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

Ofir Ben-Assuli, Tsipi Heart, Joshua R. Vest, Roni Ramon-Gonen, Nir Shlomo, Robert Klempfner

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)237-249
Number of pages13
JournalInformation Systems Management
Volume38
Issue number3
DOIs
StatePublished - 1 Jan 2021
Externally publishedYes

Keywords

  • Applying machine learning
  • Hidden Markov Models (HMM)
  • congestive heart failure
  • readmission
  • utilizing predictive analytics

Fingerprint

Dive into the research topics of 'Profiling Readmissions Using Hidden Markov Model - the Case of Congestive Heart Failure'. Together they form a unique fingerprint.

Cite this