ArticleBMC medical informatics and decision making2019
A classification framework for exploiting sparse multi-variate temporal features with application to adverse drug event detection in medical records.
Article in BMC medical informatics and decision making, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 5 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Systematic review of AI-based models in pharmacoepidemiology for adverse drug event prediction and detection.Frontiers in drug safety and regulation · 2026Pooled it
- Electronic health record-based prediction models for in-hospital adverse drug event diagnosis or prognosis: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2023Pooled it
- Methods for drug safety signal detection using routinely collected observational electronic health care data: A systematic review.Pharmacoepidemiology and drug safety · 2023Pooled it
- A Systematic Review of Time Series Classification Techniques Used in Biomedical Applications.Sensors (Basel, Switzerland) · 2022Pooled it
- Informative presence and observation in routine health data: A review of methodology for clinical risk prediction.Journal of the American Medical Informatics Association : JAMIA · 2021Pooled it
- Generative Transformers for Pharmacovigilance Signal Detection using Electronic Health Records.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Data.IEEE reviews in biomedical engineering · 2026Review
- Analytical approaches for medication management for patient safety: a scoping review.npj health systems · 2025Article
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- Development of a warning model for drug-induced liver injury in the older patients.Frontiers in pharmacology · 2025Article
- Applying AI to Structured Real-World Data for Pharmacovigilance Purposes: Scoping Review.Journal of medical Internet research · 2024Article
- KG-LIME: predicting individualized risk of adverse drug events for multiple sclerosis disease-modifying therapy.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Sequential autoencoders for feature engineering and pretraining in major depressive disorder risk prediction.JAMIA open · 2023Article
- Neural-signature methods for structured EHR prediction.BMC medical informatics and decision making · 2022Article
- Article
- Machine Learning in Causal Inference: Application in Pharmacovigilance.Drug safety · 2022Review
- Major areas of interest of artificial intelligence research applied to health care administrative data: a scoping review.Frontiers in pharmacology · 2022Article
- Finding Potential Adverse Events in the Unstructured Text of Electronic Health Care Records: Development of the Shakespeare Method.JMIRx med · 2021Article
- Evaluating risk detection methods to uncover ontogenic-mediated adverse drug effect mechanisms in children.BioData mining · 2021Article
- Identifying medication harm in hospitalised patients: a bimodal, targeted approach.Therapeutic advances in drug safety · 2020Article
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4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundAdverse drug events (ADEs) as well as other preventable adverse events in the hospital setting incur a yearly monetary cost of approximately $3.5 billion, in the United States alone. Therefore, it is of paramount importance to reduce the impact and prevalence of ADEs within the healthcare sector, not only since it will result in reducing human suffering, but also as a means to substantially reduce economical strains on the healthcare system. One approach to mitigate this problem is to employ predictive models. While existing methods have been focusing on the exploitation of static features, limited attention has been given to temporal features.
methodsIn this paper, we present a novel classification framework for detecting ADEs in complex Electronic health records (EHRs) by exploiting the temporality and sparsity of the underlying features. The proposed framework consists of three phases for transforming sparse and multi-variate time series features into a single-valued feature representation, which can then be used by any classifier. Moreover, we propose and evaluate three different strategies for leveraging feature sparsity by incorporating it into the new representation.
resultsA large-scale evaluation on 15 ADE datasets extracted from a real-world EHR system shows that the proposed framework achieves significantly improved predictive performance compared to state-of-the-art. Moreover, our framework can reveal features that are clinically consistent with medical findings on ADE detection.
conclusionsOur study and experimental findings demonstrate that temporal multi-variate features of variable length and with high sparsity can be effectively utilized to predict ADEs from EHRs. Two key advantages of our framework are that it is method agnostic, i.e., versatile, and of low computational cost, i.e., fast; hence providing an important building block for future exploitation within the domain of machine learning from EHRs.
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