Profiling Readmissions Using Hidden Markov Model - the Case of Congestive Heart Failure

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...

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Veröffentlicht in:	Information systems management 2021-07, Vol.38 (3), p.237-249
Hauptverfasser:	Ben-Assuli, Ofir, Heart, Tsipi, Vest, Joshua R., Ramon-Gonen, Roni, Shlomo, Nir, Klempfner, Robert
Format:	Artikel
Sprache:	eng
Schlagworte:	Applying machine learning Artificial intelligence congestive heart failure Cost analysis Heart failure Hidden Markov Models (HMM) Markov analysis Markov chains Patient admissions Physicians Predictive analytics readmission utilizing predictive analytics
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