Using EHRs and Machine Learning for Heart Failure Survival Analysis

Maryam Panahiazar, Vahid Taslimitehrani, Naveen Pereira, Jyotishman Pathak

Research output: Chapter in Book/Report/Conference proceedingConference contribution

44 Scopus citations


'Heart failure (HF) is a frequent health problem with high morbidity and mortality, increasing prevalence and escalating healthcare costs' [1]. By calculating a HF survival risk score based on patient-specific characteristics from Electronic Health Records (EHRs), we can identify high-risk patients and apply individualized treatment and healthy living choices to potentially reduce their mortality risk. The Seattle Heart Failure Model (SHFM) is one of the most popular models to calculate HF survival risk that uses multiple clinical variables to predict HF prognosis and also incorporates impact of HF therapy on patient outcomes. Although the SHFM has been validated across multiple cohorts [1-5], these studies were primarily done using clinical trials databases that do not reflect routine clinical care in the community. Further, the impact of contemporary therapeutic interventions, such as beta-blockers or defibrillators, was incorporated in SHFM by extrapolation from external trials. In this study, we assess the performance of SHFM using EHRs at Mayo Clinic, and sought to develop a risk prediction model using machine learning techiniques that applies routine clinical care data. Our results shows the models which were built using EHR data are more accurate (11% improvement in AUC) with the convenience of being more readily applicable in routine clinical care. Furthermore, we demonstrate that new predictive markers (such as co-morbidities) when incorporated into our models improve prognostic performance significantly (8% improvement in AUC).

Original languageEnglish (US)
Title of host publicationMEDINFO 2015
Subtitle of host publicationeHealth-Enabled Health - Proceedings of the 15th World Congress on Health and Biomedical Informatics
EditorsAndrew Georgiou, Indra Neil Sarkar, Paulo Mazzoncini de Azevedo Marques
PublisherIOS Press
Number of pages5
ISBN (Electronic)9781614995630
StatePublished - 2015
Event15th World Congress on Health and Biomedical Informatics, MEDINFO 2015 - Sao Paulo, Brazil
Duration: Aug 19 2015Aug 23 2015

Publication series

NameStudies in Health Technology and Informatics
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365


Other15th World Congress on Health and Biomedical Informatics, MEDINFO 2015
CitySao Paulo


  • Electronic Health Records
  • Heart Failure
  • Machine Learning
  • Survival Score

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management


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