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
作者：
Highlights：
• Online intelligent medical inquiry-based physician’s cognitive diagnosis big data was fused with offline EMR to obtain VEMR.
• A sequential feature map-based model for disease risk prediction was presented to obtain online users’ medical conditions.
• The optimized TRApriori method is used to mine a frequent feature map on the basis of the temporal graph.
• The frequent feature graph to obtain the online user’s reconstruction coefficient and to realize disease risk prediction.
• A neighborhood-based collaborative prediction model was presented for prediction of an online user’s possible diseases.
摘要
•Online intelligent medical inquiry-based physician’s cognitive diagnosis big data was fused with offline EMR to obtain VEMR.•A sequential feature map-based model for disease risk prediction was presented to obtain online users’ medical conditions.•The optimized TRApriori method is used to mine a frequent feature map on the basis of the temporal graph.•The frequent feature graph to obtain the online user’s reconstruction coefficient and to realize disease risk prediction.•A neighborhood-based collaborative prediction model was presented for prediction of an online user’s possible diseases.
论文关键词：Cognitive diagnosis big data,Online intelligent inquiry,Sequential feature map,Disease risk prediction,Redundancy and complementarity dispersion
论文评审过程：Received 30 November 2018, Revised 26 February 2019, Accepted 8 May 2019, Available online 28 May 2019, Version of Record 21 November 2019.
论文官网地址：https://doi.org/10.1016/j.ijinfomgt.2019.05.006
