Identification of clinical phenotypes associated with poor prognosis in patients with nonalcoholic fatty liver disease via unsupervised machine learning.

doi:10.1111/jgh.16326

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Article

Identification of clinical phenotypes associated with poor prognosis in patients with nonalcoholic fatty liver disease via unsupervised machine learning.

Journal of gastroenterology and hepatology - 1 Oct 2023

Ito Takanori, Morooka Hikaru, Takahashi Hirokazu, Fujii Hideki, Iwaki Michihiro, Hayashi Hideki, Toyoda Hidenori, Oeda Satoshi, Hyogo Hideyuki, Kawanaka Miwa, Morishita Asahiro, Munekage Kensuke, Kawata Kazuhito, Tsutsumi Tsubasa, Sawada Koji, Maeshiro Tatsuji, Tobita Hiroshi, Yoshida Yuichi, Naito Masafumi, Araki Asuka, Arakaki Shingo, Kawaguchi Takumi, Noritake Hidenao, Ono Masafumi, Masaki Tsutomu, Yasuda Satoshi, Tomita Eiichi, Yoneda Masato, Tokushige Akihiro, Ishigami Masatoshi, Kamada Yoshihiro, Ueda Shinichiro, Aishima Shinichi, Sumida Yoshio, Nakajima Atsushi, Okanoue Takeshi

Abstract excerpt

BACKGROUND AND AIMS: Both fibrosis status and body weight are important for assessing prognosis in nonalcoholic fatty liver disease (NAFLD). The aim of this study was to identify population clusters for specific clinical outcomes based on fibrosis-4 (FIB-4) index and body mass index (BMI) using an unsupervised machine learning method. METHODS: We conducted a multicenter study of 1335 biopsy-proven NAFLD patients...

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PMID: 37596843
DOI: 10.1111/jgh.16326

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