Research And Implementation Of Cancer Clinical Assistant Decision System Driven By Rule Engine

With the advent of the intelligent era in the medical profession and the explosion of medical data,hospitals have had to explore in recent years how these data can be used for patient health management.The purpose of this thesis is to use the Drools rule engine to build a system that can intelligently assess patient risk,provide early warning,and provide clinical healthcare-assisted decision making(referred to as clinical decision making)through the study of patient data.The model standardizes and dynamizes risk assessment processes,risk early warning processes and decision-making processes for clinical assistance,reducing costs for health professionals while providing better,safer and more effective services to patients.In this thesis,we focus on the following three aspects to analyze the patient history data extracted from Sichuan Cancer Hospital using association rule mining technology to extract effective rules to enrich the existing risk assessment rule base.To address the problems that hospitals are currently facing with manual duplication of data entry and lag in risk assessment,risk warning,and clinical aid decision making,this thesis integrates ETL technology and Drools rule engine technology to achieve automatic information entry and rapid reasoning of rules.At the same time,in order to cope with the dynamic changes of business requirements and reduce the learning time cost of medical and nursing staff,we propose to realize the visual management of rules based on the rule engine.Through the above three points,this thesis forms a complete solution from enriching rules to visualizing and managing rules,from automatic information extraction to executing rules to produce risk assessment,risk warning,and clinical aid decision results.The main work of this thesis is as follows.1.Medical data rule mining: Based on the association analysis index of medical record data selected by expert knowledge and clinical experience of hospital experts,the association rule algorithm FP-growth algorithm was used to complete the association rule rule mining of patient medical record data extracted from Sichuan Cancer Hospital.2.Automatic extraction of information: Based on the current problems of manual duplicate data entry and lag in risk assessment,risk warning and clinical aid decision making in hospitals,the system integrates ETL technology to realize automatic extraction of data required for risk assessment.3.Integrated rule engine: In order to realize intelligence of risk assessment,risk warning,and clinical aid decision making,this system integrates Drools rule engine technology and provides visual definition of rules and rule flow function for health care professionals based on this technology.4.Oncology clinical assisted decision-making system: Design and implement the key functions of the oncological assisted decision system based on demand analysis and the system’ s overall logical structure.