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 ePath system

Abstract Introduction The reliability of data‐driven predictions in real‐world scenarios remains uncertain. This study aimed to develop and validate a machine‐learning‐based model for predicting clinical outcomes using real‐world data from an electronic clinical pathway (ePath) system. Methods All a...

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Bibliographic Details
Main Authors:	Saori Tou, Koutarou Matsumoto, Asato Hashinokuchi, Fumihiko Kinoshita, Hideki Nakaguma, Yukio Kozuma, Rui Sugeta, Yasunobu Nohara, Takanori Yamashita, Yoshifumi Wakata, Tomoyoshi Takenaka, Kazunori Iwatani, Hidehisa Soejima, Tomoharu Yoshizumi, Naoki Nakashima, Masahiro Kamouchi
Format:	Article
Language:	English
Published:	Wiley 2025-04-01
Series:	Learning Health Systems
Subjects:	data‐driven prediction electronic clinical pathway lung cancer machine learning prolonged air leak real‐world data
Online Access:	https://doi.org/10.1002/lrh2.10469
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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 ePath system

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