利用机器学习早期检测慢性阻塞性肺病：叙述性综述

IF 4.7 2区医学 Q1 RESPIRATORY SYSTEM

Respiratory Research Pub Date : 2024-09-09 DOI:10.1186/s12931-024-02960-6

Xueting Shen, Huanbing Liu

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引用次数: 0

摘要

慢性阻塞性肺病（COPD）是一种流行性呼吸系统疾病，在全球死亡率中排名第三，给患者和社会带来了沉重负担。本综述探讨了国内外关于将机器学习（ML）应用于慢性阻塞性肺疾病早期筛查的最新研究。综述讨论了机器学习技术在慢性阻塞性肺病早期筛查中的实际应用、关键优化点和前景。目的是为未来的研究和筛查策略的制定奠定科学基础和参考框架。

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Using machine learning for early detection of chronic obstructive pulmonary disease: a narrative review

Chronic obstructive pulmonary disease (COPD) is a prevalent respiratory disease and ranks third in global mortality rates, imposing a significant burden on patients and society. This review looks at recent research, both domestically and abroad, on the application of machine learning (ML) for early COPD screening. The review discusses the practical application, key optimization points, and prospects of ML techniques in early COPD screening. The aim is to establish a scientific foundation and reference framework for future research and the development of screening strategies.

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来源期刊

Respiratory Research 医学-呼吸系统

自引率

1.70%

发文量

314

期刊介绍： Respiratory Research publishes high-quality clinical and basic research, review and commentary articles on all aspects of respiratory medicine and related diseases. As the leading fully open access journal in the field, Respiratory Research provides an essential resource for pulmonologists, allergists, immunologists and other physicians, researchers, healthcare workers and medical students with worldwide dissemination of articles resulting in high visibility and generating international discussion. Topics of specific interest include asthma, chronic obstructive pulmonary disease, cystic fibrosis, genetics, infectious diseases, interstitial lung diseases, lung development, lung tumors, occupational and environmental factors, pulmonary circulation, pulmonary pharmacology and therapeutics, respiratory immunology, respiratory physiology, and sleep-related respiratory problems.