Artificial neural network accurately predicts hepatitis B surface antigen seroclearance.

ABSTRACT:

Background & aims

Hepatitis B surface antigen (HBsAg) seroclearance and seroconversion are regarded as favorable outcomes of chronic hepatitis B (CHB). This study aimed to develop artificial neural networks (ANNs) that could accurately predict HBsAg seroclearance or seroconversion on the basis of available serum variables.

Methods

Data from 203 untreated, HBeAg-negative CHB patients with spontaneous HBsAg seroclearance (63 with HBsAg seroconversion), and 203 age- and sex-matched HBeAg-negative controls were analyzed. ANNs and logistic regression models (LRMs) were built and tested according to HBsAg seroclearance and seroconversion. Predictive accuracy was assessed with area under the receiver operating characteristic curve (AUROC).

Results

Serum quantitative HBsAg (q

SUBMITTER: Zheng MH 

PROVIDER: S-EPMC4051672 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature