Analysis of Machine Learning Techniques for Heart Failure Readmissions

Circulation: Cardiovascular Quality and Outcomes

Volumn 9, Issue 6, 2016, Pages 629-640

  Mortazavi, Bobak J ^a,d,e,g   Downing, Nicholas S ^a,d,f   Bucholz, Emily M ^a,d   Dharmarajan, Kumar ^a,d   Manhapra, Ajay ^b   Li, Shu Xia ^d   Negahban, Sahand N ^d   Krumholz, Harlan M ^a,c,d  

^a YALE UNIVERSITY SCHOOL OF MEDICINE   (United States)

^b Department of Medicine   (United States)

^c ROBERT WOOD JOHNSON FOUNDATION   (United States)

^d YALE UNIVERSITY   (United States)

^e YALE NEW HAVEN HOSPITAL   (United States)

^f BRIGHAM AND WOMEN S HOSPITAL   (United States)

^g BOSTON CHILDREN S HOSPITAL   (United States)

Author keywords

computers; heart failure; machine learning; meta analysis; patient readmission

Indexed keywords

ADULT; ARTICLE; BOOTSTRAPPING; CARDIOVASCULAR MORTALITY; COMPARATIVE EFFECTIVENESS; CONTROLLED STUDY; DATA ANALYSIS; DIAGNOSTIC TEST ACCURACY STUDY; FEMALE; HEART FAILURE; HOSPITAL READMISSION; HOSPITALIZATION; HUMAN; LOGISTIC REGRESSION ANALYSIS; MACHINE LEARNING; MAJOR CLINICAL STUDY; MALE; MIDDLE AGED; OUTCOME ASSESSMENT; PARAMETRIC TEST; POISSON DISTRIBUTION; PREDICTIVE VALUE; PRIORITY JOURNAL; RANDOM FOREST; RISK FACTOR; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL; SUPPORT VECTOR MACHINE; TELEMONITORING; VALIDATION STUDY; AGED; ALGORITHM; COMPARATIVE STUDY; DATA MINING; FACTUAL DATABASE; META ANALYSIS; NONLINEAR SYSTEM; PROCEDURES; RANDOMIZED CONTROLLED TRIAL (TOPIC); REPRODUCIBILITY; RISK ASSESSMENT; TELEMEDICINE; TIME FACTOR;

AGED; ALGORITHMS; DATA MINING; DATABASES, FACTUAL; FEMALE; HEART FAILURE; HUMANS; LOGISTIC MODELS; MALE; MIDDLE AGED; NONLINEAR DYNAMICS; PATIENT READMISSION; RANDOMIZED CONTROLLED TRIALS AS TOPIC; REPRODUCIBILITY OF RESULTS; RISK ASSESSMENT; RISK FACTORS; SUPPORT VECTOR MACHINE; TELEMEDICINE; TIME FACTORS;

EID: 84995938203     PISSN: 19417713     EISSN: 19417705     Source Type: Journal    
DOI: 10.1161/CIRCOUTCOMES.116.003039     Document Type: Article

References (36)

1. Kocher RP, Adashi EY, Hospital readmissions and the Affordable Care Act: paying for coordinated quality care. JAMA 2011 306 1794 1795. doi: 10.1001/jama.2011.1561
2. Dharmarajan K, Hsieh AF, Lin Z, Bueno H, Ross JS, Horwitz LI, Barreto-Filho JA, Kim N, Bernheim SM, Suter LG, Drye EE, Krumholz HM, Diagnoses and timing of 30-day readmissions after hospitalization for heart failure, acute myocardial infarction, or pneumonia. JAMA 2013 309 355 363. doi: 10.1001/jama.2012.216476
3. Ross JS, Chen J, Lin Z, Bueno H, Curtis JP, Keenan PS, Normand SL, Schreiner G, Spertus JA, Vidán MT, Wang Y, Wang Y, Krumholz HM, Recent national trends in readmission rates after heart failure hospitalization. Circ Heart Fail 2010 3 97 103. doi: 10.1161/CIRCHEARTFAILURE.109.885210
4. Kansagara D, Englander H, Salanitro A, Kagen D, Theobald C, Freeman M, Kripalani S, Risk prediction models for hospital readmission: a systematic review. JAMA 2011 306 1688 1698. doi: 10.1001/jama.2011.1515
5. Dharmarajan K, Krumholz HM, Strategies to reduce 30-day readmissions in older patients hospitalized with heart failure and acute myocardial infarction. Curr Geriatr Rep 2014 3 306 315. doi: 10.1007/s13670-014-0103-8
6. Ross JS, Mulvey GK, Stauffer B, Patlolla V, Bernheim SM, Keenan PS, Krumholz HM, Statistical models and patient predictors of readmission for heart failure: a systematic review. Arch Intern Med 2008 168 1371 1386. doi: 10.1001/archinte.168.13.1371
7. Rahimi K, Bennett D, Conrad N, Williams TM, Basu J, Dwight J, Woodward M, Patel A, McMurray J, MacMahon S, Risk prediction in patients with heart failure: a systematic review and analysis. JACC Heart Fail 2014 2 440 446. doi: 10.1016/j.jchf.2014.04.008
8. Lupón J, Vila J, Bayes-Genis A, Risk prediction tools in patients with heart failure. JACC Heart Fail 2015 3 267. doi: 10.1016/j.jchf.2014.10.010
9. Amarasingham R, Moore BJ, Tabak YP, Drazner MH, Clark CA, Zhang S, Reed WG, Swanson TS, Ma Y, Halm EA, An automated model to identify heart failure patients at risk for 30-day readmission or death using electronic medical record data. Med Care 2010 48 981 988. doi: 10.1097/MLR.0b013e3181ef60d9
10. Krumholz HM, Chaudhry SI, Spertus JA, Mattera JA, Hodshon B, Herrin J, Do non-clinical factors improve prediction of readmission risk?: Results from the Tele-HF study. JACC Heart Fail 2016 4 12 20. doi: 10.1016/j.jchf.2015.07.017
11. Levy WC, Anand IS, Heart failure risk prediction models: what have we learned? JACC Heart Fail 2014 2 437 439. doi: 10.1016/j.jchf.2014.05.006
12. Ouwerkerk W, Voors AA, Zwinderman AH, Factors influencing the predictive power of models for predicting mortality and/or heart failure hospitalization in patients with heart failure. JACC Heart Fail 2014 2 429 436. doi: 10.1016/j.jchf.2014.04.006
13. Weiss GM, Lockhart JW, The impact of personalization on smartphone-based activity recognition. AAAI Workshop on Activity Context Representation: Techniques and Languages 2012
14. Díaz-Uriarte R, Alvarez de Andrés S, Gene selection and classification of microarray data using random forest. BMC Bioinformatics 2006 7 3. doi: 10.1186/1471-2105-7-3
15. Statnikov A, Wang L, Aliferis CF, A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification. BMC Bioinformatics 2008 9 319. doi: 10.1186/1471-2105-9-319
16. Hastie T, Tibshirani R, Friedman J, Hastie T, Friedman J, Tibshirani R, The Elements of Statistical Learning 2009 Springer
17. Menze BH, Kelm BM, Masuch R, Himmelreich U, Bachert P, Petrich W, Hamprecht FA, A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data. BMC Bioinformatics 2009 10 213. doi: 10.1186/1471-2105-10-213
18. Bergstra J, Casagrande N, Erhan D, Eck D, Kégl B, Aggregate features and AdaBoost for music classification. Mach Learn 2006 65 473 484
19. Khammari A, Nashashibi F, Abramson Y, Laurgeau C, Vehicle detection combining gradient analysis and AdaBoost classification 2005 Intelligent Transportation Systems, 2005 Proceedings; IEEE 66 71
20. Bayati M, Braverman M, Gillam M, Mack KM, Ruiz G, Smith MS, Horvitz E, Data-driven decisions for reducing readmissions for heart failure: general methodology and case study. PLoS One 2014 9 e109264. doi: 10.1371/journal.pone.0109264
21. Chen Y-W, Lin C-J, Combining SVMs With Various Feature Selection Strategies Feature Extraction 2006 Springer 315 324
22. Pal M, Foody GM, Feature selection for classification of hyperspectral data by SVM 2010 48 Geoscience and Remote Sensing, IEEE Transactions on 2297 2307
23. Schuldt C, Laptev I, Caputo B, 2004 3 Recognizing human actions: a local SVM approach. Pattern Recognition, 2004 ICPR 2004 Proceedings of the 17th International Conference on 32 36
24. Wang S, Zhu W, Liang Z-P, 2001 2 Shape deformation: SVM regression and application to medical image segmentation. Computer Vision, 2001 ICCV 2001 Proceedings Eighth IEEE International Conference on 209 216
25. Mortazavi B, Pourhomayoun M, Nyamathi S, Wu B, Lee SI, Sarrafzadeh M, 2015 Multiple model recognition for near-realistic exergaming. Pervasive Computing and Communications (PerCom), 2015 IEEE International Conference on 140 148
26. Wiens J, Horvitz E, Guttag JV, Patient risk stratification for hospital-associated c. diff as a time-series classification task. Adv Neural Inf Process Syst 2012 467 475
27. Wang L, Porter B, Maynard C, Bryson C, Sun H, Lowy E, McDonell M, Frisbee K, Nielson C, Fihn SD, Predicting risk of hospitalization or death among patients with heart failure in the Veterans Health Administration. Am J Cardiol 2012 110 1342 1349. doi: 10.1016/j.amjcard.2012.06.038
28. Coxe S, West SG, Aiken LS, The analysis of count data: a gentle introduction to poisson regression and its alternatives. J Pers Assess 2009 91 121 136. doi: 10.1080/00223890802634175
29. Chaudhry SI, Mattera JA, Curtis JP, Spertus JA, Herrin J, Lin Z, Phillips CO, Hodshon BV, Cooper LS, Krumholz HM, Telemonitoring in patients with heart failure. N Engl J Med 2010 363 2301 2309. doi: 10.1056/NEJMoa1010029
30. Chaudhry SI, Barton B, Mattera J, Spertus J, Krumholz HM, Randomized trial of Telemonitoring to Improve Heart Failure Outcomes (Tele-HF): study design. J Card Fail 2007 13 709 714. doi: 10.1016/j.cardfail.2007.06.720
31. Dharmarajan K, Krumholz HM, Opportunities and challenges for reducing hospital revisits. Ann Intern Med 2015 162 793 794. doi: 10.7326/M15-0878
32. Breiman L, Random forests. Mach Learn 2001 45 5 32
33. Genuer R, Poggi J-M, Tuleau-Malot C, Variable selection using random forests. Pattern Recognit Lett 2010 31 2225 2236
34. Subramanian D, Subramanian V, Deswal A, Mann DL, New predictive models of heart failure mortality using time-series measurements and ensemble models. Circ Heart Fail 2011 4 456 462. doi: 10.1161/CIRCHEARTFAILURE.110.958496
35. Kerr KF, Wang Z, Janes H, McClelland RL, Psaty BM, Pepe MS, Net reclassification indices for evaluating risk prediction instruments: a critical review. Epidemiology 2014 25 114 121. doi: 10.1097/EDE.0000000000000018
36. Hilden J, Gerds TA, A note on the evaluation of novel biomarkers: do not rely on integrated discrimination improvement and net reclassification index. Stat Med 2014 33 3405 3414. doi: 10.1002/sim.5804