A machine learning model for predicting hepatocellular carcinoma risk in patients with chronic hepatitis B

Background Machine learning (ML) algorithms can be used to overcome the prognostic performance limitations of conventional hepatocellular carcinoma (HCC) risk models. We established and validated an ML‐based HCC predictive model optimized for patients with chronic hepatitis B (CHB) infections receiv...

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Veröffentlicht in:	Liver international 2023-08, Vol.43 (8), p.1813-1821
Hauptverfasser:	Lee, Hye Won, Kim, Hwiyoung, Park, Taeyun, Park, Soo Young, Chon, Young Eun, Seo, Yeon Seok, Lee, Jae Seung, park, Jun Yong, Kim, Do Young, Ahn, Sang Hoon, Kim, Beom Kyung, Kim, Seung Up
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Antiviral agents Antiviral Agents - therapeutic use antiviral therapy Carcinoma, Hepatocellular - drug therapy Carcinoma, Hepatocellular - epidemiology Carcinoma, Hepatocellular - etiology chronic hepatitis B entecavir Female Hepatitis Hepatitis B Hepatitis B, Chronic - complications Hepatitis B, Chronic - drug therapy Hepatitis B, Chronic - epidemiology Hepatocellular carcinoma Humans Learning algorithms Liver cancer Liver Neoplasms - drug therapy Liver Neoplasms - epidemiology Liver Neoplasms - etiology Machine learning Male Middle Aged prediction Prediction models prognosis Retrospective Studies Risk Risk groups risk prediction Tenofovir Tenofovir - therapeutic use Training
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