Artificial Neural Network Accurately Predicts Hepatitis B Surface Antigen Seroclearance

Ming-Hua Zheng, Wai-Kay Seto, Ke-Qing Shi, Danny Ka-Ho Wong, James Fung, Ivan Fan-Ngai Hung, Daniel Yee-Tak Fong, John Chi-Hang Yuen, Teresa Tong, Ching-Lung Lai and Man-Fung Yuen

PLOS ONE, 2014, vol. 9, issue 6, 1-11

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 (qHBsAg) and HBV DNA levels, qHBsAg and HBV DNA reduction were related to HBsAg seroclearance (P

Date: 2014
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0099422 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 99422&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0099422

DOI: 10.1371/journal.pone.0099422

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().