Data‐driven prediction of prolonged air leak after video‐assisted thoracoscopic surgery for lung cancer: Development and validation of machine‐learning‐based models using real‐world data through the <scp>ePath</scp> system
The data-driven prediction model implementing the ePath system exhibited adequate performance in predicting PAL post-video-assisted thoracoscopic surgery, optimizing variables and considering population characteristics in a real-world setting.
Authors
- Hidehisa Soejima (ORCID: https://orcid.org/0000-0001-8550-7481)
- Fumihiko Kinoshita (ORCID: https://orcid.org/0000-0002-4557-0491)
- Yoshifumi Wakata (ORCID: https://orcid.org/0000-0002-5332-2383)
- Hideki Nakaguma
- Yukio Kozuma
- 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)
- Rui Sugeta
- Koutarou Matsumoto (ORCID: https://orcid.org/0000-0003-0769-7763)
- Tomoyoshi Takenaka (ORCID: https://orcid.org/0000-0002-0278-1854)
- Asato Hashinokuchi
- Naoki Nakashima
- Takanori Yamashita (ORCID: https://orcid.org/0000-0002-9127-3703)
- Saori Tou
- Kazunori Iwatani
Institutions
- Kyushu University (JP)
- National Kyushu Medical Center (JP)
- Saiseikai Kumamoto Hospital (JP)
- Kyushu University Hospital (JP)
- Kumamoto University (JP)
Publication Details
- Journal
- Learning Health Systems
- Published
- 2024-10-11
- DOI
- https://doi.org/10.1002/lrh2.10469
- Citations
- 4
- Primary Topic
- Lung Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.68
Funders
- Japan Society for the Promotion of Science