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.

Original languageEnglish
Pages (from-to)286-291
Number of pages6
JournalComputer Methods and Programs in Biomedicine
Volume104
Issue number2
DOIs
Publication statusPublished - Nov 2011

Keywords

  • Bayes formulation
  • Clinical decision support system
  • Knowledge base

ASJC Scopus subject areas

  • Computer Science Applications
  • Software
  • Health Informatics

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