A Consistency Checker for verifying the knowledge encoded into clinical DSSs

The formalization and manipulation of complex and not yet assessed rules by clinicians are critical for Decision Support Systems (DSSs) performance in supporting remote monitoring of chronic patients. Sometimes, structural anomalies, such as inconsistency and redundancy, can occur. This work presents a novel system, named Consistency Checker, aimed at verifying the reliability of conditionaction clinical rules in knowledge-based DSSs. This system allows the verification of very complex rules having in their antecedent parts not only simple logical conditions, but also arithmetical expressions. Moreover, the Consistency Checker provides a new classification of the detected anomalies, aimed at fully describing the rule verification results, as well as a suitable knowledge representation formalism to encode condition-action rules in a general way. The system has been designed according to the Service Oriented Architecture and implemented as a Web Service within the CHRONIOUS Project.

[1]  Edward H. Shortliffe,et al.  An Approach to Verifying Completeness and Consistency in a Rule-Based Expert System , 1982, AI Mag..

[2]  Dimitrios I. Fotiadis,et al.  CHRONIOUS: A wearable system for the management of chronic disease patients , 2009, 2009 9th International Conference on Information Technology and Applications in Biomedicine.

[3]  J. Habetha.  The myheart project - Fighting cardiovascular diseases by prevention and early diagnosis , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[4]  Larry J Kricka,et al.  Improving healthcare accessibility through point-of-care technologies. , 2007, Clinical chemistry.

[5]  Ching Y. Suen,et al.  A Review of Expert Systems Evaluation Techniques , 1993 .

[6]  Asuman Dogac,et al.  SAPHIRE: A Multi-Agent System for Remote Healthcare Monitoring through Computerized Clinical Guidelines , 2007 .

[7]  R A Greenes,et al.  Improving Clinical Guidelines with Logic and Decision-table Techniques , 1994, Medical decision making : an international journal of the Society for Medical Decision Making.

[8]  Du Zhang,et al.  A technique for knowledge base verification , 1989, [Proceedings 1989] IEEE International Workshop on Tools for Artificial Intelligence.

[9]  Silvia Miksch,et al.  Knowledge-based verification of clinical guidelines by detection of anomalies , 2001, Artif. Intell. Medicine.

[10]  Keith E. Williamson,et al.  Experiences of using Verification Tools for Maintenance of Rule-Based Systems. , 1993 .

[11]  Gabriel Valiente,et al.  Verification of knowledge base redundancy and subsumption using graph transformations , 1993 .

[12]  Harold J. Steudel,et al.  A Decision-Table-Based Processor for Checking Completeness and Consistency in Rule-Based Expert Systems , 1987, Int. J. Man Mach. Stud..

[13]  Sara Colantonio,et al.  A Decision Support System for Aiding Heart Failure Management , 2009, 2009 Ninth International Conference on Intelligent Systems Design and Applications.

[14]  Kdoqi Disclaimer.  K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. , 2002, American journal of kidney diseases : the official journal of the National Kidney Foundation.

[15]  Richard N. Shiffman,et al.  Model Formulation: Representation of Clinical Practice Guidelines in Conventional and Augmented Decision Tables , 1997, J. Am. Medical Informatics Assoc..

[16]  Walton A. Perkins,et al.  Checking an Expert Systems Knowledge Base for Consistency and Completeness , 1985, IJCAI.

[17]  Ethan M Balk,et al.  K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. , 2002, American journal of kidney diseases : the official journal of the National Kidney Foundation.

[18]  Alan D. Lopez,et al.  Global and regional burden of disease and risk factors, 2001: systematic analysis of population health data , 2006, The Lancet.