An artificial intelligence algorithm for analyzing acetaminophen-associated toxic hepatitis.

Abstract

INTRODUCTION\nVery little artificial intelligence (AI) work has been performed to investigate acetaminophen-associated hepatotoxicity. The objective of this study was to develop an AI algorithm for analyzing weighted features for toxic hepatitis after acetaminophen poisoning.\n\n\nMETHODS\nThe medical records of 187 patients with acetaminophen poisoning treated at Chang Gung Memorial Hospital were reviewed. Patients were sorted into two groups according to their status of toxic hepatitis. A total of 40 clinical and laboratory features recorded on the first day of admission were selected for algorithm development. The random forest classifier (RFC) and logistic regression (LR) were used for artificial intelligence algorithm development.\n\n\nRESULTS\nThe RFC-based AI model achieved the following results: accuracy = 92.5 ± 2.6%; sensitivity = 100%; specificity = 60%; precision = 92.3 ± 3.4%; and F1 = 96.0 ± 1.8%. The area under the receiver operating characteristic curve (AUROC) was approximately 0.98. The LR-based AI model achieved the following results: accuracy = 92.00 ± 2.9%; sensitivity = 100%; specificity = 20%; precision = 92.8 ± 3.4%; recall = 98.8 ± 3.4%; and F1 = 95.6 ± 1.5%. The AUROC was approximately 0.68. The weighted features were calculated, and the 10 most important weighted features for toxic hepatitis were aspartate aminotransferase (ALT), prothrombin time, alanine aminotransferase (AST), time to hospital, platelet count, lymphocyte count, albumin, total bilirubin, body temperature and acetaminophen level.\n\n\nCONCLUSION\nThe top five weighted features for acetaminophen-associated toxic hepatitis were ALT, prothrombin time, AST, time to hospital and platelet count.