Application of machine learning techniques to high-dimensional clinical data to forecast postoperative complications

PLoS ONE

Volumn 11, Issue 5, 2016, Pages

  Thottakkara, Paul ^a,c   Ozrazgat Baslanti, Tezcan ^a   Hupf, Bradley B ^a   Rashidi, Parisa ^b   Pardalos, Panos ^c   Momcilovic, Petar ^c   Bihorac, Azra ^a  

^a UNIVERSITY OF FLORIDA COLLEGE OF MEDICINE   (United States)

^b UNIVERSITY OF FLORIDA   (United States)

^c J Crayton Pruitt Family Department of Biomedical Engineering   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACUTE KIDNEY FAILURE; ADULT; AGED; AREA UNDER THE CURVE; ARTICLE; BAYESIAN LEARNING; COHORT ANALYSIS; FEMALE; GENERALIZED ADDITIVE MODEL; HUMAN; INTERMETHOD COMPARISON; LOGISTIC REGRESSION ANALYSIS; MACHINE LEARNING; MAJOR CLINICAL STUDY; MAJOR SURGERY; MALE; MATHEMATICAL ANALYSIS; MEASUREMENT ACCURACY; NONLINEAR SYSTEM; OUTCOME ASSESSMENT; POSTOPERATIVE COMPLICATION; PREDICTION AND FORECASTING; PREDICTIVE VALUE; RECEIVER OPERATING CHARACTERISTIC; RETROSPECTIVE STUDY; SEPSIS; SUPPORT VECTOR MACHINE; VALIDATION STUDY; ACUTE KIDNEY INJURY; BIOLOGY; MIDDLE AGED; POSTOPERATIVE COMPLICATIONS; PROCEDURES; RISK; STATISTICAL MODEL;

ACUTE KIDNEY INJURY; ADULT; AGED; COHORT STUDIES; COMPUTATIONAL BIOLOGY; FEMALE; HUMANS; MACHINE LEARNING; MALE; MIDDLE AGED; MODELS, STATISTICAL; POSTOPERATIVE COMPLICATIONS; RETROSPECTIVE STUDIES; RISK; SEPSIS;

EID: 84973111641     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0155705     Document Type: Article

References (59)

1. Khan NA, Quan H, Bugar JM, Lemaire JB, Brant R, Ghali WA. Association of postoperative complications with hospital costs and length of stay in a tertiary care center. J Gen Intern Med. 2006; 21(2):177-80. Epub 2006/04/12. JGI319 [pii] doi: 10.1111/j.1525-1497.2006.00319.x PMID: 16606377
2. Hobson C, Ozrazgat-Baslanti T, Kuxhausen A, Thottakkara P, Efron PA, Moore FA, et al. Cost and Mortality Associated With Postoperative Acute Kidney Injury. Ann Surg. 2014. Epub 2014/06/03. doi: 10.1097/SLA.0000000000000732
3. Korenkevych D, Ozrazgat-Baslanti T, Thottakkara P, Hobson CE, Pardalos P, Momcilovic P, et al. The Pattern of Longitudinal Change in Serum Creatinine and 90-Day Mortality After Major Surgery. Ann Surg. 2015. doi: 10.1097/SLA.0000000000001362
4. Bihorac A, Brennan M, Ozrazgat-Baslanti T, Bozorgmehri S, Efron PA, Moore FA, et al. National surgical quality improvement program underestimates the risk associated with mild and moderate postoperative acute kidney injury. Crit Care Med. 2013; 41(11):2570-83. doi: 10.1097/CCM.0b013e31829860fc PMID: 23928835
5. Bihorac A, Yavas S, Subbiah S, Hobson CE, Schold JD, Gabrielli A, et al. Long-term risk of mortality and acute kidney injury during hospitalization after major surgery. Ann Surg. 2009; 249(5):851-8. doi: 10.1097/SLA.0b013e3181a40a0b PMID: 19387314
6. Huber M, Ozrazgat-Baslanti T, Thottakkara P, Efron PA, Feezor R, Hobson C, et al. Mortality and Cost of Acute and Chronic Kidney Disease after Vascular Surgery. Annals of vascular surgery. 2015. doi: 10.1016/j.avsg.2015.04.092
7. Ozrazgat-Baslanti T, Thottakkara P, Huber M, Berg K, Gravenstein N, Tighe P, et al. Acute and chronic kidney disease and cardiovascular morltaity after major surgeyr Annals of Surgery. 2015.
8. Zarjou A, Agarwal A. Sepsis and acute kidney injury. J Am Soc Nephrol. 2011; 22(6):999-1006. Epub 2011/05/14. doi: 10.1681/ASN.2010050484 ASN.2010050484 [pii]. PMID: 21566052
9. Copeland GP, Jones D, Walters M. POSSUM: a scoring system for surgical audit. Br J Surg. 1991; 78 (3):355-60. Epub 1991/03/01. PMID: 2021856
10. Lake AP, Williams EG. ASA classification and perioperative variables: graded anaesthesia score? Br J Anaesth. 1997; 78(2):228-9. Epub 1997/02/01.
11. Hobson CE, Singhania G, Bihorac A. Acute kidney injury in the surgical patient. Crit Care Clin. 2015.
12. Huen SC, Parikh CR. Predicting acute kidney injury after cardiac surgery: a systematic review. Ann Thorac Surg. 2012; 93(1):337-47. S0003-4975(11)02155-2 [pii] doi: 10.1016/j.athoracsur.2011.09.010 PMID: 22186469
13. Miyagawa T, Nishida N, Ohashi J, Kimura R, Fujimoto A, Kawashima M, et al. Appropriate data cleaning methods for genome-wide association study. J Hum Genet. 2008; 53(10):886-93. Epub 2008/08/13. doi: 10.1007/s10038-008-0322-y PMID: 18695938
14. Osborne JW. Is data cleaning and the testing of assumptions relevant in the 21st century? Front Psychol. 2013; 4:370. Epub 2013/06/28. doi: 10.3389/fpsyg.2013.00370 PMID: 23805118
15. Van den Broeck J, Cunningham SA, Eeckels R, Herbst K. Data cleaning: detecting, diagnosing, and editing data abnormalities. PLoS Med. 2005; 2(10):e267. Epub 2005/09/06. 04-PLME-PF-0256 [pii] doi: 10.1371/journal.pmed.0020267 PMID: 16138788
16. Chun FK, Karakiewicz PI, Briganti A, Walz J, Kattan MW, Huland H, et al. A critical appraisal of logistic regression-based nomograms, artificial neural networks, classification and regression-tree models, look-up tables and risk-group stratification models for prostate cancer. BJU Int. 2007; 99(4):794-800. Epub 2007/03/24. BJU6694 [pii] doi: 10.1111/j.1464-410X.2006.06694.x PMID: 17378842
17. Faradmal J, Soltanian AR, Roshanaei G, Khodabakhshi R, Kasaeian A. Comparison of the performance of log-logistic regression and artificial neural networks for predicting breast cancer relapse. Asian Pac J Cancer Prev. 2014; 15(14):5883-8. Epub 2014/08/02. PMID: 25081718
18. Han SR, Guikema SD, Quiring SM. Improving the predictive accuracy of hurricane power outage forecasts using generalized additive models. Risk Anal. 2009; 29(10):1443-53. Epub 2009/08/13. doi: 10.1111/j.1539-6924.2009.01280.x RISK1280 [pii]. PMID: 19671101
19. Heydari ST, Ayatollahi SM, Zare N. Comparison of artificial neural networks with logistic regression for detection of obesity. J Med Syst. 2012; 36(4):2449-54. Epub 2011/05/11. doi: 10.1007/s10916-011-9711-4 PMID: 21556898
20. Huang HH, Xu T, Yang J. Comparing logistic regression, support vector machines, and permanental classification methods in predicting hypertension. BMC Proc. 2014; 8(Suppl 1):S96. Epub 2014/12/19. doi: 10.1186/1753-6561-8-S1-S96 1753-6561-8-S1-S96 [pii]. PMID: 25519351
21. Kurt I, Ture M, Kurum AT. Comparing performances of logistic regression, classification and regression tree, and neural networks for predicting coronary artery disease. Expert Syst Appl. 2008; 34(1):366-74. doi: 10.1016/j.eswa.2006.09.004
22. Maroco J, Silva D, Rodrigues A, Guerreiro M, Santana I, de Mendonca A. Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests. BMC Res Notes. 2011; 4:299. Epub 2011/08/19. doi: 10.1186/1756-0500-4-299 1756-0500-4-299 [pii]. PMID: 21849043
23. Pochet NL, Suykens JA. Support vector machines versus logistic regression: improving prospective performance in clinical decision-making. Ultrasound Obstet Gynecol. 2006; 27(6):607-8. Epub 2006/05/23. doi: 10.1002/uog.2791 PMID: 16715467
24. Westreich D, Lessler J, Funk MJ. Propensity score estimation: neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression. J Clin Epidemiol. 2010; 63(8):826-33. Epub 2010/07/16. doi: 10.1016/j.jclinepi.2009.11.020 S0895-4356(10) 00102-2 [pii]. PMID: 20630332
25. Woo MJ, Reiter JP, Karr AF. Estimation of propensity scores using generalized additive models. Stat Med. 2008; 27(19):3805-16. Epub 2008/03/28. doi: 10.1002/sim.3278 PMID: 18366144
26. Ben Meskina S, editor On the effect of data reduction on classification accuracy. Information Technology and e-Services (ICITeS), 2013 3rd International Conference on; 2013: IEEE.
27. United States Census Bureau. American FactFinder 2010 [04/02/2011]. Available from: http://www2.census.gov/.
28. Pebesma EJ, Bivand RS. Classes and methods for spatial data in R. R News [Internet]. 2005; 5(2). Available from: http://cran.r-project.org/doc/Rnews/.
29. Wald R, Waikar SS, Liangos O, Pereira BJ, Chertow GM, Jaber BL. Acute renal failure after endovascular vs open repair of abdominal aortic aneurysm. Journal of Vascular Surgery. 2006; 43(3):460-6; discussion 6. Epub 2006/03/08. S0741-5214(05)02087-2 [pii] doi: 10.1016/j.jvs.2005.11.053 PMID: 16520155
30. Kidney Disease: Improving Global Outcomes (KDIGO) Acute Kidney Injury Work Group. Clinical Practice Guideline for Acute Kidney Injury. Kidney inter, Suppl. 2012; 2(1):1-138.
31. Dombrovskiy VY, Martin AA, Sunderram J, Paz HL. Facing the challenge: decreasing case fatality rates in severe sepsis despite increasing hospitalizations. Crit Care Med. 2005; 33(11):2555-62. Epub 2005/11/09. 00003246-200511000-00017 [pii]. PMID: 16276180
32. Agency for Healthcare Research and Quality. Patient safety indicators: technical specifications. Ver. 4.2: Agency for Healthcare Research and Quality; 2010 [cited 2012 April 9]. Available from: http://www.qualityindicators.ahrq.gov.
33. Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity Measures for Use with Administrative Data. Med Care. 1998; 36(1):8-27. PMID: 9431328
34. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987; 40(5):373-83. Epub 1987/01/01. PMID: 3558716
35. U.S. Department of Veterans Affairs VHA. National Drug File-Reference Terminology (NDF-RT™) Documentation 2015 [03/03/2015]. Available from: http://evs.nci.nih.gov/ftp1/NDF-RT/NDF-RT%20Documentation.pdf
36. Boulle M. A grouping method for categorical attributes having very large number of values. Lect Notes Artif Int. 2005; 3587:228-42.
37. Pyle D. Data preparation for data mining: Morgan Kaufmann; 1999.
38. Pang H, Jung SH. Sample size considerations of prediction-validation methods in high-dimensional data for survival outcomes. Genet Epidemiol. 2013; 37(3):276-82. Epub 2013/03/09. doi: 10.1002/gepi.21721 PMID: 23471879
39. Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): The TRIPOD Statement. Ann Intern Med. 2015; 162(1):55-63. doi: 10.7326/M14-0697 PMID: 25560714
40. Ng AY, Jordan MI. On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes. In: Dietterich TGaB, Suzanna and Ghahramani, Zoubin, editor. Advances in Neural Information Processing Systems 14 (NIPS 2001): MIT Press; 2001. p. 841-8.
41. Cortes C, Vapnik V. Support-vector networks. Mach Learn. 1995; 20(3):273-97. doi: 10.1007/BF00994018
42. Hastie T, Tibshirani R. Generalized additive models. 1st ed. London; New York: Chapman and Hall; 1990. xv, 335 p. p.
43. Wood SN. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J R Stat Soc B. 2011; 73:3-36. doi: 10.1111/j.1467-9868.2010.00749.x
44. Domingos P, Pazzani M. On the optimality of the simple Bayesian classifier under zero-one loss. Mach Learn. 1997; 29(2-3):103-30. doi: 10.1023/A:1007413511361
45. Verplancke T, Van Looy S, Benoit D, Vansteelandt S, Depuydt P, De Turck F, et al. Support vector machine versus logistic regression modeling for prediction of hospital mortality in critically ill patients with haematological malignancies. Bmc Med Inform Decis. 2008; 8. Artn 56 doi: 10.1186/1472-6947-8-56
46. Mittag F, Buchel F, Saad M, Jahn A, Schulte C, Bochdanovits Z, et al. Use of support vector machines for disease risk prediction in genome-wide association studies: concerns and opportunities. Hum Mutat. 2012; 33(12):1708-18. Epub 2012/07/11. doi: 10.1002/humu.22161 PMID: 22777693
47. Yu W, Liu T, Valdez R, Gwinn M, Khoury MJ. Application of support vector machine modeling for prediction of common diseases: the case of diabetes and pre-diabetes. BMC Med Inform Decis Mak. 2010; 10:16. doi: 10.1186/1472-6947-10-16 PMID: 20307319
48. Tibshirani R. Regression Shrinkage and Selection Via the Lasso. Journal of the Royal Statistical Society, Series B. 1994; 58:267-8.
49. Richardson R, Westley T, Gariepy G, Austin N, Nandi A. Neighborhood socioeconomic conditions and depression: a systematic review and meta-analysis. Soc Psychiatry Psychiatr Epidemiol. 2015; 50 (11):1641-56. doi: 10.1007/s00127-015-1092-4 PMID: 26164028
50. Kovalchik SA, Slaughter ME, Miles J, Friedman EM, Shih RA. Neighbourhood racial/ethnic composition and segregation and trajectories of cognitive decline among US older adults. J Epidemiol Community Health. 2015; 69(10):978-84. doi: 10.1136/jech-2015-205600 PMID: 26002968
51. Huang R, Moudon AV, Cook AJ, Drewnowski A. The spatial clustering of obesity: does the built environment matter? J Hum Nutr Diet. 2015; 28(6):604-12. doi: 10.1111/jhn.12279 PMID: 25280252
52. Ramsay SE, Morris RW, Whincup PH, Subramanian SV, Papacosta AO, Lennon LT, et al. The influence of neighbourhood-level socioeconomic deprivation on cardiovascular disease mortality in older age: longitudinal multilevel analyses from a cohort of older British men. J Epidemiol Community Health. 2015; 69(12):1224-31. doi: 10.1136/jech-2015-205542 PMID: 26285580
53. Airaksinen J, Hakulinen C, Pulkki-Raback L, Lehtimaki T, Raitakari OT, Keltikangas-Jarvinen L, et al. Neighbourhood effects in health behaviours: a test of social causation with repeat-measurement longitudinal data. Eur J Public Health. 2015. doi: 10.1093/eurpub/ckv210
54. Shariff-Marco S, Yang J, John EM, Kurian AW, Cheng I, Leung R, et al. Intersection of Race/Ethnicity and Socioeconomic Status in Mortality After Breast Cancer. J Community Health. 2015; 40(6):1287-99. doi: 10.1007/s10900-015-0052-y PMID: 26072260
55. Radford PD, Derbyshire LF, Shalhoub J, Fitzgerald JEF. Publication of surgeon specific outcome data: A review of implementation, controversies and the potential impact on surgical training. International Journal of Surgery. 2015; 13:211-6. doi: 10.1016/j.ijsu.2014.11.049 PMID: 25498494
56. Glance LG, Kellermann AL, Hannan EL, Fleisher LA, Eaton MP, Dutton RP, et al. The Impact of Anesthesiologists on Coronary Artery Bypass Graft Surgery Outcomes. Anesthesia & Analgesia. 2015; 120 (3):526-33. doi: 10.1213/ane.0000000000000522
57. American College of Surgeons National Surgical Quality Improvement Program. Surgical risk calculator 2007-2015 [12/3/2015]. Available from: http://riskcalculator.facs.org.
58. Chapman AD. Principles of data quality: GBIF; 2005.
59. Du Y, Hu W, Shyu L, editors. The effect of data reduction by independent component analysis and principal component analysis in hand motion identification. Engineering in Medicine and Biology Society, 2004 IEMBS'04 26th Annual International Conference of the IEEE; 2004: IEEE.