Cross-domain probabilistic inference in a clinical decision support system: Examples for dermatology and rheumatology

  • Authors:

  • Ying-Jui Chang;Min-Li Yeh;Chyou-Shen Lee;Chien-Yeh Hsu;Yu-Chuan (Jack) Li;Wen-Ta Chiu

  • Affiliations:

  • Graduate Institute of Medical Science, College of Medicine, Taipei Medical University, Taiwan and Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Me ...;Graduate Institute of Medical Science, College of Medicine, Taipei Medical University, Taiwan and Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Me ...;Department of Internal Medicine, Division of Allergy, Immunology and Rheumatology, Mackay Memorial Hospital, Taiwan and Mackey Medicine, Nursing and Management College, Taiwan;Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taiwan and Center of Excellence for Cancer Research (CECR), Taipei Medical Unive ...;Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taiwan and Department of Dermatology, Taipei Medical University, Wan Fang Hospit ...;School of Medicine, Taipei Medical University, Taiwan and Graduate Institute of Injury Prevention and Control, Taipei Medical University, Taiwan

  • Venue:
  • Computer Methods and Programs in Biomedicine
  • Year:
  • 2011

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Abstract

Introduction: Maintaining a large diagnostic knowledge base (KB) is a demanding task for any person or organization. Future clinical decision support system (CDSS) may rely on multiple, smaller and more focused KBs developed and maintained at different locations that work together seamlessly. A cross-domain inference tool has great clinical import and utility. Methods: We developed a modified multi-membership Bayes formulation to facilitate the cross-domain probabilistic inferencing among KBs with overlapping diseases. Two KBs developed for evaluation were non-infectious generalized blistering diseases (GBD) and autoimmune diseases (AID). After the KBs were finalized, they were evaluated separately for validity. Result: Ten cases from medical journal case reports were used to evaluate this ''cross-domain'' inference across the two KBs. The resultant non-error rate (NER) was 90%, and the average of probabilities assigned to the correct diagnosis (AVP) was 64.8% for cross-domain consultations. Conclusion: A novel formulation is now available to deal with problems occurring in a clinical diagnostic decision support system with multi-domain KBs. The utilization of this formulation will help in the development of more integrated KBs with greater focused knowledge domains.