Clinical utility of machine learning and longitudinal EHR data

Walter F. Stewart, Jason Roy, Jimeng Sun, Shahram Ebadollahi

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


The widespread adoption of electronic health records in large health systems, combined with recent advances in data mining and machine learning methods, creates opportunities for the rapid acquisition and translation of knowledge for use in clinical practice. One area of great potential is in risk prediction of chronic progressive diseases from longitudinal medical records. In this Chapter, we illustrate this potential using a case study involving prediction of heart failure. Throughout, we discuss challenges and areas in need of further development.

Original languageEnglish (US)
Pages (from-to)209-227
Number of pages19
JournalIntelligent Systems Reference Library
StatePublished - 2014
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • Information Systems and Management
  • Library and Information Sciences


  • Electronic health records
  • Hearth failure
  • Machine learning
  • Prediction models
  • Text mining


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