Artificial Intelligence (AI)-Driven Frailty Prediction Using Electronic Health Records in Hospitalized Patients With Cardiovascular Disease
Deep learning models derived from large sample sizes of phenotypic physical frailty have shown good accuracy and consistent associations with prognosis and physical functions.
Authors
- Kenji Satô (ORCID: https://orcid.org/0009-0000-4520-2560)
- Emi Maekawa (ORCID: https://orcid.org/0000-0003-0736-7940)
- Anna Kubota (ORCID: https://orcid.org/0000-0002-4819-9114)
- Masashi Yamashita (ORCID: https://orcid.org/0000-0003-4680-1068)
- Junya Ako (ORCID: https://orcid.org/0000-0001-6645-6404)
- Kazuki Hotta (ORCID: https://orcid.org/0000-0002-8639-2521)
- Kentaro Kamiya (ORCID: https://orcid.org/0009-0005-4980-2973)
- Hiroaki Miyata (ORCID: https://orcid.org/0000-0001-8800-6201)
Institutions
- Keio University (JP)
- American Research Center in Egypt (US)
- Sado General Hospital (JP)
- Kitasato University (JP)
Publication Details
- Journal
- Circulation Reports
- Published
- 2024-10-28
- DOI
- https://doi.org/10.1253/circrep.cr-24-0112
- Citations
- 8
- Primary Topic
- Frailty in Older Adults
- Type
- article
- Field-Weighted Citation Impact
- 2.75