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