임상에서의 데이터 마이닝 개념과 원칙
Basic Concepts and Principles of Data Mining in Clinical Practice
1
조회
0
다운로드
9
인용
0
참고문헌
키워드
초록
Recently, many hospitals have been adopting clinical data warehouses (CDW) as well as electronic medical records. These new hospital information systems are inevitably introducing very large amounts of clinical data that might be useful for further analysis. However, the electronic clinical data in the CDW are usually byproducts of clinical practice rather than the product of research. Therefore, they include inconsistent and sometimes erroneous information that might not have the specific context of the clinical situations. Data miners usually have various academic backgrounds such as electronics, informatics, statistics, biomedicine, and public health. If the complex situations surrounding the clinical data are not well understood, investigators performing data mining in clinical fields may have problems assessing the information they are confronted with. Here, we would like to introduce some basic concepts on the principles of data mining in clinical fields including legal and ethical considerations as well as technical concerns.
Abstract
Recently, many hospitals have been adopting clinical data warehouses (CDW) as well as electronic medical records. These new hospital information systems are inevitably introducing very large amounts of clinical data that might be useful for further analysis. However, the electronic clinical data in the CDW are usually byproducts of clinical practice rather than the product of research. Therefore, they include inconsistent and sometimes erroneous information that might not have the specific context of the clinical situations. Data miners usually have various academic backgrounds such as electronics, informatics, statistics, biomedicine, and public health. If the complex situations surrounding the clinical data are not well understood, investigators performing data mining in clinical fields may have problems assessing the information they are confronted with. Here, we would like to introduce some basic concepts on the principles of data mining in clinical fields including legal and ethical considerations as well as technical concerns.
참고문헌 (64)
- Cios KJ, "Uniqueness of medical data mining", ArtificialIntelligence in Medicine, 26(1), pp.1~24 (2002)
- Lavrac N, "An overview. in: Intelligentdata analysis in medicine and pharmacology", pp.1~13 (1997), Kluwer
- Simon SR, "Physicians and electronic health records:a statewide survey", Archives of Internal Medicine, 167(5), pp.507~512 (2007)
- Menachemi N, "Examining the adoption of electronic healthrecords and personal digital assistants by familyphysicians in Florida", Inform Prim Care, 14(1), pp.1~9 (2006)
- Park RW, "Computerized physician order entry and electronicmedical record systems in Korean teaching and generalhospitals: results of a 2004 survey", J Am Med Inform Assoc, 12(6), pp.642~647 (2005)
- Sittig F, "Asurvey of USA acute care hospitals' computer-basedprovider order entry system infusion levels", Stud HealthTechnol Inform, 129(1), pp.252~ (2007)
- DesRoches CM, "Electronic health records inambulatory care--a national survey of physicians", TheNew England Journal of Medicine, 359(1), pp.50~60 (2008)
- Dewitt JG, "Development of a datawarehouse at an academic health system: knowing aplace for the first time", Acad Med, 80(11), pp.1019~1025 (2005)
- Schubart JR, "Evaluation of a datawarehouse in an academic health sciences center", International Journal of Medical Informatics, 60(3), pp.319~333 (2000)
- Silver M, "Case study: how to apply data miningtechniques in a healthcare data warehouse", J Healthc Inf Manag, 15(2), pp.155~164 (2001)
- Zhang Q, "The application of aninstitutional clinical data warehouse to the assessmentof adverse drug reactions (ADRs). Evaluation of aminoglycosideand cephalosporin associated nephrotoxicity", Methods Inf Med, 46(5), pp.516~522 (2007)
- Kononenko I, "Machine learning for medical diagnosis:history, state of the art and perspective", Artif Intell Med, 23(1), pp.89~109 (2001)
- Lavrac N, "Selected techniques for data mining inmedicine", Artif Intell Med, 16(1), pp.3~23 (1999)
- Kopelman LM, "Minimal risk as an international ethicalstandard in research", The Journal of Medicine andPhilosophy, 29(3), pp.351~378 (2004)
- Cios KJ, "Medical data mining and knowledgediscovery", IEEE Eng Med Biol Mag, 19(4), pp.15~16 (2000)
- Cios KJ, "A knowledge discovery approach to diagnosingmyocardial perfusion", IEEE Eng Med Biol Mag, 19(4), pp.17~25 (2000)
- Yuan YC, "Multiple imputation for missing data:concepts and new development", Paper presented at:Twenty-Fifth Annual SAS Users Group International Conference (2000)
- Schafer JL, "Missing data: our view of thestate of the art", Psychol Methods, 7(2), pp.147~177 (2002)
- Harel O, "Multiple imputation: review oftheory, implementation and software", Stat Med, 26(16), pp.3057~3077 (2007)
- Haykin S, "networks and learning machines. 3rded" (2008), Prentice Hall
- Bishop CM, "Pattern recognition and machine learning.2nd ed", pp.291~358 (2005), Springer
- Rokach L, "Data mining with decision trees:theroy and applications" (2008), World Scientific Publishing Company
- Learning Bayesian networks: Thecombination of knowledge and statistical data, "Heckerman DE", Microsoft Research, ~, pp.MSR-TR-94-09 (1995)
- Heckerman DE, "Bayesian networks for data mining", Data Mining and Knowledge Discovery, 1, pp.79~119 (1997)
- Heckerman DE, "Piatetsky-Shapiro G,Smyth P, Uthurusamy R. Bayesian networks forknowledge discovery. Advances in knowledge discoveryand data mining", pp.273~305 (1996), The MIT Press
- Lee SM, "Bayesian networks for knowledgediscovery in large datasets: basics for nurse researchers", rnal of Biomedical Informatics, 36(4), pp.389~399 (2003)
- SPSS, "Clementine 12.0 modeling nodes" (2007), SPSS
- SPSS, "Clementine manual-Basic" (2007), SPSS
- Menard SW, "Applied logistic regression analysis. 2nded" (2001), Sage Publications
- Lee SM, "Logistic regressionand bayesian networks to study outcomes using largedata sets", Nursing Research, 54(2), pp.133~138 (2005)
- Tu JV, "Advantages and disadvantages of using artificialneural networks versus logistic regression for predictingmedical outcomes", Journal of Clinical Epidemiology, 49, pp.1225~1232 (1996)
- Ramaswamy S, "Multiclass cancer diagnosisusing tumor gene expression signatures", Proc Natl AcadSci U S A, 98(26), pp.15149~15154 (2001)
- Furey TS, "Support vector machine classification and validation of cancer tissue samplesusing microarray expression data", Bioinformatics, 16, pp.906~914 (2000)
- Lauritzen SL, "Local computationswith probabilities on graphical structures and theirapplication to expert systems", Journal of the Royal Statistical Society Series B, 50(2), pp.157~194 (1988)
- Eisenstein EL, "A comparison of threetechniques for rapid model development: an applicationin patient risk-stratification", Proceedings, 1996, 443~447
- Hanley JA, "The meaning and use of the areaunder a receiver operating characteristic (ROC) curve", Radiology, 143(1), pp.29~36 (1982)
- Hanley JA, "A method of comparing theareas under receiver operating characteristic curvesderived from the same cases", Radiology, 148(3), pp.839~843 (1983)
- Rowland T, "Comparison ofmultiple prediction models for ambulation followingspinal cord injury", Proceedings, 1998, 528~532
- Hosmer DW, "Goodness of fit tests for themultiple logistic regression model", Communications in Statistics, A9(10), pp.1043~1069 (1980)
- Porter CR, "Combining artificial neuralnetworks and transrectal ultrasound in the diagnosis ofprostate cancer", Oncology (Williston Park), 17(10), pp.1395~1399 (2003)
- Schwarzer G, "Artificial neuralnetworks for diagnosis and prognosis in prostate cancer", Semin Urol Oncol, 20(2), pp.89~95 (2002)
- Errejon A, "Use of artificial neuralnetworks in prostate cancer", Mol Urol, 5(4), pp.153~158 (2001)
- Murphy GP, "Use of artificial neural networks inevaluating prognostic factors determining the response to dendritic cells pulsed with PSMA peptides in prostatecancer patients", Prostate, 42(1), pp.67~72 (2000)
- Gamito EJ, "Useof artificial neural networks in the clinical staging ofprostate cancer: implications for prostate brachytherapy", Tech Urol, 6(2), pp.60~63 (2000)
- Snow PB, "Artificial neuralnetworks in the diagnosis and prognosis of prostatecancer: a pilot study", J Urol, 152(5), pp.1923~1926 (1994)
- Giles LC, "Falls in hospitalized patients:can nursing information systems data predict falls?", Computers, Informatics, Nursing, 24(3), pp.167~172 (2006)
- Tiet Q, "Suicideattempts among substance use disorder patients: aninitial step toward a decision tree for suicide management", Alcoholism: Clinical and Experimental Research, 30(6), pp.998~1005 (2006)
- Modai I, "Neural network detection of files ofsuicidal patients and suicidal profiles", Medical Informaticsand the Internet in Medicine, 24(4), pp.249~256 (1999)
- Anthony D, "An optimization ofthe Waterlow score using regression and artificialneural networks", Clinical Rehabilitation, 14(1), pp.102~109 (2000)
- Brossette SE, "Association rules and datamining in hospital infection control and public health surveillance", Journal of the American Medical Informatics Association, 5(4), pp.373~381 (1998)
- Rapeli CB, "Clinical profiles of serioussuicide attempters consecutively admitted to auniversity-based hospital: a cluster analysis study", Revista Brasileira de Psiquiatria, 27(4), pp.285~289 (2005)
- Lemeshow S, "A review of goodness of fitstatistics for use in the development of logisticregression models", American Journal of Epidemiology, 115(1), pp.92~106 (1982)
- Blum RL, "Displaying clinical data from a time-orienteddatabase", Computers in Biology and Medicine, 11(4), pp.197~210 (1981)
- Gamito EJ, "Artificial neural networks forpredictive modeling in prostate cancer", Curr Oncol Rep, 6(3), pp.216~221 (2004)
- Stephan C, "Artificial neuralnetworks: has the time come for their use in prostatecancer patients?", Nat Clin Pract Urol, 2(6), pp.262~263 (2005)
- Rodriguez Alonso A, "The utility of artificial neural networks in the prediction of prostate cancer on transrectal biopsy", Actas Urol Esp, 30(1), pp.18~24 (2006)
- Chun FK, "A critical appraisal of logisticregression-based nomograms, artificial neural networks,classification and regression-tree models, lookuptables and risk-group stratification models for prostatecancer", BJU Int, 99(4), pp.794~800 (2007)
- Cohen ME, "Neural network models forbiosignal analysis", Conf Proc IEEE Eng Med Biol Soc, 통권 : 1, pp.3537~3540 (2006)
- Elomaa T HN, "An experimental comparison ofinducing decision trees and decision lists in noisydomains", Paper presented at: 4th European Working Session on Learning (1989)
- Zupan B, "Acquiring background knowledgefor machine learning using function decomposition:a case study in rheumatology", Artif Intell Med, 14(1), pp.101~117 (1998)
- Quinlan R CP, "Inductive knowledgeacquisition: a case study. in: Applicationsof expert systems", pp.137~156 (1987), Addison-Wesley
- Horn W, "AI in medicine on its way from knowledge-intensive to data-intensive systems", Artificial Intelligence in Medicine, 23(1), pp.5~12 (2001)
- Hojker S KI, "Expertsystem's development in the management of thyroiddiseases", Paper presented at: European Congress for Nuclear Medicine (1988)
- Lesmo L SL, "Learning of fuzzy productionrules for medical diagnoses. in: Approximate reasoning in decision analysis", pp.249~260 (1982), North-Holland
