Novel machine learning models outperform risk scores in predicting hepatocellular carcinoma in patients with chronic viral hepatitis.

ABSTRACT:

Background & aims

Accurate hepatocellular carcinoma (HCC) risk prediction facilitates appropriate surveillance strategy and reduces cancer mortality. We aimed to derive and validate novel machine learning models to predict HCC in a territory-wide cohort of patients with chronic viral hepatitis (CVH) using data from the Hospital Authority Data Collaboration Lab (HADCL).

Methods

This was a territory-wide, retrospective, observational, cohort study of patients with CVH in Hong Kong in 2000-2018 identified from HADCL based on viral markers, diagnosis codes, and antiviral treatment for chronic hepatitis B and/or C. The cohort was randomly split into training and validation cohorts in a 7:3 ratio. Five popular machine learning methods, namely, logistic regression, ridge regression

SUBMITTER: Wong GL 

PROVIDER: S-EPMC8844233 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature