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Machine learning for individualized prediction of hepatocellular carcinoma development after the eradication of hepatitis C virus with antivirals

Tatsuya Minami, Masaya Sato, Hidenori Toyoda, Satoshi Yasuda, Tomoharu Yamada, T. Nakatsuka, Kenichiro Enooku, Hayato Nakagawa, Hidetaka Fujinaga, Masashi Izumiya, Yasuo Tanaka, Motoyuki Otsuka, Takamasa Ohki, Masahiro Arai, Yoshinari Asaoka, Atsushi Tanaka, Kiyomi Yasuda, Hideaki Miura, Itsuro Ogata, Toshiro Kamoshida, Kazuaki Inoue, Ryo Nakagomi, Masatoshi Akamatsu, Hiroshi Mitsui, Hajime Fujie, Keiji Ogura, Koji Uchino, Hideo Yoshida, Kazuyuki Hanajiri, T. Wada, Kiyohiko Kurai, Hisato Maekawa, Yuji Kondo, Shuntaro Obi, Takuma Teratani, Naohiko Masaki, Kayo Nagashima, Takashi Ishikawa, Naoya Kato, Hiroshi Yotsuyanagi, Kyoji Moriya, Takashi Kumada, Mitsuhiro Fujishiro, Kazuhiko Koike, Ryosuke Tateishi

2023Journal of Hepatology36 citationsDOI

Topics & Concepts

MedicineCohortHepatocellular carcinomaProportional hazards modelRandom forestMachine learningHazard ratioSupport vector machineDiscriminative modelArtificial intelligenceOncologyInternal medicineComputer scienceConfidence intervalHepatitis C virus researchHepatocellular Carcinoma Treatment and PrognosisLiver Disease Diagnosis and Treatment
Machine learning for individualized prediction of hepatocellular carcinoma development after the eradication of hepatitis C virus with antivirals | Litcius