AI RESEARCH
Bridging the Domain Divide: Supervised vs. Zero-Shot Clinical Section Segmentation from MIMIC-III to Obstetrics
arXiv CS.CL
•
ArXi:2602.17513v2 Announce Type: replace Clinical free-text notes contain vital patient information. They are structured into labelled sections; recognizing these sections has been shown to clinical decision-making and downstream NLP tasks. In this paper, we advance clinical section segmentation through three key contributions. First, we curate a new de-identified, section-labeled obstetrics notes dataset, to supplement the medical domains covered in public corpora such as MIMIC-III, on which most existing segmentation approaches are trained.