Scalable classification of infant breast milk feeding status from clinical notes using weak supervision and rule-based natural language processing
Connor Grannis, Ping Wei, Austin A Antoniou, David M Gordon, Jinyu Xu, Jianing Liu, Peter White, Sarah A Keim, Lang Li, Christopher W BartlettAbstract
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.