Currently, interpretation of health examination reports relies primarily on the physician's own experience. If health screening data could be integrated with outpatient medical records to uncover correlations between disease and abnormal test results, the physician could benefit from having additional reference resources for medical examination report interpretation and clinic diagnosis. This study used the medical database of a regional hospital in Taiwan to illustrate how association rules can be found between abnormal health examination results and outpatient illnesses. The rules can help to build up a disease-prevention knowledge database that assists healthcare providers in follow-up treatment and prevention. Furthermore, this study proposes a new algorithm, the data cutting and sorting method, or DCSM, in place of the traditional Apriori algorithm. DCSM significantly improves the mining performance of Apriori by reducing the time to scan health examination and outpatient medical records, both of which are databases of immense sizes.
AgrawalR.ImielinskiT.SwamiA.N. (1993). Data mining: A performance perspective. IEEE Transactions on Knowledge and Data Engineering5(6): 914–925.
2.
AgrawalR.SrikantR. (1994). Fast algorithms for mining association rules. Proceedings of 20th International Conference on Very Large Data Bases, pp. 487–499.
3.
AokiS.AokiY. (2006). Jingzhun jiedu jiankang jiancha baogaoshu (Interpreting health examination reports with precision). Taipei, Reisheng Culture.
4.
BerryM.J.A.LinoffG. (1997). Data mining technique for marketing, sale, and customer support (Second edition).John Wiley and Sons, Inc.
5.
BrinS.MotwaniR.UllmanJ.D.TsurS. (1997). Dynamic itemset counting and implication rules for market basket data. ACM SIGMOD International Conference on Management of Data, pp. 255–264.
6.
ChenC.J. (2009). The cause-and-effect model and the exploration of pathogens—the marvellous adventures of epidemiology. NTU Alumni Bimonthly65: 24–31.
7.
ChenH.K. (2010). Shanyong Jiankang jiancha—jibing yufang de lichi (Health examination—an effective disease-prevention measure). Journal of Healthcare Quality4(2): 20–23.
8.
ChenM.F. (2007). Taida yiyuan jiankang guanli zhongxin de gaizao (Renovation of NTU hospital health management centre). NTU Alumni Bimonthly52: 55–57.
9.
ChenY. (2001). Kandong Jianjian Baogao de diyi ben shu (How to read health examination reports).Taipei JiuyiBookhouse Culture.
10.
ChenY.LinZ. (2005). Zhuanjia jieda huayan baogao (Expert interpretation of test results).Shanghai, Shanghai Scientific and Technological Literature Publishing House.
11.
ChengW.L. (2005). Jiankang jiancha quanshu (The complete guide to physical examination).Taipei, Business Weekly Publications.
12.
ChienC.F.ChenL.F. (2008). Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry. Expert Systems with Applications34: 280–290.
13.
ChuangT.KungJ.ChengJ. (2006). Yi ziliao tankan jishu jianli binghuan jiuyi daoyin—yi changweike binghuan wei li (Establishment of the patient guide using data mining techniques). Journal of Taiwan Association for Medical Informatics15(1): 17–34.
14.
GeeL.AbbottJ.HartA.ConwayS.P.EtheringtonC.WebbA.K. (2005). Associations between clinical variables and quality of life in adults with cystic fibrosis. Journal of Cystic Fibrosis4(1): 59–66.
15.
GrupeF.H.OwrangM.M. (1995). Database mining discovering new knowledge and cooperative advantage. Information System Management12(4): 26–31.
16.
HanJ.PeiJ.YinY. (2000). Mining frequent patterns without candidate generation. Proceedings of 2000 ACM International Conference on Management of Data (SIGMOD'00), pp. 1–12.
17.
HoegerW.W.K.HoegerS.A. (1994). Principles and labs for physical fitness and wellness. Englewood CO, Morton Publishing Company.
18.
HuangY.C.LiaoM.C. (2011). The application of a neural network on the construction of the health management system of hospital staff. Journal of Quality18(4): 315–331.
19.
InatomiK. (1997). Chingsong jiedu yiyuan jiancha baogao (Interpreting health check-up reports with ease), trans. ChangTaipeiR.S., Taiwan Industrial Culture.
20.
JongS.P.ChenM.S.YuP.S. (1995). An effective hash based algorithm for mining association rules. SIGMOD Conference: 175–186.
21.
KeissnerC. (1998). Data mining for the enterprise. Proceedings of the 31st Annual Hawaii International Conference on System Science (HICSS 98) IEEE7, pp. 295–304.
22.
LeavellH.R.ClarkE.G. (1965). Preventive medicine for the doctor in his community (Third edition).New York, McGraw-Hill.
23.
McGinnisM.J.Williams-RussoP.KnickmanJ.R. (2002). The case for more active policy attention to health promotion. Health Affairs21(2):78–93.
24.
PalaniappanS.AwangR. (2008). Intelligent heart disease prediction system using data mining techniques. IEEE International Conference on Computer Systems and Applications, pp. 108–115.
YongqianQ.GuoY.LiX.WangQ.ChenH.CuicD. (2007). The diagnostic rules of peripheral lung cancer preliminary study based on data mining technique. Journal of Nanjing Medical University21(3): 190–195.
