Medical decision support systems based on machine learning

This dissertation discusses three problems from different areas of medical research and their machine learning solutions. Each solution is a distinct type of decision support system. They show three common properties: personalized health care decision support, reduction of the use of medical resources, and improvement of outcomes. The first decision support system assists individual hospital selection. This system can help a user make the best decision in terms of the combination of mortality, complication, and travel distance. Both machine learning and optimization techniques are utilized in this type of decision support system. Machine learning methods, such as Support Vector Machines, learn a decision function. Next, the function is transformed into an objective function and then optimization methods are used to find the values of decision variables to reach the desired outcome with the most confidence. The second decision support system assists diagnostic decisions in a sequential decision-making setting by finding the most promising tests and suggesting a diagnosis. The system can speed up the diagnostic process, reduce overuse of medical tests, save costs, and improve the accuracy of diagnosis. In this study, the system finds the test most likely to confirm a diagnosis based on the pre-test probability computed from the patient’s information including symptoms and the results of previous tests. If the patient’s disease post-test probability is higher than the treatment threshold, a diagnostic decision will be made, and vice versa. Otherwise, the patient needs more tests to help make a decision. The system will then recommend the next optimal test and repeat the same process. The third decision support system recommends the best lifestyle changes for an individual to lower the risk of cardiovascular disease (CVD). As in the hospital

[1]  John A. Cowan,et al.  The volume-outcome effect for abdominal aortic surgery: differences in case-mix or complications? , 2002, Archives of surgery.

[2]  R. Detrano,et al.  International application of a new probability algorithm for the diagnosis of coronary artery disease. , 1989, The American journal of cardiology.

[3]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[4]  D. Buckeridge,et al.  Systematic Review: Surveillance Systems for Early Detection of Bioterrorism-Related Diseases , 2004, Annals of Internal Medicine.

[5]  David Dagan Feng,et al.  Guest Editorial Introduction to the Special Issue on Advances in Clinical and Health-Care Knowledge Management , 2005, IEEE Transactions on Information Technology in Biomedicine.

[6]  Foster J. Provost,et al.  Active feature-value acquisition for classifier induction , 2004, Fourth IEEE International Conference on Data Mining (ICDM'04).

[7]  Russell Greiner,et al.  Learning and Classifying Under Hard Budgets , 2005, ECML.

[8]  Edward A. Feigenbaum,et al.  The Art of Artificial Intelligence: Themes and Case Studies of Knowledge Engineering , 1977, IJCAI.

[9]  J. Kupersmith,et al.  Quality of Care in Teaching Hospitals: A Literature Review , 2005, Academic medicine : journal of the Association of American Medical Colleges.

[10]  James Theiler,et al.  Online Feature Selection using Grafting , 2003, ICML.

[11]  D. Bates,et al.  Effects of computerized physician order entry and clinical decision support systems on medication safety: a systematic review. , 2003, Archives of internal medicine.

[12]  I. Ihse,et al.  The Volume-Outcome Relationship in Cancer Surgery: A Hard Sell , 2003, Annals of surgery.

[13]  J. Weissman,et al.  Teaching hospitals and quality of care: a review of the literature. , 2002, The Milbank quarterly.

[14]  Ming-Te Tsai,et al.  Expert system of a crude oil distillation unit for process optimization using neural networks , 2004, Expert Syst. Appl..

[15]  Rich Caruana,et al.  Predicting good probabilities with supervised learning , 2005, ICML.

[16]  Thomas G. Dietterich,et al.  Pruning Improves Heuristic Search for Cost-Sensitive Learning , 2002, ICML.

[17]  Andrew Kusiak,et al.  Optimization of Temporal Processes: A Model Predictive Control Approach , 2009, IEEE Transactions on Evolutionary Computation.

[18]  Thomas H. Payne,et al.  Review Paper: Medication-related Clinical Decision Support in Computerized Provider Order Entry Systems: A Review , 2007, J. Am. Medical Informatics Assoc..

[19]  Ron Kohavi,et al.  Irrelevant Features and the Subset Selection Problem , 1994, ICML.

[20]  William J. Clancey,et al.  Heuristic Classification , 1986, Artif. Intell..

[21]  H. Lehmann,et al.  Clinical Decision Support Systems (cdsss) Have Been Hailed for Their Potential to Reduce Medical Errors Clinical Decision Support Systems for the Practice of Evidence-based Medicine , 2022 .

[22]  David G. Stork,et al.  Pattern Classification , 1973 .

[23]  Peter D. Turney.  Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm , 1994, J. Artif. Intell. Res..

[24]  J. C. Bean.  Genetics and random keys for sequencing amd optimization , 1993 .

[25]  Irene Fraser,et al.  Volume thresholds and hospital characteristics in the United States. , 2003, Health affairs.

[26]  Sara L McLafferty,et al.  GIS and health care. , 2003, Annual review of public health.

[27]  Hyeran Byun,et al.  A Survey on Pattern Recognition Applications of Support Vector Machines , 2003, Int. J. Pattern Recognit. Artif. Intell..

[28]  John Platt,et al.  Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods , 1999 .

[29]  Ron Kohavi,et al.  Lazy Decision Trees , 1996, AAAI/IAAI, Vol. 1.

[30]  Farhi Marir,et al.  Case-based reasoning: A review , 1994, The Knowledge Engineering Review.

[31]  L. Hayden,et al.  Ten Commandments for Effective Clinical Decision Support: Making the Practice of Evidence-based Medicine a Reality , 2011 .

[32]  Kenneth DeJong,et al.  Robust feature selection algorithms , 1993, Proceedings of 1993 IEEE Conference on Tools with Al (TAI-93).

[33]  Harlan M Krumholz,et al.  JCAHO accreditation and quality of care for acute myocardial infarction. , 2003, Health affairs.

[34]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[35]  Sholom M. Weiss,et al.  Knowledge-based data mining , 2003, KDD '03.

[36]  David W. Aha,et al.  A Review and Empirical Evaluation of Feature Weighting Methods for a Class of Lazy Learning Algorithms , 1997, Artificial Intelligence Review.

[37]  Sanjay Saint,et al.  Impact of patient risk on the hospital volume-outcome relationship in coronary artery bypass grafting. , 2005, Archives of internal medicine.

[38]  Zhiqiang Zheng,et al.  On active learning for data acquisition , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[39]  Ethan A Halm,et al.  Is Volume Related to Outcome in Health Care? A Systematic Review and Methodologic Critique of the Literature , 2002, Annals of Internal Medicine.

[40]  Ian Watson,et al.  The client‐centred approach: expert system maintenance , 1992 .

[41]  Paulo J. G. Lisboa,et al.  The Use of Artificial Neural Networks in Decision Support in Cancer: a Systematic Review , 2005 .

[42]  Charles X. Ling,et al.  Data Mining for Direct Marketing: Problems and Solutions , 1998, KDD.

[43]  Afschin Gandjour,et al.  Threshold Volumes Associated With Higher Survival in Health Care: A Systematic Review , 2003, Medical care.

[44]  Bianca Zadrozny,et al.  Transforming classifier scores into accurate multiclass probability estimates , 2002, KDD.

[45]  C. Gatsonis,et al.  Designing studies to ensure that estimates of test accuracy are transferable , 2002, BMJ : British Medical Journal.

[46]  P. Maurette.  [To err is human: building a safer health system]. , 2002, Annales francaises d'anesthesie et de reanimation.

[47]  John Fox,et al.  Disseminating medical knowledge: the PROforma approach , 1998, Artif. Intell. Medicine.

[48]  R B Haynes,et al.  Evidence base of clinical diagnosis: The architecture of diagnostic research , 2002 .

[49]  Pedro M. Domingos.  Control-Sensitive Feature Selection for Lazy Learners , 1997, Artificial Intelligence Review.

[50]  B. Hillner,et al.  Hospital and physician volume or specialization and outcomes in cancer treatment: importance in quality of cancer care. , 2000, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[51]  P. Bossuyt,et al.  Sources of Variation and Bias in Studies of Diagnostic Accuracy , 2004, Annals of Internal Medicine.

[52]  Peter Jackson,et al.  Introduction to expert systems , 1986 .

[53]  J. Kassirer,et al.  The threshold approach to clinical decision making. , 1980, The New England journal of medicine.

[54]  A. Elstein,et al.  Clinical problem solving and diagnostic decision making: selective review of the cognitive literature , 2002, BMJ : British Medical Journal.

[55]  Chih-Lin Chi,et al.  Building a hospital referral expert system with a Prediction and Optimization-Based Decision Support System algorithm , 2008, J. Biomed. Informatics.

[56]  Diederick E. Grobbee,et al.  Limitations of Sensitivity, Specificity, Likelihood Ratio, and Bayes' Theorem in Assessing Diagnostic Probabilities: A Clinical Example , 1997, Epidemiology.

[57]  David J. Spiegelhalter,et al.  Machine Learning, Neural and Statistical Classification , 2009 .

[58]  Paul Compton,et al.  Inductive knowledge acquisition: a case study , 1987 .

[59]  R. Deyo,et al.  Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. , 1992, Journal of clinical epidemiology.

[60]  Léopold Simar,et al.  Computer Intensive Methods in Statistics , 1994 .

[61]  Richard S. Johannes,et al.  Using the ADAP Learning Algorithm to Forecast the Onset of Diabetes Mellitus , 1988 .

[62]  James C. Bean,et al.  Genetic Algorithms and Random Keys for Sequencing and Optimization , 1994, INFORMS J. Comput..

[63]  Hiroshi Motoda,et al.  Feature Extraction, Construction and Selection: A Data Mining Perspective , 1998 .

[64]  Foster J. Provost,et al.  Active Feature-Value Acquisition , 2009, Manag. Sci..

[65]  Matthew M. Huntbach,et al.  The Art in Artificial Intelligence , 1999 .

[66]  Robert A. Greenes,et al.  Research Paper: The GuideLine Interchange Format: A Model for Representing Guidelines , 1998, J. Am. Medical Informatics Assoc..

[67]  Pedro M. Domingos.  MetaCost: a general method for making classifiers cost-sensitive , 1999, KDD '99.

[68]  Glenis Moore,et al.  The art of artificial intelligence , 1987 .

[69]  Thomas R. Miller,et al.  What would be the effect of referral to high-volume hospitals in a largely rural state? , 2004, The Journal of rural health : official journal of the American Rural Health Association and the National Rural Health Care Association.

[70]  Claire Cardie,et al.  Examining Locally Varying Weights for Nearest Neighbor Algorithms , 1997, ICCBR.

[71]  Ann M Coulston,et al.  The challenge to customize. , 2003, Journal of the American Dietetic Association.

[72]  Andrew W. Moore,et al.  Locally Weighted Learning , 1997, Artificial Intelligence Review.

[73]  Justin B Dimick,et al.  Regional availability of high-volume hospitals for major surgery. , 2004, Health affairs.

[74]  Patricia Wright,et al.  Helping people assess the health risks from lifestyle choices: Comparing a computer decision aid with customized printed alternative , 2004, Communication & medicine.

[75]  S. G. Axline,et al.  Computer-based consultations in clinical therapeutics: explanation and rule acquisition capabilities of the MYCIN system. , 1975, Computers and biomedical research, an international journal.

[76]  Chih-Lin Chi,et al.  The optimal diagnostic decision sequence. , 2008, AMIA ... Annual Symposium proceedings. AMIA Symposium.

[77]  Qiang Yang,et al.  Test strategies for cost-sensitive decision trees , 2006, IEEE Transactions on Knowledge and Data Engineering.

[78]  Paul S. Bradley,et al.  Feature Selection via Mathematical Programming , 1997, INFORMS J. Comput..

[79]  Inwig,et al.  Designing studies to ensure that estimates of test accuracy are transferable , 2002, BMJ : British Medical Journal.

[80]  J. Birkmeyer,et al.  Hospital volume and surgical mortality in the United States. , 2002, The New England journal of medicine.

[81]  C. Steiner,et al.  Comorbidity measures for use with administrative data. , 1998, Medical care.

[82]  Stephen E. Fienberg,et al.  The Comparison and Evaluation of Forecasters. , 1983 .

[83]  J. Birkmeyer,et al.  Regionalization of high-risk surgery and implications for patient travel times. , 2003, JAMA.

[84]  T. Osler,et al.  Is the hospital volume-mortality relationship in coronary artery bypass surgery the same for low-risk versus high-risk patients? , 2003, The Annals of thoracic surgery.

[85]  Chih-Lin Chi,et al.  A data mining technique for risk-stratification diagnosis. , 2007, AMIA ... Annual Symposium proceedings. AMIA Symposium.

[86]  Peter D. Turney.  Types of Cost in Inductive Concept Learning , 2002, ArXiv.

[87]  Yoshiteru Ishida,et al.  Using Global Properties for Qualitative Reasoning: A Qualitative System Theory , 1989, IJCAI.

[88]  Margaret H. Dunham,et al.  Data Mining: Introductory and Advanced Topics , 2002 .

[89]  Qiang Yang,et al.  Test-cost sensitive naive Bayes classification , 2004, Fourth IEEE International Conference on Data Mining (ICDM'04).

[90]  Qiang Yang,et al.  Decision trees with minimal costs , 2004, ICML.

[91]  David W. Aha,et al.  Feature Weighting for Lazy Learning Algorithms , 1998 .

[92]  George Hripcsak,et al.  Issues and Structures for Sharing Medical Knowledge Among Decision-Making Systems: The 1989 Arden Homestead Retreat , 1989 .

[93]  Tim Menzies.  Knowledge Elicitation: the State of the Art , 2000 .

[94]  Arie Hasman,et al.  Approaches for creating computer-interpretable guidelines that facilitate decision support , 2004, Artif. Intell. Medicine.

[95]  Sang-Chan Park,et al.  MBNR: Case-Based Reasoning with Local Feature Weighting by Neural Network , 2004, Applied Intelligence.

[96]  G. Chapman,et al.  [Medical decision making]. , 1976, Lakartidningen.

[97]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.