Computers, Volume 10, Issue 1 (January 2021) – 13 articles
Cover Story (view full-size image): Population health management is the automated process of using big data to define patient cohorts and stratify groups by risk, with the final aim of improving clinical outcomes and quality of life while also reducing healthcare costs. This paper presents the trade-off of multiple machine learning algorithms to identify high-risk patients, which are usually affected by multimorbidity and represent major healthcare system users. Input datasets consist of administrative and socioeconomic data from periods of different lengths. Random Forest with 1 year of historical data achieves the best results, enabling real-time risk prediction updates whenever new data are collected and giving physicians the possibility to define appropriate personalized medicine for patients. View this paper
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