Random forests on ubiquitous data for heart failure 30-day readmissions prediction

Proceedings - 2013 12th International Conference on Machine Learning and Applications, ICMLA 2013

Volumn 2, Issue , 2013, Pages 415-421

  Vedomske, Michael A ^a   Brown, Donald E ^a   Harrison Jr , James H ^a  

^a UNIVERSITY OF VIRGINIA   (United States)

Author keywords

30 day readmission; claims data; Health informatics; heart failure; random forests

Indexed keywords

DECISION TREES; FORECASTING; HOSPITALS; LEARNING SYSTEMS;

30 DAY READMISSION; CLAIMS DATA; HEALTH INFORMATICS; HEART FAILURE; RANDOM FORESTS;

CARDIOLOGY;

EID: 84899451803     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICMLA.2013.158     Document Type: Conference Paper

References (38)

1. A. Elixhauser and C. Steiner, "Readmissions to U.S. hospitals by dignosis, 2010," Statistical Brief 153, Agency for Healthcare Research and Quality, Rockville, MD, Apr. 2013.
2. A. F. Hernandez, M. A. Greiner, G. C. Fonarow, B. G. Hammill, P. A. Heidenreich, C. W. Yancy, E. D. Peterson, and L. H. Curtis, "Relationship between early physician follow-up and 30-day readmission among medicare beneficiaries hospitalized for heart failure," JAMA: The Journal of the American Medical Association, vol. 303, no. 17, p. 17161722, 2010.
3. L. Horwitz, C. Partovian, Z. Lin, J. Herrin, J. Grady, M. Conover, J. Montague, C. Dillaway, K. Bartczak, and J. Ross, "Hospital-wide (allcondition) 30-day risk-standardized readmission measure," tech. rep., Centers for Medicare & Medicaid Services, 2011.
4. Fonarow GC, Adams KF, Abraham WT, Yancy CW, Boscardin W, ADHERE Scientific Advisory Committee, Study Group, and and Investigators f, "Risk stratification for in-hospital mortality in acutely decompensated heart failure: Classification and regression tree analysis," JAMA, vol. 293, pp. 572-580, Feb. 2005.
5. P. S. Keenan, S.-L. T. Normand, Z. Lin, E. E. Drye, K. R. Bhat, J. S. Ross, J. D. Schuur, B. D. Stauffer, S. M. Bernheim, A. J. Epstein, Y. Wang, J. Herrin, J. Chen, J. J. Federer, J. A. Mattera, Y. Wang, and H. M. Krumholz, "An administrative claims measure suitable for profiling hospital performance on the basis of 30-day allcause readmission rates among patients with heart failure," Circulation: Cardiovascular Quality and Outcomes, vol. 1, pp. 29-37, Sept. 2008. PMID: 20031785.
6. R. Amarasingham, B. J. Moore, Y. P. Tabak, M. H. Drazner, C. A. Clark, S. Zhang, W. G. Reed, T. S. Swanson, Y. Ma, and E. A. Halm, "An automated model to identify heart failure patients at risk for 30-day readmission or death using electronic medical record data:," Medical Care, vol. 48, pp. 981-988, Nov. 2010.
7. B. G. Hammill, L. H. Curtis, G. C. Fonarow, P. A. Heidenreich, C. W. Yancy, E. D. Peterson, and A. F. Hernandez, "Incremental value of clinical data beyond claims data in predicting 30-day outcomes after heart failure hospitalization," Circulation: Cardiovascular Quality and Outcomes, vol. 4, pp. 60-67, Jan. 2011. PMID: 21139093.
8. D. S. Lee, P. C. Austin, J. L. Rouleau, P. P. Liu, D. Naimark, and J. V. Tu, "Predicting mortality among patients hospitalized for heart failure," JAMA: The Journal of the American Medical Association, vol. 290, pp. 2581-2587, Nov. 2003.
9. A. G. Au, F. A. McAlister, J. A. Bakal, J. Ezekowitz, P. Kaul, and C. Van Walraven, "Predicting the risk of unplanned readmission or death within 30 days of discharge after a heart failure hospitalization," American heart journal, 2012.
10. E. Bradley, O. Yakusheva, L. Horwitz, H. Sipsma, and J. Fletcher, "Identifying patients at increased risk for unplanned readmission," Medical Care, vol. 51, pp. 761-766, Sept. 2013.
11. M. Rothman, S. Rothman, and J. Beals IV, "Development and validation of a continuous measure of patient condition using the electronic medical record," Journal of Biomedical Informatics, vol. In Press, 2013.
12. C. M. DesRoches, C. Worzala, M. S. Joshi, P. D. Kralovec, and A. K. Jha, "Small, nonteaching, and rural hospitals continue to be slow in adopting electronic health record systems," Health Affairs, vol. 31, pp. 1092-1099, May 2012. PMID: 22535503.
13. S. Levant and C. DeFrances, "Electronic collection of inpatient and ambulatory hospital care data: National hospital care survey," in Proceedings of the 13th Annual International Conference on Digital Government Research, dg.o ?12, (New York, NY, USA), p. 200205, ACM, 2012.
14. M. Zuniga, D. Anderson, and K. Alexander, "Heart disease and stroke in rural america," Rural healthy people, 2010.
15. C. D. McNaughton, S. P. Collins, S. Kripalani, R. Rothman, W. H. Self, C. Jenkins, K. Miller, P. Arbogast, A. Naftilan, and R. S. Dittus, "Low numeracy is associated with increased odds of 30-day emergency department or hospital recidivism for patients with acute heart failure," Circulation: Heart Failure, vol. 6, no. 1, p. 4046, 2013.
16. W. C. Levy, D. Mozaffarian, D. T. Linker, S. C. Sutradhar, S. D. Anker, A. B. Cropp, I. Anand, A. Maggioni, P. Burton, M. D. Sullivan, B. Pitt, P. A. Poole-Wilson, D. L. Mann, and M. Packer, "The seattle heart failure model prediction of survival in heart failure," Circulation, vol. 113, pp. 1424-1433, Mar. 2006. PMID: 16534009.
17. D. Logeart, G. Thabut, P. Jourdain, C. Chavelas, P. Beyne, F. Beauvais, E. Bouvier, and A. C. Solal, "Predischarge b-type natriuretic peptide assay for identifying patients at high risk of re-admission after decompensated heart failure," Journal of the American College of Cardiology, vol. 43, pp. 635-641, Feb. 2004.
18. H. M. Krumholz, Y.-T. Chen, Y. Wang, V. Vaccarino, M. J. Radford, and R. I. Horwitz, "Predictors of readmission among elderly survivors of admission with heart failure," American heart journal, vol. 139, no. 1, p. 7277, 2000.
19. H. M. Krumholz, J. Amatruda, G. L. Smith, J. A. Mattera, S. A. Roumanis, M. J. Radford, P. Crombie, and V. Vaccarino, "Randomized trial of an education and support intervention to prevent readmission of patients with heart failure," Journal of the American College of Cardiology, vol. 39, no. 1, p. 8389, 2002.
20. M. D. Naylor, D. A. Brooten, R. L. Campbell, G. Maislin, K. M. McCauley, and J. S. Schwartz, "Transitional care of older adults hospitalized with heart failure: A randomized, controlled trial," Journal of the American Geriatrics Society, vol. 52, no. 5, p. 675684, 2004.
21. M. W. Rich, V. Beckham, C. Wittenberg, C. L. Leven, K. E. Freedland, and R. M. Carney, "A multidisciplinary intervention to prevent the readmission of elderly patients with congestive heart failure," New England Journal of Medicine, vol. 333, no. 18, p. 11901195, 1995.
22. S. Stewart, J. E. Marley, and J. D. Horowitz, "Effects of a multidisciplinary, home-based intervention on planned readmissions and survival among patients with chronic congestive heart failure: A randomised controlled study," The Lancet, vol. 354, no. 9184, p. 10771083, 1999.
23. E. Hsich, E. Z. Gorodeski, E. H. Blackstone, H. Ishwaran, and M. S. Lauer, "Identifying important risk factors for survival in patient with systolic heart failure using random survival forests," Circulation: Cardiovascular Quality and Outcomes, vol. 4, pp. 39-45, Jan. 2011. PMID: 21098782.
24. Y.-J. Son, H.-G. Kim, E.-H. Kim, S. Choi, and S.-K. Lee, "Application of support vector machine for prediction of medication adherence in heart failure patients," Healthcare Informatics Research, vol. 16, no. 4, p. 253, 2010.
25. S. Cholleti, A. Post, J. Gao, X. Lin, W. Bornstein, D. Cantrell, and J. Saltz, "Leveraging derived data elements in data analytic models for understanding and predicting hospital readmissions," in AMIA Annual Symposium Proceedings, vol. 2012, p. 103, 2012.
26. X. Lin, A Predictive Random Forest Model on Hospital 30-Day Readmission using Electronic Health Records. MSPH, Emory University, May 2012.
27. U. Public Health Sciences, "UVa clinical data repository," May 2012.
28. M. Beebe, J. A. Dalton, M. Espronceda, D. D. Evans, R. L. Glenn, and G. Green, CPT: Standard: Current Procedural Terminology. Amer Medical Assn, 2007.
29. N. C. f. H. S. (US), C. o. C. Classifications, C. o. Professional, H. Activities, and W. H. Organization, The International Classification of Diseases, 9th Revision, Clinical Modification: ICD. 9. CM. Commission on Professional and Hospital Activities, 1978.
30. DHHS, "Guidance regarding methods for de-identification of protected health information in accordance with the health insurance portability and accountability act (HIPAA) privacy rule," 2013.
31. D. E. Beck and D. A. Margolin, "Physician coding and reimbursement," The Ochsner Journal, vol. 7, no. 1, pp. 8-15, 2007. PMID: 21603473 PMCID: PMC3096340.
32. M. E. Charlson, P. Pompei, K. L. Ales, and C. R. MacKenzie, "A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation 1," Journal of chronic diseases, vol. 40, no. 5, p. 373383, 1987.
33. T. D. A. of Health Care, "PERCENT OF PATIENTS READMITTED WITHIN 30 DAYS OF DISCHARGE, BY COHORT," Online Report Generation 1, The Dartmouth Institute for Health Policy and Clinical Practice, 2010.
34. L. Breiman, "Random forests," Machine learning, vol. 45, no. 1, p. 532, 2001.
35. L. Breiman and A. Cutler, "Random forests-classification description," Department of Statistics Homepage, 2007.
36. C. Chen, A. Liaw, and L. Breiman, "Using random forest to learn imbalanced data," University of California, Berkeley, 2004.
37. K. J. O?Malley, K. F. Cook, M. D. Price, K. R. Wildes, J. F. Hurdle, and C. M. Ashton, "Measuring diagnoses: ICD code accuracy," Health Services Research, vol. 40, no. 5p2, p. 16201639, 2005.
38. E. P. McCarthy, L. I. Iezzoni, R. B. Davis, R. H. Palmer, M. Cahalane, M. B. Hamel, K. Mukamal, R. S. Phillips, and D. T. Davies, "Does clinical evidence support ICD-9-CM diagnosis coding of complications," Medical Care, vol. 38, pp. 868-876, Aug. 2000. ArticleType: Researcharticle /Full publication date: Aug., 2000 /Copyright 2000 Lippincott Williams & Wilkins.