ArticlePLoS computational biology2021
Machine learning liver-injuring drug interactions with non-steroidal anti-inflammatory drugs (NSAIDs) from a retrospective electronic health record (EHR) cohort.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
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Who cites it
31 citing papers in PubMed, 1 synthesis or guideline pooled it, 44 citations in OpenAlex.
- Systematic review of AI-based models in pharmacoepidemiology for adverse drug event prediction and detection.Frontiers in drug safety and regulation · 2026Pooled it
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- PregMedNet: Multifaceted maternal medication impacts on neonatal complications.Nature communications · 2026Article
- Cardiometabolic parameters and novel hematological indices in women with migraine.Journal of medical biochemistry · 2026Article
- Recombinant Human KAI1/CD82 Attenuates Glucocorticoid-Induced Muscle Atrophy by Promoting Myogenic Differentiation.International journal of molecular sciences · 2026Article
- PregMedNet: Multifaceted Maternal Medication Impacts on Neonatal Complications.medRxiv : the preprint server for health sciences · 2025Article
- Artificial Intelligence: Applications in Pharmacovigilance Signal Management.Pharmaceutical medicine · 2025Review
- Applying AI to Structured Real-World Data for Pharmacovigilance Purposes: Scoping Review.Journal of medical Internet research · 2024Article
- Efficient analysis of drug interactions in liver injury: a retrospective study leveraging natural language processing and machine learning.BMC medical research methodology · 2024Article
- Implications of Stabilometric Assessment in Determining Functional Deficits in Patients with Severe Knee Osteoarthritis: Observational Study.Journal of clinical medicine · 2024Article
- Medical Informatics Operating Room Vitals and Events Repository (MOVER): a public-access operating room database.JAMIA open · 2023Article
- Prenatal and Neonatal Pulmonary Thrombosis as a Potential Complication of SARS-CoV-2 Infection in Late Pregnancy.International journal of molecular sciences · 2023Article
- Effects of Turmeric Extract on Age-Related Skeletal Muscle Atrophy in Senescence-Accelerated Mice.Life (Basel, Switzerland) · 2023Article
- A story of the potential effect of non-steroidal anti-inflammatory drugs (NSAIDs) in Parkinson's disease: beneficial or detrimental effects.Inflammopharmacology · 2023Review
- Early Administration of Vancomycin Inhibits Pulmonary Embolism by Remodeling Gut Microbiota.Journal of personalized medicine · 2023Article
- Concordance of Non-Alcoholic Fatty Liver Disease and Associated Factors among Older Married Couples in China.International journal of environmental research and public health · 2023Article
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
Drug-drug interactions account for up to 30% of adverse drug reactions. Increasing prevalence of electronic health records (EHRs) offers a unique opportunity to build machine learning algorithms to identify drug-drug interactions that drive adverse events. In this study, we investigated hospitalizations' data to study drug interactions with non-steroidal anti-inflammatory drugs (NSAIDS) that result in drug-induced liver injury (DILI). We propose a logistic regression based machine learning algorithm that unearths several known interactions from an EHR dataset of about 400,000 hospitalization. Our proposed modeling framework is successful in detecting 87.5% of the positive controls, which are defined by drugs known to interact with diclofenac causing an increased risk of DILI, and correctly ranks aggregate risk of DILI for eight commonly prescribed NSAIDs. We found that our modeling framework is particularly successful in inferring associations of drug-drug interactions from relatively small EHR datasets. Furthermore, we have identified a novel and potentially hepatotoxic interaction that might occur during concomitant use of meloxicam and esomeprazole, which are commonly prescribed together to allay NSAID-induced gastrointestinal (GI) bleeding. Empirically, we validate our approach against prior methods for signal detection on EHR datasets, in which our proposed approach outperforms all the compared methods across most metrics, such as area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC).
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