Compguide: Acquisition and editing of computer-interpretable guidelines

The formalization of Clinical Practice Guidelines (CPGs) as Computer-Interpretable Guidelines (CIGs) has the potential to positively influence the behaviour of health practitioners by being available at the point and time of care. Existing tools for acquiring and editing CIGs for automatic interpretation present limitations in their ease of use and the support they offer to a CIG encoder. Besides characterizing these limitations and identifying improvements to include in future tools, this work describes the CompGuide Editor, a Protégé tool for the management of CIGs that guides a user throughout the several steps of CIG encoding, without requiring the user to have programming knowledge, and through the use of interfaces that are simple and intuitive.FCT - Fuel Cell Technologies Program (SFRH/BD/85291/2012)info:eu-repo/semantics/publishedVersio

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

Conceptual graph-based knowledge representation for supporting reasoning in African traditional medicine

Although African patients use both conventional or modern and traditional healthcare simultaneously, it has been proven that 80% of people rely on African traditional medicine (ATM). ATM includes medical activities stemming from practices, customs and traditions which were integral to the distinctive African cultures. It is based mainly on the oral transfer of knowledge, with the risk of losing critical knowledge. Moreover, practices differ according to the regions and the availability of medicinal plants. Therefore, it is necessary to compile tacit, disseminated and complex knowledge from various Tradi-Practitioners (TP) in order to determine interesting patterns for treating a given disease. Knowledge engineering methods for traditional medicine are useful to model suitably complex information needs, formalize knowledge of domain experts and highlight the effective practices for their integration to conventional medicine. The work described in this paper presents an approach which addresses two issues. First it aims at proposing a formal representation model of ATM knowledge and practices to facilitate their sharing and reusing. Then, it aims at providing a visual reasoning mechanism for selecting best available procedures and medicinal plants to treat diseases. The approach is based on the use of the Delphi method for capturing knowledge from various experts which necessitate reaching a consensus. Conceptual graph formalism is used to model ATM knowledge with visual reasoning capabilities and processes. The nested conceptual graphs are used to visually express the semantic meaning of Computational Tree Logic (CTL) constructs that are useful for formal specification of temporal properties of ATM domain knowledge. Our approach presents the advantage of mitigating knowledge loss with conceptual development assistance to improve the quality of ATM care (medical diagnosis and therapeutics), but also patient safety (drug monitoring)

Intelligence-based medicine

Despite seven hundred thousand new medical references last year, the relationship between a given set of medical features and specific pathophysiology, treatment, and criteria of improvement is often weak. Moreover, the generalization of evidences obtained in specific settings may lead to under-treat or to over-treat a significant proportion of patients. We expose an application of the cybernetic loop, based on traditional medical steps: nosology, semeiology, pathophysiology, therapy and on the four transitions between these steps. This approach leads to formulate eight basic questions evaluating the steps in terms of reproducibility and the transitions in terms of predictivity. We detail two practical applications: 1) the evaluation of a medical decision (implantation of an internal cardioverter-defibrillator) and 2) the evaluation of a specific study (EPHESUS). Using this loop allows to determine clearly when evidence is lacking and/or to which extend an evidence really increases the medical knowledge or just creates a market

Towards a New Science of a Clinical Data Intelligence

In this paper we define Clinical Data Intelligence as the analysis of data generated in the clinical routine with the goal of improving patient care. We define a science of a Clinical Data Intelligence as a data analysis that permits the derivation of scientific, i.e., generalizable and reliable results. We argue that a science of a Clinical Data Intelligence is sensible in the context of a Big Data analysis, i.e., with data from many patients and with complete patient information. We discuss that Clinical Data Intelligence requires the joint efforts of knowledge engineering, information extraction (from textual and other unstructured data), and statistics and statistical machine learning. We describe some of our main results as conjectures and relate them to a recently funded research project involving two major German university hospitals.Comment: NIPS 2013 Workshop: Machine Learning for Clinical Data Analysis and Healthcare, 201

Exploiting thesauri knowledge in medical guideline formalization

Abstract. As in software product lifecycle, the effort spent in maintaining medical knowledge in guidelines can be reduced, if modularization, formalization and tracking of domain knowledge are employed across the guideline development phases. We propose to exploit and combine knowledge templates with medical background knowledge from existing thesauri in order to produce reusable building blocks used in guideline development. These templates enable easier guideline formalization, by describing how chunks of medical knowledge can be combined into more complex ones and how they are linked to a textual representation. By linking our ontology used in guideline formalization with existing thesauri, we can use compilations of thesauri knowledge as building blocks for modeling and maintaining the content of a medical guideline. Our paper investigates whether medical knowledge acquired from several medical thesauri can be molded on a guideline pattern, such that it supports building of executable models of guidelines. Keywords: Linguistic and Control Patterns, Guideline Modelling and Formalization. 1. Objective Evidence-based clinical guidelines, representing disseminated state-of-the-art medical practice, undergo frequent changes due to new research results, and require permanent maintenance, similar to that required in a software project. Existing guideline modelin

Rule-based Formalization of Eligibility Criteria for Clinical Trials

Abstract. In this paper, we propose a rule-based formalization of eli-gibility criteria for clinical trials. The rule-based formalization is imple-mented by using the logic programming language Prolog. Compared with existing formalizations such as pattern-based and script-based languages, the rule-based formalization has the advantages of being declarative, ex-pressive, reusable and easy to maintain. Our rule-based formalization is based on a general framework for eligibility criteria containing three types of knowledge: (1) trial-specific knowledge, (2) domain-specific knowledge and (3) common knowledge. This framework enables the reuse of several parts of the formalization of eligibility criteria. We have implemented the proposed rule-based formalization in SemanticCT, a semantically-enabled system for clinical trials, showing the feasibility of using our rule-based formalization of eligibility criteria for supporting patient re-cruitment in clinical trial systems.