Machine learning models to further identify advantaged populations that can achieve functional cure of chronic hepatitis B virus infection after receiving Peg-IFN alpha treatment.

Journal: International journal of medical informatics
PMID: 39454328

Abstract

OBJECTIVE: Functional cure is currently the highest goal of hepatitis B virus(HBV) treatment.Pegylated interferon(Peg-IFN) alpha is an important drug for this purpose,but even in the hepatitis B e antigen(HBeAg)-negative population,there is still a portion of the population respond poorly to it.Therefore,it is important to explore the influencing factors affecting the response rate of Peg-IFN alpha and establish a prediction model to further identify advantaged populations.

Authors

  • Wenting Zhong
    Department of Infectious Disease, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China.
  • Che Wang
    School of Software, East China Jiaotong University, Nanchang, Jiangxi, 330013, China.
  • Jia Wang
    Institute of Special Animal and Plant Sciences, Chinese Academy of Agricultural Sciences, Changchun, Jilin, China.
  • Tianyan Chen
    Department of Infectious Diseases, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.

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