An integration profile of rule engines for clinical decision support systems

Rule engine has become an indispensable component for many clinical decision support systems. Due to the complexity and heterogeneity of clinical data, one big challenge for rule-based clinical applications is mapping the data from various data sources to rule variables. This paper proposed a rule engine integration profile that uses a shared ontology between the rule engine and external systems to facilitate data acquisition. Based on the integration profile, a diagnostic clinical decision support application was successfully deployed in a Chinese hospital.

[1]  Sahana Murthy,et al.  Modeling and Executing Electronic Health Records Driven Phenotyping Algorithms using the NQF Quality Data Model and JBoss® Drools Engine , 2012, AMIA.

[2]  S. K. Sarma,et al.  JESS Based Expert System Architecture for Diagnosis of Rice Plant Diseases: Design and Prototype Development , 2013, 2013 4th International Conference on Intelligent Systems, Modelling and Simulation.

[3]  Huilong Duan,et al.  Mobilizing clinical decision support to facilitate knowledge translation: A case study in China , 2015, Comput. Biol. Medicine.

[4]  Mirjam K. Rommers,et al.  Evaluation of rule effectiveness and positive predictive value of clinical rules in a Dutch clinical decision support system in daily hospital pharmacy practice , 2013, Artif. Intell. Medicine.

[5]  Robert M. Wygant.  CLIPS - a powerful development and delivery expert system tool , 1989 .

[6]  Douglas A. Talbert,et al.  Experience using a programmable rules engine to implement a complex medical protocol during order entry , 2000, AMIA.

[7]  Arie Hasman,et al.  Development of a guideline-based decision support system with explanation facilities for outpatient therapy , 2008, Comput. Methods Programs Biomed..

[8]  Hans-Ulrich Prokosch,et al.  Accessing complex patient data from Arden Syntax Medical Logic Modules , 2018, Artif. Intell. Medicine.

[9]  Lien Fu Lai,et al.  Extending FuzzyCLIPS for parallelizing data-dependent fuzzy expert systems , 2010, The Journal of Supercomputing.

[10]  A Hasman,et al.  GASTON: an architecture for the acquisition and execution of clinical guideline-application tasks. , 2000, Medical informatics and the Internet in medicine.