mlsanityData to 14 Oct 2026

ClinStructor: AI-Powered Structuring of Unstructured Clinical Texts

arXiv 2511.11883 ↗First posted 14 Nov 2025cs.CL cs.LGcited by 1

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Abstract (arXiv)

Clinical notes contain valuable, context-rich information, but their unstructured format introduces several challenges, including unintended biases (e.g., gender or racial bias), and poor generalization across clinical settings (e.g., models trained on one EHR system may perform poorly on another due to format differences) and poor interpretability. To address these issues, we present ClinStructor, a pipeline that leverages large language models (LLMs) to convert clinical free-text into structured, task-specific question-answer pairs prior to predictive modeling. Our method substantially enhances transparency and controllability and only leads to a modest reduction in predictive performance (a 2-3% drop in AUC), compared to direct fine-tuning, on the ICU mortality prediction task. ClinStructor lays a strong foundation for building reliable, interpretable, and generalizable machine learning models in clinical environments.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 33 of its 59 references that are in the map. First posted on arXiv 14 Nov 2025.

Builds on

33 of 59 references.

References · 33

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21 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026CliniCIRCA: A Modular LLM Framework for Constructing Longitudinal Mental Health Patient Journeys from Raw EHR Narrativescited by 0