Research Of Data Mining At New Rural Cooperative Medical System

Rsearch ObjectChief objective: In order to increase New Rural Cooperative Medical System(NRCMS) governor's quantity decision-making ability by history data, NRCMS Data-Mining(DM) flow, subject matter for DM and it's practice value were studied. Particular objective:①making NRCMS-DM flow standardization;②understanding the operation question for NRCMS;③designing NRCMS Data Warehouse models;④carrying out NRCMS DM by some NRCMS county, and some advice could be put forward for NRCMS.Study MethodsFirstly, finding out NRCMS's general situation and DM's common model by literature research, and making the frame for NRCMS DM.Secondly, to affirm NRCMS'main operation question by understanding how working for NRCMS, using public choice.Thirdly, according to DW's conceptual model, logistic model and physical model design flow, making NRCMS-DW's conceptual model and logistic model, from the manager information asking.Lastly ,design NRCMS's DM data stream model on SPSS Clemention 12.0. finding out strong association rules in NRCMS compensation subject data-base by GRI association arithmetic. To find out abnormity medical consumed data model for countryman who joining NRCMS. Training a model to forecast who may be hospitalization and which one may be an abnormity in hospitalization.Outcomes1. The quantity of NRCMS county went up percent 72.3 annual from 2004 to 2008 year, coverage rise from percent 75.2 to percent 91.5, the NRCMS fund for everybody increased 3-4 times. 65 thousands countryman for a manager to manage. After 2006 year, four policy file about NRCMS's manage information were made out on health ministry, 1.8 hundred billion fund were arranged. NRCMS's information system building on the data management, analysis and data mining could be realized by 2010 year.2. According to CRISP-DM, data mining on NRCMS should carry out on four step: understanding the operation question, preparing data, data mining, appraising and implementing.3. Eight question were correlation with NRCMS: financing, disease risk, compensate for medical consumption, medical service, countryman's health, benefit adscription for countryman or hospital. The importance score of all of which in turn are 8,8,8,7,5,3,3.4. NRCMS DW model: conceptual model was build on individual, hospital and compensate three subject matter, and the relation model of which are that individual-compensate (1-1), compensate-hospital (1-m), individual-hospital (m-m). DW logical data model was designed as star model. The data cube should be both for family account or outpatient and single for hospitalization database.5. Disease risk association rule:"pregnancy, childbirthd and co-childbed disease"have the highest percent on compensation facts(26.56%),after that in turn were digest disease(18.37%) ,"trauma and poisoning"(10.98%), circle system disease(10.42%) and cancer(6.01%). A rule of"fracture"=>"male"was found, the support is 6.32% and the confidence is 73.76% for which. Another rule about medical consumption were found as that:"pregnancy, childbirth etc."and"in county hospital"=>"low consumption","digest diseases"and"in county or township hospital"=>"middle low consumption", ("fracture"and"in county hospital")or"in city hospital"=>"high consumption"6. The distribution law of countryman's disease risk: the percent of people in county hospital is the highest that 47.53%, then is that in township hospital(28.88%). The main disease are digest disease and fracture, and male sufferer were more than female. Cancer sufferer are the most in city hospital. Average hospital days, fee for medicine and compensation by NRCMS increase as the level of hospital asked for service(P<0.05).7. To inspect abnormity data by hospital institute, hospital days , disease and fee for service. Some data were taken out as: some female sufferer stay 270 days in township hospital for both side groin lymph turgescence , another 75 age female sufferer stay 175 days in county hospital for chronic bronchitis.……. the most sufferer have fracture disease in county abnormity data.8. Forecasting for hospitalization: make"yes"or"no"hospital as output, select the feature attribute, CHAID tree model show that: hospital rate is 22.97% in poor group lead by disease. People who are poor as disease and have chronic disease, then the rate be in hospital will be 58.28%. poor and have chronic disease will be in hospital at the rate of 14.29%.9. Forecasting for abnormity data of hospitalization: take out 10% abnormity data by clustering, then make"T"or"F"as the output label, after selecting character attributes, and select two model those is C5.1 and Network which. forecast efficiency are higher. Some question hiding in black box of network could be found out by rules coming from C5.1 tree model: person who above 65age and be in township hospital or below 11age and be in city hospital would happen abnormity consumption. Above city level hospital also would easily be an abnormity consumption. But person who above 11age and below 58age would not happen abnormity consumption no matter what which lever hospital.Conclusion1. To enhance the decision-making ability for NRCMS managers by building NRCMS DW and DM system, that has been an important topic.2. The compensation topic should be the first subject matter for building NRCMS DW model.3. The percent who gain compensation for childbirth at hospital is the highest. While the most disease economic burden come from digest disease and fracture/suddenness hurt for people (especially male)at the study county.4. Aging, chronic disease, low income and children's difficulty disease could be the main factors for abnormity data object happen .adviceTo promote countryman's health by making more health care for pregnancy and lying-in women working; To control countryman's disease risk, suddenness hurt and digest disease should be take more attention to prevent. To decreasing NRCMS fund risk, disease management should provide for chronic and/or aging patient, avoid inconsequence medical afford.the main Innovations1. Using standardization study, to put forward the subject matter for NRCMS DM by policy theory. NRCMS DW's conceptual data model and logical data model were designed from NCMS operation view.2. Designing data stream model at SPSS Clemention 12.0 for NRCMS DM.3. Some NRCMS DM questions were studied, and more interesting conclusion for practice were enduced.