How Machine Learning Can Help Close Evidence Gaps for Drug Safety in Pregnant Women
By
Michelle Falci
May 27, 2026
Clinical Scorecard: Utilizing Machine Learning to Address Evidence Deficiencies in Medication Safety for Pregnant Women
At a Glance
Category Detail
Condition Medication safety in pregnant women Key Mechanisms Machine learning and data mining to analyze medication exposure and outcomes Target Population Pregnant women and their healthcare providers Care Setting Clinical research and maternal healthcare
Key Highlights
79% of pregnant women take at least one medication without sufficient evidence on safety. Only 4% of clinical trials in the past decade included pregnant women. Machine learning is being used to analyze large datasets to improve drug safety recommendations. Guideline-Based Recommendations
Diagnosis
Inclusion of pregnant women in clinical research to improve evidence on medication safety. Management
Utilization of machine learning to assess medication risks and benefits for pregnant women. Monitoring & Follow-up
Continuous evaluation of medication effects on pregnant women through data mining. Risks
Potential biases in machine learning models if not properly interpreted. Patient & Prescribing Data
Pregnant women taking medications
Need for evidence-based guidelines to inform medication use during pregnancy.
Clinical Best Practices
Ensure transparency in machine learning models to trace decision pathways. Combine causal inference with machine learning for better risk assessment. Related Resources & Content
