Application of data mining for examining polypharmacy and adverse effects in cardiology patients

被引：6

作者：

Cerrito P. ^{[1
,2
,3
]}

机构：

[1] Jewish Hosp. Heart/Lung Institute, Louisville

[2] Department of Mathematics, University of Louisville, Louisville

[3] Jewish Hosp. Heart/Lung Institute, Louisville, KY 40202

来源：

Cardiovascular Toxicology | 2001年 / 1卷 / 3期

关键词：

Cardiovascular patients; Data mining; Drug interactions; Polypharmacy;

D O I：

10.1385/CT:1:3:177

中图分类号：

学科分类号：

摘要：

This article comments upon the use of data mining tools to examine clinical data. Many cardiovascular patients have co-morbid diseases that put them at risk for polypharmacy, or severe adverse reactions from the interactions of multiple medications. Clinical trials typically use too few patients with stringent inclusion/exclusion criteria that prevent an examination of the issue of polypharmacy. However, clinical data collected in the course of patient treatment can be used in conjunction with data mining to find meaningful results.

引用

页码：177 / 179

页数：2

共 10 条

[1]

Fineberg S.E., The treatment of hypertension and dyslipidemia in diabetes mellitus, Primary Care, 26, 4, pp. 951-964, (1999)

[2]

Parmley W., How many medicines do patients with heart failure need?, Circulation, 103, 12, (2001)

[3]

Cohn J.N., Goldstein S.O., Greenberg B.H., Lorell B.H., Bourge R.C., Jaski B.E., Et al., A dose-dependent increase in mortality with vesnarinone among patients with severe heart failure, N. Engl. J. Med., 339, 25, pp. 1810-1816, (1998)

[4]

French A.R., Mason T., Nelson A.M., O'Fallon W.M., Gabriel S.E., Increased mortality in adults with a history of juvenile rheumatoid arthritis: A population-based study, Arthritis Rheum., 44, 3, pp. 523-527, (2001)

[5]

Strain J.J., Caliendo G., Alexis J.D., Lowe R.S., Karim A., Loigman M., Cardiac drug and psychotropic drug interactions: Significance and recommendations, Gen. Hosp. Psychiatry, 21, 6, pp. 408-429, (1999)

[6]

Lindquist M., Stahl M., Bate A., Edwards I.R., Meyboom R.H., A retrospective evaluation of a data mining approach to aid finding new adverse drug reaction signals in the WHO international database, Drug Safety, 23, 6, pp. 533-542, (2000)

[7]

Willenheimer R., Swedberg K., Dressing heart-failure patients on Savile Row-tailored treatment, Lancet, 355, pp. 2012-2013, (2000)

[8]

Holmes J.H., Durbin D.R., Winston F.K., Discovery of predictive models in an injury surveillance database: An application of data mining in clinical research, Proceedings of the AMIA Annual Symposium, pp. 359-363, (2000)

[9]

Downs S.M., Wallace M.Y., Mining association rules from a pediatric primary care decision support system, Proceedings of the AMIA Annual Symposium 2000, pp. 200-204, (2000)

[10]

Bate A., Lindquist M., Edwards I.R., Olsson S., Orre R., Lasner A., Et al., A Bayesian neural network method for adverse drug reaction signal generation, Eur. J. Clin. Pharmacol., 54, pp. 315-321, (1998)

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