ArticleScientific reports2017
Development of A Machine Learning Algorithm to Classify Drugs Of Unknown Fetal Effect.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses 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
16 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Enabling pregnant women and their physicians to make informed medication decisions using artificial intelligence.Journal of pharmacokinetics and pharmacodynamics · 2020Pooled it
- Engineered tissues and strategies to overcome challenges in drug development.Advanced drug delivery reviews · 2020Pooled it
- Evaluating the Performance of Traditional Pharmacoepidemiologic and Machine Learning Models to Predict Pregnancies at Risk of Major Congenital Malformations.Birth defects research · 2026Article
- AI in Childbirth and Emerging Alternatives to Traditional Fertility Treatments.Reproductive sciences (Thousand Oaks, Calif.) · 2026Review
- Applying AI to Structured Real-World Data for Pharmacovigilance Purposes: Scoping Review.Journal of medical Internet research · 2024Article
- Enhancing inclusivity in clinical trials: Model-informed drug development for pregnant individuals in the era of personalized medicine.CPT: pharmacometrics & systems pharmacology · 2024Article
- Effects of gestational haloperidol exposure on mRNA expressions related to glutamate and GABA receptors in offspring.IBRO neuroscience reports · 2023Article
- Use of Electronic Health Record Data for Drug Safety Signal Identification: A Scoping Review.Drug safety · 2023Article
- Data-Driven Modeling of Pregnancy-Related Complications.Trends in molecular medicine · 2021Review
- Using Machine Learning to Predict Complications in Pregnancy: A Systematic Review.Frontiers in bioengineering and biotechnology · 2021Review
- Machine Learning Within Studies of Early-Life Environmental Exposures and Child Health: Review of the Current Literature and Discussion of Next Steps.Current environmental health reports · 2020Review
- Machine learning on drug-specific data to predict small molecule teratogenicity.Reproductive toxicology (Elmsford, N.Y.) · 2020Article
- Data-driven queries between medications and spontaneous preterm birth among 2.5 million pregnancies.Birth defects research · 2019Article
- Exploiting machine learning for end-to-end drug discovery and development.Nature materials · 2019Article
- ODAL: A one-shot distributed algorithm to perform logistic regressions on electronic health records data from multiple clinical sites.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2019Article
- Electronic Medical Records Enable Precision Medicine Approaches for Celiac Disease.Journal of pediatric gastroenterology and nutrition · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Many drugs commonly prescribed during pregnancy lack a fetal safety recommendation - called FDA 'category C' drugs. This study aims to classify these drugs into harmful and safe categories using knowledge gained from chemoinformatics (i.e., pharmacological similarity with drugs of known fetal effect) and empirical data (i.e., derived from Electronic Health Records). Our fetal loss cohort contains 14,922 affected and 33,043 unaffected pregnancies and our congenital anomalies cohort contains 5,658 affected and 31,240 unaffected infants. We trained a random forest to classify drugs of unknown pregnancy class into harmful or safe categories, focusing on two distinct outcomes: fetal loss and congenital anomalies. Our models achieved an out-of-bag accuracy of 91% for fetal loss and 87% for congenital anomalies outperforming null models. Fifty-seven 'category C' medications were classified as harmful for fetal loss and eleven for congenital anomalies. This includes medications with documented harmful effects, including naproxen, ibuprofen and rubella live vaccine. We also identified several novel drugs, e.g., haloperidol, that increased the risk of fetal loss. Our approach provides important information on the harmfulness of 'category C' drugs. This is needed, as no FDA recommendation exists for these drugs' fetal safety.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.