From Formal to Textual

UIDB/03213/2020 UIDP/03213/2020This paper aims to show how Terminology can help foster interoperability and more effective knowledge representation, organisation and sharing in the biomedical field, and on the other hand, support specialised communication among various stakeholders. SNOMED CT will be used to illustrate this, with the focus being on formal and textual (or natural language) definitions – the latter currently underrepresented in this resource - and on how a doubledimensional terminological approach can benefit textual definition drafting, thereby assisting the work carried out by SNOMED CT national translation teams.publishersversionpublishe

Retrospective checking of compliance with practice guidelines for acute stroke care: a novel experiment using openEHR’s Guideline Definition Language

BACKGROUND: Providing scalable clinical decision support (CDS) across institutions that use different electronic health record (EHR) systems has been a challenge for medical informatics researchers. The lack of commonly shared EHR models and terminology bindings has been recognised as a major barrier to sharing CDS content among different organisations. The openEHR Guideline Definition Language (GDL) expresses CDS content based on openEHR archetypes and can support any clinical terminologies or natural languages. Our aim was to explore in an experimental setting the practicability of GDL and its underlying archetype formalism. A further aim was to report on the artefacts produced by this new technological approach in this particular experiment. We modelled and automatically executed compliance checking rules from clinical practice guidelines for acute stroke care. METHODS: We extracted rules from the European clinical practice guidelines as well as from treatment contraindications for acute stroke care and represented them using GDL. Then we executed the rules retrospectively on 49 mock patient cases to check the cases’ compliance with the guidelines, and manually validated the execution results. We used openEHR archetypes, GDL rules, the openEHR reference information model, reference terminologies and the Data Archetype Definition Language. We utilised the open-sourced GDL Editor for authoring GDL rules, the international archetype repository for reusing archetypes, the open-sourced Ocean Archetype Editor for authoring or modifying archetypes and the CDS Workbench for executing GDL rules on patient data. RESULTS: We successfully represented clinical rules about 14 out of 19 contraindications for thrombolysis and other aspects of acute stroke care with 80 GDL rules. These rules are based on 14 reused international archetypes (one of which was modified), 2 newly created archetypes and 51 terminology bindings (to three terminologies). Our manual compliance checks for 49 mock patients were a complete match versus the automated compliance results. CONCLUSIONS: Shareable guideline knowledge for use in automated retrospective checking of guideline compliance may be achievable using GDL. Whether the same GDL rules can be used for at-the-point-of-care CDS remains unknown

Proceedings

Proceedings of the Workshop CHAT 2011: Creation, Harmonization and Application of Terminology Resources. Editors: Tatiana Gornostay and Andrejs Vasiļjevs. NEALT Proceedings Series, Vol. 12 (2011). © 2011 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/16956

Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: NL

Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: N

Towards natural language question generation for the validation of ontologies and mappings

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)The increasing number of open-access ontologies and their key role in several applications such as decision-support systems highlight the importance of their validation. Human expertise is crucial for the validation of ontologies from a domain point-of-view. However, the growing number of ontologies and their fast evolution over time make manual validation challenging. Methods: We propose a novel semi-automatic approach based on the generation of natural language (NL) questions to support the validation of ontologies and their evolution. The proposed approach includes the automatic generation, factorization and ordering of NL questions from medical ontologies. The final validation and correction is performed by submitting these questions to domain experts and automatically analyzing their feedback. We also propose a second approach for the validation of mappings impacted by ontology changes. The method exploits the context of the changes to propose correction alternatives presented as Multiple Choice Questions. Results: This research provides a question optimization strategy to maximize the validation of ontology entities with a reduced number of questions. We evaluate our approach for the validation of three medical ontologies. We also evaluate the feasibility and efficiency of our mappings validation approach in the context of ontology evolution. These experiments are performed with different versions of SNOMED-CT and ICD9. Conclusions: The obtained experimental results suggest the feasibility and adequacy of our approach to support the validation of interconnected and evolving ontologies. Results also suggest that taking into account RDFS and OWL entailment helps reducing the number of questions and validation time. The application of our approach to validate mapping evolution also shows the difficulty of adapting mapping evolution over time and highlights the importance of semi-automatic validation.The increasing number of open-access ontologies and their key role in several applications such as decision-support systems highlight the importance of their validation. Human expertise is crucial for the validation of ontologies from a domain point-of-vi7115FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)2014/14890-

Implementation of workflow engine technology to deliver basic clinical decision support functionality

BACKGROUND: Workflow engine technology represents a new class of software with the ability to graphically model step-based knowledge. We present application of this novel technology to the domain of clinical decision support. Successful implementation of decision support within an electronic health record (EHR) remains an unsolved research challenge. Previous research efforts were mostly based on healthcare-specific representation standards and execution engines and did not reach wide adoption. We focus on two challenges in decision support systems: the ability to test decision logic on retrospective data prior prospective deployment and the challenge of user-friendly representation of clinical logic. RESULTS: We present our implementation of a workflow engine technology that addresses the two above-described challenges in delivering clinical decision support. Our system is based on a cross-industry standard of XML (extensible markup language) process definition language (XPDL). The core components of the system are a workflow editor for modeling clinical scenarios and a workflow engine for execution of those scenarios. We demonstrate, with an open-source and publicly available workflow suite, that clinical decision support logic can be executed on retrospective data. The same flowchart-based representation can also function in a prospective mode where the system can be integrated with an EHR system and respond to real-time clinical events. We limit the scope of our implementation to decision support content generation (which can be EHR system vendor independent). We do not focus on supporting complex decision support content delivery mechanisms due to lack of standardization of EHR systems in this area. We present results of our evaluation of the flowchart-based graphical notation as well as architectural evaluation of our implementation using an established evaluation framework for clinical decision support architecture. CONCLUSIONS: We describe an implementation of a free workflow technology software suite (available at http://code.google.com/p/healthflow) and its application in the domain of clinical decision support. Our implementation seamlessly supports clinical logic testing on retrospective data and offers a user-friendly knowledge representation paradigm. With the presented software implementation, we demonstrate that workflow engine technology can provide a decision support platform which evaluates well against an established clinical decision support architecture evaluation framework. Due to cross-industry usage of workflow engine technology, we can expect significant future functionality enhancements that will further improve the technology's capacity to serve as a clinical decision support platform

UK phenomics platform for developing and validating electronic health record phenotypes: CALIBER

Objective: Electronic health records (EHRs) are a rich source of information on human diseases, but the information is variably structured, fragmented, curated using different coding systems, and collected for purposes other than medical research. We describe an approach for developing, validating, and sharing reproducible phenotypes from national structured EHR in the United Kingdom with applications for translational research. Materials and Methods: We implemented a rule-based phenotyping framework, with up to 6 approaches of validation. We applied our framework to a sample of 15 million individuals in a national EHR data source (population-based primary care, all ages) linked to hospitalization and death records in England. Data comprised continuous measurements (for example, blood pressure; medication information; coded diagnoses, symptoms, procedures, and referrals), recorded using 5 controlled clinical terminologies: (1) read (primary care, subset of SNOMED-CT [Systematized Nomenclature of Medicine Clinical Terms]), (2) International Classification of Diseases–Ninth Revision and Tenth Revision (secondary care diagnoses and cause of mortality), (3) Office of Population Censuses and Surveys Classification of Surgical Operations and Procedures, Fourth Revision (hospital surgical procedures), and (4) DMþD prescription codes. Results: Using the CALIBER phenotyping framework, we created algorithms for 51 diseases, syndromes, biomarkers, and lifestyle risk factors and provide up to 6 validation approaches. The EHR phenotypes are curated in the open-access CALIBER Portal (https://www.caliberresearch.org/portal) and have been used by 40 national and international research groups in 60 peer-reviewed publications. Conclusions: We describe a UK EHR phenomics approach within the CALIBER EHR data platform with initial evidence of validity and use, as an important step toward international use of UK EHR data for health research

Doctor of Philosophy

dissertationDisease-specific ontologies, designed to structure and represent the medical knowledge about disease etiology, diagnosis, treatment, and prognosis, are essential for many advanced applications, such as predictive modeling, cohort identification, and clinical decision support. However, manually building disease-specific ontologies is very labor-intensive, especially in the process of knowledge acquisition. On the other hand, medical knowledge has been documented in a variety of biomedical knowledge resources, such as textbook, clinical guidelines, research articles, and clinical data repositories, which offers a great opportunity for an automated knowledge acquisition. In this dissertation, we aim to facilitate the large-scale development of disease-specific ontologies through automated extraction of disease-specific vocabularies from existing biomedical knowledge resources. Three separate studies presented in this dissertation explored both manual and automated vocabulary extraction. The first study addresses the question of whether disease-specific reference vocabularies derived from manual concept acquisition can achieve a near-saturated coverage (or near the greatest possible amount of disease-pertinent concepts) by using a small number of literature sources. Using a general-purpose, manual acquisition approach we developed, this study concludes that a small number of expert-curated biomedical literature resources can prove sufficient for acquiring near-saturated disease-specific vocabularies. The second and third studies introduce automated techniques for extracting disease-specific vocabularies from both MEDLINE citations (title and abstract) and a clinical data repository. In the second study, we developed and assessed a pipeline-based system which extracts disease-specific treatments from PubMed citations. The system has achieved a mean precision of 0.8 for the top 100 extracted treatment concepts. In the third study, we applied classification models to reduce irrelevant disease-concepts associations extracted from MEDLINE citations and electronic medical records. This study suggested the combination of measures of relevance from disparate sources to improve the identification of true-relevant concepts through classification and also demonstrated the generalizability of the studied classification model to new diseases. With the studies, we concluded that existing biomedical knowledge resources are valuable sources for extracting disease-concept associations, from which classification based on statistical measures of relevance could assist a semi-automated generation of disease-specific vocabularies