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.

Formalization and computation of quality measures based on electronic medical records

Ambiguous definitions of quality measures in natural language impede their automated computability and also the reproducibility, validity, timeliness, traceability, comparability, and interpretability of computed results. Therefore, quality measures should be formalized before their release. We have previously developed and successfully applied a method for clinical indicator formalization (CLIF). The objective of our present study is to test whether CLIF is generalizable--that is, applicable to a large set of heterogeneous measures of different types and from various domains. We formalized the entire set of 159 Dutch quality measures for general practice, which contains structure, process, and outcome measures and covers seven domains. We relied on a web-based tool to facilitate the application of our method. Subsequently, we computed the measures on the basis of a large database of real patient data. Our CLIF method enabled us to fully formalize 100% of the measures. Owing to missing functionality, the accompanying tool could support full formalization of only 86% of the quality measures into Structured Query Language (SQL) queries. The remaining 14% of the measures required manual application of our CLIF method by directly translating the respective criteria into SQL. The results obtained by computing the measures show a strong correlation with results computed independently by two other parties. The CLIF method covers all quality measures after having been extended by an additional step. Our web tool requires further refinement for CLIF to be applied completely automatically. We therefore conclude that CLIF is sufficiently generalizable to be able to formalize the entire set of Dutch quality measures for general practic

Comparison of reasoners for large ontologies in the OWL 2 EL profile

This paper provides a survey to and a comparison of state-of-the-art Semantic Web reasoners that succeed in classifying large ontologies expressed in the tractable OWL 2 EL profile. Reasoners are characterized along several dimensions: The first dimension comprises underlying reasoning characteristics, such as the employed reasoning method and its correctness as well as the expressivity and worst-case computational complexity of its supported language and whether the reasoner supports incremental classification, rules, justifications for inconsistent concepts and ABox reasoning tasks. The second dimension is practical usability: whether the reasoner implements the OWL API and can be used via OWLlink, whether it is available as Protégé plugin, on which platforms it runs, whether its source is open or closed and which license it comes with. The last dimension contains performance indicators that can be evaluated empirically, such as classification, concept satisfiability, subsumption checking and consistency checking performance as well as required heap space and practical correctness, which is determined by comparing the computed concept hierarchies with each other. For the very large ontology SNOMED CT, which is released both in stated and inferred form, we test whether the computed concept hierarchies are correct by comparing them to the inferred form of the official distribution. The reasoners are categorized along the defined characteristics and benchmarked against well-known biomedical ontologies. The main conclusion from this study is that reasoners vary significantly with regard to all included characteristics, and therefore a critical assessment and evaluation of requirements is needed before selecting a reasoner for a real-life application