Literature DB >> 20726735

ESP: an expert system for poisoning diagnosis and management.

Riza Theresa B Batista-Navarro1, Diana A Bandojo, M A Jaymee Krisette Gatapia, Reggie Nicolo C Santos, Alvin B Marcelo, Lynn Crisanta R Panganiban, Prospero C Naval.   

Abstract

We describe a clinical decision support system (CDSS) designed to provide timely information germane to poisoning. The CDSS aids medical decision making through recommendations to clinicians for immediate evaluation. The system is implemented as a rule-based expert system with two major components: the knowledge base and the inference engine. The knowledge base serves as the database which contains relevant poisoning information and rules that are used by the inference engine in making decisions. This expert system accepts signs and symptoms observed from a patient as input and presents a list of possible poisoning types with the corresponding management procedures which may be considered in making the final diagnosis. A knowledge acquisition tool (KAT) that allows toxicological experts to update the knowledge base was also developed. This article describes the architecture of the fully featured system, the design of the CDSS and the KAT as web applications, the utilisation of the inferencing mechanism of C Language Integrated Production System (CLIPS), which is an expert system shell that helps the system in decision-making tasks, the methods used as well as problems encountered. We also present the results obtained after testing the system and propose some recommendations for future work.

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Year:  2010        PMID: 20726735     DOI: 10.3109/17538157.2010.490624

Source DB:  PubMed          Journal:  Inform Health Soc Care        ISSN: 1753-8157            Impact factor:   2.439


  1 in total

1.  Clinical Decision Support Systems: Effective Solution for Diagnosis, Treatment, and Management of Patients Affected by Poisoning.

Authors:  Zahra Mahmoudvand; Shahin Shadnia; Sharareh Rostam Niakan; Marjan Ghazisaeedi
Journal:  Iran J Public Health       Date:  2019-07       Impact factor: 1.429

  1 in total

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