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Open Access

A Case-Finding Clinical Decision Support System to Identify Subjects with Chronic Obstructive Pulmonary Disease Based on Public Health Data

Department of Pulmonary and Critical Care Medicine, China-Japan Friendship Hospital, Beijing 100029, China
School of Software Engineering, the Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
Intelligent Healthcare Unit, Baidu Inc, Beijing 100093, China
Department of Automation, Tsinghua University, Beijing 100084, China
Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100005, China
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Abstract

Chronic obstructive pulmonary disease (COPD) is a serious chronic respiratory disease. Improving the ability to identify patients with COPD in primary medical institutions is important to prevent and treat the disease. With the continuous development of medical digitization, the application of big data informatization in the medical and health fields has become possible. Recently, applying innovative technologies such as big data analysis, machine learning, and artificial intelligence-assisted decision-making in the medical field has become an interdisciplinary research hotspot. Based on the identification and diagnosis of COPD in the high-risk population, this study proposes a convenient and effective clinical decision support system to help identify patients with COPD in primary health institutions. The results of the preliminary experiments show that the proposed method is convenient and effective compared with the existing methods.

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Tsinghua Science and Technology
Pages 525-540

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Cite this article:
Lin X, Lei Y, Chen J, et al. A Case-Finding Clinical Decision Support System to Identify Subjects with Chronic Obstructive Pulmonary Disease Based on Public Health Data. Tsinghua Science and Technology, 2023, 28(3): 525-540. https://doi.org/10.26599/TST.2022.9010010

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Received: 03 March 2022
Accepted: 07 April 2022
Published: 13 December 2022
© The author(s) 2023.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).