Artificial intelligence predicts direct-acting antivirals failure among hepatitis C virus patients: A nationwide hepatitis C virus registry program
Journal
Clinical and molecular hepatology
Journal Volume
30
Journal Issue
1
Date Issued
2024-01
Author(s)
Lu, Ming-Ying
Huang, Chung-Feng
Hung, Chao-Hung
Tai, Chi-Ming
Mo, Lein-Ray
Kuo, Hsing-Tao
Tseng, Kuo-Chih
Lo, Ching-Chu
Bair, Ming-Jong
Wang, Szu-Jen
Huang, Jee-Fu
Yeh, Ming-Lun
Chen, Chun-Ting
Tsai, Ming-Chang
Huang, Chien-Wei
Lee, Pei-Lun
Yang, Tzeng-Hue
Huang, Yi-Hsiang
Chong, Lee-Won
Chen, Chien-Lin
Yang, Chi-Chieh
Yang, Sheng-Shun
Cheng, Pin-Nan
Hsieh, Tsai-Yuan
Hu, Jui-Ting
Wu, Wen-Chih
Cheng, Chien-Yu
Chen, Guei-Ying
Zhou, Guo-Xiong
Tsai, Wei-Lun
Kao, Chien-Neng
Lin, Chih-Lang
Wang, Chia-Chi
Lin, Ta-Ya
Lin, Chih-Lin
Su, Wei-Wen
Lee, Tzong-Hsi
Chang, Te-Sheng
Dai, Chia-Yen
Lin, Han-Chieh
Chuang, Wan-Long
Peng, Cheng-Yuan
Tsai, Chun-Wei-
Chen, Chi-Yi
Yu, Ming-Lung
Abstract
Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1-3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy.
Subjects
Algorithms; Antiviral agents; Artificial intelligence; Hepatitis C virus; Machine learning
Type
journal article