Phenotyping to predict 12-month health outcomes of older general medicine patients
Clusters created using hierarchical, K-means and Louvain community detection algorithms identified well-separated patient phenotypes that were consistently associated with age-related adverse health outcomes. Frailty data was the most valuable data type for predicting most health outcomes.
Authors
- Campbell Thompson (ORCID: https://orcid.org/0000-0002-5164-3327)
- Richard John Woodman (ORCID: https://orcid.org/0000-0002-4094-1222)
- Kimberly Bryant
- Patrick Russell (ORCID: https://orcid.org/0000-0002-2534-3371)
- Arduino Aleksander Mangoni (ORCID: https://orcid.org/0000-0001-8699-1412)
- Michael J. Sorich (ORCID: https://orcid.org/0000-0003-1999-866X)
- Alberto Pilotto (ORCID: https://orcid.org/0000-0002-8615-1955)
Institutions
- Royal Adelaide Hospital (AU)
- Flinders University (AU)
- Flinders Medical Centre (AU)
- Ente Ospedaliero Ospedali Galliera (IT)
- Women's and Children's Health Network (AU)
- University of Bari Aldo Moro (IT)
- The University of Adelaide (AU)
Publication Details
- Journal
- Aging Clinical and Experimental Research
- Published
- 2025-02-22
- DOI
- https://doi.org/10.1007/s40520-024-02924-2
- Citations
- 4
- Primary Topic
- Frailty in Older Adults
- Type
- article
- Field-Weighted Citation Impact
- 4.90
Funders
- Flinders University