Machine Learning For Risk Prediction After Heart Failure Emergency Department Visit or Hospital Admission Using Administrative Health Data
ML-based modelling with deep feature synthesis provided superior risk stratification for HF patients at 30-days and 1-year follow-up after an ED visit or hospitalization using data from a large administrative regional healthcare system.
Authors
- Jonathan G. Howlett (ORCID: https://orcid.org/0000-0002-0535-9771)
- Justin Adrian Ezekowitz (ORCID: https://orcid.org/0000-0002-2724-4086)
- Nowell Mark Fine (ORCID: https://orcid.org/0000-0002-6546-6587)
- Russell Greiner (ORCID: https://orcid.org/0000-0001-8327-934X)
- Padma Kaul (ORCID: https://orcid.org/0000-0003-2239-3944)
- Finlay A. McAlister (ORCID: https://orcid.org/0000-0001-7435-3341)
- Weijie Sun (ORCID: https://orcid.org/0000-0003-3099-0298)
- James A. White (ORCID: https://orcid.org/0000-0002-5297-4507)
- Sunil V. Kalmady (ORCID: https://orcid.org/0000-0002-4876-9121)
Institutions
- University of Alberta (CA)
- Northern Alberta Institute of Technology (CA)
- Stephens College (US)
- Canadian VIGOUR Centre (CA)
- Libin Cardiovascular Institute of Alberta (CA)
Publication Details
- Journal
- PLOS Digital Health
- Published
- 2024-10-25
- DOI
- https://doi.org/10.1371/journal.pdig.0000636
- Citations
- 4
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 1.39
Funders
- University of Alberta
- Servier