Development of interpretable machine learning models to predict in‐hospital prognosis of acute heart failure patients

Abstract Aims In recent years, there has been remarkable development in machine learning (ML) models, showing a trend towards high prediction performance. ML models with high prediction performance often become structurally complex and are frequently perceived as black boxes, hindering intuitive int...

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Bibliographic Details

Published in:	ESC Heart Failure
Main Authors:	Munekazu Tanaka, Hirohiko Kohjitani, Erika Yamamoto, Takeshi Morimoto, Takao Kato, Hidenori Yaku, Yasutaka Inuzuka, Yodo Tamaki, Neiko Ozasa, Yuta Seko, Masayuki Shiba, Yusuke Yoshikawa, Yugo Yamashita, Takeshi Kitai, Ryoji Taniguchi, Moritake Iguchi, Kazuya Nagao, Takafumi Kawai, Akihiro Komasa, Yuichi Kawase, Takashi Morinaga, Mamoru Toyofuku, Yutaka Furukawa, Kenji Ando, Kazushige Kadota, Yukihito Sato, Koichiro Kuwahara, Yasushi Okuno, Takeshi Kimura, Koh Ono, the KCHF Study Investigators
Format:	Article
Language:	English
Published:	Wiley 2024-10-01
Subjects:	Acute heart failure Machine learning Explainable model SHAP Decision tree model
Online Access:	https://doi.org/10.1002/ehf2.14834