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Research and Application of Clinical Behavior Anomaly Detection Based on Association Rules

Author: YuanXiaoDong
Tutor: YangHeBiao
School: Jiangsu University
Course: Applied Computer Technology
Keywords: Clinical behavior Anomaly Detection Sequence association rules Frequent patterns Cheat
CLC: TP311.13
Type: Master's thesis
Year: 2010
Downloads: 73
Quote: 0
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Abstract


Social medical insurance system as a relationship to the national economy system, on the one hand, the protection of the health of all workers, free from the threat of injury to play an important role; On the other hand, due to the lack of effective medical procedure codes of conduct and standardization, illegal phenomena occur, which will not only damage the interests of patients, causing tension in the doctor-patient relationship, but also result in the waste of medical resources, hindering the development of medical and health undertakings. Therefore, from the clinical diagnosis and treatment process mining abnormal behavior, has a very important significance for the specification of hospitals, clinics behavior, prevention of irregularities and fraud, reduce medical resource wastage. Research background and research status and by association rule mining method based on the medical profession \building domain-oriented anomaly detection model for screening clinical events abnormal treatment behavior. In this paper, the main work is as follows: 1, the paper discusses the mainstream anomaly detection method, analyzed clinical mining of association rules. The medical field analysis to study the characteristics of the medical data, and clinical data preprocessing method; analyzed the clinical behavior data having timing characteristics and the single disease clinics behavior characteristics based on clinical path for lack of algorithms to improve the GSP algorithm proposed the persistent time constraints frequent sequence mining algorithms CBS the GSPA, by introducing the concept of legal sub-sequence descriptor sequence timing, use of time constraint relationship pair sequence sequence legitimacy the judgment to ensure that continue to generate the correct candidate and count the support of the clinical standards of conduct legitimate sub-sequence, thus completing the sequence mode cuts, and then found that the clinical data frequent behavior sequence mode; 3, for the clinical behavior of the time-bound characteristics, by analyzing the legitimacy constrained by frequent sequence generation rules proposed the ARCBS (Association Rule of Clinical BehaviorSequence) algorithm to mining clinical behavior sequence association rules, and as a basis to build the clinical behavior anomaly detection model; 4, the design of a prototype system framework, association rules extraction, anomaly detection and other aspects of the implementation, evaluation of the effect of the anomaly detection to verify the availability of the model.

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