Prediction and management of readmission risk for congestive heart failure

HEALTHINF 2014 - 7th International Conference on Health Informatics, Proceedings; Part of 7th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2014

Volumn , Issue , 2014, Pages 523-528

  Roy, Senjuti Basu ^a   Chin, Si Chi ^a  

^a UNIVERSITY OF WASHINGTON   (United States)

Author keywords

Hospital readmission risk prediction; Predictive modeling; Readmission risk management

Indexed keywords

BIOMEDICAL ENGINEERING; CARDIOLOGY; DATA MINING; FORECASTING; HOSPITALS; MEDICAL INFORMATICS; PATIENT TREATMENT; PREDICTIVE ANALYTICS; RISK ASSESSMENT;

CONGESTIVE HEART FAILURES; HEALTH CARE INFORMATICS; HEALTHCARE INDUSTRY; HOSPITAL DISCHARGE; PREDICTIVE MODELING; RISK MANAGEMENT STRATEGIES; RISK PREDICTIONS; SEQUENCE MINING;

RISK MANAGEMENT;

EID: 84902322039     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.5220/0004915805230528     Document Type: Conference Paper

References (16)

1. Han, J. and Kamber, M. (2006). Data mining: concepts and techniques. Morgan Kaufmann
2. Hardin, J. W., Schmeidiche, H., and Carroll, R. J. (2003). The regression-calibration method for fitting generalized linear models with additive measurement error. Stata Journal, 3(4):361-372
3. Jencks, S. F., Williams, M. V., and Coleman, E. A. (2009). Rehospitalizations among patients in the medicare fee-for-service program. New England Journal of Medicine, 360(14):1418-1428
4. Johnson, R. A. andWichern, D.W., editors (1988). Applied multivariate statistical analysis. Prentice-Hall, Inc., Upper Saddle River, NJ, USA
5. Kansagara D, E. H. (2011). Risk prediction models for hospital readmission: A systematic review. JAMA, 306(15):1688-1698
6. Koelling, T. M., Johnson, M. L., Cody, R. J., and Aaronson, K. D. (2005). Discharge education improves clinical outcomes in patients with chronic heart failure. Circulation, 111(2):179-185
7. Krumholz HM, P. E. (1997). Readmission after hospitalization for congestive heart failure among medicare beneficiaries. Archives of Internal Medicine, 157(1):99-04
8. Naylor MD, B. D. (1999). Comprehensive discharge planning and home follow-up of hospitalized elders: A randomized clinical trial. JAMA: The Journal of the American Medical Association, 281(7):613-620
9. Phillips CO, W. S. (2004). Comprehensive discharge planning with postdischarge support for older patients with congestive heart failure: A meta-analysis. JAMA: The Journal of the American Medical Association, 291(11):1358-1367
10. Plessis, J. L. D. and Merwe, A. J. V. D. (2004). Inferences in multivariate bayesian calibration. JSTOR. Rich, M. W., Beckham, V., Wittenberg, C., Leven, C. L
11. Freedland, K. E., and Carney, R. M. (1995). A multidisciplinary intervention to prevent the readmission of elderly patients with congestive heart failure. New England Journal of Medicine, 333(18):1190-1195
12. Schneider, J. K., Hornberger, S., Booker, J., Davis, A., and Kralicek, R. (1993). A medication discharge planning program measuring the effect on readmissions. Clinical Nursing Research, 2(1):41-53
13. Walraven, C. v., Bennett, C., Jennings, A., Austin, P. C., and Forster, A. J. (2011). Proportion of hospital readmissions deemed avoidable: a systematic review. Canadian Medical Association Journal, 183(7):E391-E402
14. Zolfaghar, K., Agarwal, J., Sistla, D., Chin, S.-C., Roy, S. B., and Verbiest, N. (2013a). Risk-o-meter: an intelligent clinical risk calculator. In KDD, pages 1518-1521
15. Zolfaghar, K., Meadem, N., Sistla, D., Chin, S.-C., Roy, S. B., Verbiest, N., and Teredesai, A. (2013b). Exploring preprocessing techniques for prediction of risk of readmission for congestive heart failure patients. In Data Mining and Healthcare Workshop
16. Zolfaghar, K., Verbiest, N., Agarwal, J., Meadem, N., Chin, S.-C., Roy, S. B., Teredesai, A., Hazel, D., Amoroso, P., and Reed, L. (2013c). Predicting riskof-readmission for congestive heart failure patients: A multi-layer approach. CoRR, abs/1306.2094