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Machine Learning in Healthcare Informatics pp 181–207Cite as

Using Machine Learning to Plan Rehabilitation for Home Care Clients: Beyond “Black-Box” Predictions

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Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 56))

Abstract

Resistance to adopting machine-learning algorithms in clinical practice may be due to a perception that these are “black-box” techniques and incompatible with decision-making based on evidence and clinical experience. We believe this resistance is unfortunate, given the increasing availability of large databases containing assessment information that could benefit from machine-learning and data-mining techniques, thereby providing a new and important source of evidence upon which to base clinical decisions. We have focused our investigation on the clinical applications of machine-learning algorithms on older persons in a home care rehabilitation setting. Data for this research were obtained from standardized client assessments using the comprehensive RAI-Home Care (RAI-HC) assessment instrument. Our work has shown that machine-learning algorithms can produce better decisions than standard clinical protocols. More importantly, we have shown that machine-learning algorithms can do much more than make “black-box” predictions; they can generate important new clinical and scientific insights. These insights can be used to make better decisions about treatment plans for patients and about resource allocation for healthcare services, resulting in better outcomes for patients, and in a more efficient and effective healthcare system.

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References

  1. Akaike H (1974) A new look at the statistical model identification. IEEE T Automat Contr 19:716–723

    Article  MathSciNet  MATH  Google Scholar 

  2. Armstrong JJ, Stolee P, Hirdes JP, Poss JW (2010) Examining three frailty conceptualizations in their ability to predict negative outcomes for home care clients. Age Ageing 39:755–758

    Article  Google Scholar 

  3. Bayes T (1763) An essay towards solving a problem in the doctrine of chances. Phil Trans Roy Soc 53:370–418

    Google Scholar 

  4. Begg R, Kamruzzaman J (2005) A machine learning approach for automated recognition of movement patterns using basic, kinetic and kinematic gait data. J Biomech 38:401–408

    Article  Google Scholar 

  5. Bishop CM (2006) Pattern recognition and machine learning. Springer, New York

    MATH  Google Scholar 

  6. Boser B, Guyon I, Vapnik VN (1992) A training algorithm for optimal margin classifiers. In: Proceeding 5th annual workshop computer learn theory. ACM Press, New York

    Google Scholar 

  7. Borrie MJ, Stolee P, Knoefel FD, Wells JL, Seabrook JA (2005) Current best practices in geriatric rehabilitation in Canada. Geriatr Today Can J Geriatr Med Geriatr Psych 8:148–153

    Google Scholar 

  8. Breiman L (1996) Bagging predictors. Mach Learn 24:123–140

    MathSciNet  MATH  Google Scholar 

  9. Breiman L (2001) Random forests. Mach Learn 45:5–32

    Article  MATH  Google Scholar 

  10. Breiman L (2001) Statistical modeling: the two cultures. Stat Sci 16:199–231

    Article  MathSciNet  MATH  Google Scholar 

  11. Colombo M, Guaita A, Cottino M, Previderé G, Ferrari D, Vitali S (2004) The impact of cognitive impairment on the rehabilitation process in geriatrics. Arch Gerontol Geriatr Suppl 9:85–92

    Article  Google Scholar 

  12. Cover TM, Hart PE (1967) Nearest neighbor pattern classification. IEEE T Inform Theory 13:21–27

    Article  MATH  Google Scholar 

  13. Cristianini N, Shawe-Taylor J (2002) An introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge University Press, New York

    Google Scholar 

  14. Diwan S, Shugarman LR, Fries BE (2004) Problem identification and care plan responses in a home and community-based services program. Med Care 23:193–211

    Google Scholar 

  15. Efron B (1979) Bootstrap methods: another look at the jackknife. Ann Stat 7:1–26

    Article  MathSciNet  MATH  Google Scholar 

  16. Efron B, Hastie TJ, Johnstone IM, Tibshirani RJ (2004) Least angle regression (with discussion). Ann Stat 32:407–499

    Article  MathSciNet  MATH  Google Scholar 

  17. Fan J, Li R (2001) Variable selection via nonconcave penalized likelihood and its oracle properties. J Amer Statist Assoc 96:1348–1360

    Article  MathSciNet  MATH  Google Scholar 

  18. Freund Y, Schapire R (1996) Experiments with a new boosting algorithm. Machine Learning Proc 13th Int Conf. Morgan Kauffman, San Francisco, pp 148–156

    Google Scholar 

  19. Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Stat 29:1189–1232

    Article  MATH  Google Scholar 

  20. Friedman JH, Hastie TJ, Tibshirani RJ (2000) Additive logistic regression: A statistical view of boosting. Ann Stat 28:337–407

    Article  MathSciNet  MATH  Google Scholar 

  21. Ghisla MK, Cossi S, Timpini A, Baroni F, Facchi E, Marengoni A (2007) Predictors of successful rehabilitation in geriatric patients: subgroup analysis of patients with cognitive impairment. Aging Clin Exp Res 19:417–423

    Article  Google Scholar 

  22. Gill PE, Murray W, Wright MH (1986) Practical optimization. Academic Press, London

    Google Scholar 

  23. Gilmore S, Hofmann-Wellenhof R, Soyer HP (2010) A support vector machine for decision support in melanoma recognition. Exp Dermatol 19:830–835

    Article  Google Scholar 

  24. Hanks RA, Rapport LJ, Millis SR, Deshpande SA (1999) Measures of executive functioning as predictors of functional ability and social integration in a rehabilitation sample. Arch Phys Med Rehabil 80:1030–1037

    Article  Google Scholar 

  25. Harrison RF, Kennedy RL (2005) Artificial neural network models for prediction of acute coronary syndromes using clinical data from the time of presentation. Ann Emerg Med 46:431–439

    Article  Google Scholar 

  26. Hastie TJ, Tibshirani RJ, Friedman JF (2001) The elements of statistical learning: data mining, Inference and Prediction. Springer, New York

    Book  Google Scholar 

  27. Hepburn B (2010) Healthcare in Ontario is cracking under stress. Toronto Star, August

    Google Scholar 

  28. Hershkovitz A, Kalandariov Z, Hermush V, Weiss R, Brill S (2007) Factors affecting short-term rehabilitation outcomes of disabled elderly patients with proximal hip fracture. Arch Phys Med Rehabil 88:916–921

    Article  Google Scholar 

  29. Hirdes JP, Fries BE, Morris JN, Steel K, Mor V, Frijters DH et al (1999) Integrated health information systems based on the RAI/MDS series of instruments. Health Manage Forum 12:30–40

    Article  Google Scholar 

  30. Hirdes JP, Frijters D, Teare G (2003) The MDS-CHESS scale: a new measure to predict mortality in institutionalized older people. J Am Geriatr Soc 51:96–100

    Article  Google Scholar 

  31. Hirdes JP, Fries BE, Morris JN, Ikegami N, Zimmerman D, Dalby DM et al (2004) Home Care Quality Indicators (HCQIs) based on the MDS-HC. Gerontologist 44:665–679

    Article  Google Scholar 

  32. Hirdes JP, Poss JW, Curtin-Telegdi N (2008) The method for assigning priority levels (MAPLe): a new decision-support system for allocating home care resources. BMC Med 6:9

    Article  Google Scholar 

  33. Ji SY, Smith R, Huynh T, Najarian K (2009) A comparative analysis of multi-level computer-assisted decision-making systems for traumatic injuries. BMC Med Inform Decis Mak 9:2

    Article  Google Scholar 

  34. Kulminski AM, Ukraintseva SV, Kulminskaya IV, Arbeev KG, Land K, Yashin AI (2008) Cumulative deficits better characterize susceptibility to death in elderly people than phenotypic frailty: lessons from the cardiovascular health study. J Am Geriatr Soc 56:898–903

    Article  Google Scholar 

  35. Lau HY, Tong KY, Zhu H (2009) Support vector machine for classification of walking conditions of persons after stroke with dropped foot. Hum Mov Sci 28:504–514

    Article  Google Scholar 

  36. Landi F, Tua E, Onder G, Carrara B, Sgadari A, Rinaldi C et al (2000) Minimum data set for home care: a valid instrument to assess frail older people living in the community. Med Care 38:1184–1190

    Article  Google Scholar 

  37. Lucas P (2004) Bayesian analysis, pattern analysis, and data mining in healthcare. Curr Opin Crit Care 10:399–403

    Article  Google Scholar 

  38. McCullagh P, Nelder JA (1989) Generalized Linear Models. Chapman and Hall, Boca Raton

    Book  MATH  Google Scholar 

  39. Meier L, van de Geer S, Bühlmann P (2008) The group Lasso for logistic regression. J Royal Stat Soc B Met 70:53–71

    Article  MATH  Google Scholar 

  40. Meinshausen N (2007) Relaxed Lasso. Comput Stat Data An 52:374–393

    Article  MathSciNet  MATH  Google Scholar 

  41. Meinshausen N, Bühlmann P (2010) Stability selection (with discussion). J Royal Stat Soc B Met 72:417–473

    Article  Google Scholar 

  42. Melin R, Fugl-Meyer AR (2003) On prediction of vocational rehabilitation outcome at a Swedish employability institute. J Rehabil Med 35:284–289

    Article  Google Scholar 

  43. Miller AJ (2002) Subset selection in regression. Chapman and Hall, New York

    Book  MATH  Google Scholar 

  44. Mitnitski AB, Graham JE, Mogilner AJ, Rockwood K (2002) Frailty, fitness and late-life mortality in relation to chronological and biological age. BMC Geriatr 2:1

    Article  Google Scholar 

  45. Mitnitski AB, Mogilner AJ, Graham JE, Rockwood K (2003) Techniques for knowledge discovery in existing biomedical databases: Estimation of individual aging effects in cognition in relation to dementia. J Clinical Epidemiol 56:116–123

    Article  Google Scholar 

  46. Morris JN, Fries BE, Mehr DR, Hawes C, Phillips C, Mor V et al (1994) MDS cognitive performance scale. J Gerontol 49:M174–M182

    Article  Google Scholar 

  47. Morris JN, Fries BE, Morris SA (1999) Scaling ADLs within the MDS. J Gerontol Med Sci 54:M546–M553

    Article  Google Scholar 

  48. Morris JN, Fries BE, Steel K, Ikegami N, Bernabei R, Carpenter GI et al (1997) Comprehensive clinical assessment in community setting: applicability of the MDS-HC. J Am Geriatr Soc 45:1017–1024

    Google Scholar 

  49. Morris JN, Fries BE, Steel K, Ikegami N, Bernabei R (1999) Primer on use of the Minimum Data Set-Home Care (MDS-HC) version 2.0© and the Client Assessment Protocols (CAPs). Hebrew Rehabilitation Center for Aged, Boston

    Google Scholar 

  50. Naglie G, Tansey C, Kirkland JL, Ogilvie-Harris DJ, Detsky AS, Etchells E, Tomlinson G, O’Rourke K, Goldlist B (2002) Interdisciplinary inpatient care for elderly people with hip fracture: a randomized controlled trial. Can Med Assoc J 167:25–32

    Google Scholar 

  51. Ottenbacher KJ, Linn RT, Smith PM, Illig SB, Mancuso M, Granger CV (2004) Comparison of logistic regression and neural network analysis applied to predicting living setting after hip fracture. Ann Epidemiol 14:551–559

    Article  Google Scholar 

  52. Pearce CB, Gunn SR, Ahmed A, Johnson CD (2006) Machine learning can improve prediction of severity in acute pancreatitis using admission values of APACHE II score and C-reactive protein. Pancreatology 6:123–131

    Article  Google Scholar 

  53. Pepe MS (2003) The statistical evaluation of medical tests for classification and prediction. Oxford University Press, New York

    MATH  Google Scholar 

  54. Price RK, Spitznagel EL, Downey TJ, Meyer DJ, Risk NK, El-Ghassawy OG (2000) Applying artificial neural network models to clinical decision making. Psychol Assess 12:40–51

    Article  Google Scholar 

  55. Radchenko P, James G (2008) Variable inclusion and shrinkage algorithms. J Amer Statist Assoc 103:1304–1315

    Article  MathSciNet  MATH  Google Scholar 

  56. Ramos-Pollán R, Guevara-López MA, Suárez-Ortega C, Díaz-Herrero G, Franco-Valiente JM, Rubio-Del-Solar M, González-de-Posada N, Vaz MA, Loureiro J, Ramos I (2012) Discovering mammography-based machine learning classifiers for breast cancer diagnosis. J Med Syst 36(4):2259–2269

    Google Scholar 

  57. Rockwood K, Mitnitski AB (2007) Frailty in relation to the accumulation of deficits. J Gerontol A Biol Sci Med Sci 62:722–727

    Article  Google Scholar 

  58. Shmueli G (2010) To explain or to predict? Stat Sci 25:289–310

    Article  MathSciNet  Google Scholar 

  59. Searle S, Mitnitski AB, Gahbauer E, Gill T, Rockwood K (2008) A standard procedure for creating a frailty index. BMC Geriatr 8:24

    Article  Google Scholar 

  60. Stolee P, Borrie MJ, Cook S, Hollomby J, the participants of the Canadian Consensus Workshop on Geriatric Rehabilitation (2004) A research agenda for geriatric rehabilitation: the Canadian consensus. Geriatr Today J Can Geriatr Soc 7:38–42

    Google Scholar 

  61. Tam SF, Cheing GLY, Hui-Chan SWY (2004) Predicting osteoarthritic knee rehabilitation outcome by using a prediction model using data mining techniques. Int J Rehabil Res 27:65–69

    Article  Google Scholar 

  62. Thorsen L, Gjerset GM, Loge JH, Kiserud CE, Skovlund E, Fløtten T, Fosså SD (2011) Cancer patients’ needs for rehabilitation services. Acta Oncol 50:212–222

    Article  Google Scholar 

  63. Tibshirani R (1996) Regression shrinkage and selection via the Lasso. J Royal Stat Soc B 58:267–288

    MathSciNet  MATH  Google Scholar 

  64. Vapnik VN (1995) The nature of statistical learning theory. Springer, New York

    Book  MATH  Google Scholar 

  65. Wells JL, Seabrook JA, Stolee P, Borrie MJ, Knoefel F (2003) State of the art in geriatric rehabilitation. Part I: review of frailty and comprehensive geriatric assessment. Arch Phys Med Rehabil 84:890–897

    Article  Google Scholar 

  66. Wells JL, Seabrook JA, Stolee P, Borrie MJ, Knoefel F (2003) State of the art in geriatric rehabilitation. Part II: clinical challenges. Arch Phys Med Rehabil 84:898–903

    Article  Google Scholar 

  67. Williams AP, Lum JM, Deber R, Montgomery R, Kuluski K, Peckham A et al (2009) Aging at home: integrating community-based care for older persons. Healthc Pap 10:8–21

    Google Scholar 

  68. Xin L, Zhu M (2012) Stochastic stepwise ensembles for variable selection. J Comput Graph Stat 21:275–294

    Article  MathSciNet  Google Scholar 

  69. Zou H (2006) The adaptive Lasso and its oracle properties. J Am Stat Assoc 101:1418–1429

    Article  MATH  Google Scholar 

  70. Zou H, Hastie TJ (2005) Regularization and variable selection via the elastic net. J Royal Stat Soc B 67:301–320

    Article  MathSciNet  MATH  Google Scholar 

  71. Zhu M (2008) Kernels and ensembles: perspectives on statistical learning. Am Stat 62:97–109

    Article  Google Scholar 

  72. Zhu M, Chipman HA (2006) Darwinian evolution in parallel universes: a parallel genetic algorithm for variable selection. Technometrics 48:491–502

    Article  MathSciNet  Google Scholar 

  73. Zhu M, Chen W, Hirdes JP, Stolee P (2007) The K-nearest neighbors algorithm predicted rehabilitation potential better than current clinical assessment protocol. J Clin Epidemiol 60:1015–1021

    Article  Google Scholar 

  74. Zhu M, Zhang Z, Hirdes JP, Stolee P (2007) Using machine learning algorithms to guide rehabilitation planning for home care clients. BMC Med Inform Dec Mak 7:41

    Article  Google Scholar 

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Acknowledgments

The InfoRehab project is supported by the Canadian Institutes of Health Research (CIHR). We thank Chloe Wu for her assistance with the management of data.

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Authors and Affiliations

  1. Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, N2L 3G1, Canada

    Mu Zhu & Lu Cheng

  2. School of Public Health and Health Systems, University of Waterloo, Waterloo, ON, N2L 3G1, Canada

    Joshua J. Armstrong, Jeff W. Poss, John P. Hirdes & Paul Stolee

Authors
  1. Mu Zhu
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  2. Lu Cheng
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  3. Joshua J. Armstrong
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  4. Jeff W. Poss
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  5. John P. Hirdes
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  6. Paul Stolee
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Corresponding author

Correspondence to Mu Zhu .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Louisiana Tech University, Ruston, Louisiana, USA

    Sumeet Dua

  2. Ngee Ann Polytechnic, Singapore, Singapore

    U. Rajendra Acharya

  3. Department of Health Informatics and Information Management, Louisiana Tech University, Ruston, Louisiana, USA

    Prerna Dua

Appendix: Evaluation of Binary Predictions

Appendix: Evaluation of Binary Predictions

Suppose we have a certain procedure (whether an algorithm or a protocol) for predicting binary outcomes of either zero or one. The false positive (FP) and false negative (FN) rates are intuitive measures of the prediction performance. They are the probabilities of the two types of errors the procedure can make, namely, calling a true zero a one (FP) and calling a true one a zero (FN).

The positive diagnostic likelihood ratio \( (DLR + ) \), and the negative diagnostic likelihood ratio \( (DLR - ) \) are less intuitive but extremely useful measures; they

“quantify the change in the odds of [the true outcome] obtained by knowledge of [the prediction]” or “the increase in knowledge about [the true outcome] gained through [the prediction]” [53].

Let

$$ prior - odds = \frac{{P\left( {outcome = 1} \right)}}{{P\left( {outcome = 0} \right)}}, $$
$$ posterior - odds\left( {prediction} \right) = \frac{{P\left( {outcome = 1|prediction} \right)}}{{P\left( {outcome = 0|prediction} \right)}}. $$

By a simple application of Bayes’ theorem [3], it can be shown that

$$ posterior - odds\left( {prediction = 1} \right) = \left( {DLR + } \right) \times \left( {prior - odds} \right), $$
$$ posterior - odds\left( {prediction = 0} \right) = \left( {DLR - } \right) \times \left( {prior - odds} \right). $$

Therefore, \( DLR + \) can be interpreted as the factor by which a prediction of one can increase the prior odds, and \( DLR{-} \) can be interpreted as the factor by which a prediction of zero can decrease the prior odds. Therefore, informative prediction procedures should have \( DLR{+} > 1 \) and \( DLR\,- < 1 \). Given two prediction methods, A and B, A can be said to be more informative than B if \( DLR{+} (A)\, > DLR + (B) \) and if \( DLR{-} (A) < DLR - (B) \). The use of \( DLR + \) and \( DLR - \) to evaluate procedures for making binary predictions has been gaining popularity in the last two decades [53].

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Zhu, M., Cheng, L., Armstrong, J.J., Poss, J.W., Hirdes, J.P., Stolee, P. (2014). Using Machine Learning to Plan Rehabilitation for Home Care Clients: Beyond “Black-Box” Predictions. In: Dua, S., Acharya, U., Dua, P. (eds) Machine Learning in Healthcare Informatics. Intelligent Systems Reference Library, vol 56. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40017-9_9

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  • DOI: https://doi.org/10.1007/978-3-642-40017-9_9

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