The Construction and Application of a Clinical Decision Support System for Cardiovascular Diseases: Multimodal Data-Driven Development and Validation Study

Abstract BackgroundDue to the acceleration of the aging population and the prevalence of unhealthy lifestyles, the incidence of cardiovascular diseases (CVDs) in China continues to grow. However, due to the uneven distribution of medical resources across regions and significan...

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Main Authors: Shumei Miao, Pei Ji, Yongqian Zhu, Haoyu Meng, Mang Jing, Rongrong Sheng, Xiaoliang Zhang, Hailong Ding, Jianjun Guo, Wen Gao, Guanyu Yang, Yun Liu
Format: Article
Language:English
Published: JMIR Publications 2025-03-01
Series:JMIR Medical Informatics
Online Access:https://medinform.jmir.org/2025/1/e63186
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author Shumei Miao
Pei Ji
Yongqian Zhu
Haoyu Meng
Mang Jing
Rongrong Sheng
Xiaoliang Zhang
Hailong Ding
Jianjun Guo
Wen Gao
Guanyu Yang
Yun Liu
author_facet Shumei Miao
Pei Ji
Yongqian Zhu
Haoyu Meng
Mang Jing
Rongrong Sheng
Xiaoliang Zhang
Hailong Ding
Jianjun Guo
Wen Gao
Guanyu Yang
Yun Liu
author_sort Shumei Miao
collection DOAJ
description Abstract BackgroundDue to the acceleration of the aging population and the prevalence of unhealthy lifestyles, the incidence of cardiovascular diseases (CVDs) in China continues to grow. However, due to the uneven distribution of medical resources across regions and significant disparities in diagnostic and treatment levels, the diagnosis and management of CVDs face considerable challenges. ObjectiveThe purpose of this study is to build a cardiovascular diagnosis and treatment knowledge base by using new technology, form an auxiliary decision support system, and integrate it into the doctor’s workstation, to improve the assessment rate and treatment standardization rate. This study offers new ideas for the prevention and management of CVDs. MethodsThis study designed a clinical decision support system (CDSS) with data, learning, knowledge, and application layers. It integrates multimodal data from hospital laboratory information systems, hospital information systems, electronic medical records, electrocardiography, nursing, and other systems to build a knowledge model. The unstructured data were segmented using natural language processing technology, and medical entity words and entity combination relationships were extracted using IDCNN (iterated dilated convolutional neural network) and TextCNN (text convolutional neural network). The CDSS refers to global CVD assessment indicators to design quality control strategies and an intelligent treatment plan recommendation engine map, establishing a big data analysis platform to achieve multidimensional, visualized data statistics for management decision support. ResultsThe CDSS system is embedded and interfaced with the physician workstation, triggering in real-time during the clinical diagnosis and treatment process. It establishes a 3-tier assessment control through pop-up windows and screen domination operations. Based on the intelligent diagnostic and treatment reminders of the CDSS, patients are given intervention treatments. The important risk assessment and diagnosis rate indicators significantly improved after the system came into use, and gradually increased within 2 years. The indicators of mandatory control, directly became 100% after the CDSS was online. The CDSS enhanced the standardization of clinical diagnosis and treatment. ConclusionsThis study establishes a specialized knowledge base for CVDs, combined with clinical multimodal information, to intelligently assess and stratify cardiovascular patients. It automatically recommends intervention treatments based on assessments and clinical characterizations, proving to be an effective exploration of using a CDSS to build a disease-specific intelligent system.
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spelling doaj-art-ee2980b75b074e6aa1dbb5542f566dfd2025-08-20T02:58:37ZengJMIR PublicationsJMIR Medical Informatics2291-96942025-03-0113e63186e6318610.2196/63186The Construction and Application of a Clinical Decision Support System for Cardiovascular Diseases: Multimodal Data-Driven Development and Validation StudyShumei Miaohttp://orcid.org/0000-0001-6101-8288Pei Jihttp://orcid.org/0000-0003-1754-0970Yongqian Zhuhttp://orcid.org/0009-0000-6335-8796Haoyu Menghttp://orcid.org/0000-0002-7602-3327Mang Jinghttp://orcid.org/0009-0009-6191-3413Rongrong Shenghttp://orcid.org/0009-0009-1873-6255Xiaoliang Zhanghttp://orcid.org/0009-0005-7598-1733Hailong Dinghttp://orcid.org/0009-0005-0587-8802Jianjun Guohttp://orcid.org/0009-0009-8526-5705Wen Gaohttp://orcid.org/0000-0002-0749-7676Guanyu Yanghttp://orcid.org/0000-0003-3704-1722Yun Liuhttp://orcid.org/0000-0002-6431-4469 Abstract BackgroundDue to the acceleration of the aging population and the prevalence of unhealthy lifestyles, the incidence of cardiovascular diseases (CVDs) in China continues to grow. However, due to the uneven distribution of medical resources across regions and significant disparities in diagnostic and treatment levels, the diagnosis and management of CVDs face considerable challenges. ObjectiveThe purpose of this study is to build a cardiovascular diagnosis and treatment knowledge base by using new technology, form an auxiliary decision support system, and integrate it into the doctor’s workstation, to improve the assessment rate and treatment standardization rate. This study offers new ideas for the prevention and management of CVDs. MethodsThis study designed a clinical decision support system (CDSS) with data, learning, knowledge, and application layers. It integrates multimodal data from hospital laboratory information systems, hospital information systems, electronic medical records, electrocardiography, nursing, and other systems to build a knowledge model. The unstructured data were segmented using natural language processing technology, and medical entity words and entity combination relationships were extracted using IDCNN (iterated dilated convolutional neural network) and TextCNN (text convolutional neural network). The CDSS refers to global CVD assessment indicators to design quality control strategies and an intelligent treatment plan recommendation engine map, establishing a big data analysis platform to achieve multidimensional, visualized data statistics for management decision support. ResultsThe CDSS system is embedded and interfaced with the physician workstation, triggering in real-time during the clinical diagnosis and treatment process. It establishes a 3-tier assessment control through pop-up windows and screen domination operations. Based on the intelligent diagnostic and treatment reminders of the CDSS, patients are given intervention treatments. The important risk assessment and diagnosis rate indicators significantly improved after the system came into use, and gradually increased within 2 years. The indicators of mandatory control, directly became 100% after the CDSS was online. The CDSS enhanced the standardization of clinical diagnosis and treatment. ConclusionsThis study establishes a specialized knowledge base for CVDs, combined with clinical multimodal information, to intelligently assess and stratify cardiovascular patients. It automatically recommends intervention treatments based on assessments and clinical characterizations, proving to be an effective exploration of using a CDSS to build a disease-specific intelligent system.https://medinform.jmir.org/2025/1/e63186
spellingShingle Shumei Miao
Pei Ji
Yongqian Zhu
Haoyu Meng
Mang Jing
Rongrong Sheng
Xiaoliang Zhang
Hailong Ding
Jianjun Guo
Wen Gao
Guanyu Yang
Yun Liu
The Construction and Application of a Clinical Decision Support System for Cardiovascular Diseases: Multimodal Data-Driven Development and Validation Study
JMIR Medical Informatics
title The Construction and Application of a Clinical Decision Support System for Cardiovascular Diseases: Multimodal Data-Driven Development and Validation Study
title_full The Construction and Application of a Clinical Decision Support System for Cardiovascular Diseases: Multimodal Data-Driven Development and Validation Study
title_fullStr The Construction and Application of a Clinical Decision Support System for Cardiovascular Diseases: Multimodal Data-Driven Development and Validation Study
title_full_unstemmed The Construction and Application of a Clinical Decision Support System for Cardiovascular Diseases: Multimodal Data-Driven Development and Validation Study
title_short The Construction and Application of a Clinical Decision Support System for Cardiovascular Diseases: Multimodal Data-Driven Development and Validation Study
title_sort construction and application of a clinical decision support system for cardiovascular diseases multimodal data driven development and validation study
url https://medinform.jmir.org/2025/1/e63186
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