Fuzzy Logic in Clinical Practice Decision Support Systems

Computerized clinical guidelines can provide significant benefits to health outcomes and costs, however, their effective implementation presents significant problems. Vagueness and ambiguity inherent in natural (textual) clinical guidelines is not readily amenable to formulating automated alerts or advice. Fuzzy logic allows us to formalize the treatment of vagueness in a decision support architecture. This paper discusses sources of fuzziness in clinical practice guidelines. We consider how fuzzy logic can be applied and give a set of heuristics for the clinical guideline knowledge engineer for addressing uncertainty in practice guidelines. We describe the specific applicability of fuzzy logic to the decision support behavior of Care Plan On-Line, an intranet-based chronic care planning system for General Practitioners

Development and implementation of clinical guidelines : an artificial intelligence perspective

Clinical practice guidelines in paper format are still the preferred form of delivery of medical knowledge and recommendations to healthcare professionals. Their current support and development process have well identified limitations to which the healthcare community has been continuously searching solutions. Artificial intelligence may create the conditions and provide the tools to address many, if not all, of these limitations.. This paper presents a comprehensive and up to date review of computer-interpretable guideline approaches, namely Arden Syntax, GLIF, PROforma, Asbru, GLARE and SAGE. It also provides an assessment of how well these approaches respond to the challenges posed by paper-based guidelines and addresses topics of Artificial intelligence that could provide a solution to the shortcomings of clinical guidelines. Among the topics addressed by this paper are expert systems, case-based reasoning, medical ontologies and reasoning under uncertainty, with a special focus on methodologies for assessing quality of information when managing incomplete information. Finally, an analysis is made of the fundamental requirements of a guideline model and the importance that standard terminologies and models for clinical data have in the semantic and syntactic interoperability between a guideline execution engine and the software tools used in clinical settings. It is also proposed a line of research that includes the development of an ontology for clinical practice guidelines and a decision model for a guideline-based expert system that manages non-compliance with clinical guidelines and uncertainty.This work is funded by national funds through the FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within project PEst-OE/EEI/UI0752/2011"

Writing clinical practice guidelines in controlled natural language

Clinicians could benefit from decision support systems incorporating the knowledge contained in clinical practice guidelines. However, the unstructured form of these guidelines makes them unsuitable for formal representation. To address this challenge we translated a complete set of pediatric guideline recommendations into Attempto Controlled English (ACE). One experienced pediatrician, one physician and a knowledge engineer assessed that a suitably extended version of ACE can accurately and naturally represent the clinical concepts and the proposed actions of the guidelines. Currently, we are developing a systematic and replicable approach to authoring guideline recommendations in ACE

Representation of clinical practice guideline components in OWL

Serie : Advances in intelligent systems and computing, ISSN 2194-5357, vol. 221The main purpose to attain with the advent of clinical decision sup-port systems is either to improve the quality of patient care or to reduce the oc-currence of clinical malpractice, such as medical errors and defensive medicine. It is therefore necessary a machine-readable support to integrate the recommen-dations of Clinical Practice Guidelines in such systems. CompGuide is a Com-puter-Interpretable Guideline model developed under Ontology Web Language that offers support for administrative information concerning a guideline, work-flow procedures, and the definition of clinical and temporal constraints. When compared to other models of the same type, besides having a comprehensive task network model, it introduces new temporal representations and the possi-bility of reusing pre-existing knowledge and integrating it in a guideline.(undefined

The Use of SNOMED CT for Representing Concepts Used in Preoperative Guidelines

The use of guidelines to improve quality of care depends on presenting them in a standard machine-interpretable form and using common terms in guidelines as well as in patient records. In this study, the use of SNOMED CT for representing concepts used in preoperative assessment guidelines was evaluated. Terms used in six of these guidelines were mapped to this terminology. Mappings were presented based on three scores: no match, partial match, and complete match. As eleven of the terms were repeatedly used in different guidelines, we analyzed the results based on “token” and “type” coverage. Of 133 extracted terms from guidelines, 107 terms should be covered by SNOMED CT of which 87% was completely represented by this terminology. Our study showed that SNOMED CT content should be extended before preoperative assessment guidelines can be completely automated

Representing Clinical Practice Guidelines with Declarative Programming

Clinical practice guidelines (CPG) describe recommended actions for diagnosis and treatment of various patient conditions. These guidelines are most often presented in a narrative form, requiring time from a physician’s already busy schedule and careful study, considering the guidelines may contain poor organization and lack clear, descriptive evidence for recommendations. Too often, this means that the information provided by guideline authors is ignored in clinical practice. Over the past few decades, much effort has gone into translating clinical practice guidelines into clinical-decision support systems to make guideline information more accessible and improve physician-patient interactions. To contribute to physicians’ accessibility of guideline information, we attempted to develop a methodology to represent clinical practice guidelines as computer-implementable guidelines (CIG) with declarative programming. There are many obstacles in this implementation, such as underspecified conditions for recommendations, lack of knowledge and consensus in several areas, and heavy use of ambiguous terms. We report the measures we took to counter each of these issues, which allowed us to ultimately produce several models that could serve as computer-implementable guidelines for use in clinical practice. Through close analysis of our guideline implementation process, we hope to recognize patterns of knowledge and issues in the medical domain that will ease future clinical practice guideline implementation

The role of ontologies and decision frameworks in computer-interpretable guideline execution

Computer-Interpretable Guidelines (CIGs) are machine readable representations of Clinical Practice Guidelines (CPGs) that serve as the knowledge base in many knowledge-based systems oriented towards clinical decision support. Herein we disclose a comprehensive CIG representation model based on Web Ontology Language (OWL) along with its main components. Additionally, we present results revealing the expressiveness of the model regarding a selected set of CPGs. The CIG model then serves as the basis of an architecture for an execution system that is able to manage incomplete information regarding the state of a patient through Speculative Computation. The architecture allows for the generation of clinical scenarios when there is missing information for clinical parameters.FCT - Fundação para a Ciência e a Tecnologia (SFRH/BD/85291/ 2012)info:eu-repo/semantics/publishedVersio

Facilitating pre-operative assessment guidelines representation using SNOMED CT

Objective: To investigate whether SNOMED CT covers the terms used in pre-operative assessment guidelines, and if necessary, how the measured content coverage can be improved. Pre-operative assessment guidelines were retrieved from the websites of (inter)national anesthesiarelated societies. The recommendations in the guidelines were rewritten to ‘‘IF condition THEN action” statements to facilitate data extraction. Terms were extracted from the IF–THEN statements and mapped to SNOMED CT. Content coverage was measured by using three scores: no match, partial match and complete match. Non-covered concepts were evaluated against the SNOMED CT editorial documentation. Results: From 6 guidelines, 133 terms were extracted, of which 71% (n = 94) completely matched with SNOMED CT concepts. Disregarding the vague concepts in the included guidelines SNOMED CT’s content coverage was 89%. Of the 39 non-completely covered concepts, 69% violated at least one of SNOMED CT’s editorial principles or rules. These concepts were categorized based on four categories: non-reproducibility, classification-derived phrases, numeric ranges, and procedures categorized by complexity. Conclusion: Guidelines include vague terms that cannot be well supported by terminological systems thereby hampering guideline-based decision support systems. This vagueness reduces the content coverage of SNOMED CT in representing concepts used in the pre-operative assessment guidelines. Formalization of the guidelines using SNOMED CT is feasible but to optimize this, first the vagueness of some guideline concepts should be resolved and a few currently missing but relevant concepts should be added to SNOMED CT