Predicting Mortality and Readmission in Obstructive Sleep Apnea via LLM-Expanded Clinical Concepts

Obstructive Sleep Apnea (OSA) is a common sleep disorder associated with serious health risks. This study leverages large language models (LLMs) to process and interpret clinical narratives in electronic health records. It develops clinically meaningful lexicons for predicting mortality and readmiss...

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Bibliografski detalji

Glavni autori:	Awwal Ahmed, Anthony Rispoli, Carrie Wasieloski, Ifrah Khurram, Rafael Zamora-Resendiz, Destinee Morrow, Aijuan Dong, Silvia Crivelli
Format:	Artigo
Jezik:	Inglês
Izdano:	MDPI AG 2026-03-01
Serija:	Big Data and Cognitive Computing
Teme:	obstructive sleep apnea large language models natural language processing mortality hospital readmission machine learning
Online pristup:	https://www.mdpi.com/2504-2289/10/3/97
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