Article

Empirical Review of LLM-driven Classification of Multidimensional Sleep Health Mentions from Free-Text Clinical Notes

2025-06-05

Abstract excerpt

Accurate multidimensional sleep health (MSH) information is often fragmented and inconsistently represented within hospital infrastructures, leaving crucial details buried in unstructured clinical notes rather than discrete fields. This inconsistency complicates large-scale phenotyping, secondary analyses, and clinical decision support regarding sleep-related outcomes. In this work, we systematically explore conte...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 19f7ac7e-9059-5040-881b-6c62e777ad2d
DOI: 10.1101/2025.06.04.25328983

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Empirical Review of LLM-driven Classification of Multidimensional Sleep Health Mentions from Free-Text Clinical NotesDOI 10.1101/2025.06.04.25328983Read

Automated Insomnia Phenotyping from Electronic Health Records: Leveraging Large Language Models to Decode Clinical NarrativesSemantic proximity Read

Zero-shot extraction of seizure outcomes from clinical notes using generative pretrained transformersSemantic proximity Read

A Comparative Investigation of Zero-shot Prompting and Fine-tuning for Clinical Note SummarizationSemantic proximity Read

A Systematic Exploration of LLM Behavior for EHR PhenotypingSemantic proximity Read

Evaluating approaches of training a generative large language model for multi-label classification of unstructured electronic health recordsSemantic proximity Read

clickBrick Prompt Engineering: Optimizing Large Language Model Performance in Clinical PsychiatrySemantic proximity Read

SleepBert: An Intelligent Clinical Encyclopaedia for Sleep Disorders Using Large Language ModelsSemantic proximity Read

Fine-Tuning PubMedBERT for Hierarchical Condition Category Classification from Clinical NotesSemantic proximity Read

Semantic Encoding in Medical LLMs for Vocabulary StandardisationSemantic proximity Read

From Narratives to Diagnosis: A Machine Learning Framework for Classifying Sleep Disorders in Aging Populations: The sleepCare PlatformSemantic proximity Read

A Large Language Model-Based Generative Natural Language Processing Framework Finetuned on Clinical Notes Accurately Extracts Headache Frequency from Electronic Health RecordsSemantic proximity Read

Combining Clinician Expertise with Prompt Engineering enhances Small Language Models Reliability for Cancer Entity Recognition in Electronic Health RecordsSemantic proximity Read