Expert System Shells for Rapid Clinical Decision Support Module Development: An ESTA Demonstration of a Simple Rule-Based System for the Diagnosis of Vaginal Discharge.
10.4258/hir.2012.18.4.252
- Author:
Maged N KAMEL BOULOS
1
Author Information
1. Faculty of Health, Education and Society, Plymouth University, Devon, UK. mnkboulos@ieee.org
- Publication Type:Original Article
- Keywords:
Expert Systems;
Knowledge Bases;
Clinical Decision Support Systems;
Computer-Assisted Decision Making;
Software Design
- MeSH:
Archives;
Caregivers;
Decision Making, Computer-Assisted;
Decision Support Systems, Clinical;
Expert Systems;
Humans;
Knowledge Bases;
Peer Review;
Product Packaging;
Software Design;
Students, Nursing;
Tablets;
Uncertainty;
Vaginal Discharge
- From:Healthcare Informatics Research
2012;18(4):252-258
- CountryRepublic of Korea
- Language:English
-
Abstract:
OBJECTIVES: This study demonstrates the feasibility of using expert system shells for rapid clinical decision support module development. METHODS: A readily available expert system shell was used to build a simple rule-based system for the crude diagnosis of vaginal discharge. Pictures and 'canned text explanations' are extensively used throughout the program to enhance its intuitiveness and educational dimension. All the steps involved in developing the system are documented. RESULTS: The system runs under Microsoft Windows and is available as a free download at http://healthcybermap.org/vagdisch.zip (the distribution archive includes both the program's executable and the commented knowledge base source as a text document). The limitations of the demonstration system, such as the lack of provisions for assessing uncertainty or various degrees of severity of a sign or symptom, are discussed in detail. Ways of improving the system, such as porting it to the Web and packaging it as an app for smartphones and tablets, are also presented. CONCLUSIONS: An easy-to-use expert system shell enables clinicians to rapidly become their own 'knowledge engineers' and develop concise evidence-based decision support modules of simple to moderate complexity, targeting clinical practitioners, medical and nursing students, as well as patients, their lay carers and the general public (where appropriate). In the spirit of the social Web, it is hoped that an online repository can be created to peer review, share and re-use knowledge base modules covering various clinical problems and algorithms, as a service to the clinical community.
