Feasibility of machine learning-based modeling and prediction using multiple centers data to assess intrahepatic cholangiocarcinoma outcomes

Feasibility of machine learning-based modeling and prediction using multiple centers data to assess intrahepatic cholangiocarcinoma outcomes

AbstractBackground and aims Currently, there are still no definitive consensus in the treatment of intrahepatic cholangiocarcinoma (iCCA). This study aimed to build a clinical decision support tool based on machine learning using the Surveillance, Epidemiology, and End Results (SEER) database and th...

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Bibliographic Details
Main Authors: Shuang-Nan Zhou, Da-Wei Jv, Xiang-Fei Meng, Jing-Jing Zhang, Chun Liu, Ze-Yi Wu, Na Hong, Yin-Ying Lu, Ning Zhang
Format: Article
Language:English
Published: Taylor & Francis Group 2023-12-01
Series:Annals of Medicine
Subjects:
Intrahepatic cholangiocarcinoma
machine-learning
prognostic system
clinical decision
Online Access:https://www.tandfonline.com/doi/10.1080/07853890.2022.2160008

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https://www.tandfonline.com/doi/10.1080/07853890.2022.2160008

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