Bridging the Domain Divide: Supervised vs. Zero-Shot Clinical Section Segmentation from MIMIC-III to Obstetrics

• URL: http://arxiv.org/abs/2602.17513v1
• Date: Thu, 19 Feb 2026 16:25:07 GMT
• Title: Bridging the Domain Divide: Supervised vs. Zero-Shot Clinical Section Segmentation from MIMIC-III to Obstetrics
• Authors: Baris Karacan, Barbara Di Eugenio, Patrick Thornton,
• Abstract summary: We advance clinical section segmentation through three key contributions.<n>We curate a new de-identified, section-labeled obstetrics notes dataset.<n>We evaluate transformer-based supervised models for section segmentation on a curated subset of MIMIC-III.
• Score: 6.451459735222387
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Clinical free-text notes contain vital patient information. They are structured into labelled sections; recognizing these sections has been shown to support 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. Second, we systematically evaluate transformer-based supervised models for section segmentation on a curated subset of MIMIC-III (in-domain), and on the new obstetrics dataset (out-of-domain). Third, we conduct the first head-to-head comparison of supervised models for medical section segmentation with zero-shot large language models. Our results show that while supervised models perform strongly in-domain, their performance drops substantially out-of-domain. In contrast, zero-shot models demonstrate robust out-of-domain adaptability once hallucinated section headers are corrected. These findings underscore the importance of developing domain-specific clinical resources and highlight zero-shot segmentation as a promising direction for applying healthcare NLP beyond well-studied corpora, as long as hallucinations are appropriately managed.

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