Using Machine Learning to Predict the Duration of Atrial Fibrillation: Model Development and Validation
ML models predicting AF duration improved the diagnostic ability of cardiologists. However, cardiologists did not entirely rely on the ML model's prediction, even if they were aware of their diagnostic capability limitations.
Authors
- T Nishimura
- Satoaki Matoba (ORCID: https://orcid.org/0000-0002-9373-4331)
- Nobunari Tomura (ORCID: https://orcid.org/0000-0002-0045-8631)
- Satoshi Shimoo (ORCID: https://orcid.org/0000-0001-6868-2827)
- Hibiki Iwakoshi (ORCID: https://orcid.org/0000-0002-4680-9388)
- Keiji Inoue (ORCID: https://orcid.org/0000-0001-5321-2216)
- Masahiro Makino (ORCID: https://orcid.org/0009-0001-2048-5020)
- Jun Munakata (ORCID: https://orcid.org/0009-0002-3070-8010)
- Keitaro Senoo (ORCID: https://orcid.org/0000-0002-8597-7165)
- Hirokazu Shiraishi (ORCID: https://orcid.org/0000-0002-2421-3647)
- Taku Okawa (ORCID: https://orcid.org/0009-0003-2671-6330)
- Kohei Kawai (ORCID: https://orcid.org/0009-0009-8885-2446)
Institutions
- Kyoto Prefectural University of Medicine (JP)
- Japanese Red Cross Society Kyoto Daini Hospital (JP)
Publication Details
- Journal
- JMIR Medical Informatics
- Published
- 2024-10-13
- DOI
- https://doi.org/10.2196/63795
- Primary Topic
- Atrial Fibrillation Management and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 0.00