Scalable classification of infant breast milk feeding status from clinical notes using weak supervision and rule-based natural language processing

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Abstract
Objective: Extract infant breast milk feeding status from electronic health record clinical notes to support observational drug-safety studies. Materials and Methods: Infant notes (19 489; 228 infants) were weakly labeled by large language model (LLM) and validated on 1045 manually reviewed notes. Using these labels, we built a rule-based medspaCy classifier from a curated feeding term set and compared it with ClinPhen and fine-tuned Bidirectional Encoder Representations from Transformers (BERT) document classifiers. Maternal postpartum notes (23 mothers; 616 partitions) were manually labeled to compare BERT with prompt-based language models. Results: LLM labels achieved 99.62% accuracy (macro F1 99.32%). medspaCy achieved 94.36% accuracy and 90.40% balanced accuracy (macro F1 0.90) and was applied to 52 278 mother–infant dyads. On maternal notes, the prompt-based model improved Positive-class F1 vs BERT (0.96 vs 0.82). Discussion: LLMs were most effective for scalable labeling; deterministic rules supported transparent large-scale inference. Conclusion: Weak supervision plus lightweight rules can convert sparse breastfeeding documentation into structured variables at-scale.
Funding Information
  • The Ohio State University Clinical and Translational Science Institute (P30HD106451)
  • National Center for Advancing Translational Sciences
  • National Institutes of Health (UM1TR004548)

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