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This paper is in the following e-collection/theme issue:

Machine Learning (3362) Clinical Information and Decision Making (3931) Decision Support for Health Professionals (2376) Electronic Health Records (1803) E-Health / Health Services Research and New Models of Care (1242) Frailty Detection, Assessment and Prediction (251) AI in Older Adult Care (249)

Published on in Vol 14 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/95478, first published .
Doctor handing patient a clipboard for signature in medical office

Development and Internal Validation of an Electronic Medical Record–Based Machine Learning Model for Frailty Prediction in Hospitalized Patients: Retrospective Cohort Study

Development and Internal Validation of an Electronic Medical Record–Based Machine Learning Model for Frailty Prediction in Hospitalized Patients: Retrospective Cohort Study

Authors of this article:

Georgina Christine Laurea Szabo1 Author Orcid Image ;   Madison Bills2, 3 Author Orcid Image ;   Matthew Williams2 Author Orcid Image ;   Annie Conway4 Author Orcid Image ;   Leesa Van Bochove1 Author Orcid Image ;   Gerry O'Callaghan2, 3 Author Orcid Image
Article Authors Cited by Tweetations Metrics

Georgina Christine Laurea Szabo   1 ;   Madison Bills   2, 3 ;   Matthew Williams   2 ;   Annie Conway   4 ;   Leesa Van Bochove   1 ;   Gerry O'Callaghan   2, 3

1 SA Health, Commission on Excellence and Innovation in Health, Adelaide, South Australia, Australia

2 Central Adelaide Local Health Network, Adelaide, South Australia, Australia

3 School of Public Health, Adelaide University, Adelaide, South Australia, Australia

4 Adelaide EpiCentre, Central Adelaide Local Health Network, Adelaide, South Australia, Australia

Corresponding Author:

  • Madison Bills
  • Central Adelaide Local Health Network
  • Royal Adelaide Hospital, 1 Port Road
  • Adelaide, South Australia 5000
  • Australia
  • Phone: 61 0432831520
  • Email: Madison.bills@sa.gov.au