A Machine Learning Prediction Model of Adult Obstructive Sleep Apnea Based on Systematically Evaluated Common Clinical Biochemical Indicators

IF 4.7 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC ACS Applied Electronic Materials Pub Date : 2024-04-01 DOI:10.2147/nss.s453794
Jiewei Huang, Jiajing Zhuang, Huaxian Zheng, Ling Yao, Qingquan Chen, Jiaqi Wang, Chunmei Fan
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Abstract

Objective: Obstructive sleep apnea (OSA) is a common and potentially fatal sleep disorder. The purpose of this study was to construct an objective and easy-to-promote model based on common clinical biochemical indicators and demographic data for OSA screening
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基于系统评估的常见临床生化指标的成人阻塞性睡眠呼吸暂停机器学习预测模型
目的:阻塞性睡眠呼吸暂停(OSA阻塞性睡眠呼吸暂停(OSA)是一种常见且可能致命的睡眠障碍。本研究旨在根据常见的临床生化指标和人口统计学数据,构建一个客观且易于推广的模型,用于筛查 OSA
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来源期刊
CiteScore
7.20
自引率
4.30%
发文量
567
期刊介绍: ACS Applied Electronic Materials is an interdisciplinary journal publishing original research covering all aspects of electronic materials. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrate knowledge in the areas of materials science, engineering, optics, physics, and chemistry into important applications of electronic materials. Sample research topics that span the journal's scope are inorganic, organic, ionic and polymeric materials with properties that include conducting, semiconducting, superconducting, insulating, dielectric, magnetic, optoelectronic, piezoelectric, ferroelectric and thermoelectric. Indexed/​Abstracted: Web of Science SCIE Scopus CAS INSPEC Portico
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