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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 dat
آیا می توان از طریق استعلام هوشمند تحت شرایط تجارت الکترونیکی ، وضعیت یک بیمار را پیش بینی کرد؟ پیش بینی خطر ابتلا به بیماری مبتنی بر ویژگی های توالی بر ویژگی های انتخاب شده از تشخیص شناختی داده های بزرگ-2020 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. Keywords: Cognitive diagnosis big data | Online intelligent inquiry | Sequential feature map | Disease risk prediction | Redundancy and complementarity dispersion |
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