A personalized infectious disease risk prediction system
作者:
Highlights:
• Personal risk factor is not the only factor that affecting a person's risk contracting of an infectious disease.
• Few modifications from the existing knowledge representations are added to (1) capture all factors affecting an infectious disease risk in a person, and (2) allow automatic generation of a prediction model.
• A special-purpose algorithm was built to automatically generate an equivalent prediction model from the knowledge representation to predict the personalized infectious disease risks.
• A system which aims to incorporate current data and declarative knowledge, as well as tailoring the knowledge-base, algorithm, and the generated prediction model is presented.
• Evaluations for three major infectious diseases at country-level are included with intention to seek the reliability of the personalized infectious disease risk probabilities from the generated prediction model.
摘要
•Personal risk factor is not the only factor that affecting a person's risk contracting of an infectious disease.•Few modifications from the existing knowledge representations are added to (1) capture all factors affecting an infectious disease risk in a person, and (2) allow automatic generation of a prediction model.•A special-purpose algorithm was built to automatically generate an equivalent prediction model from the knowledge representation to predict the personalized infectious disease risks.•A system which aims to incorporate current data and declarative knowledge, as well as tailoring the knowledge-base, algorithm, and the generated prediction model is presented.•Evaluations for three major infectious diseases at country-level are included with intention to seek the reliability of the personalized infectious disease risk probabilities from the generated prediction model.
论文关键词:Infectious disease,Risk prediction,Bayesian network,Ontology,Rule
论文评审过程:Received 20 March 2018, Revised 18 April 2019, Accepted 18 April 2019, Available online 18 April 2019, Version of Record 4 May 2019.
论文官网地址:https://doi.org/10.1016/j.eswa.2019.04.042