International journal of medical informatics
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Inpatient falls, many resulting in injury or death, are a serious problem in hospital settings. Existing falls risk assessment tools, such as the Morse Fall Scale, give a risk score based on a set of factors, but don't necessarily signal which factors are most important for predicting falls. Artificial intelligence (AI) methods provide an opportunity to improve predictive performance while also identifying the most important risk factors associated with hospital-acquired falls. We can glean insight into these risk factors by applying classification tree, bagging, random forest, and adaptive boosting methods applied to Electronic Health Record (EHR) data. ⋯ Machine learning methods have the potential to identify the most relevant and novel factors for the detection of hospitalized patients at risk of falling, which would improve the quality of patient care, and to more fully support healthcare provider and organizational leadership decision-making. Nurses would be able to enhance their judgement to caring for patients at risk for falls. Our study may also serve as a reference for the development of AI-based prediction models of other iatrogenic conditions. To our knowledge, this is the first study to report the importance of patient, clinical, and organizational features based on the use of AI approaches.
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Information technologies have been vital during the COVID-19 pandemic. Telehealth and telemedicine services, especially, fulfilled their promise by allowing patients to receive advice and care at a distance, making it safer for all concerned. Over the preceding years, professional societies, governments, and scholars examined ethical, legal, and social issues (ELSI) related to telemedicine and telehealth. Primary concerns evident from reviewing this literature have been quality of care, access, consent, and privacy. ⋯ Clinicians and organizations need updated guidelines for ethical use of telemedicine and telehealth care, and decision- and policy-makers need evidence to inform decisions. The variety of newly implemented telemedicine services is an on-going natural experiment presenting an unparalleled opportunity to develop an evidence-based way forward. The paper recommends evaluation using an applied ethics, context-sensitive approach that explores interactions among multiple factors and considerations. It suggests evaluation questions to investigate ethical, social, and legal issues through multi-method, sociotechnical, interpretive and ethnographic, and interactionist evaluation approaches. Such evaluation can help telehealth, and other information technologies, be integrated into healthcare ethically and effectively.
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The Coronavirus Disease 2019 (COVID-19) has currently ravaged through the world, resulting in over thirteen million confirmed cases and over five hundred thousand deaths, a complete change in daily life as we know it, worldwide lockdowns, travel restrictions, as well as heightened hygiene measures and physical distancing. Being able to analyse and predict the spread of this epidemic-causing disease is hence of utmost importance now, especially as it would help in the reasoning behind important decisions drastically affecting countries and their people, as well as in ensuring efficient resource and utility management. However, the needs of the people and specific conditions of the spread are varying widely from country to country. Hence, this article has two fold objectives: (i) conduct an in-depth statistical analysis of COVID-19 affected patients in India, (ii) propose a mathematical model for the prediction of spread of COVID-19 cases in India. ⋯ The proposed system showed an accuracy of 90.36% for prediction since the first COVID-19 case in India, and 96.67% accuracy over the month of April. Predicted number of cases for the next day is found to be a function of the numbers over the last 3 days, but with an 'increase' factor influenced by the last 10 days. It is noticed that males are affected more than females. It is also noticed that in India, the number of people in each age bucket is steadily decreasing, with the largest number of adults infected being the youngest ones-a departure from the world trend. The model is self-correcting as it improves its predictions every day, by incorporating the previous day's data into the trend-line for the following days. This model can thus be used dynamically not only to predict the spread of COVID-19 in India, but also to check the effect of various government measures in a short span of time after they are implemented.
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With the recent advances in computational science, machine-learning methods have been increasingly used in medical research. Because such projects usually require both a clinician and a computational data scientist, there is a need for interdisciplinary research collaboration. However, there has been no published analysis of research collaboration networks in cardiovascular medicine using machine intelligence. ⋯ A co-authorship network analysis revealed a structure of collaboration networks in the application of machine learning in the field of cardiovascular disease, which can be useful for planning future scientific collaboration.