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The Construction and Application of a Clinical Decision Support System for Cardiovascular Diseases: Multimodal
Shumei Miao1,2, Pei Ji2, Yongqian Zhu3
1School of Computer Science and Engineering, Southeast University, No.2 Sipailou, Nanjing, 210096, China, 86 02552090872.
Insights
A new clinical decision support system (CDSS) improves cardiovascular disease (CVD) assessment and treatment standardization in China. This intelligent system enhances patient care by integrating multimodal data and providing real-time clinical decision support.
Area of Science:
- Medical Informatics
- Artificial Intelligence in Healthcare
- Cardiovascular Medicine
Background:
- Rising incidence of cardiovascular diseases (CVDs) in China due to aging population and unhealthy lifestyles.
- Challenges in CVD diagnosis and management stemming from uneven medical resource distribution and varying treatment standards.
Purpose of the Study:
- To develop a cardiovascular diagnosis and treatment knowledge base using novel technology.
- To create an auxiliary clinical decision support system (CDSS) integrated into physician workstations.
- To enhance CVD assessment accuracy and treatment standardization rates.
Main Methods:
- Designed a layered CDSS integrating multimodal data (laboratory, HIS, EMR, ECG, nursing).
- Utilized natural language processing, IDCNN, and TextCNN for unstructured data analysis and knowledge extraction.
- Developed a big data platform for multidimensional data statistics and management decision support.
Main Results:
- Real-time CDSS integration into physician workstations improved risk assessment and diagnosis rates significantly.
- Mandatory control indicators reached 100% post-CDSS implementation.
- Enhanced the overall standardization of clinical diagnosis and treatment for cardiovascular diseases.
Conclusions:
- Established a specialized CVD knowledge base integrated with clinical multimodal information for intelligent patient assessment and stratification.
- Demonstrated the effectiveness of the CDSS in automatically recommending intervention treatments.
- Proved the value of disease-specific intelligent systems built using CDSS for cardiovascular care.
Background:
Due 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.
Objective:
The 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.
Methods:
This 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.
Results:
The 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.
Conclusions:
This 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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