Identifying Key Variances in Clinical Pathways Associated With Prolonged Hospital Stays Using Machine Learning and ePath Real-World Data: Model Development and Validation Study

Saori Tou   ¹ , MS ;   Koutarou Matsumoto   ¹ , MPH, PhD ;   Asato Hashinokuchi   ² , MD ;   Fumihiko Kinoshita   ² , MD, PhD ;   Yasunobu Nohara   ³ , PhD ;   Takanori Yamashita   ⁴ , PhD ;   Yoshifumi Wakata   ⁵ , MD, PhD ;   Tomoyoshi Takenaka   ² , MD, PhD ;   Hidehisa Soejima   ⁶ , MD, PhD ;   Tomoharu Yoshizumi   ² , MD, PhD ;   Naoki Nakashima   ^{4, 7} , MD, PhD ;   Masahiro Kamouchi   ^{1, 8} , MD, PhD

¹ Department of Health Care Administration and Management, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan

² Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan

³ Big Data Science and Technology, Faculty of Advanced Science and Technology, Kumamoto University, Kumamoto, Japan

⁴ Medical Information Center, Kyushu University Hospital, Fukuoka, Japan

⁵ Department of Medical Informatics, Tokushima University Hospital, Tokushima, Japan

⁶ Institute for Medical Information Research and Analysis, Saiseikai Kumamoto Hospital, Kumamoto, Japan

⁷ Department of Medical Informatics, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan

⁸ Center for Cohort Studies, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan