Miao Shumei, Ji Pei, Zhu Yongqian, Meng Haoyu, Jing Mang, Sheng Rongrong, Zhang Xiaoliang, Ding Hailong, Guo Jianjun, Gao Wen, Yang Guanyu, Liu Yun
School of Computer Science and Engineering, Southeast University, No.2 Sipailou, Nanjing, 210096, China, 86 02552090872.
Department of Information, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
JMIR Med Inform. 2025 Mar 3;13:e63186. doi: 10.2196/63186.
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.
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.
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.
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.
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.
由于人口老龄化加速以及不健康生活方式的流行，中国心血管疾病（CVD）的发病率持续上升。然而，由于医疗资源在各地区分布不均以及诊疗水平存在显著差异，CVD的诊断和管理面临着相当大的挑战。
本研究旨在利用新技术构建心血管疾病诊断与治疗知识库，形成辅助决策支持系统，并将其集成到医生工作站中，以提高评估率和治疗标准化率。本研究为CVD的预防和管理提供了新思路。
本研究设计了一个具有数据层、学习层、知识层和应用层的临床决策支持系统（CDSS）。它整合了来自医院实验室信息系统、医院信息系统、电子病历、心电图、护理等系统的多模态数据，以构建知识模型。使用自然语言处理技术对非结构化数据进行分词，并使用迭代扩张卷积神经网络（IDCNN）和文本卷积神经网络（TextCNN）提取医学实体词和实体组合关系。CDSS参考全球CVD评估指标来设计质量控制策略和智能治疗方案推荐引擎图，建立大数据分析平台以实现多维度、可视化的数据统计，为管理决策提供支持。
CDSS系统嵌入并与医生工作站接口，在临床诊疗过程中实时触发。通过弹出窗口和屏幕控制操作建立了三级评估控制。基于CDSS的智能诊疗提醒，对患者进行干预治疗。系统投入使用后，重要风险评估和诊断率指标显著提高，并在2年内逐渐上升。强制控制指标在CDSS上线后直接达到100%。CDSS提高了临床诊疗的标准化程度。
本研究建立了CVD的专业知识库，结合临床多模态信息，对心血管疾病患者进行智能评估和分层。它根据评估和临床特征自动推荐干预治疗方案，证明了利用CDSS构建特定疾病智能系统的有效探索。
