华中科技大学硕士学位论文
ABSTRACT
[Purpose] To master the technical and theoretical knowledge of clinical decision support systems, analyze the progress and research status of CDSS applications at home and abroad, summarize the system architecture of CDSS, and propose to establish a system of clinical decision support system based on big data. Finally, type 2 diabetes is used for disease prediction analysis as an example, and a diabetes prediction system is implemented preliminarily.
[Methods] The research methods used in this thesis include literature research method, decision tree algorithm, Bayesian network algorithm, neural network algorithm and empirical analysis method. The literature research method was used to analyze the progress and research status of CDSS applications at home and abroad. The general clinical decision support system architecture and big data forecasting model were summarized to provide the theoretical support for this paper. Through system modeling and business process construction, the modular approach builds a CDSS- architecture based on big data; clementine tool was used to build models such as decision tree, Bayesian network, and neural network algorithms to achieve diabetes prediction and empirical analysis.
[Results] ⑴ An architecture model of clinical decision support system based on big data was constructed. The architecture includes data source, data integration, big data analysis, decision analysis, and human-computer interaction. The paper also analyzed the application functions of based on big data CDSS, including recommendations, tips, alerts, forecasts, and etc. ⑵ Diabetes prediction was used as an example to conduct an empirical analysis, and three methods of diabetes prediction were completed including decision tree, Bayesian network and neural network . The average prediction completion rate of Bayesian network was 95.07%, and the other two predicted completion rates were 100%. the prediction accuracy of the three models respectively were 93.20%±1.68%, 83.47%±1.18%, and 90.78%±1.97%. ⑶ Initially designed and implemented the diabetes prediction system
华中科技大学硕士学位论文
and completed the data query, input and prediction functions.
[Conclusions] In the empirical analysis of diabetes prediction, the prediction effects of the three models are different. The completion rate of Bayesian network prediction is lower, and the Bayesian prediction accuracy rate is lower than both decision trees and neural networks. The sensitivity average bit dissimilarity is low. In general, the decision tree and neural network model have better prediction performance than Bayesian networks. The disease prediction model and empirical analysis process conducted this time can be thought to provide ideas for follow-up decision-making aids based on big data, and has a positive effect on improving the quality and efficiency of medical services.
Keywords: Big Data; Clinical Decision Support System; Disease Prediction; Diabetes