ArticleDigital health
Machine learning-based risk prediction model for medication administration errors in neonatal intensive care units: A prospective direct observational study.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence and computational methods in the Asia-Pacific pharmacovigilance landscape: a systematic review.International journal of public health · 2026Pooled it
- Effect of a Mobile App on Medication Errors During Simulated Pediatric Cardiopulmonary Resuscitation: PEDIDOSIS App vs. Conventional Methods. A Multicenter Simulation-based Randomized Controlled Trial.Journal of medical systems · 2026Trial
- Design, implementation, and evaluation of neonatal abstinence syndrome care management web application: a multimethod study.Scientific reports · 2026Trial
- A clinical decision support system for predicting drug-related adverse effects in pediatric patients with congenital heart disease: Development and retrospective evaluation.The Journal of international medical research · 2026Article
- Article
- Artificial intelligence-guided nutritional therapy in the ICU.Current opinion in clinical nutrition and metabolic care · 2026Review
- Machine learning for medication error detection: a scoping review.Research square · 2026Article
- Early diagnosis and prognostic prediction of secondary bloodstream infections caused byFrontiers in cellular and infection microbiology · 2025Article
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Authors and funding
9 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Objective: Neonates' physiological immaturity and complex dosing requirements heighten their susceptibility to medication administration errors (MAEs), with the potential for severe harm and substantial economic impact on healthcare systems. Developing an effective risk prediction model for MAEs is crucial to reduce and prevent harm. Methods: This national-level, multicentre, prospective direct observational study was conducted in neonatal intensive care units (NICUs) of five public hospitals in Malaysia. Randomly selected nurses were directly observed during medication preparation and administration. Each observation was independently assessed for errors. Ten machine learning (ML) algorithms were applied with features derived from systematic reviews, incident reports, and expert consensus. Model performance, prioritising F1-score for MAEs, was evaluated using various measures. Feature importance was determined using the permutation-feature importance for robust comparison across ML algorithms. Results: A total of 1093 doses were administered to 170 neonates, with mean age and birth weight of 33.43 (SD ± 5.13) weeks and 1.94 (SD ± 0.95) kg, respectively. F1-scores for the ten models ranged from 76.15% to 83.28%. Adaptive boosting (AdaBoost) emerged as the best-performing model (F1-score: 83.28%, accuracy: 77.63%, area under the receiver operating characteristic: 82.95%, precision: 84.72%, sensitivity: 81.88% and negative predictive value: 64.00%). The most influential features in AdaBoost were the intravenous route of administration, working hours, and nursing experience. Conclusions: This study developed and validated an ML-based model to predict the presence of MAEs among neonates in NICUs. AdaBoost was identified as the best-performing algorithm. Utilising the model's predictions, healthcare providers can potentially reduce MAE occurrence through timely interventions.
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