Representing temporal patterns in computer-interpretable clinical Guidelines

Computer-interpretable Guidelines (CIGs) as machine-readable versions of clinical protocols have to provide appropriate constructs for the representation of different aspects of medical knowledge, namely administrative information, workflows of procedures, clinical constraints and temporal constraints. This work focuses on the latter, by aiming to develop a comprehensive representation of temporal constraints for machine readable formats of clinical protocols and provide a proper execution engine that deals with different time patterns and constraints placed on them. A model for the representation of time is presented for the CompGuide ontology in Ontology Web language (OWL) along with a comparison with the available formalisms in this field.This work is part-funded by ERDF - European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT - Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within project FCOMP-01-0124-FEDER-028980 and project Scope UID/CEC/00319/2013.The work of Tiago Oliveira is supported by a FCT grant with the reference SFRH/BD/85291/2012.info:eu-repo/semantics/publishedVersio

Context-based task ontologies for clinical guidelines

Evidence-based medicine relies on the execution of clinical practice guidelines and protocols. A great deal of of effort has been invested in the development of various tools which automate the representation and execution of the recommendations contained within such guidelines and protocols by creating Computer Interpretable Guideline Models (CIGMs). Context-based task ontologies (CTOs), based on standard terminology systems like UMLS, form one of the core components of such a model. We have created DAML+OIL-based CTOs for the tasks mentioned in the WHO guideline for hypertension management, drawing comparisons also with other related guidelines. The advantages of CTOs include: contextualization of ontologies, providing ontologies tailored to specific aspects of the phenomena of interest, dividing the complexity involved in creating ontologies into different levels, providing a methodology by means of which the task recommendations contained within guidelines can be integrated into the clinical practices of a health care set-up

Computerization of workflows, guidelines and care pathways: a review of implementation challenges for process-oriented health information systems

There is a need to integrate the various theoretical frameworks and formalisms for modeling clinical guidelines, workflows, and pathways, in order to move beyond providing support for individual clinical decisions and toward the provision of process-oriented, patient-centered, health information systems (HIS). In this review, we analyze the challenges in developing process-oriented HIS that formally model guidelines, workflows, and care pathways. A qualitative meta-synthesis was performed on studies published in English between 1995 and 2010 that addressed the modeling process and reported the exposition of a new methodology, model, system implementation, or system architecture. Thematic analysis, principal component analysis (PCA) and data visualisation techniques were used to identify and cluster the underlying implementation ‘challenge’ themes. One hundred and eight relevant studies were selected for review. Twenty-five underlying ‘challenge’ themes were identified. These were clustered into 10 distinct groups, from which a conceptual model of the implementation process was developed. We found that the development of systems supporting individual clinical decisions is evolving toward the implementation of adaptable care pathways on the semantic web, incorporating formal, clinical, and organizational ontologies, and the use of workflow management systems. These architectures now need to be implemented and evaluated on a wider scale within clinical settings

Generating conflict-free treatments for patients with comorbidity using ASP

Conflicts in recommended medical interventions regularly arise when multiple treatments are simultaneously needed for patients with comorbid diseases. An approach that can automatically repair such inconsistencies and generate conflict-free combined treatments is thus a valuable aid for clinicians. In this paper we propose an answer set programming based method that detects and repairs conflicts between treatments. The answer sets of the program directly correspond to proposed treatments, accounting for multiple possible solutions if they exist. We also include the possibility to take preferences based on drug-drug interactions into account while solving inconsistencies. We show in a case study that our method results in more preferred treatments

Recommending treatments for comorbid patients using word-based and phrase-based alignment methods

The problem of finding treatments for patients diagnosed with multiple diseases (i.e.~a comorbidity) is an important research topic in the medical literature. In this paper, we propose a new data driven approach to recommend treatments for these comorbidities using word-based and phrase-based alignment methods. The most popular methods currently rely on combining specific information from individual diseases (e.g.~procedures, tests, etc.), then aim to detect and repair the conflicts that arise in the combined treatments. This proves to be a challenge especially in the cases where the studied comorbidities contain large numbers of diseases. In contrast, our methods rely on training a translation model using previous medical records to find treatments for newly diagnosed comorbidities. We also explore the use of additional criteria in the form of a drug interactions penalty and a treatment popularity score to select the best treatment in the case where multiple valid translations for a single comorbidity are available