Distinguishing acute poisoning agents using machine learning models derived from the National Poison Data System (NPDS)
- سال انتشار: 1402
- محل انتشار: اولین کنگره بین المللی هوش مصنوعی در علوم پزشکی
- کد COI اختصاصی: AIMS01_243
- زبان مقاله: انگلیسی
- تعداد مشاهده: 236
نویسندگان
Data Science Institute, Southern Methodist University, Dallas, TX, USA
Medical Toxicology and Drug Abuse Research Center (MTDRC), Birjand University of Medical Sciences (BUMS), Birjand, Iran
Student Research Committee, Birjand University of Medical Sciences, Birjand, Iran
Data Science Institute, Southern Methodist University, Dallas, TX, USA- Department of Emergency Medicine, University of Colorado School of Medicine, Aurora, CO, USA
CPC Clinical Research ۲۱۱۵ N Scranton St Suite ۲۰۴۰
Data Scientist, Hiperdyne Corporation, Tokyo, Japan
چکیده
Background and aims: Acute poisoning is known to be a globally major health problem and dueto a patient’s inability to verbally convey the exposure history, obtaining an accurate exposurehistory can be challenging. Artificial intelligence (AI) uses logic and decision trees to replicatehuman decision-making. Machine learning (ML) is a subgroup of AI that benefit from statisticalmodels to execute tasks based on inference but not definite instructions, which allows the modelmore flexibility to learn and improve itself. This study aimed to develop a machine-learning algorithmthat can predict and distinguish eight poisoning agents based on their clinical symptoms.Method: Data were obtained from the National Poison Data System from ۲۰۱۴ to ۲۰۱۸, forpatients with single-agent exposure to ۸ drugs/drug classes (lithium, aspirin, benzodiazepines,bupropion, acetaminophen, calcium channel blockers, diphenhydramine, and sulfonylureas). Thefour classifier prediction models were applied including LightGBM, Logistic regression, XGBoost,and CatBoost. Standard performance measures were also calculated including accuracy,F۱-score, recall, precision, and specificity.Results: In total ۲۰۱۰۳۱ cases were included to develop and test the algorithms. Accuracy amongthese four models ranging from ۷۷-۸۰%, precision, and F۱-scores were ۷۶-۸۰%, and recall was۷۷-۷۸%. Overall specificity was ۹۲% for all four models. Accuracy was highest for distinguishingsulfonylureas, acetaminophen, benzodiazepines, and diphenhydramine poisoning. F۱ scores werereported to be highest for correctly classifying sulfonylureas, acetaminophen, and benzodiazepinepoisonings. Also, recall was highest for sulfonylureas, acetaminophen, and benzodiazepines, andlowest for bupropion. Specificity was > ۹۹% for models of sulfonylureas, lithium, calcium channelblockers, and aspirin.Conclusion: LightGBM and CatBoost classifier prediction models had the highest performanceof those which have been tested. The algorithms were most accurate in classifying sulfonylureapoisonings, followed by acetaminophen, benzodiazepines, diphenhydramine, and bupropion poisoning.Although the clinical utility has not been studied, with further development in this subjectthis may serve as a useful diagnostic aid.کلیدواژه ها
Machine learning, Artificial intelligence, Toxicology, Poisoning, Overdoseمقالات مرتبط جدید
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