Can the development of a patient's condition be predicted through intelligent inquiry under the e-health business mode? Sequential feature map-based disease risk prediction upon features selected from cognitive diagnosis big data
Can the development of a patient's condition be predicted through intelligent inquiry under the e-health business mode? Sequential feature map-based disease risk prediction upon features selected from cognitive diagnosis big data
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电子健康业务模式下能否通过智能查询预测患者病情发展？
DOI：
10.1016/j.ijinfomgt.2019.05.006
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发表时间：
2020-02
影响因子：
21
通讯作者：
Wang Zongrun
中科院分区：
文献类型：
--
作者：
Liu Xin;Zhou Yanju;Wang Zongrun
数据驱动模式推动了预防医学的研究。在疾病风险预测方面，医生的临床认知诊断数据可以用于疾病的早期预防，从而降低医疗成本，提高医疗服务的可及性，降低医疗风险。然而，电子健康商业模式下的智能问诊研究并未涉及医生对患者病情的认知，没有提供诊断大数据，忽视了线上线下医疗数据共同活动产生的融合文本信息的价值，未能从在线问诊数据驱动的角度深入分析高阶信息短缺导致的冗余互补分散现象。此外，单纯基于离线临床认知诊断数据进行风险预测无疑会降低预测精度。重要的是，相关研究很少考虑不同医疗事件的时间关系，没有对模式爆炸的实际问题进行详细分析，没有提出智能描绘图的思路，也没有根据从地图中获得的子图进行相关风险预测。为此，提出一种基于冗余互补色散特征选择的医生在线认知诊断大数据特征选择模型的疾病风险预测方法，实现在线智能问诊认知诊断大数据特征选择；对获得的特征进行智能排序，用于后续高维信息短缺补偿；将认知诊断大数据补偿后的关键特征信息与离线电子病历（EMR）融合，形成虚拟电子病历（VEMR）。将形成的VEMR与序列特征图的方法相结合进行建模，提出基于序列特征图的疾病风险预测模型，以获取在线用户的医疗状况。提出了一种基于邻域的协作预测模型，用于预测在线智能医疗查询用户未来可能患的疾病，并对疾病的风险概率进行智能排序。实验中，以在线智能医疗查询用户的VEMR作为模拟实验的基础，预测慢性阻塞性肺病（OCPD）人群和风湿性心脏病（RHD）人群的疾病风险。实验表明，所提出的方法在 VEMR 中表现出相对良好的度量性能，并改进了疾病风险预测。
The data-driven mode has promoted the researches of preventive medicine. In prediction of disease risks, physicians’ clinical cognitive diagnosis data can be used for early prevention of diseases and, therefore, to reduce medical cost, to improve accessibility of medical services and to lower medical risk. However, researches involved no physicians’ cognition of patients’ conditions in intelligent inquiry under e-health business mode, offered no diagnosis big data, neglected the values of the fused text information generated by joint activities of online and offline medical data, and failed to thoroughly analyze the phenomenon of redundancy-complementarity dispersion caused by high-order information shortage from the online inquiry data-driven perspective. Besides, the risk prediction simply based on offline clinical cognitive diagnosis data undoubtedly reduces prediction precision. Importantly, relevant researches rarely considered temporal relationships of different medical events, did not conduct detailed analysis on practical problems of pattern explosion, did not offer a thought of intelligent portrayal map, and did not conduct relevant risk prediction based on the sub-maps obtained from the map. In consequence, the paper presents a disease risk prediction method with the model for redundancy-complementarity dispersion-based feature selection from physicians’ online cognitive diagnosis big data to realize features selection from the cognitive diagnosis big data of online intelligent inquiry; the obtained features were ranked intelligently for subsequent high-dimensional information shortage compensation; the compensated key feature information of the cognitive diagnosis big data was fused with offline electronic medical record (EMR) to form the virtual electronic medical record (VEMR). The formed VEMR was combined with the method of the sequential feature map for modelling, and a sequential feature map-based model for disease risk prediction was presented to obtain online users’ medical conditions. A neighborhood-based collaborative prediction model was presented for prediction of an online intelligent medical inquiry user’s possible diseases in the future and to intelligently rank the risk probabilities of the diseases. In the experiments, the online intelligent medical inquiry users’ VEMRs were used as the foundation of the simulation experiments to predict disease risks in chronic obstructive pulmonary disease (OCPD) population and rheumatic heart disease (RHD) population. The experiments demonstrated that the presented method showed relatively good metric performances in the VEMR and improved disease risk prediction.
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影响因子：
3.9
作者：
Pedersen AB;Mikkelsen EM;Cronin-Fenton D;Kristensen NR;Pham TM;Pedersen L;Petersen I
通讯作者：
Petersen I
DOI：
10.1016/j.eswa.2017.06.004
发表时间：
2017-11
期刊：
Expert Syst. Appl.
影响因子：
--
作者：
Mahdieh Zabihimayvan;Reza Sadeghi;H. N. Rude;Derek Doran
通讯作者：
Mahdieh Zabihimayvan;Reza Sadeghi;H. N. Rude;Derek Doran
DOI：
10.1016/j.eswa.2017.04.021
发表时间：
2017-10
期刊：
Expert Syst. Appl.
影响因子：
--
作者：
Lei Zhang;D. Zhang;Ming-ming Sun;Fangmei Chen
通讯作者：
Lei Zhang;D. Zhang;Ming-ming Sun;Fangmei Chen
长 QT 综合征的风险分层。
DOI：
10.1056/nejm200308283490916
发表时间：
2003
期刊：
The New England journal of medicine
影响因子：
--
作者：
Ellinor,PatrickT;Milan,DavidJ;MacRae,CalumA
通讯作者：
MacRae,CalumA
影响因子：
3
作者：
Ludmila Dymova;Pavel V. Sevastjanov;A. Tikhonenko
通讯作者：
Ludmila Dymova;Pavel V. Sevastjanov;A. Tikhonenko