What question did this study set out to answer?

The aim is to create and assess machine learning models that predict medication harm among hospitalized adult patients.

January 21, 2026Therapeutic Advances in Drug Safety0 citationsOpen Access

Machine learning risk prediction models for medication harm in hospitalised adult patients

JLJonathan Yong Jie LamThe University of Queensland MBMichael BarrasThe University of Queensland ISIan ScottThe University of Queensland

Key Points

The aim is to create and assess machine learning models that predict medication harm among hospitalized adult patients.
Used a cohort dataset from 279 hospitalized adult patients.
Trained eight machine learning models to identify patients at risk of medication harm.
Utilized various identification methods for medication harm cases, including chart reviews and coding.
Applied recursive feature elimination for feature selection and addressed class imbalance with oversampling.
Model performance was evaluated using stratified 10-fold cross-validation.
The random forest model showed the highest performance with an area under the ROC curve of 0.76.
Achieved precision of 0.50 and recall of 0.62, indicating good sensitivity and specificity.
Identified key predictive features including length of stay, age, and specific medication use.
Overall accuracy of the model was 0.86 and specificity was 0.90.

Abstract

Background: Medication harm is a significant healthcare challenge in hospitalised adult patients. Machine learning (ML) approaches offer the potential to improve prediction accuracy for medication harm by capturing complex relationships among clinical risk factors that traditional statistical models may not detect. Objective: To develop and evaluate ML models for predicting medication harm in hospitalised adult patients. Design: ML study involving secondary use of a prospectively collected hospital cohort dataset. Methods: This study used data from 279 adult patients admitted to general medical and geriatric wards of a tertiary hospital, among whom 40 experienced 51 medication harm events. Eight ML models were trained and evaluated for identifying patients at risk of medication harm. Medication harm cases were identified through detailed chart reviews, trigger tools, voluntary incident reporting, and International Classification of Diseases version 10 discharge coding. Data were pre-processed with missing values imputed using median imputation. Ten predictive features were selected using recursive feature elimination and clinical expert opinion. Models were trained using stratified 10-fold cross-validation with an 80/20 train-test split. Class imbalance was addressed using an oversampling approach. Results: A random forest model demonstrated the highest performance, achieving an area under the receiver operating characteristic curve of 0.76, precision of 0.50, recall of 0.62, F1 score of 0.54, accuracy of 0.86, specificity of 0.90, and an area under the precision-recall curve of 0.47. Predictive features of importance included length of stay, depression, dementia, insulin use, number of medications (⩾15), age (⩾65), opioid use, and antibiotic use. Conclusion: This study highlights the potential of ML models to predict medication harm, enabling early identification of high-risk patients for preventive interventions. Interdisciplinary collaboration is essential in developing robust, clinically relevant models that can be used to improve patient safety.

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