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Annually, 10% of patients with atrial fibrillation (AF) or venous thromboembolism (VTE) treated with non-vitamin K oral anticoagulants undergo diagnostic or therapeutic procedures. This subanalysis of the multicenter, prospective, observational Edoxaban Management in Diagnostic and Therapeutic Procedures real-world registry included patients in Europe and Asia with AF or VTE who underwent transcatheter cardiovascular (CV) procedures. Edoxaban interruption and clinical outcomes were assessed for all arterial or venous access procedures and stratified by bleeding risk. Overall, 2695 procedures were reported; 755 (28.0%) were transcatheter CV procedures, of which 373 (49.4%) were arterial access and 382 (50.6%) were venous access procedures. Patients with arterial versus venous access procedures had significantly higher bleeding and stroke and thromboembolism risk scores ( P < 0.0001 for both) and underwent procedures that were more frequently classified as having a higher European Heart Rhythm Association bleeding risk. Edoxaban was interrupted in 59.5% (222) arterial versus 42.4% (162) venous access procedures, mostly either only preprocedurally or both pre- and postprocedurally. The combined incidence of clinically relevant ischemic or bleeding event rates and deaths was low (0.8 events/100 procedures). This subanalysis showed that while edoxaban was interrupted in approximately half of all interventions, ischemic events and major bleeding were low, suggesting transcatheter CV procedures can be performed safely in high-risk patients with AF or VTE. Patient and procedural factors should be considered to personalize the decision of edoxaban management around the time of a transcatheter CV procedure. Clinical trial registration number: NCT02950168, NCT02951039

Abstract Background Optimising periprocedural management of direct oral anticoagulation in patients with atrial fibrillation on chronic treatment undergoing major surgeries is an important aspect of balancing the risk of surgery-related bleeding with the risk of thromboembolic events, which may vary by surgery type. Methods This subanalysis of the prospective EMIT-AF/VTE programme assessed periprocedural-edoxaban management, according to physicians’ decisions, and bleeding and thromboembolic event rates in patients who underwent major vs. nonmajor surgeries. Edoxaban interruption and clinical outcomes were compared between major vs. nonmajor surgeries and between renal function subgroups (creatinine clearance [CrCL] ≤ 50 mL/min vs. > 50 mL/min). Results We included 276 major and 512 nonmajor surgeries. The median pre- and postprocedural duration of edoxaban interruption in major vs. nonmajor surgeries was 4 vs. 1 days, whereas median duration of interruption for those with preprocedural-only and postprocedural-only interruption was 2 vs. 1 days and 2 vs. 0 days, respectively (P

Abstract Introduction Literature reviews support continuing anticoagulation during dental procedures. However, studies often present grouped anticoagulation data, and information on individual anticoagulant management would be helpful to dentists. The Edoxaban Management in Diagnostic and Therapeutic Procedures (EMIT-AF/VTE) programme (NCT02950168; NCT02951039) demonstrated low periprocedural bleeding and thrombotic event rates in patients with atrial fibrillation receiving edoxaban. Aims To report periprocedural edoxaban interruption and clinical events in patients from EMIT-AF/VTE who underwent dental procedures. Methods Dental procedures were categorised by type (cleaning/noncleaning). Edoxaban interruption, bleeding events, and thrombotic events were observed 5 days preprocedure through 29 days postprocedure. Results Overall, 196 patients underwent 350 cleaning and/or noncleaning procedures; most patients (171/196 [87.2%]) underwent noncleaning procedures (282/350 [80.6%]), whereas 48/196 (24.5%) underwent 68/350 (19.4%) cleaning procedures. Edoxaban was uninterrupted for most cleanings (53/68 [77.9%]). Preprocedural interruption was common for single and multiple tooth extractions (single, 67/100 [67.0%]; multiple, 16/30 [53.3%]). The only major bleeding occurred after an unrelated cleaning. Minor bleeding occurred in 1/68 (1.5%) cleaning and 4/282 (1.4%) noncleaning procedures. There were no thrombotic events. Conclusions For most cleanings, edoxaban was not interrupted, whereas preprocedural interruption was more common for tooth extractions. Overall, bleeding rates were low, and no thrombotic events occurred.

Patients treated with edoxaban may require diagnostic and therapeutic procedures that involve edoxaban interruption. Although heparin bridging strategies are not recommended, heparin is frequently used in clinical practice. However, whether heparin use decreases thromboembolic risk remains unclear, and the potential for increased periprocedural bleeding remains a concern. Here, we report factors predicting edoxaban interruption and the use of heparin bridging strategies and associated clinical events from Global EMIT-AF/VTE, a multicenter, prospective, noninterventional study (Clinicaltrials.gov NCT02950168). Eligible patients are adults with atrial fibrillation or venous thromboembolism treated with edoxaban who underwent a diagnostic or therapeutic procedure. Edoxaban interruption, heparin bridging strategies, and clinical event data were collected from 5 days before procedure through 29 days afterwards. Edoxaban was interrupted in 1222/2089 procedures (58.5%); a heparin bridging strategy was used during 178 (14.6%) of these interruptions. Patients who received periprocedural heparin had higher baseline HAS-BLED (2.4±1.0 vs 1.9±1.1, P 3 and high EHRA procedural risk predicted both edoxaban interruption and the use of a heparin bridging strategy, whereas CHA 2 DS 2 -VASc scores did not predict either. Bleeding and ischemic event rates were low; the all-bleeding rate was higher with the use of a heparin bridging strategy versus without (6.2% vs 3.1%, P = 0.04). Periprocedural heparin use was associated with higher bleeding rates, but not with lower thromboembolic risk. Individual patient and procedural bleeding risks appear to contribute more than stroke risk to clinicians’ consideration of a heparin bridging strategy.

Recently, various sophisticated methods, including machine learning and artificial intelligence, have been employed to examine health-related data. Medical professionals are acquiring enhanced diagnostic and treatment abilities by utilizing machine learning applications in the healthcare domain. Medical data have been used by many researchers to detect diseases and identify patterns. In the current literature, there are very few studies that address machine learning algorithms to improve healthcare data accuracy and efficiency. We examined the effectiveness of machine learning algorithms in improving time series healthcare metrics for heart rate data transmission (accuracy and efficiency). In this paper, we reviewed several machine learning algorithms in healthcare applications. After a comprehensive overview and investigation of supervised and unsupervised machine learning algorithms, we also demonstrated time series tasks based on past values (along with reviewing their feasibility for both small and large datasets).

Background: Augmented reality (AR) technology can simultaneously superimpose virtual images on real objects, which is different from conventional image-guided solutions. After wearing AR head-mounted devices (ARHMDs), doctors can have the ability to see through the spine without shifting their eyes to an external navigation screen. Objective: This review aims to summarize the current application of ARHMDs in spine surgery, and analyze the research problems and future potentials. Methods: We performed a review of the application of ARHMDs in spinal surgery. Pubmed and google schoolar were searched from database inception to November 1, 2021, for studies on the application of ARHMDs in spine surgery. Summarize studies with definite technical routes, workflows, and effective assessment. Results: 16 studies were included in this review, with surgical applications focusing on vertebroplasty, pedicle screw placement, rod bending. According to the different image registration and tracking methods, the researches involved contour registration, body surface marker registration and spinous process marker registration. Most of the results showed that ARHMDs could be competent for these clinical navigation tasks, and were non-inferior when comparing to free-hand (FH). Some studies even met the standard of CANS, but require the assistance of intraoperative CBCT or CT. Conclusions: At present, ARHMDs can assist clinical tasks through image registration and tracking. However, due to the insufficient ARHMD hardware performance and the need of professional engineers in software development, it is not enough to complete high-precision navigation surgery alone. With the rapid development in science and technology. The ARHMD technology is still a promising method that will play an important role in navigated spine surgery.

Background and Aim: Intramedullary spinal cord glioma has no evident boundary with normal spinal cord tissue. The rate of successful surgical resection of intramedullary spinal cord glioma is low. Well-established animal models for intramedullary spinal cord glioma can help promote translation from related basic therapy research to clinical applications. In this study, we established a rat model of intramedullary spinal cord glioma. Materials and Methods: A total of 23 male Sprague-Dawley (SD) rats were randomized into blank control (n = 3) and experimental (n = 20) groups. The blank control group received intramedullary injection of Dulbecco's modified Eagle medium (DMEM) and the experimental group was injected with DMEM containing C6 glioma cells. The neurological states of these rats were evaluated using the Basso, Beattie, and Bresnahan scale (BBB). Tumor sizes were measured by magnetic resonance imaging. The histopathological analysis was performed to observe the growth of infiltrating tumors. All procedures involving animals were approved by the Ethics Committee of the Laboratory Animal Facility Biomedical Analysis Center, Tsinghua University (Beijing, China; approval No. 17-WGH1). Results: On postoperative 7 days, the experimental group presented with a significant progressive decrease in motor function (mean BBB score 15.00 ± 1.20) compared with the blank control group (20.67 ± 0.47, P < 0.01). All rats in the experimental group showed exponential tumor growth and had an average survival of up to 5 weeks after tumor cell implantation. The tumor sizes were 3.18 ± 0.21 mm3, 68.55 ± 3.38 mm3, and 345.28 ± 22.57 mm3 on postoperative 7, 14, and 28 days. The histopathological analysis illustrated that the growth of infiltrating tumors followed the longitudinal axis of the spinal cord. Conclusions: Thus, we have established a SD rat model of intramedullary spinal cord glioma, and we found that our findings are reproducible and homogeneous. These positive results provide solid bases for further studies of intramedullary spinal cord glioma.

pH sensors are increasingly being utilized in the biomedical field and have been implicated in health applications that aim to improve the monitoring and treatment of patients. In this work, a previously developed Titanium Nitride (TiN) solid-state pH sensor is further enhanced, with the potential to be used for pH regulation inside the human body and for other biomedical, industrial, and environmental applications. One of the main limitations of existing solid-state pH sensors is their reduced performance in high redox mediums. The potential shift E0 value of the previously developed TiN pH electrode in the presence of oxidizing or reducing agents is 30 mV. To minimize this redox shift, a Nafion-modified TiN electrode was developed, tested, and evaluated in various mediums. The Nafion-modified electrode has been shown to shift the E0 value by only 2 mV, providing increased accuracy in highly redox samples while maintaining acceptable reaction times. Overcoming the redox interference for pH measurement enables several advantages of the Nafion-modified TiN electrode over the standard pH glass electrode, implicating its use in medical diagnosis, real-time health monitoring, and further development of miniaturized smart sensors.

Abstract Background Although effective results of many studies support the use of spinal cord stimulation in chronic pain patients, no randomized controlled trial has been undertaken in China to date. CITRIP is a multicenter, prospective, randomized, withdrawal study designed to evaluate the clinical effectiveness and safety of spinal cord stimulation plus remote programming management in patients with intractable trunk or limb pain. Method Participants will be recruited in approximately 10 centers across China. Eligible participants with intractable trunk or limb and an average visual analog scale (VAS) score ≥ 5 will undergo a spinal cord stimulation test. Participants with VAS score reduction ≥ 50% could move forward to receive implantation of an implanted pulse generator. In the withdrawal period at 3-month follow-up visit, participants randomized to the experimental group (EG) will undergo continuous stimulation while ceasing the stimulation in the control group (CG). The outcome assessment will occur at baseline and at 1, 3 (pre- and post-randomization), and 6 months. The primary outcome is the difference of maximal VAS score between EG and CG in the withdrawal period compared with baseline before the withdrawal period. Additional outcomes include VAS score change at 1-, 3-, and 6-month follow-ups; responder rate (VAS score improving by 50%); achievement rate of a desirable pain state (VAS score ≤ 4); awake times during sleep; Beck Depression Inventory for depression evaluation; short-form 36 for quality of life evaluation; drug usage; and satisfaction rating of the device. Adverse events will be collected. The primary analysis will follow the intention-to-treat principle. Discussion The CITRIP study seeks to evaluate the effectiveness and safety of a randomized withdrawal trial of spinal cord stimulation for patients with intractable trunk or limb pain. Trial registration ClinicalTrials.gov NCT03858790 . Registered on March 1, 2019, retrospectively registered

Abstract Background EdoxabaN versus warfarin in subjectS UndeRgoing cardiovErsion of Atrial Fibrillation evaluated use of nonvitamin K antagonist oral anticoagulant edoxaban vs enoxaparin‐warfarin in patients with nonvalvular atrial fibrillation undergoing electrical cardioversion. Hypothesis To assess clinical factors related to successful or unsuccessful cardioversion. To evaluate whether differences in adverse events based on anticoagulation strategy may exist. Methods In this multicenter prospective randomized open‐label blinded end‐point evaluation trial, 2199 patients were randomized to edoxaban 60 mg once daily (30 mg for creatinine clearance 15‐50 mL/min, weight ≤ 60 kg, and/or concomitant use of P‐glycoprotein inhibitor) or enoxaparin‐warfarin. Successful cardioversion was confirmed by 12‐lead electrocardiography‐documented sinus rhythm. Results Cardioversion was successful in 1578 patients; in 355 patients, cardioversion was unsuccessful. Male, high body weight, high body mass index (BMI), coronary artery disease, concomitant aspirin, or prior statins use were more common in patients with unsuccessful cardioversion; international normalized ratio control did not differ by cardioversion success. On multivariate analysis, gender (P

There has been an increasing prevalence of ad-hoc networks for various purposes and applications. These include Low Power Wide Area Networks (LPWAN) and Wireless Body Area Networks (WBAN) which have emerging applications in health monitoring as well as user location tracking in emergency settings. Further applications can include real-time actuation of IoT equipment, and activation of emergency alarms through the inference of a user's situation using sensors and personal devices through a LPWAN. This has potential benefits for military networks and applications regarding the health of soldiers and field personnel during a mission. Due to the wireless nature of ad-hoc network devices, it is crucial to conserve battery power for sensors and equipment which transmit data to a central server. An inference system can be applied to devices to reduce data size for transfer and subsequently reduce battery consumption, however this could result in compromising accuracy. This paper presents a framework for secure automated messaging and data fusion as a solution to address the challenges of requiring data size reduction whilst maintaining a satisfactory accuracy rate. A Multilayer Inference System (MIS) was used to conserve the battery power of devices such as wearables and sensor devices. The results for this system showed a data reduction of 97.9% whilst maintaining satisfactory accuracy against existing single layer inference methods. Authentication accuracy can be further enhanced with additional biometrics and health data information.

Monitoring a patient’s vital signs is considered one of the most challenging problems in telehealth systems, especially when patients reside in remote locations. Companies now use IoT devices such as wearable devices to participate in telehealth systems. However, the steady adoption of wearables can result in a significant increase in the volume of data being collected and transmitted. As these devices run on limited battery power, they can run out of power quickly due to the high processing requirements of the device for data collection and transmission. Given the importance of medical data, it is imperative that all transmitted data adhere to strict integrity and availability requirements. Reducing the volume of healthcare data and the frequency of transmission can improve a device’s battery life via an inference algorithm. Furthermore, this approach creates issues for improving transmission metrics related to accuracy and efficiency, which are traded-off against each other, with increasing accuracy reducing efficiency. This paper demonstrates that machine learning (ML) can be used to overcome the trade-off problem. The damped least-squares algorithm (DLSA) is used to enhance both metrics by taking fewer samples for transmission whilst maintaining accuracy. The algorithm is tested with a standard heart rate dataset to compare the metrics. The results showed that the DLSA provides the best performance, with an efficiency of 3.33 times for reduced sample data size and an accuracy of 95.6%, with similar accuracies observed in seven different sampling cases adopted for testing that demonstrate improved efficiency. This proposed method significantly improve both metrics using ML without sacrificing one metric over the other compared to existing methods with high efficiency.

As COVID-19 rampages throughout the world and has a major impact on the healthcare system, non-emergency medical procedures have nearly come to a halt due to appropriate resource reallocation. However, pain never stops, particularly for patients with chronic intractable pain and implanted spinal cord stimulation (SCS) devices. The isolation required to fight this pandemic makes it impossible for such patients to adjust the parameters or configuration of the device on site. Although telemedicine has shown a great effect in many healthcare scenarios, there have been fewer applications of such technology focusing on the interaction with implanted devices. Here, we introduce the first remote and wireless programming system that enables healthcare providers to perform video-based real-time programming and palliative medicine for pain patients with a SCS implant. During the COVID-19 pandemic from January 23, 2020, the date of lockdown of Wuhan, to April 30, 2020, 34 sessions of remote programming were conducted with 16 patients. Thirteen of the 16 patients required programming for parameter optimization. Improvement was achieved with programming adjustment in 12 of 13 (92.3%) cases. Eleven of the 16 (68.8%) patients reported that the system was user-friendly and met their needs. Five patients complained of an unstable connection resulting from the low network speed initially, and three of these patients solved this problem. In summary, we demonstrated that a remote wireless programming system can deliver safe and effective programming operations of implantable SCS device, thereby providing palliative care of value to the most vulnerable chronic pain patients during a pandemic.Clinical Trial Registrationwww.clinicaltrials.gov, identifier NCT 03858790.

Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through deep learning based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be efficient with a better accuracy that can work on lightweight computational devices. The proposed model is efficient in maintaining stateful information for precise predictions. A grey-level co-occurrence matrix is used for assessing the progress of diseased growth. The performance has been compared against other state-of-the-art models such as Fine-Tuned Neural Networks (FTNN), Convolutional Neural Network (CNN), Very Deep Convolutional Networks for Large-Scale Image Recognition developed by Visual Geometry Group (VGG), and convolutional neural network architecture that expanded with few changes. The HAM10000 dataset is used and the proposed method has outperformed other methods with more than 85% accuracy. Its robustness in recognizing the affected region much faster with almost 2× lesser computations than the conventional MobileNet model results in minimal computational efforts. Furthermore, a mobile application is designed for instant and proper action. It helps the patient and dermatologists identify the type of disease from the affected region’s image at the initial stage of the skin disease. These findings suggest that the proposed system can help general practitioners efficiently and effectively diagnose skin conditions, thereby reducing further complications and morbidity.

Privacy protection in electronic healthcare applications is an important consideration, due to the sensitive nature of personal health data. Internet of Health Things (IoHT) networks that are used within a healthcare setting have unique challenges and security requirements (integrity, authentication, privacy, and availability) that must also be balanced with the need to maintain efficiency in order to conserve battery power, which can be a significant limitation in IoHT devices and networks. Data are usually transferred without undergoing filtering or optimization, and this traffic can overload sensors and cause rapid battery consumption when interacting with IoHT networks. This poses certain restrictions on the practical implementation of these devices. In order to address these issues, this paper proposes a privacy-preserving two-tier data inference framework solution that conserves battery consumption by inferring the sensed data and reducing data size for transmission, while also protecting sensitive data from leakage to adversaries. The results from experimental evaluations on efficiency and privacy show the validity of the proposed scheme, as well as significant data savings without compromising data transmission accuracy, which contributes to energy efficiency of IoHT sensor devices.

Background Anticoagulation in patients with malignancy and atrial fibrillation is challenging because of enhanced risks for thrombosis and bleeding and the frequent need for invasive procedures. Data on direct oral antagonists in such patients are sparse. Methods and Results The ENGAGE AF‐TIMI 48 (Effective Anticoagulation With Factor Xa Next Generation in Atrial Fibrillation–Thrombolysis in Myocardial Infarction Study 48) trial randomized 21 105 patients with atrial fibrillation to edoxaban or warfarin. Patients with malignancy, defined as a postrandomization new diagnosis or recurrence of remote cancer, were followed up over a median of 2.8 years. Adjusted Cox proportional hazard models were used to evaluate the safety and efficacy of edoxaban versus warfarin. Over a median of 495 days (interquartile range, 230–771 days), 1153 patients (5.5%) were diagnosed with new or recurrent malignancy, most commonly involving the gastrointestinal tract (20.6%), prostate (13.6%), and lung (11.1%). Malignancy was associated with increased risk of death (adjusted hazard ratio [HR], 3.12; 95% confidence interval [CI], 2.78–3.50) and major bleeding (adjusted HR, 2.45; 95% CI, 2.07–2.89), but not stroke/systemic embolism (adjusted HR, 1.08; 95% CI, 0.83–1.42). Relative outcomes with higher‐dose edoxaban versus warfarin were consistent regardless of malignancy status for stroke/systemic embolism (HR, 0.60 [95% CI, 0.31–1.15] for malignancy versus HR, 0.89 [95% CI, 0.76–1.05] for no malignancy; interaction P=0.25) and major bleeding (HR, 0.98 [95% CI, 0.69–1.40] for malignancy versus HR, 0.79 [95% CI, 0.69–1.05] for no malignancy; interaction P=0.31). There was, however, a significant treatment interaction for the composite ischemic end point (ischemic stroke/systemic embolism/myocardial infarction), with greater efficacy of higher‐dose edoxaban versus warfarin in patients with malignancy (HR, 0.54; 95% CI, 0.31–0.93) compared with no malignancy (HR, 1.02; 95% CI, 0.88–1.18; interaction P=0.026). Conclusions In patients with atrial fibrillation who develop malignancy, the efficacy and safety profile of edoxaban relative to warfarin is preserved, and it may represent a more practical alternative.

Among the various factors affecting long distance driving are the risks imposed by a driver’s health, which could result in road accidents. Internet of Things (IoT) and mobile health (mHealth) technologies are increasingly being recognized for their role in monitoring various health parameters of such drivers. IoT devices such as wearable devices could be used efficiently in vehicles by professional long-distance drivers. However, the adoption of such technologies is limited by security and privacy issues. This paper proposes a novel vehicular alarm notification system by integrating mHealth data of the driver with vehicular data for improving vehicle safety. The purpose of the system is to minimize road accidents by integrating the emerging mHealth technologies with the vehicular information system (VIS) using wireless body area network sensors and devices in a secure and lightweight framework. An integrated secure system could facilitate adoptability to provide timely notifications of emergencies to drivers in order to avoid road accidents and take appropriate follow-up actions. Furthermore, the information gathered by the integrated system could assist health service providers to address the driver’s root cause of potential health risks. The lightweight security framework adopted in this study is based on an efficient trust model that can improve the adoption of an mHealth integrated vehicular alarm notification system. Our contribution is to provide a real-time alarm notification service to avoid potential traffic accidents by monitoring the health status of long-distance drivers, achieved through incorporating an affordable and lightweight security system to secure health data transfer.

Transferring data from a sensor or monitoring device in electronic health, vehicular informatics, or Internet of Things (IoT) networks has had the enduring challenge of improving data accuracy with relative efficiency. Previous works have proposed the use of an inference system at the sensor device to minimize the data transfer frequency as well as the size of data to save network usage and battery resources. This has been implemented using various algorithms in sampling and inference, with a tradeoff between accuracy and efficiency. This paper proposes to enhance the accuracy without compromising efficiency by introducing new algorithms in sampling through a hybrid inference method. The experimental results show that accuracy can be significantly improved, whilst the efficiency is not diminished. These algorithms will contribute to saving operation and maintenance costs in data sampling, where resources of computational and battery are constrained and limited, such as in wireless personal area networks emerged with IoT networks.

Due to the prevalence and constantly increasing risk of cyber-attacks, new and evolving security mechanisms are required to protect information and networks and ensure the basic security principles of confidentiality, integrity, and availability—referred to as the CIA triad. While confidentiality and integrity can be achieved using Secure Sockets Layer (SSL)/Transport Layer Security (TLS) certificates, these depend on the correct authentication of servers, which could be compromised due to man-in-the-middle (MITM) attacks. Many existing solutions have practical limitations due to their operational complexity, deployment costs, as well as adversaries. We propose a novel scheme to detect MITM attacks with minimal intervention and workload to the network and systems. Our proposed model applies a novel inferencing scheme for detecting true anomalies in transmission time at a trusted time server (TTS) using time-based verification of sent and received messages. The key contribution of this paper is the ability to automatically detect MITM attacks with trusted verification of the transmission time using a learning-based inferencing algorithm. When used in conjunction with existing systems, such as intrusion detection systems (IDS), which require comprehensive configuration and network resource costs, it can provide a robust solution that addresses these practical limitations while saving costs by providing assurance.

BackgroundElderly patients with atrial fibrillation are at higher risk of both ischemic and bleeding events compared to younger patients. In a prespecified analysis from the ENGAGE AF‐TIMI 48 trial, we evaluate clinical outcomes with edoxaban versus warfarin according to age. Methods and ResultsTwenty‐one thousand one‐hundred and five patients enrolled in the ENGAGE AF‐TIMI 48 trial were stratified into 3 prespecified age groups:

With the emergence of technologies such as electronic health and mobile health (eHealth/mHealth), cloud computing, big data, and the Internet of Things (IoT), health related data are increasing and many applications such as smartphone apps and wearable devices that provide wellness and fitness tracking are entering the market. Some apps provide health related data such as sleep monitoring, heart rate measuring, and calorie expenditure collected and processed by the devices and servers in the cloud. These requirements can be extended to provide a personalized life expectancy (PLE) for the purpose of wellbeing and encouraging lifestyle improvement. No existing works provide this PLE information that is developed and customized for the individual. This article is based on the concurrent models and methodologies to calculate and predict life expectancy (LE) and proposes an idea of using multi-phased approaches to the solution as the project requires an immense and broad range of work to accomplish. As a result, the current prediction of LE, which was found to be up to a maximum of five years could potentially be extended to a lifetime prediction by utilizing generic health data. In this article, the novel idea of the solution proposing a PLE on an individual basis, which can be extended to lifetime is presented in addition to the existing works.

Some applications, including disaster monitoring and recovery networks, use low-power wide-area networks (LPWAN). LPWAN sensors capture data bits and transmit them to public carrier networks (e.g., cellular networks) via dedicated gateways. One of the challenges encountered in disaster management scenarios revolves around the carry/forward sensed data and geographical location information dissemination to the disaster relief operatives (disaster relief agency; DRA) to identify, characterise, and prioritise the affected areas. There are network topology options to reach its destination, including cellular, circuit switched, and peer-to-peer networks. In the context of natural disaster prediction, it is vital to access geographical location data as well as the timestamp. This paper proposes the usage of Pseudo A Number (PAN), that is, the calling party address, which is used by every network to include the location information instead of the actual calling party address of the gateway in LPWAN. This PAN information can be further analysed by the DRA to identify the affected areas and predict the complications of the disaster impacts in addition to the past history of damages. This paper aims to propose a solution that can predict disaster proceedings based on propagation and the velocity of impact using vector calculation of the location data and the timestamp, which are transmitted by sensors through the PAN of the gateway in LPWAN.