Skip to main content
SpringerLink
Log in

Machine Learning for Early DRG Classification

  • Chapter
  • First Online:
Optimizing Hospital-wide Patient Scheduling

Part of the book series: Lecture Notes in Economics and Mathematical Systems ((LNE,volume 674))

Abstract

In this chapter, a literature review of machine learning methods is provided with a special focus on attribute selection and classification methods successfully employed in health care. Similarities and differences between the machine learning methods addressed in this dissertation and the approaches available from the literature are highlighted. Afterwards, techniques for selecting relevant and non-redundant attributes for early DRG classification are presented. Finally, different classification techniques are described in detail.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Bibliography

  1. http://www.scopus.com. Last accessed 22 May 2013

  2. ISI Web of Knowledge Journal Citation Report (JCR) 2011, Thomson Reuters, http://webofknowledge.com. Last accessed 22 May 2013

  3. M. Abad-Grau, J. Ierache, C. Cervino, P. Sebastiani, Evolution and challenges in the design of computational systems for triage assistance. J. Biomed. Inform. 41(3), 432–441 (2008)

    Google Scholar 

  4. R. Acharya, O. Faust, A. Alvin, S. Sree, F. Molinari, L. Saba, A. Nicolaides, J. Suri, Symptomatic vs. asymptomatic plaque classification in carotid ultrasound. J. Med. Syst. 36(3), 1861–1871 (2012)

    Google Scholar 

  5. M. Aldape-Pérez, C. Yáñez Márquez, O. Camacho-Nieto, A.J. Argüelles-Cruz, An associative memory approach to medical decision support systems. Comput. Methods Programs Biomed. 106(3), 287–307 (2012)

    Google Scholar 

  6. F. Alemi, M. Torii, M. Atherton, D. Pattie, K. Cox, Bayesian processing of context-dependent text: reasons for appointments can improve detection of influenza. Med. Decis. Mak. 32(2), 1–9 (2012)

    Google Scholar 

  7. C.F. Aliferis, A. Statnikov, I. Tsamardinos, S. Mani, X.D. Koutsoukos, Local causal and Markov blanket induction for causal discovery and feature selection for classification Part I: algorithms and empirical evaluation. J. Mach. Learn. Res. 11(1), 171–234 (2010)

    Google Scholar 

  8. K. Ambert, A. Cohen, A system for classifying disease comorbidity status from medical discharge summaries using automated hotspot and negated concept detection. J. Am. Med. Inform. Assoc. 16(4), 590–595 (2009)

    Google Scholar 

  9. C. Arizmendi, A. Vellido, E. Romero, Classification of human brain tumours from MRS data using discrete wavelet transform and Bayesian neural networks. Expert Syst. Appl. 39(5), 5223–5232 (2012)

    Google Scholar 

  10. L. Atallah, B. Lo, R. Ali, R. King, G.-Z. Yang, Real-time activity classification using ambient and wearable sensors. IEEE Trans. Inf. Technol. Biomed. 13(6), 1031–1039 (2009)

    Google Scholar 

  11. O. Aziz, L. Atallah, B. Lo, E. Gray, T. Athanasiou, A. Darzi, G.-Z. Yang, Ear-worn body sensor network device: an objective tool for functional postoperative home recovery monitoring. J. Am. Med. Inform. Assoc. 18(2), 156–159 (2011)

    Google Scholar 

  12. M. Bacauskiene, A. Verikas, A. Gelzinis, A. Vegiene, Random forests based monitoring of human larynx using questionnaire data. Expert Syst. Appl. 39(5), 5506–5512 (2012)

    Google Scholar 

  13. X. Bai, R. Padman, J. Ramsey, P. Spirtes, Tabu search-enhanced graphical models for classification in high dimensions. INFORMS J. Comput. 20(3), 423–437 (2008)

    Google Scholar 

  14. M. Banerjee, Y. Ding, A.-M. Noone, Identifying representative trees from ensembles. Stat. Med. 31(15), 1601–1616 (2012)

    Google Scholar 

  15. V. Baskaran, A. Guergachi, R. Bali, R. Naguib, Predicting breast screening attendance using machine learning techniques. IEEE Trans. Inf. Technol. Biomed. 15(2), 251–259 (2011)

    Google Scholar 

  16. D. Bertsimas, M. Bjarnadóttir, M. Kane, J. Kryder, R. Pandey, S. Vempala, G. Wang, Algorithmic prediction of health-care costs. Oper. Res. 56(6), 1382–1392 (2008)

    Google Scholar 

  17. C. Bishop, Pattern Recognition and Machine Learning (Springer, New York, 2006)

    Google Scholar 

  18. A. Boxwala, J. Kim, J. Grillo, L. Ohno-Machado, Using statistical and machine learning to help institutions detect suspicious access to electronic health records. J. Am. Med. Inform. Assoc. 18(4), 498–505 (2011)

    Google Scholar 

  19. M. Budnik, B. Krawczyk, On optimal settings of classification tree ensembles for medical decision support. Health Inform. J. 19(1), 3–15 (2013)

    Google Scholar 

  20. R. Busse, U. Nimptsch, T. Mansky, Measuring, monitoring and managing quality in Germany’s hospitals. Health Aff. 28(2), 294–304 (2009)

    Google Scholar 

  21. C.-L. Chang, C.-H. Chen, Applying decision tree and neural network to increase quality of dermatologic diagnosis. Expert Syst. Appl. 36(2), 4035–4041 (2009)

    Google Scholar 

  22. P.-C. Chang, J.-J. Lin, C.-H. Liu, An attribute weight assignment and particle swarm optimization algorithm for medical database classifications. Comput. Methods Programs Biomed. 107(3), 382–392 (2012)

    Google Scholar 

  23. G. Chen, J. Warren, P. Riddle, Semantic space models for classification of consumer webpages on metadata attributes. J. Biomed. Inform. 43(5), 725–735 (2010)

    Google Scholar 

  24. B. Cho, H. Yu, K.-W. Kim, T. Kim, I. Kim, S. Kim, Application of irregular and unbalanced data to predict diabetic nephropathy using visualization and feature selection methods. Artif. Intell. Med. 42(1), 37–53 (2008)

    Google Scholar 

  25. B. Cho, H. Yu, J. Lee, Y. Chee, I. Kim, S. Kim, Nonlinear support vector machine visualization for risk factor analysis using nomograms and localized radial basis function kernels. IEEE Trans. Inf. Technol. Biomed. 12(2), 247–256 (2008)

    Google Scholar 

  26. A. Chu, H. Ahn, B. Halwan, B. Kalmin, E. Artifon, A. Barkun, M. Lagoudakis, A. Kumar, A decision support system to facilitate management of patients with acute gastrointestinal bleeding. Artif. Intell. Med. 42(3), 247–259 (2008)

    Google Scholar 

  27. M. Conway, S. Doan, A. Kawazoe, N. Collier, Classifying disease outbreak reports using n-grams and semantic features. Int. J. Med. Inform. 78(12), 47–58 (2009)

    Google Scholar 

  28. A. Daemen, D. Timmerman, T. Van den Bosch, C. Bottomley, E. Kirk, C. Van Holsbeke, L. Valentin, T. Bourne, B. De Moor, Improved modeling of clinical data with kernel methods. Artif. Intell. Med. 54(2), 103–114 (2012)

    Google Scholar 

  29. D. Delen, C. Fuller, C. McCann, D. Ray, Analysis of healthcare coverage: a data mining approach. Expert Syst. Appl. 36(2), 995–1003 (2009)

    Google Scholar 

  30. D. Delen, A. Oztekin, L. Tomak, An analytic approach to better understanding and management of coronary surgeries. Decis. Support Syst. 52(3), 698–705 (2012)

    Google Scholar 

  31. J. Denny, A. Spickard, K. Johnson, N. Peterson, J. Peterson, R. Miller, Evaluation of a method to identify and categorize section headers in clinical documents. J. Am. Med. Inform. Assoc. 16(6), 806–815 (2009)

    Google Scholar 

  32. J. DeShazo, A. Turner, An interactive and user-centered computer system to predict physician’s disease judgments in discharge summaries. J. Biomed. Inform. 43(2), 218–223 (2010)

    Google Scholar 

  33. S. Dreiseitl, L. Ohno-Machado, Logistic regression and artificial neural network classification models: a methodology review. J. Biomed. Inform. 35(5–6), 352–359 (2002)

    Google Scholar 

  34. R. Erol, S. Oǧulata, C. Şahin, Z. Alparslan, A radial basis function neural network (RBFNN) approach for structural classification of thyroid diseases. J. Med. Syst. 32(3), 215–220 (2008)

    Google Scholar 

  35. L. Fan, K.-L. Poh, P. Zhou, Partition-conditional ICA for Bayesian classification of microarray data. Expert Syst. Appl. 37(12), 8188–8192 (2010)

    Google Scholar 

  36. Y.-J. Fan, W. Chaovalitwongse, Optimizing feature selection to improve medical diagnosis. Ann. Oper. Res. 174(1), 169–183 (2010)

    Google Scholar 

  37. R. Farkas, G. Szarvas, I. Hegedüs, A. Almási, V. Vincze, R. Ormándi, R. Busa-Fekete, Semi-automated construction of decision rules to predict morbidities from clinical texts. J. Am. Med. Inform. Assoc. 16(4), 601–605 (2009)

    Google Scholar 

  38. H. Fei, C. Chu, N. Meskens, A. Artiba, Solving surgical cases assignment problem by a branch-and-price approach. Int. J. Prod. Econ. 112(1), 96–108 (2008)

    Google Scholar 

  39. G. Fiol, P. Haug, Classification models for the prediction of clinicians’ information needs. J. Biomed. Inform. 42(1), 82–89 (2009)

    Google Scholar 

  40. O. Frunza, D. Inkpen, S. Matwin, W. Klement, P. O’Blenis, Exploiting the systematic review protocol for classification of medical abstracts. Artif. Intell. Med. 51(1), 17–25 (2011)

    Google Scholar 

  41. S. Fu, M. Desmarais, Tradeoff analysis of different Markov blanket local learning approaches, in Advances in Knowledge Discovery and Data Mining, ed. by T. Washio, E. Suzuki, K. Ting, A. Inokuchi. Volume 5012 of Lecture Notes in Computer Science (Springer, Berlin/Heidelberg, 2008), pp. 562–571

    Google Scholar 

  42. S. Ghazavi, T. Liao, Medical data mining by fuzzy modeling with selected features. Artif. Intell. Med. 43(3), 195–206 (2008)

    Google Scholar 

  43. M. Gietzelt, G. Nemitz, K.-H. Wolf, H. Meyer zu Schwabedissen, R. Haux, M. Marschollek, A clinical study to assess fall risk using a single waist accelerometer. Inform. Health Soc. Care 34(4), 181–188 (2009)

    Google Scholar 

  44. D. Glotsos, I. Kalatzis, P. Theocharakis, P. Georgiadis, A. Daskalakis, K. Ninos, P. Zoumboulis, A. Filippidou, D. Cavouras, A multi-classifier system for the characterization of normal, infectious, and cancerous prostate tissues employing transrectal ultrasound images. Comput. Methods Programs Biomed. 97(1), 53–61 (2010)

    Google Scholar 

  45. I. Goldstein, O. Uzuner, Specializing for predicting obesity and its co-morbidities. J. Biomed. Inform. 42(5), 873–886 (2009)

    Google Scholar 

  46. J. Goodson, W. Jang, Assessing nursing home care quality through Bayesian networks. Health Care Manag. Sci. 11(4), 382–392 (2008)

    Google Scholar 

  47. T. Grubinger, C. Kobel, K.-P. Pfeiffer, Regression tree construction by bootstrap: model search for DRG-systems applied to Austrian health-data. BMC Med. Inform. Decis. Mak. 10(9), 1–11 (2010)

    Google Scholar 

  48. M. Hall, G. Holmes, Benchmarking attribute selection techniques for discrete class data mining. IEEE Trans. Knowl. Data Eng. 15(6), 1437–1447 (2003)

    Google Scholar 

  49. M. Hariharan, L. Chee, S. Yaacob, Analysis of infant cry through weighted linear prediction cepstral coefficients and probabilistic neural network. J. Med. Syst. 36(3), 1309–1315 (2012)

    Google Scholar 

  50. P. Harper, A review and comparison of classification algorithms for medical decision making. Health Policy 71(3), 315–331 (2005)

    Google Scholar 

  51. R.C. Holte, Very simple classification rules perform well on most commonly used datasets. Mach. Learn. 11(1), 63–90 (1993)

    Google Scholar 

  52. N.-C. Hsieh, L.-P. Hung, C.-C. Shih, H.-C. Keh, C.-H. Chan, Intelligent postoperative morbidity prediction of heart disease using artificial intelligence techniques. J. Med. Syst. 36(3), 1809–1820 (2012)

    Google Scholar 

  53. Y. Hu, T. Ku, R. Jan, K. Wang, Y. Tseng, S. Yang, Decision tree-based learning to predict patient controlled analgesia consumption and readjustment. BMC Med. Inform. Decis. Mak. 12(11), 131 (2012)

    Google Scholar 

  54. Y.-H. Hu, F. Wu, C.-L. Lo, C.-T. Tai, Predicting warfarin dosage from clinical data: a supervised learning approach. Artif. Intell. Med. 56(1), 27–34 (2012)

    Google Scholar 

  55. S. Huang, L. Wulsin, H. Li, J. Guo, Dimensionality reduction for knowledge discovery in medical claims database: application to antidepressant medication utilization study. Comput. Methods Programs Biomed. 93(2), 115–123 (2009)

    Google Scholar 

  56. N. Ishii, A. Koike, Y. Yamamoto, T. Takagi, Figure classification in biomedical literature to elucidate disease mechanisms, based on pathways. Artif. Intell. Med. 49(3), 135–143 (2010)

    Google Scholar 

  57. E. Joffe, O. Havakuk, J. Herskovic, V. Patel, E. Bernstam, Collaborative knowledge acquisition for the design of context-aware alert systems. J. Am. Med. Inform. Assoc. 19(6), 988–994 (2012)

    Google Scholar 

  58. H. Kahramanli, N. Allahverdi, Design of a hybrid system for the diabetes and heart diseases. Expert Syst. Appl. 35(1–2), 82–89 (2008)

    Google Scholar 

  59. B.-Y. Kang, D.-W. Kim, H.-G. Kim, Two-phase chief complaint mapping to the UMLS metathesaurus in Korean electronic medical records. IEEE Trans. Inf. Technol. Biomed. 13(1), 78–86 (2009)

    Google Scholar 

  60. S. Kara, B. Aksebzeci, H. Kodaz, S. Güneş, E. Kaya, H. Özbilge, Medical application of information gain-based artificial immune recognition system (IG-AIRS): classification of microorganism species. Expert Syst. Appl. 36(3), 5168–5172 (2009)

    Google Scholar 

  61. M. Khalilia, S. Chakraborty, M. Popescu, Predicting disease risks from highly imbalanced data using random forest. BMC Med. Inform. Decis. Mak. 11(51), 1–13 (2011)

    Google Scholar 

  62. K. Kira, L.A. Rendell, A practical approach to feature selection, in Proceedings of the 9th International Conference on Machine Learning, Aberdeen, 1992, pp. 249–256

    Google Scholar 

  63. J. Kittler, M. Hatef, R. Duin, J. Matas, On combining classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 20(3), 226–239 (1998)

    Google Scholar 

  64. H. Kodaz, S. Özşen, A. Arslan, S. Güneş, Medical application of information gain based artificial immune recognition system (AIRS): diagnosis of thyroid disease. Expert Syst. Appl. 36(2), 3086–3092 (2009)

    Google Scholar 

  65. R. Kohavi, G. John, Wrappers for feature subset selection. Artif. Intell. 97(1–2), 273–324 (1997)

    Google Scholar 

  66. I. Kononenko, Machine learning for medical diagnosis: history, state of the art and perspective. Artif. Intell. Med. 23(1), 89–109 (2001)

    Google Scholar 

  67. P. Kralj Novak, N. Lavrač, D. Gamberger, A. Krstačić, CSM-SD: methodology for contrast set mining through subgroup discovery. J. Biomed. Inform. 42(1), 113–122 (2009)

    Google Scholar 

  68. M. Krizmaric, M. Verlic, G. Stiglic, S. Grmec, P. Kokol, Intelligent analysis in predicting outcome of out-of-hospital cardiac arrest. Comput. Methods Programs Biomed. 95(2), 22–32 (2009)

    Google Scholar 

  69. S. Kumar, M. Madheswaran, An improved medical decision support system to identify the diabetic retinopathy using fundus images. J. Med. Syst. 36(6), 3573–3581 (2012)

    Google Scholar 

  70. L. Kuncheva, Combining Pattern Classifiers (Wiley, Hoboken, 2004)

    Google Scholar 

  71. S.-M. Lee, P. Abbott, Bayesian networks for knowledge discovery in large datasets: basics for nurse researchers. J. Biomed. Inform. 36(4–5), 389–399 (2003)

    Google Scholar 

  72. T.-T. Lee, C.-Y. Liu, Y.-H. Kuo, M. Mills, J.-G. Fong, C. Hung, Application of data mining to the identification of critical factors in patient falls using a web-based reporting system. Int. J. Med. Inform. 80(2), 141–150 (2011)

    Google Scholar 

  73. W.-I. Lee, B.-Y. Shih, Y.-S. Chung, The exploration of consumers’ behavior in choosing hospital by the application of neural network. Expert Syst. Appl. 34(2), 806–816 (2008)

    Google Scholar 

  74. D.-C. Li, C.-W. Liu, A class possibility based kernel to increase classification accuracy for small data sets using support vector machines. Expert Syst. Appl. 37(4), 3104–3110 (2010)

    Google Scholar 

  75. D.-C. Li, C.-W. Liu, S. Hu, A fuzzy-based data transformation for feature extraction to increase classification performance with small medical data sets. Artif. Intell. Med. 52(1), 45–52 (2011)

    Google Scholar 

  76. S.-T. Li, C.-C. Chen, F. Huang, Conceptual-driven classification for coding advise in health insurance reimbursement. Artif. Intell. Med. 51(1), 27–41 (2011)

    Google Scholar 

  77. Z. Li, F. Liu, L. Antieau, Y. Cao, H. Yu, Lancet: a high precision medication event extraction system for clinical text. J. Am. Med. Inform. Assoc. 17(5), 563–567 (2010)

    Google Scholar 

  78. C. Liang, L. Peng, An automated diagnosis system of liver disease using artificial immune and genetic algorithms. J. Med. Syst. 37(2), 1–10 (2013)

    Google Scholar 

  79. J.-H. Lin, P. Haug, Exploiting missing clinical data in Bayesian network modeling for predicting medical problems. J. Biomed. Inform. 41(1), 1–14 (2008)

    Google Scholar 

  80. F. Liou, Y. Tang, J. Chen, Detecting hospital fraud and claim abuse through diabetic outpatient services. Health Care Manag. Sci. 11(4), 353–358 (2008)

    Google Scholar 

  81. F. Liu, L. Antieau, H. Yu, Toward automated consumer question answering: automatically separating consumer questions from professional questions in the healthcare domain. J. Biomed. Inform. 44(6), 1032–1038 (2011)

    Google Scholar 

  82. P. Luukka, Similarity classifier in diagnosis of bladder cancer. Comput. Methods Programs Biomed. 89(1), 43–49 (2008)

    Google Scholar 

  83. S. Lv, X. Wang, Y. Cui, J. Jin, Y. Sun, Y. Tang, Y. Bai, Y. Wang, L. Zhou, Application of attention network test and demographic information to detect mild cognitive impairment via combining feature selection with support vector machine. Comput. Methods Programs Biomed. 97(1), 11–18 (2010)

    Google Scholar 

  84. D. Mackay, Information Theory, Inference and Learning Algorithms (Cambridge University Press, Cambridge, 2003)

    Google Scholar 

  85. I. Maglogiannis, E. Loukis, E. Zafiropoulos, A. Stasis, Support vectors machine-based identification of heart valve diseases using heart sounds. Comput. Methods Programs Biomed. 95(1), 47–61 (2009)

    Google Scholar 

  86. F. Mancini, F. Sousa, F. Teixeira, A. Falcão, A. Hummel, T. da Costa, P. Calado, L. de Araújo, I. Pisa, Use of medical subject headings (MeSH) in Portuguese for categorizing web-based healthcare content. J. Biomed. Inform. 44(2), 299–309 (2011)

    Google Scholar 

  87. D. Margaritis, Learning Bayesian network model structure from data. Ph.D. thesis, School of Computer Science, Carnegie Mellon University, 2003

    Google Scholar 

  88. C. Meek, Complete orientation rules for patterns. Technical report, Carnegie Mellon University, 1995

    Google Scholar 

  89. K. Miettinen, M. Juhola, Classification of otoneurological cases according to Bayesian probabilistic models. J. Med. Syst. 34(2), 119–130 (2010)

    Google Scholar 

  90. C. Morioka, S. El-Saden, W. Pope, J. Sayre, G. Duckwiler, F. Meng, A. Bui, H. Kangarloo, A methodology to integrate clinical data for the efficient assessment of brain-tumor patients. Inform. Health Soc. Care 33(1), 55–68 (2008)

    Google Scholar 

  91. D. Mowery, J. Wiebe, S. Visweswaran, H. Harkema, W. Chapman, Building an automated SOAP classifier for emergency department reports. J. Biomed. Inform. 45(1), 71–81 (2012)

    Google Scholar 

  92. T. Mu, T. Pataky, A. Findlow, M. Aung, J. Goulermas, Automated nonlinear feature generation and classification of foot pressure lesions. IEEE Trans. Inf. Technol. Biomed. 14(2), 418–424 (2010)

    Google Scholar 

  93. R. Nagarajan, M. Scutari, S. Lèbre, Bayesian Networks in R (Springer, New York, 2013)

    Google Scholar 

  94. A. Nguyen, M. Lawley, D. Hansen, R. Bowman, B. Clarke, E. Duhig, S. Colquist, Symbolic rule-based classification of lung cancer stages from free-text pathology reports. J. Am. Med. Inform. Assoc. 17(4), 440–445 (2010)

    Google Scholar 

  95. J.H. Oh, P. Gurnani, J. Schorge, K.P. Rosenblatt, J.X. Gao, An extended Markov blanket approach to proteomic biomarker detection from high-resolution mass spectrometry data. IEEE Trans. Inf. Technol. Biomed. 13(2), 195–206 (2009)

    Google Scholar 

  96. L. Ohno-Machado, Modeling medical prognosis: survival analysis techniques. J. Biomed. Inform. 34(6), 428–439 (2001)

    Google Scholar 

  97. A. Oniśko, M. Druzdzel, Impact of precision of Bayesian network parameters on accuracy of medical diagnostic systems. Artif. Intell. Med. 57(3), 197–206 (2013)

    Google Scholar 

  98. A. Ozcift, Enhanced cancer recognition system based on random forests feature elimination algorithm. J. Med. Syst. 36(4), 2577–2585 (2011)

    Google Scholar 

  99. A. Oztekin, D. Delen, Z.J. Kong, Predicting the graft survival for heart-lung transplantation patients: an integrated data mining methodology. Int. J. Med. Inform. 78(12), 84–96 (2009)

    Google Scholar 

  100. S. Pakhomov, P. Hanson, S. Bjornsen, S. Smith, Automatic classification of foot examination findings using clinical notes and machine learning. J. Am. Med. Inform. Assoc. 15(2), 198–202 (2008)

    Google Scholar 

  101. S. Parhizi, L. Steege, K. Pasupathy, Mining the relationships between psychosocial factors and fatigue dimensions among registered nurses. Int. J. Ind. Ergon. 43(1), 82–90 (2013)

    Google Scholar 

  102. J. Patrick, M. Li, High accuracy information extraction of medication information from clinical notes: 2009 i2b2 medication extraction challenge. J. Am. Med. Inform. Assoc. 17(5), 524–527 (2010)

    Google Scholar 

  103. J. Patrick, D. Nguyen, Y. Wang, M. Li, A knowledge discovery and reuse pipeline for information extraction in clinical notes. J. Am. Med. Inform. Assoc. 18(5), 574–579 (2011)

    Google Scholar 

  104. J. Pearl, Causality: Models, Reasoning and Inference (Cambridge University Press, Cambridge, 2000)

    Google Scholar 

  105. L. Pecchia, P. Melillo, M. Sansone, M. Bracale, Discrimination power of short-term heart rate variability measures for CHF assessment. IEEE Trans. Inf. Technol. Biomed. 15(1), 40–46 (2011)

    Google Scholar 

  106. P. Petrantonakis, L. Hadjileontiadis, Emotion recognition from EEG using higher order crossings. IEEE Trans. Inf. Technol. Biomed. 14(2), 186–197 (2010)

    Google Scholar 

  107. V. Podgorelec, P. Kokol, Genetic algorithm based system for patient scheduling in highly constrained situations. J. Med. Syst. 21(6), 417–427 (1997)

    Google Scholar 

  108. V. Podgorelec, P. Kokol, B. Stiglic, I. Rozman, Decision trees: an overview and their use in medicine. J. Med. Syst. 26(5), 445–463 (2002)

    Google Scholar 

  109. K. Polat, Application of attribute weighting method based on clustering centers to discrimination of linearly non-separable medical datasets. J. Med. Syst. 36(4), 2657–2673 (2011)

    Google Scholar 

  110. K. Polat, S. Güneş, A new feature selection method on classification of medical datasets: Kernel F-score feature selection. Expert Syst. Appl. 36(7), 10367–10373 (2009)

    Google Scholar 

  111. K. Polat, S. Kara, A. Güven, S. Güneş, Usage of class dependency based feature selection and fuzzy weighted pre-processing methods on classification of macular disease. Expert Syst. Appl. 36(2), 2584–2591 (2009)

    Google Scholar 

  112. J. Pollettini, S. Panico, J. Daneluzzi, R. Tinós, J. Baranauskas, A. MacEdo, Using machine learning classifiers to assist healthcare-related decisions: classification of electronic patient records. J. Med. Syst. 36(6), 3861–3874 (2012)

    Google Scholar 

  113. J. Quinlan, C4.5: Programs for Machine Learning (Morgan Kaufman, San Mateo, 1992)

    Google Scholar 

  114. R. Ramiarina, R. Almeida, W. Pereira, Hospital costs estimation and prediction as a function of patient and admission characteristics. Int. J. Health Plan. Manag. 23(4), 345–355 (2007)

    Google Scholar 

  115. J. Ramsey, A PC-style Markov blanket search for high dimensional datasets. Technical report, Carnegie Mellon University, 2006

    Google Scholar 

  116. J. Rasku, H. Joutsijoki, I. Pyykkö, M. Juhola, Prediction of a state of a subject on the basis of a stabilogram signal and video oculography test. Comput. Methods Programs Biomed. 108(2), 580–588 (2012)

    Google Scholar 

  117. A. Razavi, H. Gill, H. Ahlfeldt, N. Shahsavar, Non-compliance with a postmastectomy radiotherapy guideline: decision tree and cause analysis. BMC Med. Inform. Decis. Mak. 8(41), 1–8 (2008)

    Google Scholar 

  118. P. Revesz, T. Triplet, Classification integration and reclassification using constraint databases. Artif. Intell. Med. 49(2), 79–91 (2010)

    Google Scholar 

  119. B. Rink, S. Harabagiu, K. Roberts, Automatic extraction of relations between medical concepts in clinical texts. J. Am. Med. Inform. Assoc. 18(5), 594–600 (2011)

    Google Scholar 

  120. K. Roberts, S. Harabagiu, A flexible framework for deriving assertions from electronic medical records. J. Am. Med. Inform. Assoc. 18(5), 568–573 (2011)

    Google Scholar 

  121. M. Robnik-Šikonja, I. Kononenko, Theoretical and empirical analysis of ReliefF and RReliefF. Mach. Learn. 53(1), 23–69 (2003)

    Google Scholar 

  122. Y. Roumani, J. May, D. Strum, L. Vargas, Classifying highly imbalanced ICU data. Health Care Manag. Sci. 16(2), 119–128 (2013)

    Google Scholar 

  123. S. Rubrichi, S. Quaglini, Summary of product characteristics content extraction for a safe drugs usage. J. Biomed. Inform. 45(2), 231–239 (2012)

    Google Scholar 

  124. M. Sariyar, A. Borg, Bagging, bumping, multiview, and active learning for record linkage with empirical results on patient identity data. Comput. Methods Programs Biomed. 108(3), 1160–1169 (2012)

    Google Scholar 

  125. M. Sariyar, A. Borg, K. Pommerening, Evaluation of record linkage methods for iterative insertions. Methods Inf. Med. 48(5), 429–437 (2009)

    Google Scholar 

  126. M. Sariyar, A. Borg, K. Pommerening, Missing values in deduplication of electronic patient data. J. Am. Med. Inform. Assoc. 19(1), 76–82 (2012)

    Google Scholar 

  127. M. Scotch, M. Duggal, C. Brandt, Z. Lin, R. Shiffman, Use of statistical analysis in the biomedical informatics literature. J. Am. Med. Inform. Assoc. 17(1), 3–5 (2010)

    Google Scholar 

  128. M. Scutari, Learning Bayesian networks with the bnlearn package. J. Stat. Softw. 35(3), 1–22 (2010)

    Google Scholar 

  129. M. Scutari, K. Strimmer, Introduction to graphical modelling, in Handbook of Statistical Systems Biology, chapter 11, ed. by M. Stumpf, D. Balding, M. Girolami (Wiley, Chichester, 2010), pp. 235–254

    Google Scholar 

  130. R. Shanmugam, A diagnostic methodology for hazy data with “borderline” cases. J. Med. Syst. 34(2), 161–177 (2010)

    Google Scholar 

  131. M.J. Sharma, S.J. Yu, Benchmark optimization and attribute identification for improvement of container terminals. Eur. J. Oper. Res. 201(2), 568–580 (2009)

    Google Scholar 

  132. A. Smith, C. Nugent, S. McClean, Evaluation of inherent performance of intelligent medical decision support systems: utilising neural networks as an example. Artif. Intell. Med. 27(1), 1–27 (2003)

    Google Scholar 

  133. Z. Song, Z. Ji, J.-G. Ma, B. Sputh, U. Acharya, O. Faust, A systematic approach to embedded biomedical decision making. Comput. Methods Programs Biomed. 108(2), 656–664 (2012)

    Google Scholar 

  134. N. Sriraam, EEG based automated detection of auditory loss: a pilot study. Expert Syst. Appl. 39(1), 723–731 (2012)

    Google Scholar 

  135. I. Štajduhar, B. Dalbelo-Bašić, Learning Bayesian networks from survival data using weighting censored instances. J. Biomed. Inform. 43(4), 613–622 (2010)

    Google Scholar 

  136. I. Štajduhar, B. Dalbelo-Bašić, Uncensoring censored data for machine learning: a likelihood-based approach. Expert Syst. Appl. 39(8), 7226–7234 (2012)

    Google Scholar 

  137. R. Stoean, C. Stoean, Modeling medical decision making by support vector machines, explaining by rules of evolutionary algorithms with feature selection. Expert Syst. Appl. 40(7), 2677–2686 (2013)

    Google Scholar 

  138. R. Stoean, C. Stoean, M. Lupsor, H. Stefanescu, R. Badea, Evolutionary-driven support vector machines for determining the degree of liver fibrosis in chronic hepatitis C. Artif. Intell. Med. 51(1), 53–65 (2011)

    Google Scholar 

  139. N. Sut, O. Simsek, Comparison of regression tree data mining methods for prediction of mortality in head injury. Expert Syst. Appl. 38(12), 15534–15539 (2011)

    Google Scholar 

  140. M. Tagluk, N. Sezgin, M. Akin, Estimation of sleep stages by an artificial neural network employing EEG, EMG and EOG. J. Med. Syst. 34(4), 717–725 (2010)

    Google Scholar 

  141. M. Testik, B. Ozkaya, S. Aksu, O. Ozcebe, Discovering blood donor arrival patterns using data mining: a method to investigate service quality at blood centers. J. Med. Syst. 36(2), 579–594 (2012)

    Google Scholar 

  142. I. Tsamardinos, C. Aliferis, A. Statnikov, Algorithms for large scale Markov blanket discovery, in Proceedings of the 16th International Florida Artificial Intelligence Research Society Conference, St. Augustine, 2003, pp 376–381

    Google Scholar 

  143. K. Tufan, S. Kara, F. Latifoglu, S. Aydin, A. Kiriş, U. Özkuvanci, Non-invasive diagnosis of stress urinary incontinence sub types using wavelet analysis, shannon entropy and principal component analysis. J. Med. Syst. 36(4), 2159–2169 (2012)

    Google Scholar 

  144. E. Übeyli, K. Ilbay, G. Ilbay, D. Sahin, G. Akansel, Differentiation of two subtypes of adult hydrocephalus by mixture of experts. J. Med. Syst. 34(3), 281–290 (2010)

    Google Scholar 

  145. O. Uzuner, J. Mailoa, R. Ryan, T. Sibanda, Semantic relations for problem-oriented medical records. Artif. Intell. Med. 50(2), 63–73 (2010)

    Google Scholar 

  146. B. Van Calster, G. Condous, E. Kirk, T. Bourne, D. Timmerman, S. Van Huffel, An application of methods for the probabilistic three-class classification of pregnancies of unknown location. Artif. Intell. Med. 46(2), 139–154 (2009)

    Google Scholar 

  147. K. Varpa, K. Iltanen, M. Juhola, Machine learning method for knowledge discovery experimented with otoneurological data. Comput. Methods Programs Biomed. 91(2), 154–164 (2008)

    Google Scholar 

  148. C. Vidrighin, R. Potolea, Proicet: a cost-sensitive system for prostate cancer data. Health Inform. J. 14(4), 297–307 (2008)

    Google Scholar 

  149. K. Wagholikar, S. Mangrulkar, A. Deshpande, V. Sundararajan, Evaluation of fuzzy relation method for medical decision support. J. Med. Syst. 36(1), 233–239 (2012)

    Google Scholar 

  150. L. Wasserman, All of Statistics: A Concise Course in Statistical Inference (Springer, New York, 2004)

    Google Scholar 

  151. R. Wicentowski, M. Sydes, Using implicit information to identify smoking status in smoke-blind medical discharge summaries. J. Am. Med. Inform. Assoc. 15(1), 29–31 (2008)

    Google Scholar 

  152. I. Witten, E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. (Morgan Kaufmann, San Francisco, 2005)

    Google Scholar 

  153. J. Wu, J. Roy, W. Stewart, Prediction modeling using EHR data: challenges, strategies, and a comparison of machine learning approaches. Med. Care 48(6), 106–113 (2010)

    Google Scholar 

  154. Y. Xu, K. Hong, J. Tsujii, E.-C. Chang, Feature engineering combined with machine learning and rule-based methods for structured information extraction from narrative clinical discharge summaries. J. Am. Med. Inform. Assoc. 19(5), 824–832 (2012)

    Google Scholar 

  155. Y. Xu, J. Liu, J. Wu, Y. Wang, Z. Tu, J.-T. Sun, J. Tsujii, E.-C. Chang, A classification approach to coreference in discharge summaries: 2011 i2b2 challenge. J. Am. Med. Inform. Assoc. 19(5), 897–905 (2012)

    Google Scholar 

  156. Q. Yan, H. Yan, F. Han, X. Wei, T. Zhu, SVM-based decision support system for clinic aided tracheal intubation predication with multiple features. Expert Syst. Appl. 36(3), 6588–6592 (2009)

    Google Scholar 

  157. L. Yu, H. Liu, Efficient feature selection via analysis of relevance and redundancy. J. Mach. Learn. Res. 5(1), 1205–1224 (2004)

    Google Scholar 

  158. M. Yu, A. Rhuma, S. Naqvi, L. Wang, J. Chambers, A posture recognition-based fall detection system for monitoring an elderly person in a smart home environment. IEEE Trans. Inf. Technol. Biomed. 16(6), 1274–1286 (2012)

    Google Scholar 

  159. F. Zakeri, H. Behnam, N. Ahmadinejad, Classification of benign and malignant breast masses based on shape and texture features in sonography images. J. Med. Syst. 36(3), 1621–1627 (2012)

    Google Scholar 

  160. J. Zheng, W. Chapman, T. Miller, C. Lin, R. Crowley, G. Savova, A system for coreference resolution for the clinical narrative. J. Am. Med. Inform. Assoc. 19(4), 660–667 (2012)

    Google Scholar 

  161. W. Zhong, R. Chow, J. He, Clinical charge profiles prediction for patients diagnosed with chronic diseases using multi-level support vector machine. Expert Syst. Appl. 39(1), 1474–1483 (2012)

    Google Scholar 

  162. X. Zhou, S. Chen, B. Liu, R. Zhang, Y. Wang, P. Li, Y. Guo, H. Zhang, Z. Gao, X. Yan, Development of traditional chinese medicine clinical data warehouse for medical knowledge discovery and decision support. Artif. Intell. Med. 48(2–3), 139–152 (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. TUM School of Management, Technische Universität München, München, Germany

    Daniel Gartner

Authors
  1. Daniel Gartner
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Gartner, D. (2014). Machine Learning for Early DRG Classification. In: Optimizing Hospital-wide Patient Scheduling. Lecture Notes in Economics and Mathematical Systems, vol 674. Springer, Cham. https://doi.org/10.1007/978-3-319-04066-0_2

Download citation

Publish with us

Policies and ethics

Search

Navigation