Identifying Key Variances in Clinical Pathways Associated With Prolonged Hospital Stays Using Machine Learning and ePath Real-World Data: Model Development and Validation Study
A machine learning-based model using ePath data effectively identified critical variances in the clinical pathways associated with PLOS. This automated tool may enhance clinical decision-making and improve patient management.
Authors
- Hidehisa Soejima (ORCID: https://orcid.org/0000-0001-8550-7481)
- Naoki Nakashima (ORCID: https://orcid.org/0000-0003-4926-8772)
- Fumihiko Kinoshita (ORCID: https://orcid.org/0000-0002-4557-0491)
- Yoshifumi Wakata (ORCID: https://orcid.org/0000-0002-5332-2383)
- Masahiro Kamouchi (ORCID: https://orcid.org/0000-0002-5348-8762)
- Tomoharu Yoshizumi (ORCID: https://orcid.org/0000-0002-4497-1816)
- Yasunobu Nohara (ORCID: https://orcid.org/0000-0002-2805-6597)
- Koutarou Matsumoto (ORCID: https://orcid.org/0000-0003-0769-7763)
- Tomoyoshi Takenaka (ORCID: https://orcid.org/0000-0002-0278-1854)
- Asato Hashinokuchi
- Takanori Yamashita (ORCID: https://orcid.org/0000-0002-9127-3703)
- Saori Tou
Institutions
- Kyushu University (JP)
- Saiseikai Kumamoto Hospital (JP)
- Kyushu University Hospital (JP)
- Tokushima University Hospital (JP)
- Kumamoto University (JP)
Publication Details
- Journal
- JMIR Medical Informatics
- Published
- 2025-10-08
- DOI
- https://doi.org/10.2196/71617
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00