ArticlePatterns (New York, N.Y.)2020
Using Machine Learning to Identify Adverse Drug Effects Posing Increased Risk to Women.
Article in Patterns (New York, N.Y.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 48 citations in OpenAlex.
- Sex bias consideration in healthcare machine-learning research: a systematic review in rheumatoid arthritis.BMJ open · 2025Pooled it
- Adverse Events Associated with Clascoterone: A Real-World Pharmacovigilance Study of the Food and Drug Administration Adverse Event Reporting System.Therapeutic innovation & regulatory science · 2026Article
- OnSIDES database: Extracting adverse drug events from drug labels using natural language processing models.Med (New York, N.Y.) · 2025Article
- Evaluating the effectiveness of AI-enhanced "One Body, Two Wings" pharmacovigilance models in China: a nationwide survey on medication safety and risk management.Frontiers in health services · 2025Article
- Precision Adverse Drug Reactions Prediction with Heterogeneous Graph Neural Network.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- The Role of Vitamin D Metabolism Genes and Their Genomic Background in Shaping Cyclosporine A Dosage Parameters after Kidney Transplantation.Journal of clinical medicine · 2024Article
- Review
- Sex-biased gene expression and gene-regulatory networks of sex-biased adverse event drug targets and drug metabolism genes.BMC pharmacology & toxicology · 2024Article
- Artificial intelligence for optimizing benefits and minimizing risks of pharmacological therapies: challenges and opportunities.Frontiers in drug safety and regulation · 2024Review
- Sex-biased gene expression and gene-regulatory networks of sex-biased adverse event drug targets and drug metabolism genes.bioRxiv : the preprint server for biology · 2023Article
- Bottom-up and top-down paradigms of artificial intelligence research approaches to healthcare data science using growing real-world big data.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Identifying Safety Subgroups at Risk: Assessing the Agreement Between Statistical Alerting and Patient Subgroup Risk.Drug safety · 2023Article
- Building a knowledge graph to enable precision medicine.Scientific data · 2023Article
- Machine Learning in Causal Inference: Application in Pharmacovigilance.Drug safety · 2022Review
- Considerations and challenges for sex-aware drug repurposing.Biology of sex differences · 2022Review
- No population left behind: Improving paediatric drug safety using informatics and systems biology.British journal of clinical pharmacology · 2022Review
- A Computational Framework for Identifying Age Risks in Drug-Adverse Event Pairs.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2022Article
- Integrating machine learning with electronic health record data to facilitate detection of prolactin level and pharmacovigilance signals in olanzapine-treated patients.Frontiers in endocrinology · 2022Article
- Quantifying the Severity of Adverse Drug Reactions Using Social Media: Network Analysis.Journal of medical Internet research · 2021Article
- Addressing bias in big data and AI for health care: A call for open science.Patterns (New York, N.Y.) · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Adverse drug reactions are the fourth leading cause of death in the US. Although women take longer to metabolize medications and experience twice the risk of developing adverse reactions compared with men, these sex differences are not comprehensively understood. Real-world clinical data provide an opportunity to estimate safety effects in otherwise understudied populations, i.e., women. These data, however, are subject to confounding biases and correlated covariates. We present AwareDX, a pharmacovigilance algorithm that leverages advances in machine learning to predict sex risks. Our algorithm mitigates these biases and quantifies the differential risk of a drug causing an adverse event in either men or women. AwareDX demonstrates high precision during validation against clinical literature and pharmacogenetic mechanisms. We present a resource of 20,817 adverse drug effects posing sex-specific risks. AwareDX, and this resource, present an opportunity to minimize adverse events by tailoring drug prescription and dosage to sex.
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.