Impact of selected pre-processing techniques on prediction of risk of early readmission for diabetic patients in India

International Journal of Diabetes in Developing Countries

Volumn 36, Issue 4, 2016, Pages 469-476

  Duggal, Reena ^a   Shukla, Suren ^b   Chandra, Sarika ^c   Shukla, Balvinder ^a   Khatri, Sunil Kumar ^a  

^a AMITY UNIVERSITY   (India)

^b OHUM Healthcare Solutions Private Ltd   (India)

^c Kailash Hospital   (India)

Author keywords

Data mining; Diabetes; Feature selection; Missing value imputation; Pre processing; Predicting readmission rates

Indexed keywords

ADULT; AREA UNDER THE CURVE; CLASSIFIER; DATA MINING; DECISION TREE; DIABETIC PATIENT; ELECTRONIC MEDICAL RECORD; EXPERIMENTAL STUDY; HOSPITAL READMISSION; HUMAN; INDIA; LOGISTIC REGRESSION ANALYSIS; MAJOR CLINICAL STUDY; MODEL; PREDICTION; RECALL; YOUNG ADULT;

EID: 85007601515     PISSN: 09733930     EISSN: 19983832     Source Type: Journal    
DOI: 10.1007/s13410-016-0495-4     Document Type: Article

References (34)

1. Dungan KM. The effect of diabetes on hospital readmissions. J Diabet Sci Technol. 2012;6(5):1045–52.
2. Kar S. Reducing readmission in the hospital through integrated care cycle [Internet]. Openforum.hbs.org. 2014 [cited 10 September] (2015). Available from: https://openforum.hbs.org/challenge/hbs-hms-health-acceleration-challenge/innovations/reducing-readmission-in-the-hospital-through-integrated-care-cycle
3. Silverstein MD, Qin H, Mercer SQ, Fong J, Haydar Z. Risk factors for 30-day hospital readmission in patients? 65 years of age. In Baylor University Medical Center. Proceedings 2008; 21 Suppl 4:363. Baylor University Medical Center.
4. Strack B, DeShazo JP, Gennings C, Olmo JL, Ventura S, Cios KJ, Clore JN. Impact of HbA1c measurement on hospital readmission rates: analysis of 70,000 clinical database patient records. BioMed Res Int. 2014;3:2014.
5. Eby E, Hardwick C, Yu M, Gelwicks S, Deschamps K, Xie J, George T. Predictors of 30 day hospital readmission in patients with type 2 diabetes: a retrospective, case–control, database study. Curr Med Res Opin. 2015;31(1):107–14.
6. Rubin DJ. Hospital readmission of patients with diabetes. Curr Diabet Rep. 2015;15(4):1–9.
7. Rubin DJ, Donnell-Jackson K, Jhingan R, Golden SH, Paranjape A. Early readmission among patients with diabetes: a qualitative assessment of contributing factors. J Diabet Complicat. 2014;28(6):869–73.
8. Billings J, Dixon J, Mijanovich T, Wennberg D. Case finding for patients at risk of readmission to hospital: development of algorithm to identify high risk patients. BMJ. 2006;333(7563):327.
9. Lagoe RJ, Nanno DS, Luziani ME. Quantitative tools for addressing hospital readmissions. BMC Res Notes. 2012;5(1):620.
10. Donnan PT, Dorward DW, Mutch B, Morris AD. Development and validation of a model for predicting emergency admissions over the next year (PEONY): a UK historical cohort study. Arch Int Med. 2008;168(13):1416–22.
11. van Walraven C, Wong J, Hawken S, Forster AJ. Comparing methods to calculate hospital-specific rates of early death or urgent readmission. Can Med Assoc J. 2012;184(15):E810–7.
12. Donzé J, Aujesky D, Williams D, Schnipper JL. Potentially avoidable 30-day hospital readmissions in medical patients: derivation and validation of a prediction model. JAMA Int Med. 2013;173(8):632–8.
13. van Walraven C, Dhalla IA, Bell C, Etchells E, Stiell IG, Zarnke K, Austin PC, Forster AJ. Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community. Can Med Assoc J. 2010;182(6):551–7.
14. Billings J, Blunt I, Steventon A, Georghiou T, Lewis G, Bardsley M. Development of a predictive model to identify inpatients at risk of re-admission within 30 days of discharge (PARR-30). BMJ open. 2012;2(4):e001667.
15. AbdelRahman SE, Zhang M, Bray BE, Kawamoto K. A three-step approach for the derivation and validation of high-performing predictive models using an operational dataset: congestive heart failure readmission case study. BMC Med Inform Decis Making. 2014;14(1):1.
16. Meadem N, Verbiest N, Zolfaghar K, Agarwal J, Chin SC, Roy SB. Exploring preprocessing techniques for prediction of risk of readmission for congestive heart failure patients. In Data mining and healthcare (DMH), at International Conference on Knowledge Discovery and Data Mining (KDD) 2013.
17. Duggal R, Khatri SK, Shukla B. Improving patient matching: single patient view for clinical decision support using Big Data analytics. In Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions), 2015 4th International Conference on 2015 Sep 2 (pp. 1–6). IEEE.
18. Duggal, Reena, Shukla, B. & Khatri, S. K. Big Data Analytics in Indian healthcare system—opportunities and challenges, National Conference on Computing, Communication and Information Processing 2015 (NCCCIP-2015), ISBN: 978–93–84935-27-6, (DOI: NCCIP2015/NERIST/02/03–05-2015/CP28).
19. Chen JY, Ma Q, Chen H, Yermilov I. New bundled world: quality of care and readmission in diabetes patients. J Diabet Sci Technol. 2012;6(3):563–71.
20. Radovanovic S, Vukicevic M, Kovacevic A, Stiglic G, Obradovic Z. Domain knowledge based hierarchical feature selection for 30-day hospital readmission prediction. In Artificial intelligence in medicine. Springer International Publishing; 2015 pp. 96–100.
21. Hosseinzadeh A, Izadi M, Verma A, Precup D, Buckeridge D. Assessing the predictability of hospital readmission using machine learning. In Twenty-Fifth IAAI Conference; 2013.
22. Shams I, Ajorlou S, Yang K. A predictive analytics approach to reducing 30-day avoidable readmissions among patients with heart failure, acute myocardial infarction, pneumonia, or COPD. Health Care Manag Sci. 2015;18(1):19–34.
23. Zolfaghar K, Verbiest N, Agarwal J, Meadem N, Chin SC, Roy SB, Teredesai A, Hazel D, Amoroso P, Reed L. Predicting risk-of-readmission for congestive heart failure patients: a multi-layer approach. arXiv preprint arXiv:1306.2094. 2013.
24. Braga P, Portela F, Santos MF, Rua F. Data mining models to predict patient’s readmission in intensive care units.
25. Vukicevic M, Radovanovic S, Kovacevic A, Stiglic G, Obradovic Z. Improving hospital readmission prediction using domain knowledge based virtual examples. In Knowledge management in organizations Springer International Publishing; 2015 pp. 695–706.
26. Han J, Kamber M. Data mining. 2nd ed. Amsterdam: Elsevier; 2006. p. 72–85.310-317
27. Hall MA, Smith LA. Feature subset selection: a correlation based filter approach. In International Conference on Neural Information Processing and Intelligent Information Systems; 1997 pp. 855–858.
28. Peng L, Lei L. A review of missing data treatment methods. Intell Inf Manag Syst Technol. 2005;1(3):412–9.
29. Su X, Khoshgoftaar TM, Greiner R. Using imputation techniques to help learn accurate classifiers. In Tools with artificial intelligence, 2008. ICTAI’08. 20th IEEE International Conference on 2008; 1:437–444. IEEE.
30. Hosmer Jr DW, Lemeshow S. Applied logistic regression. 2nd ed. John Wiley & Sons; 2004.
31. Lee EW. Selecting the best prediction model for readmission. J Prev Med Public Health. 2012;45(4):259–66.
32. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH. The WEKA data mining software: an update. ACM SIGKDD Explor Newsletter. 2009;11(1):10–8.
33. Zolfaghar K, Meadem N, Teredesai A, Roy SB, Chin SC, Muckian B. Big data solutions for predicting risk-of-readmission for congestive heart failure patients. InBig Data, 2013 I.E. International Conference on; 2013pp. 64–71. IEEE.
34. Chin SC, Zolfaghar K, Roy SB, Teredesai A, Amoroso P. Divide-n-Discover discretization based data exploration framework for healthcare analytics. Healthinf 2014; 329-333.