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| 一种面向临床决策推理的可视诊疗技术 |
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赵睿1,2, 朱卫国3,4,5, 马翠霞2, 滕东兴2
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1.中国科学院大学, 北京 100049;2.中国科学院 软件研究所 人机交互技术与智能信息处理实验室, 北京 100190;3.中国医学科学院 北京协和医学院, 北京 100730;4.北京协和医院 普通内科, 北京 100730;5.北京协和医院 信息管理处, 北京 100730
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| 摘要: |
| 海量医学信息的快速增长已远远超出人类认知能力，医疗服务环境和用户人群的复杂多样性使得海量数据难以在现有能力和工具的支持下满足广大用户对于信息服务的需求.临床诊疗服务的可视化、智能化程度不高导致现有的医学知识服务水平难以保证海量资源信息的充分利用.在分析了临床诊疗环境下人机协同认知特性的基础上给出了一种基于语义层次的信息组织方式；分析了符合该数据组织模式的可视形态及自然的可视交互技术；在上述工作的基础上构建了一个面向临床决策推理的可视诊疗分析框架，并给出了原型系统实例加以验证.结果表明，通过结合交互式可视化和自动分析技术，可以有效地帮助人们从海量数据中获取到有用的信息模式，减轻人们对数据进行分析的负担，为医疗诊断过程提供决策支持服务. |
| 关键词: 可视化诊疗 人机交互 临床决策 人机协同 协同认知 海量医疗信息 |
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| 基金项目:北京协和医院杰出青年基金（JQ201509)；国家重点基础研究发展计划（973)（2016YFB1001200)；国家高技术研究发展计划（863)（2015AA050204) |
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| Visualized Diagnosing Technique for Clinical Decision-Making and Reasoning |
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ZHAO Rui1,2, ZHU Wei-Guo3,4,5, MA Cui-Xia2, TENG Dong-Xing2
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1.University of Chinese Academy of Sciences, Beijing 100049, China;2.Intelligence Engineering Laboratory, Institute of Software, The Chinese Academy of Sciences, Beijing 100190, China;3.Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100730, China;4.Division of General Internal Medicine, Peking Union Medical College Hospital, Beijing 100730, China;5.Division of Information Management, Peking Union Medical College Hospital, Beijing 100730, China
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| Abstract: |
| Explosive growth in medical information has exceeded human's cognitive skills and abilities. Traditional information management tools are challenged in satisfying people's needs of information service due to the diversity of clinical service environment and users. The existing medical service is difficult to meet the needs of the massive information processing and utilizing due to the development of intelligence and visualization of clinical service. This paper analyzes the collaborative cognitive feature between human and computer on the basis of scenarios of clinical process; presents an approach to information organization based on sematic; and proposes the technique of visual organization and interaction for large-scale medical data. Moreover, a user-centered visual analysis framework is proposed for clinical decision-making and reasoning. Finally an example of application is given to illustrate how to facilitate users to gain useful insights from the massive data by combining automated analysis techniques with interactive visualizations. Experiments show that visual analytics facilitate users to deal with information overloads and aid medical diagnosis decision. |
| Key words: visual diagnosis human-computer interaction clinical decision-making human-computer collaboration cognitive collaboration large-scale medical information |
