Integrating Wearable Devices and Recommendation System: Towards a Next Generation Healthcare Service Delivery

Researchers have identified lifestyle diseases as a major threat to human civilization. These diseases gradually progress without giving any warning and result in a sudden health aggravation that leads to a medical emergency. As such, individuals can only avoid the life-threatening condition if they regularly monitor their health status. Health recommendation systems allow users to continuously monitor their health and deliver proper health advice to them. Also, continuous health monitoring depends on the real-time data exchange between health solution providers and users. In this regard, healthcare providers have begun to use wearable devices and recommendation systems to collect data in real time and to manage health conditions based on the generated data. However, we lack literature that has examined how individuals use wearable devices, what type of data the devices collect, and how providers use the data for delivering solutions to users. Thus, we decided to explore the available literature in this domain to understand how wearable devices can provide solutions to consumers. We also extended our focus to cover current health service delivery frameworks with the help of recommender systems. Thus, this study reviews health-monitoring services by conglomerating both wearable device and recommendation system to come up with personalized health and fitness solutions. Additionally, the paper elucidates key components of an advanced-level real-time monitoring service framework to guide future research and practice in this domain

The use of artificial intelligence in nephrology

Introduction and methods Artificial Intelligence(AI) is a relatively new branch of science that studies the display of intelligent behavior by machines and its use in advanced analysis and computation. Due to the potential use of AI, it has also been introduced into medicine and nephrology. The following article is an analysis of the current knowledge on the potential of AI in nephrology and its relevance to clinicians based on the latest publications contained in the PubMed and Google Scholar databases. Stage of knowledge AI found its application in the prognosis of the development of IgA nephropathy thanks to the use of a neural network, which by analyzing the results of research and the drugs used in a large group of patients has learned to detect patients at high risk of developing severe complications at the beginning of the disease. What is more, AI makes it possible to detect DKD earlier and delay renal replacement therapy. In patients undergoing hemodialysis, artificial intelligence developed a model that calculated the appropriate duration of the procedure and adjusted drugs to control blood pressure. Another example of the use of AI is its use in relation to patients undergoing kidney transplantation. The AI calculates the beneficial concentration of an immunosuppressive drug specifically for a given patient, which allows clinicians to limit adverse effects. Summary AI is a breakthrough technology that is constantly being developed. Despite the high cost of implementing this technology, it is believed that it could represent the future of medicine and be a new way in treatment techniques and in the early detection of diseases in nephrology

Kidney Ailment Prediction under Data Imbalance

Chronic Kidney Disease (CKD) is the leading cause for kidney failure. It is a global health problem affecting approximately 10% of the world population and about 15% of US adults. Chronic Kidney Diseases do not generally show any disease specific symptoms in early stages thus it is hard to detect and prevent such diseases. Early detection and classification are the key factors in managing Chronic Kidney Diseases. In this thesis, we propose a new machine learning technique for Kidney Ailment Prediction. We focus on two key issues in machine learning, especially in its application to disease prediction. One is related to class imbalance problem. This occurs when at least one of the classes are represented by significantly smaller number of samples than the others in the training set. The problem with imbalanced dataset is that the classifiers tend to classify all samples as majority class, ignoring the minority class samples. The second issue is on the specific type of data to be used for a given problem. Here, we focused on predicting kidney diseases based on patient information extracted from laboratory and questionnaire data. Most recent approaches for predicting kidney diseases or other chronic diseases rely on the usage of prescription drugs. In this study, we focus on biomarker and anthropometry data of patients to analyze and predict kidney-related diseases. In this research, we adopted a learning approach which involves repeated random data sub-sampling to tackle the class imbalance problem. This technique divides the samples into multiple sub-samples, while keeping each training sub-sample completely balanced. We then trained classification models on the balanced data to predict the risk of kidney failure. Further, we developed an intelligent fusion mechanism to combine information from both the biomarker and anthropometry data sets for improved prediction accuracy and stability. Results are included to demonstrate the performance

Diabetes in the time of COVID-19 pandemic: A knife with two sharp ends

512-520Interactions of current pandemic COVID-19 and pre-existing major health burden Diabetes Mellitus have posed a serious global public health crisis. The emergence of COVID-19 as a communicable viral infection along with the presence of non-communicable diabetes, have transformed the health system into a knife with two sharp ends. Though diabetes worldwide is almost 20 times more than COVID-19 positive cases, the severe virulence and pathogenesis coincides with the routine treatment and pathogenesis of diabetes making it one of the most serious comorbid factors. The first three deaths due to COVID-19 reported in China were diabetes patients. The severity of the association of diabetes with COVID-19 ranges from 5 to 20%. Type 1 diabetes mellitus and type 2 diabetes mellitus increase the susceptibility to infections and their complications. The present study was attempted to review probable interaction between these two global health burdens and possible suggestive management to control their detrimental effect. An intensive online search was conducted using two databases, PubMed and Google Scholar. Most hypothesized pathways for COVID-19 infection are the ACE2 receptors and RAAS system followed by the DPP4 receptor pathway. This review proposes that proper and timely management of the COVD-19 patients with diabetes comorbidity might reduce COVID-19 disease burden

Medical Devices Information Systems in Primary Care

People who suffer from chronic diseases are becoming more involved in remote monitoring processes each year. The market acceptance of remote care programmes, connecting patients through medical devices as part of the treatment regime, is spreading worldwide. Healthcare providers use medical devices to monitor, in various ways, the chronically ill population, namely people with diabetes and hypertension. However, most hospital and service provider information systems do not conform to the same important data standards, making interoperability and information sharing difficult. In this sense, the Multimorbidity Health Information System (METHIS) project is a multidisciplinary, goal-oriented, design-science-based intervention aiming to improve physician-patient communication and patient engagement. It focuses on multimorbidity and ageing, encompassing patients with more than one chronic disease and over 65 years old. The proposed solution is a Clinical Medical Devices Information (CMDI) system and data model which contains standardised information about chronic patients, medical devices and other data sets to be included in the METHIS System. With this framework, the system can perform consistently and reliably while meeting all relevant regulatory requirements or standards. Based on this dissertation and the METHIS project’s complementary work, implementing the CMDI in various Family Health Unit (FHU) in Portugal will make it possible to combat the diversity and loss of telemonitoring information.A cada ano, os doentes crónicos estão mais envolvidos em processos de telemonitorização. A aceitação pelo mercado de programas de cuidados à distância, ligando doentes através de dispositivos médicos como parte do regime de tratamento, está a espalhar-se por todo o mundo. Os prestadores de cuidados de saúde utilizam dispositivos médicos para monitorizar, de várias formas, a população cronicamente doente, nomeadamente as pessoas com diabetes e hipertensão arterial. No entanto, a maioria dos sistemas de informação dos hospitais e prestadores de serviços não estão em conformidade com as mesmas normas, o que dificulta a interoperabilidade e a partilha de informação. Neste sentido, o projeto METHIS é uma intervenção multidisciplinar, baseada em Design Science, que visa melhorar a comunicação entre médico e doente e o envolvimento do mesmo. Tem como foco a multimorbidade e o envelhecimento, englobando doentes com várias doenças crónicas e com idade superior a 65 anos. A solução proposta é um sistema e o correspondente modelo de dados CMDI que contém informação padronizada sobre doentes crónicos, dispositivos médicos e outros conjuntos de dados a serem incluídos no Sistema METHIS. Com este modelo de dados, o sistema possui a informação para poder funcionar de forma consistente e fiável, cumprindo todos os requisitos ou normas regulamentares relevantes. Com base nesta dissertação e no trabalho complementar do projeto METHIS, a implementação da base de dados CMDI em vários Unidades de Saúde em Portugal tornará possível combater a diversidade e a perda de informação na telemonitorização

Self-monitoring Practices, Attitudes, and Needs of Individuals with Bipolar Disorder: Implications for the Design of Technologies to Manage Mental Health

Objective To understand self-monitoring strategies used independently of clinical treatment by individuals with bipolar disorder (BD), in order to recommend technology design principles to support mental health management. Materials and Methods Participants with BD (N = 552) were recruited through the Depression and Bipolar Support Alliance, the International Bipolar Foundation, and WeSearchTogether.org to complete a survey of closed- and open-ended questions. In this study, we focus on descriptive results and qualitative analyses. Results Individuals reported primarily self-monitoring items related to their bipolar disorder (mood, sleep, finances, exercise, and social interactions), with an increasing trend towards the use of digital tracking methods observed. Most participants reported having positive experiences with technology-based tracking because it enables self-reflection and agency regarding health management and also enhances lines of communication with treatment teams. Reported challenges stem from poor usability or difficulty interpreting self-tracked data. Discussion Two major implications for technology-based self-monitoring emerged from our results. First, technologies can be designed to be more condition-oriented, intuitive, and proactive. Second, more automated forms of digital symptom tracking and intervention are desired, and our results suggest the feasibility of detecting and predicting emotional states from patterns of technology usage. However, we also uncovered tension points, namely that technology designed to support mental health can also be a disruptor. Conclusion This study provides increased understanding of self-monitoring practices, attitudes, and needs of individuals with bipolar disorder. This knowledge bears implications for clinical researchers and practitioners seeking insight into how individuals independently self-manage their condition as well as for researchers designing monitoring technologies to support mental health management

Bayesian Network Models of Causal Interventions in Healthcare Decision Making: Literature Review and Software Evaluation

This report summarises the outcomes of a systematic literature search to identify Bayesian network models used to support decision making in healthcare. After describing the search methodology, the selected research papers are briefly reviewed, with the view to identify publicly available models and datasets that are well suited to analysis using the causal interventional analysis software tool developed in Wang B, Lyle C, Kwiatkowska M (2021). Finally, an experimental evaluation of applying the software on a selection of models is carried out and preliminary results are reported.Comment: 50 pages (19 + 31 Appendix

Updating the determinants of health model in the information age

In 1991, Dahlgren and Whitehead produced a highly influential model of the determinants of health that has since been used by numerous national and international public health organizations globally. The purpose of the model is to enable interventions that improve health to be addressed at four key policy levels. It is not a model of health or disease; instead the model is structured around health policy decision-making. However the model needs an update, since it was devised there has been a digital revolution that has transformed every aspect of: human life, our cities, society and the fundamental principles upon which the global economy operates. The article examines the impact of Information and Communication Technologies (ICT) on the determinants of health. ICT has given rise to a new Information Age that is implicated in many of the major global health issues today. Addressing contemporary health issues requires intervention at the level of ICT, particularly as health communication online is central to the delivery and dissemination of public health policies