International journal of medical informatics · 2025
A web-based tool utilizing machine learning algorithms for predicting illicit drug use in emergency departments.
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Lu Tsung-Chien, Lin Chih-Chuan, Weng Te-I, Chou Fan-Ya, Fang Cheng-Chung, TEDAS Research Group
Аннотация
BACKGROUND: Identifying illicit drug use through urine testing is time-consuming in the era of new psychoactive substances. This study aimed to develop a machine learning (ML) prediction model for early identification of illicit drug use in suspected emergency department (ED) patients.
METHODS: Data from the Taiwan Emergency Department Drug Abuse Surveillance (TEDAS) database (2020-2023) was used. Six feature categories-demographics, triage data, referral source, symptoms, physical findings, and clinical characteristics-were included. The primary outcome was positive urine results for illicit drugs, confirmed by liquid chromatography-tandem mass spectrometry. Data were divided chronologically into training/validation and testing sets. Three supervised ML algorithms, including random forest, CatBoost, and light gradient boosting machine, were tested using K-fold cross-validation, and performance was evaluated by the area under the receiver operating characteristic curve (AUC) in the test set.
RESULTS: The analysis included 13,615 urine test results from ED cases, identifying 3,185 positive cases (23.4%). A total of 9,529 cases (2020-2022) formed the training/validation cohort, and 4,086 (2023) were used for testing. Twenty features were used to construct the prediction model. The CatBoost classifier performed best, achieving an AUC of 0.846 (95% confidence interval [CI]: 0.831-0.859) in the testing cohort. A web-based tool and mobile apps were implemented to assist emergency physicians in predicting illicit drug use.
CONCLUSIONS: The machine learning model effectively predicts illicit drug use in ED patients and has been successfully implemented for free access. Further analysis is needed to assess post-implementation performance and its potential for use in other countries.
METHODS: Data from the Taiwan Emergency Department Drug Abuse Surveillance (TEDAS) database (2020-2023) was used. Six feature categories-demographics, triage data, referral source, symptoms, physical findings, and clinical characteristics-were included. The primary outcome was positive urine results for illicit drugs, confirmed by liquid chromatography-tandem mass spectrometry. Data were divided chronologically into training/validation and testing sets. Three supervised ML algorithms, including random forest, CatBoost, and light gradient boosting machine, were tested using K-fold cross-validation, and performance was evaluated by the area under the receiver operating characteristic curve (AUC) in the test set.
RESULTS: The analysis included 13,615 urine test results from ED cases, identifying 3,185 positive cases (23.4%). A total of 9,529 cases (2020-2022) formed the training/validation cohort, and 4,086 (2023) were used for testing. Twenty features were used to construct the prediction model. The CatBoost classifier performed best, achieving an AUC of 0.846 (95% confidence interval [CI]: 0.831-0.859) in the testing cohort. A web-based tool and mobile apps were implemented to assist emergency physicians in predicting illicit drug use.
CONCLUSIONS: The machine learning model effectively predicts illicit drug use in ED patients and has been successfully implemented for free access. Further analysis is needed to assess post-implementation performance and its potential for use in other countries.