Introduction: There is a paucity of literature analyzing outcome of chlorpyrifos intoxication. Methods: A total of 40 patients with chlorpyrifos intoxication were seen at Chang Gung Memorial Hospital between 2008 and 2017. Patients were stratified into two subgroups according to their prognosis, as good ( n = 12) or poor ( n = 28). Good prognosis group were defined as patients who survived without serious complications, and poor prognosis group included patients who died and survived after development of severe complications. Demographic, clinical, laboratory, and mortality data were obtained for analysis. Results: Patients aged 53.8 ± 16.3 years and most were male (80.0%). All patients (100.0%) developed acute cholinergic crisis such as emesis (45.0%), respiratory failure (42.5%), tachycardia (30.0%), kidney injury (22.5%), and seizure (7.5%). Intermediate syndrome developed in 12.5% of patients, but none had delayed neuropathy (0%). The poor prognosis group suffered higher incidences of respiratory failure ( p = 0.011), kidney injury ( p = 0.026), and prolonged corrected QT interval ( p = 0.000), and they had higher blood urea nitrogen level ( p = 0.041), lower Glasgow coma scale score ( p = 0.011), and lower monocyte count ( p = 0.023) than good prognosis group. All patients were treated with atropine and pralidoxime therapy, but six patients (15.0%) still died of intoxication. In a multivariate logistic regression model, blood urea nitrogen was a significant risk factor for poor prognosis (odds ratio: 1.375, 95% confidence interval: 1.001–1.889, p = 0.049). Nevertheless, no mortality risk factor could be identified. Conclusion: The mortality rate of patients with chlorpyrifos intoxication was 15.0%. Furthermore, acute cholinergic crisis, intermediate syndrome, and delayed neuropathy developed in 100.0%, 12.5%, and 0% of patients, respectively.

Introduction: Very 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. Methods: The 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. Results: The 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. Conclusion: The top five weighted features for acetaminophen-associated toxic hepatitis were ALT, prothrombin time, AST, time to hospital and platelet count.

There are specific characteristics in the thin film transistor liquid crystal display (TFT-LCD) industry, such as unexpected demand fluctuation, customized products that each customer will designate a specific key component, long lead time of procurement, and short product life cycle. This research presents a genetic algorithm integrated with a neural network model for monthly sales forecasting of TFT-LCD products in Taiwan. The practical situation and its requirements are explained and two systematic approaches are discussed: (a) a K-means clustering for historic sales data and new sales is forecasted by mapping its inputs into one of these clustered data, and (b) a neural network integrated with a genetic algorithm for supervised learning of the functional behaviour of time-series data and their approximation. The evolving neural network model is applied for modelling the system's behaviour with the possibility of exploiting expert information and systematic optimization. The model has been tested and satisfying results are shown with practical data.