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

Ying Jui Chang, Min Li Yeh, Chyou Shen Lee, Chien Yeh Hsu, Yu Chuan Jack Li, Wen Ta Chiu

研究成果: 雜誌貢獻文章

7 引文 斯高帕斯(Scopus)

摘要

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.

原文英語
頁(從 - 到)286-291
頁數6
期刊Computer Methods and Programs in Biomedicine
104
發行號2
DOIs
出版狀態已發佈 - 十一月 2011

ASJC Scopus subject areas

  • Computer Science Applications
  • Software
  • Health Informatics

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