Using Inductive Rules in Medical Case-Based Reasoning System

Multiple disorders are a daily problem in medical diagnosis and treatment, while most expert systems make an implicit assumption that only single disorder occurs in a single patient. In our paper, we show the need for performing multiple disorders diagnosis, and investigate a way of using inductive rules in our Case-based Reasoning System for diagnosing multiple disorder cases. We applied our approach to two medical casebases taken from real world applications demonstrating the promise of the research. The method also has the potential to be applied to other multiple fault domains, e.g. car failure diagnosis.

[1]  Barry Smyth,et al.  Advances in Case-Based Reasoning , 1996, Lecture Notes in Computer Science.

[2]  Rainer Schmidt,et al.  CBR in Medicine , 1998, Case-Based Reasoning Technology.

[3]  H. Hamilton,et al.  The Nature and Characteristics of Psychiatric Comorbidity in Incarcerated Adolescents , 1998, Canadian journal of psychiatry. Revue canadienne de psychiatrie.

[4]  Edward H. Shortliffe,et al.  Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project (The Addison-Wesley series in artificial intelligence) , 1984 .

[5]  Yeona Jang,et al.  HYDI: a hybrid system with feedback for diagnosing multiple disorders , 1993 .

[6]  Hanna Wasyluk,et al.  Extension of the HEPAR II Model to Multiple-Disorder Diagnosis , 2000, Intelligent Information Systems.

[7]  H. E. Pople,et al.  Internist-I, an Experimental Computer-Based Diagnostic Consultant for General Internal Medicine , 1982 .

[8]  Frank Puppe,et al.  A Diagnostic Expert System for Structured Reports, Quality Assessment, and Training of Residents in Sonography , 2004, Medizinische Klinik.

[9]  Bruce G. Buchanan,et al.  The MYCIN Experiments of the Stanford Heuristic Programming Project , 1985 .

[10]  Lucila Ohno-Machado,et al.  A genetic algorithm approach to multi-disorder diagnosis , 2000, Artif. Intell. Medicine.

[11]  Tony R. Martinez,et al.  Improved Heterogeneous Distance Functions , 1996, J. Artif. Intell. Res..

[12]  J. Reggia,et al.  Abductive Inference Models for Diagnostic Problem-Solving , 1990, Symbolic Computation.

[13]  Frank Puppe,et al.  Inductive Learning for Case-Based Diagnosis with Multiple Faults , 2002, ECCBR.

[14]  Upendra Dave,et al.  Probabilistic Reasoning and Bayesian Belief Networks , 1996 .

[15]  Raymond J. Mooney,et al.  Inductive Learning For Abductive Diagnosis , 1994, AAAI.

[16]  Frank Puppe,et al.  Evaluation of two Strategies for Case-Based Diagnosis handling Multiple Faults , 2003, Wissensmanagement.

[17]  Frank Puppe,et al.  Quality Measures for Semi-Automatic Learning of Simple Diagnostic Rule Bases , 2004 .

[18]  Frank Puppe,et al.  Quality Measures and Semi-automatic Mining of Diagnostic Rule Bases , 2004, INAP/WLP.