algorithm 4 with negation for fever without UTI symptoms. Natural language processing (NLP) was used for processing free-text medical notes. The algorithms were validated in 1258 potential UTI episodes from January to March 2012 and results extrapolated to all UTI episodes within this period (N 1/4 16,712). The reference standard for HA-UTIs was manual record review according to the European Centre for Disease Prevention and Control (and US Centers for Disease Control and Prevention) definitions by trained healthcare personnel. Findings: Of the 1258 UTI episodes, 163 fulfilled the ECDC HA-UTI definition and the algorithms classified 391, 150, 189, 194, and 153 UTI episodes, respectively, as HA-UTI. Algorithms 1, 2, and 3 had insufficient performances. Algorithm 4 achieved better performance and algorithm 5 performed best for surveillance purposes with sensitivity 0.667 (95% confidence interval: 0.594-0.733), specificity 0.997 (0.996-0.998), positive predictive value 0.719 (0.624-0.807) and negative predictive value 0.997 (0.996-0.997). Conclusion: A fully automated surveillance algorithm based on NLP to find UTI symptoms in free-text had acceptable performance to detect HA-UTI compared to manua