Profiling Readmissions Using Hidden Markov Model-The Case Of Congestive Heart Failure
INFORMATION SYSTEMS MANAGEMENT(2021)
摘要
Reducing costly hospital readmissions of patients with Congestive Heart Failure (CHF) is important. We analyzed 4,661 CHF patients (from 2007 to 2017) using Hidden Markov Models in order to profile CHF readmission risk over time. This method proved practical in identifying three patient groups with distinctive characteristics, which might guide physicians in tailoring personalized care to prevent hospital readmission. We thus demonstrate how applying appropriate AI analytics can save costs and improve the quality of care.
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关键词
Applying machine learning, utilizing predictive analytics, congestive heart failure, Hidden Markov Models (HMM), readmission
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