A predictive analytics framework for identifying patients at risk of developing multiple medical complications caused by chronic diseases

© 2019 Elsevier B.V. Chronic diseases often cause several medical complications. This paper aims to predict multiple complications among patients with a chronic disease. The literature uses single-task learning algorithms to predict complications independently and assumes no correlation among complications of chronic diseases. We propose two methods (independent prediction of complications with single-task learning and concurrent prediction of complications with multi-task learning) and show that medical complications of chronic diseases can be correlated. We use a case study and compare the performance of these two methods by predicting complications of hypertrophic cardiomyopathy on 106 predictors in 1078 electronic medical records from April 2009-April 2017, inclusive. The methods are implemented using logistic regression, artificial neural networks, decision trees, and support vector machines. The results show multi-task learning with logistic regression improves the performance of predictions in terms of both discrimination and calibration

Electronic games for aged care and rehabilitation

The declining cognitive and motor abilities has become a major problem in the health care of the elderly, often leading to potentially fatal falls. Current rehabilitation strategies to address this issue include routine physiotherapy which is often dull and boring for the patient, leading to poor adherence. In recent years, the use of video games in physical therapy has reportedly had a positive affect on rehabilitation strategies. We propose using a modified version of a music video game, Dance Dance Revolution (DDR), to motivate the elderly and increase adherence to rehabilitation. We also present the design of a mobile monitoring system which allows the health professional to monitor the patient's progress. ©2009 IEEE

Agent-based monitoring of functional rehabilitation using video games

In recent years, there has been an increasing trend towards using video games for health applications. In particular interactive video games, where an individual interacts with the game by moving their limbs or whole body, have started to find application in the field of rehabilitation medicine. The often dull and repetitive nature of rehabilitation exercise can be transformed into an activity to which patients happily adhere via the use of engaging video games that are enjoyable to play. One additional potential benefit of video game use in rehabilitation is that patients can continue to interact with the video game system in their own home following discharge from hospital. As such, video games may offer a means for rehabilitation specialists to remotely assess compliance of patients with their rehabilitation therapy and monitor changes in function over time. Although the use of technology for monitoring health at home is now widespread, an as yet unexplored challenge lies in integrating information technologies with rehabilitation games. This keeps the health professional informed about compliance and progress of the video game exercise, while the patient performs her/his prescribed rehabilitation routine at home. Therefore, there is a strong need for a computational framework to support the medical professional and patient by using an agent-based architecture. Agents are pieces of software that act on behalf of human roles, involved in rehabilitation process. The objective of this chapter is to thus address major issues in designing an agent-based mobile monitoring system for rehabilitation treatments. The chapter also suggests how to remotely measure the patient's progress in rehabilitation treatments while the patient plays video games at home. © 2010 Springer-Verlag Berlin Heidelberg

A decade of research on the use of three-dimensional virtual worlds in health care: A systematic literature review

Background: A three-dimensional virtual world (3DVW) is a computer-simulated electronic 3D virtual environment that users can explore, inhabit, communicate, and interact with via avatars, which are graphical representations of the users. Since the early 2000s, 3DVWs have emerged as a technology that has much to offer the health care sector. Objective: The purpose of this study was to characterize different application areas of various 3DVWs in health and medical context and categorize them into meaningful categories. Methods: This study employs a systematic literature review on the application areas of 3DVWs in health care. Our search resulted in 62 papers from five top-ranking scientific databases published from 1990 to 2013 that describe the use of 3DVWs for health care specific purposes. We noted a growth in the number of academic studies on the topic since 2006. Results: We found a wide range of application areas for 3DVWs in health care and classified them into the following six categories: academic education, professional education, treatment, evaluation, lifestyle, and modeling. The education category, including professional and academic education, contains the largest number of papers (n=34), of which 23 are related to the academic education category and 11 to the professional education category. Nine papers are allocated to treatment category, and 8 papers have contents related to evaluation. In 4 of the papers, the authors used 3DVWs for modeling, and 3 papers targeted lifestyle purposes. The results indicate that most of the research to date has focused on education in health care. We also found that most studies were undertaken in just two countries, the United States and the United Kingdom. Conclusions: 3D virtual worlds present several innovative ways to carry out a wide variety of health-related activities. The big picture of application areas of 3DVWs presented in this review could be of value and offer insights to both the health care community and researchers

Recent Research Areas and Grand Challenges in Security of Electronic Medical Records

This study undertook a literature survey to provide a taxonomy that represents research areas of Electronic Medical Record (EMR). We identified the following areas of research and classified them into eight main categories: design and implementation, evaluation, adoption, impacts, medical research, integration, EMR data design and management, and policy and standards. Even though EMR improves care quality and efficiency in a positive way, some negative perceptions by the health user community (health professionals and health service managers) should not be neglected. By categorizing EMR research articles, we reveal a clear set of grand challenges of EMR in the future education and research in health informatics, biomedical engineering, and related areas. A big picture of EMR research areas presented in this study helps health community to find scientific methods for various grand challenges in EMR