An intelligent model for the classification of children's occupational therapy problems

Expert Systems with Applications

Volumn 39, Issue 5, 2012, Pages 5233-5242

  Yeh, Yu Ling ^a,b   Hou, Tung Hsu ^b   Chang, Wen Yen ^c  

^a NAN KAI UNIVERSITY OF TECHNOLOGY   (Taiwan)

^b NATIONAL YUNLIN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

^c Yuan Sheng Hospital   (Taiwan)

Author keywords

Artificial neural network; Children occupational therapy; Classification; Classification and regression trees

Indexed keywords

ARTIFICIAL NEURAL NETWORK; ATTRIBUTE VALUES; BINARY VARIABLES; CLASSIFICATION AND REGRESSION TREE; CLASSIFICATION AND REGRESSION TREES; CLASSIFICATION MODELS; CLASSIFICATION TREES; CRITICAL ATTRIBUTES; DATA SETS; INTELLIGENT CLASSIFICATION; INTELLIGENT MODELS; INTERPERSONAL SKILLS; MACHINE LEARNING TECHNIQUES; MAKING DECISION; MUSCLE TONE; PREDICATORS; REAL PROBLEMS; SUPPORTING SYSTEMS; TRAINED NEURAL NETWORKS; VISUAL STIMULUS;

FORESTRY; NEURAL NETWORKS; OCCUPATIONAL THERAPY; RATING; REGRESSION ANALYSIS;

CLASSIFICATION (OF INFORMATION);

CLASSIFICATION; FORESTRY; INFORMATION RETRIEVAL; NEURAL NETWORKS; REGRESSION ANALYSIS; TREES;

EID: 84855868297     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2011.11.016     Document Type: Article

References (48)

1. H.A. Abbass An evolutionary artificial neural networks approach for breast cancer diagnosis Artificial Intelligence in Medicine 25 3 2005 265 281
2. E. Aki, and A. Turan The efficiency of ophthalmologic interventions and occupational therapy in children with cerebral palsy and poor vision European Journal of Paediatric Neurology 12 Suppl. 1 2008 59
3. A.S. Allen, P.N. Pratt, and J.C. Smith Occupational therapy for children (2nd ed.; pp. 192-196) 1996 Maryland Heights MI Mosby
4. American Occupational Therapy Association - AOTA Definition of occupational therapy practice for state regulation (Policy 5.3.1) American Journal of Occupational Therapy 47 1993 1117 1121
5. R. Andrews, J. Diederich, and A.B. Tickle A survey and critique of techniques for extracting rules from trained artificial neural networks Knowledge-Based Systems 8 6 1995 373 389
6. C. Archambeaua, J. Delbekeb, C. Veraartb, and M. Verleysena Prediction of visual perceptions with artificial neural networks in a visual prosthesis for the blind Artificial Intelligence in Medicine 32 3 2004 183 194
7. M.V. Baldelli, R. Boiardi, P. Ferrari, S. Bianchi, and M.H. Bianchi Occupational therapy and tools for monitoring the effectiveness of therapy Archives of Gerontology and Geriatrics 38 Suppl. 1 2004 45 50
8. C.M. Bishop Neural networks for pattern recognition 1995 Oxford University Press Oxford, UK
9. L. Breiman, J.H. Friedman, R.A. Olshen, and C.I. Stone Classification and regression trees 1984 Wadsworth International Group Belmont, CA
10. T.F. Chu, and T.C. Chang The study of education and career of Occupational Therapy in Taiwan Journal of Taiwan Occupational Therapy Research and Practice 5 1 2009 78 91
11. Craven, M. W. (1996). Extracting comprehensible models from trained neural networks. Doctoral dissertation. University of Wisconsin Madison.
12. Craven, M. W.; Shavlik, J. W. (1994). Using sampling and queries to extract rules from trained neural networks. In Paper session presented in the Proc. Eleventh Int. Conf. on Machine Learning, San Francisco, California, USA (pp. 37-45).
13. M. Ferguson, D. Carlson, S. Zivnuska, and D. Whitten Is it better to receive than to give? Empathy in the conflict-distress relationship Journal of Occupational Health Psychology 15 3 2010 304 315
14. L.M. Fu Rule generation from neural networks IEEE Transactions on Systems Man Cybernetics 24 7 1994 1114 1124
15. N. Gazzola, and A. Stalikas Therapist interpretations and client processes in three therapeutic modalities: Implications for psychotherapy integration Journal of Psychotherapy Integration 14 4 2004 397 418
16. C.F. Gomi, H. Taras, and D.B. Granet Can occupational therapy improve vision skills associated with reading/writing? Journal of American Association for Pediatric Ophthalmology and Strabismus 11 1 2007 79
17. S. Haykin Neural networks: A comprehensive foundation 1994 McMillan New York, NY
18. Integral Solutions Limited (2006). Neural networks algorithms. Clementine 10.1 Algorithms Guide.
19. E. Kim Occupational stress: A survey of psychotherapists in Korea and the United States International Journal of Stress Management 14 1 2007 111 120
20. P. Kitsantas, M. Hollander, and L. Li Using classification trees to access low birth weight outcomes Artificial Intelligence in Medicine 38 3 2006 275 289
21. M. Klatt Integrating yoga, meditation, and occupational therapy for inner-city children The Journal of Science and Healing 5 3 2009 152 153
22. R. Krishnan, G. Sivakumar, and P. Bhattacharya Extracting decision trees from trained neural networks Pattern Recognition 32 1999 1999 2009
23. P. Langley, and H.A. Simon Applications of machine learning and rule induction Communications of ACM 38 11 1995 54 64
24. R.H. Lin An intelligent model for liver disease diagnosis Artificial Intelligence in Medicine 47 1 2009 53 62
25. M.M. Linehan, B.N. Cochran, C.M. Mar, E.R. Levensky, and K.A. Comtois Therapeutic burnout among borderline personality disordered clients and their therapists: Development and evaluation of two adaptations of the maslach burnout inventory Cognitive and Behavioral Practice 7 3 2000 329 337
26. P.J.G. Lisboa, E.C. Ifeachor, and P.S. Szczepaniak Artificial neural network in biomedicine 2000 Springer-Verlag London
27. R.P. Marble, and J.C. Healy A neural network approach to the diagnosis of morbidity outcomes in trauma care Artificial Intelligence in Medicine 15 3 1999 299 307
28. M.K. Markey, G.D. Tourassi, and C.E. Floyd Decision tree classification of proteins identified by mass spectrometry of blood serum samples from people with and without lung cancer Proteomics 3 9 2003 1678 1679
29. Mehta, M.; Agrawal, R.; Rissanen, J. (1996). SLIQ: A fast scalable classifier for data mining. In Paper session presented in Proc. of the fifth International Conf. on Extending Database Technology, Avignon, France (pp. 18-32).
30. B.A. Mobley, E. Schechter, W.E. Moore, P.A. McKee, and J.E. Eichner Predictions of coronary artery stenosis by artificial neural network Artificial Intelligence in Medicine 18 3 2000 187 203
31. Occupational Therapy Basics (2007). Who Might Need Occupational Therapy? Retrieved July 6, 2009. Available from http://kidshealth.org/parent/system/ill/ occupational-therapy.html#.
32. J. Paetz Knowledge-based approach to septic shock patient data using a neural network with trapezoidal activation functions Artificial Intelligence in Medicine 28 2 2003 207 230
33. J.R. Quinlan Induction of decision trees. Machine learning (pp. 81-106) 1986 Morgan Kaufmann San Francisco, CA
34. J.R. Quinlan C4.5: Programs for machine learning 1993 Morgan Kaufmann San Francisco, CA
35. M.A. Razi, and K. Athappilly A comparative analysis of neural network (NNs), nonlinear regression and classification and regression tree (CART) models Expert Systems with Applications 29 1 2005 65 74
36. L.G. Richards, N.K. Latham, D.U. Jette, L. Rosenberg, R.J. Smout, and G. DeJong Characterizing occupational therapy practice in stroke rehabilitation Archives of Physical Medicine and Rehabilitation 86 12, Suppl. 1 2005 51 60
37. A. Rivard, V. Hollis, J. Darrah, H.M. Madill, and S. Warren Therapists' perspectives on the management and delivery of occupational therapy and physical therapy services Healthcare Management Forum 18 2 2005 9 13
38. P.S. Roberts, J.A. Vegher, M. Gilewski, A. Bender, and R.V. Riggs Client-centered occupational therapy using constraint-induced therapy Journal of Stroke and Cerebrovascular Diseases 14 3 2005 115 121
39. W.N. Robiner The mental health professions: Workforce supply and demand, issues, and challenges Clinical Psychology Review 26 5 2006 600 625
40. S. Russell, and P. Norvig Artificial intelligence: A modern approach 2002 Prentice-Hall Upper Saddle River, NJ
41. S.L. Salzberg Locating protein coding regions in human DNA using a decision tree algorithm Journal of Computational Biology 2 3 1995 473 485
42. S.L. Salzberg, A.L. Delcher, K. Fasman, and J. Henderson A decision tree system for finding genes in DNA Journal of Computational Biology 5 4 1998 667 680
43. M.S. Sanders, and E.J. McCormick Human factors in engineering and design International ed. 1993 McGraw Hill Taipei, Taiwan
44. D. Steinburg, and P. Colla CART - classification and regression trees 1997 Salford Systems San Diego, CA
45. G.G. Towell, and J.W. Shavlik Extracting refined rules from knowledge-based neural networks Machine Learning 13 1 1993 71 101
46. D. West, and V. West Model selection for a medical diagnostic decision support system: A breast cancer detection case Artificial Intelligence in Medicine 20 3 2000 183 204
47. T.K. Wu, S.C. Huang, and Y.R. Meng Evaluation of ANN and SVM classifiers as predictors to the diagnosis of students with learning disabilities Expert Systems with Applications 34 3 2008 1846 1856
48. Z.H. Zhou, Y. Jiang, Y.B. Yang, and S.F. Chen Lung cancer cell identification based on artificial neural network ensembles Artificial Intelligence in Medicine 24 1 2002 25 36