The environment ontology in 2016: bridging domains with increased scope, semantic density, and interoperation.

J Biomed Semantics. 2016;7(1):57

Authors: Buttigieg PL, Pafilis E, Lewis SE, Schildhauer MP, Walls RL, Mungall CJ

Abstract
BACKGROUND: The Environment Ontology (ENVO; http://www.environmentontology.org/ ), first described in 2013, is a resource and research target for the semantically controlled description of environmental entities. The ontology's initial aim was the representation of the biomes, environmental features, and environmental materials pertinent to genomic and microbiome-related investigations. However, the need for environmental semantics is common to a multitude of fields, and ENVO's use has steadily grown since its initial description. We have thus expanded, enhanced, and generalised the ontology to support its increasingly diverse applications.
METHODS: We have updated our development suite to promote expressivity, consistency, and speed: we now develop ENVO in the Web Ontology Language (OWL) and employ templating methods to accelerate class creation. We have also taken steps to better align ENVO with the Open Biological and Biomedical Ontologies (OBO) Foundry principles and interoperate with existing OBO ontologies. Further, we applied text-mining approaches to extract habitat information from the Encyclopedia of Life and automatically create experimental habitat classes within ENVO.
RESULTS: Relative to its state in 2013, ENVO's content, scope, and implementation have been enhanced and much of its existing content revised for improved semantic representation. ENVO now offers representations of habitats, environmental processes, anthropogenic environments, and entities relevant to environmental health initiatives and the global Sustainable Development Agenda for 2030. Several branches of ENVO have been used to incubate and seed new ontologies in previously unrepresented domains such as food and agronomy. The current release version of the ontology, in OWL format, is available at http://purl.obolibrary.org/obo/envo.owl .
CONCLUSIONS: ENVO has been shaped into an ontology which bridges multiple domains including biomedicine, natural and anthropogenic ecology, 'omics, and socioeconomic development. Through continued interactions with our users and partners, particularly those performing data archiving and sythesis, we anticipate that ENVO's growth will accelerate in 2017. As always, we invite further contributions and collaboration to advance the semantic representation of the environment, ranging from geographic features and environmental materials, across habitats and ecosystems, to everyday objects in household settings.

PMID: 27664130 [PubMed - as supplied by publisher]

Impact of Predicting Health Care Utilization Via Web Search Behavior: A Data-Driven Analysis.

J Med Internet Res. 2016;18(9):e251

Authors: Agarwal V, Zhang L, Zhu J, Fang S, Cheng T, Hong C, Shah NH

Abstract
BACKGROUND: By recent estimates, the steady rise in health care costs has deprived more than 45 million Americans of health care services and has encouraged health care providers to better understand the key drivers of health care utilization from a population health management perspective. Prior studies suggest the feasibility of mining population-level patterns of health care resource utilization from observational analysis of Internet search logs; however, the utility of the endeavor to the various stakeholders in a health ecosystem remains unclear.
OBJECTIVE: The aim was to carry out a closed-loop evaluation of the utility of health care use predictions using the conversion rates of advertisements that were displayed to the predicted future utilizers as a surrogate. The statistical models to predict the probability of user's future visit to a medical facility were built using effective predictors of health care resource utilization, extracted from a deidentified dataset of geotagged mobile Internet search logs representing searches made by users of the Baidu search engine between March 2015 and May 2015.
METHODS: We inferred presence within the geofence of a medical facility from location and duration information from users' search logs and putatively assigned medical facility visit labels to qualifying search logs. We constructed a matrix of general, semantic, and location-based features from search logs of users that had 42 or more search days preceding a medical facility visit as well as from search logs of users that had no medical visits and trained statistical learners for predicting future medical visits. We then carried out a closed-loop evaluation of the utility of health care use predictions using the show conversion rates of advertisements displayed to the predicted future utilizers. In the context of behaviorally targeted advertising, wherein health care providers are interested in minimizing their cost per conversion, the association between show conversion rate and predicted utilization score, served as a surrogate measure of the model's utility.
RESULTS: We obtained the highest area under the curve (0.796) in medical visit prediction with our random forests model and daywise features. Ablating feature categories one at a time showed that the model performance worsened the most when location features were dropped. An online evaluation in which advertisements were served to users who had a high predicted probability of a future medical visit showed a 3.96% increase in the show conversion rate.
CONCLUSIONS: Results from our experiments done in a research setting suggest that it is possible to accurately predict future patient visits from geotagged mobile search logs. Results from the offline and online experiments on the utility of health utilization predictions suggest that such prediction can have utility for health care providers.

PMID: 27655225 [PubMed - as supplied by publisher]

Semantic processing of EHR data for clinical research.

J Biomed Inform. 2015 Dec;58:247-59

Authors: Sun H, Depraetere K, De Roo J, Mels G, De Vloed B, Twagirumukiza M, Colaert D

Abstract
There is a growing need to semantically process and integrate clinical data from different sources for clinical research. This paper presents an approach to integrate EHRs from heterogeneous resources and generate integrated data in different data formats or semantics to support various clinical research applications. The proposed approach builds semantic data virtualization layers on top of data sources, which generate data in the requested semantics or formats on demand. This approach avoids upfront dumping to and synchronizing of the data with various representations. Data from different EHR systems are first mapped to RDF data with source semantics, and then converted to representations with harmonized domain semantics where domain ontologies and terminologies are used to improve reusability. It is also possible to further convert data to application semantics and store the converted results in clinical research databases, e.g. i2b2, OMOP, to support different clinical research settings. Semantic conversions between different representations are explicitly expressed using N3 rules and executed by an N3 Reasoner (EYE), which can also generate proofs of the conversion processes. The solution presented in this paper has been applied to real-world applications that process large scale EHR data.

PMID: 26515501 [PubMed - indexed for MEDLINE]

Integrating HL7 RIM and ontology for unified knowledge and data representation in clinical decision support systems.

Comput Methods Programs Biomed. 2016 Jan;123:94-108

Authors: Zhang YF, Tian Y, Zhou TS, Araki K, Li JS

Abstract
BACKGROUND AND OBJECTIVES: The broad adoption of clinical decision support systems within clinical practice has been hampered mainly by the difficulty in expressing domain knowledge and patient data in a unified formalism. This paper presents a semantic-based approach to the unified representation of healthcare domain knowledge and patient data for practical clinical decision making applications.
METHODS: A four-phase knowledge engineering cycle is implemented to develop a semantic healthcare knowledge base based on an HL7 reference information model, including an ontology to model domain knowledge and patient data and an expression repository to encode clinical decision making rules and queries. A semantic clinical decision support system is designed to provide patient-specific healthcare recommendations based on the knowledge base and patient data.
RESULTS: The proposed solution is evaluated in the case study of type 2 diabetes mellitus inpatient management. The knowledge base is successfully instantiated with relevant domain knowledge and testing patient data. Ontology-level evaluation confirms model validity. Application-level evaluation of diagnostic accuracy reaches a sensitivity of 97.5%, a specificity of 100%, and a precision of 98%; an acceptance rate of 97.3% is given by domain experts for the recommended care plan orders.
CONCLUSIONS: The proposed solution has been successfully validated in the case study as providing clinical decision support at a high accuracy and acceptance rate. The evaluation results demonstrate the technical feasibility and application prospect of our approach.

PMID: 26474836 [PubMed - indexed for MEDLINE]

The role of ontologies in biological and biomedical research: a functional perspective.

Brief Bioinform. 2015 Nov;16(6):1069-80

Authors: Hoehndorf R, Schofield PN, Gkoutos GV

Abstract
Ontologies are widely used in biological and biomedical research. Their success lies in their combination of four main features present in almost all ontologies: provision of standard identifiers for classes and relations that represent the phenomena within a domain; provision of a vocabulary for a domain; provision of metadata that describes the intended meaning of the classes and relations in ontologies; and the provision of machine-readable axioms and definitions that enable computational access to some aspects of the meaning of classes and relations. While each of these features enables applications that facilitate data integration, data access and analysis, a great potential lies in the possibility of combining these four features to support integrative analysis and interpretation of multimodal data. Here, we provide a functional perspective on ontologies in biology and biomedicine, focusing on what ontologies can do and describing how they can be used in support of integrative research. We also outline perspectives for using ontologies in data-driven science, in particular their application in structured data mining and machine learning applications.

PMID: 25863278 [PubMed - indexed for MEDLINE]

Large-scale Cross-modality Search via Collective Matrix Factorization Hashing.

IEEE Trans Image Process. 2016 Sep 8;

Authors: Ding G, Guo Y, Zhou J, Gao Y

Abstract
By transforming data into binary representation, i.e., Hashing, we can perform high-speed search with low storage cost, and thus Hashing has collected increasing research interest in the recent years. Recently, how to generate Hashcode for multimodal data (e.g., images with textual tags, documents with photos, etc) for large-scale cross-modality search (e.g., searching semantically related images in database for a document query) is an important research issue because of the fast growth of multimodal data in the Web. To address this issue, a novel framework for multimodal Hashing is proposed, termed as Collective Matrix Factorization Hashing (CMFH). The key idea of CMFH is to learn unified Hashcodes for different modalities of one multimodal instance in the shared latent semantic space in which different modalities can be effectively connected. Therefore, accurate cross-modality search is supported. Based on the general framework, we extend it in the unsupervised scenario where it tries to preserve the Euclidean structure, and in the supervised scenario where it fully exploits the label information of data. The corresponding theoretical analysis and the optimization algorithms are given. We conducted comprehensive experiments on three benchmark datasets for cross-modality search. The experimental results demonstrate that CMFH can significantly outperform several state-of-the-art cross-modality Hashing methods, which validates the effectiveness of the proposed CMFH.

PMID: 27623584 [PubMed - as supplied by publisher]

FoodWiki: a Mobile App Examines Side Effects of Food Additives Via Semantic Web.

J Med Syst. 2016 Feb;40(2):41

Authors: Çelik Ertuğrul D

Abstract
In this article, a research project on mobile safe food consumption system (FoodWiki) is discussed that performs its own inferencing rules in its own knowledge base. Currently, the developed rules examines the side effects that are causing some health risks: heart disease, diabetes, allergy, and asthma as initial. There are thousands compounds added to the processed food by food producers with numerous effects on the food: to add color, stabilize, texturize, preserve, sweeten, thicken, add flavor, soften, emulsify, and so forth. Those commonly used ingredients or compounds in manufactured foods may have many side effects that cause several health risks such as heart disease, hypertension, cholesterol, asthma, diabetes, allergies, alzheimer etc. according to World Health Organization. Safety in food consumption, especially by patients in these risk groups, has become crucial, given that such health problems are ranked in the top ten health risks around the world. It is needed personal e-health knowledge base systems to help patients take control of their safe food consumption. The systems with advanced semantic knowledge base can provide recommendations of appropriate foods before consumption by individuals. The proposed FoodWiki system is using a concept based search mechanism that performs on thousands food compounds to provide more relevant information.

PMID: 26590979 [PubMed - indexed for MEDLINE]

Protein aggregation, structural disorder and RNA-binding ability: a new approach for physico-chemical and gene ontology classification of multiple datasets.

BMC Genomics. 2015;16:1071

Authors: Klus P, Ponti RD, Livi CM, Tartaglia GG

Abstract
BACKGROUND: Comparison between multiple protein datasets requires the choice of an appropriate reference system and a number of variables to describe their differences. Here we introduce an innovative approach to discriminate multiple protein datasets (multiCM) and to measure enrichments in gene ontology terms (cleverGO) using semantic similarities.
RESULTS: We illustrate the powerfulness of our approach by investigating the links between RNA-binding ability and other protein features, such as structural disorder and aggregation, in S. cerevisiae, C. elegans, M. musculus and H. sapiens. Our results are in striking agreement with available experimental evidence and unravel features that are key to understand the mechanisms regulating cellular homeostasis.
CONCLUSIONS: In an intuitive way, multiCM and cleverGO provide accurate classifications of physico-chemical features and annotations of biological processes, molecular functions and cellular components, which is extremely useful for the discovery and characterization of new trends in protein datasets. The multiCM and cleverGO can be freely accessed on the Web at http://www.tartaglialab.com/cs_multi/submission and http://www.tartaglialab.com/GO_analyser/universal . Each of the pages contains links to the corresponding documentation and tutorial.

PMID: 26673865 [PubMed - indexed for MEDLINE]

The health care and life sciences community profile for dataset descriptions.

PeerJ. 2016;4:e2331

Authors: Dumontier M, Gray AJ, Marshall MS, Alexiev V, Ansell P, Bader G, Baran J, Bolleman JT, Callahan A, Cruz-Toledo J, Gaudet P, Gombocz EA, Gonzalez-Beltran AN, Groth P, Haendel M, Ito M, Jupp S, Juty N, Katayama T, Kobayashi N, Krishnaswami K, Laibe C, Le Novère N, Lin S, Malone J, Miller M, Mungall CJ, Rietveld L, Wimalaratne SM, Yamaguchi A

Abstract
Access to consistent, high-quality metadata is critical to finding, understanding, and reusing scientific data. However, while there are many relevant vocabularies for the annotation of a dataset, none sufficiently captures all the necessary metadata. This prevents uniform indexing and querying of dataset repositories. Towards providing a practical guide for producing a high quality description of biomedical datasets, the W3C Semantic Web for Health Care and the Life Sciences Interest Group (HCLSIG) identified Resource Description Framework (RDF) vocabularies that could be used to specify common metadata elements and their value sets. The resulting guideline covers elements of description, identification, attribution, versioning, provenance, and content summarization. This guideline reuses existing vocabularies, and is intended to meet key functional requirements including indexing, discovery, exchange, query, and retrieval of datasets, thereby enabling the publication of FAIR data. The resulting metadata profile is generic and could be used by other domains with an interest in providing machine readable descriptions of versioned datasets.

PMID: 27602295 [PubMed]

Exploiting Semantic Web Technologies to Develop OWL-Based Clinical Practice Guideline Execution Engines.

IEEE J Biomed Health Inform. 2016 Jan;20(1):388-98

Authors: Jafarpour B, Abidi SR, Abidi SS

Abstract
Computerizing paper-based CPG and then executing them can provide evidence-informed decision support to physicians at the point of care. Semantic web technologies especially web ontology language (OWL) ontologies have been profusely used to represent computerized CPG. Using semantic web reasoning capabilities to execute OWL-based computerized CPG unties them from a specific custom-built CPG execution engine and increases their shareability as any OWL reasoner and triple store can be utilized for CPG execution. However, existing semantic web reasoning-based CPG execution engines suffer from lack of ability to execute CPG with high levels of expressivity, high cognitive load of computerization of paper-based CPG and updating their computerized versions. In order to address these limitations, we have developed three CPG execution engines based on OWL 1 DL, OWL 2 DL and OWL 2 DL + semantic web rule language (SWRL). OWL 1 DL serves as the base execution engine capable of executing a wide range of CPG constructs, however for executing highly complex CPG the OWL 2 DL and OWL 2 DL + SWRL offer additional executional capabilities. We evaluated the technical performance and medical correctness of our execution engines using a range of CPG. Technical evaluations show the efficiency of our CPG execution engines in terms of CPU time and validity of the generated recommendation in comparison to existing CPG execution engines. Medical evaluations by domain experts show the validity of the CPG-mediated therapy plans in terms of relevance, safety, and ordering for a wide range of patient scenarios.

PMID: 25532198 [PubMed - indexed for MEDLINE]

InteGO2: a web tool for measuring and visualizing gene semantic similarities using Gene Ontology.

BMC Genomics. 2016;17 Suppl 5:530

Authors: Peng J, Li H, Liu Y, Juan L, Jiang Q, Wang Y, Chen J

Abstract
BACKGROUND: The Gene Ontology (GO) has been used in high-throughput omics research as a major bioinformatics resource. The hierarchical structure of GO provides users a convenient platform for biological information abstraction and hypothesis testing. Computational methods have been developed to identify functionally similar genes. However, none of the existing measurements take into account all the rich information in GO. Similarly, using these existing methods, web-based applications have been constructed to compute gene functional similarities, and to provide pure text-based outputs. Without a graphical visualization interface, it is difficult for result interpretation.
RESULTS: We present InteGO2, a web tool that allows researchers to calculate the GO-based gene semantic similarities using seven widely used GO-based similarity measurements. Also, we provide an integrative measurement that synergistically integrates all the individual measurements to improve the overall performance. Using HTML5 and cytoscape.js, we provide a graphical interface in InteGO2 to visualize the resulting gene functional association networks.
CONCLUSIONS: InteGO2 is an easy-to-use HTML5 based web tool. With it, researchers can measure gene or gene product functional similarity conveniently, and visualize the network of functional interactions in a graphical interface. InteGO2 can be accessed via http://mlg.hit.edu.cn:8089/ .

PMID: 27586009 [PubMed - in process]

Designing a patient monitoring system for bipolar disorder using Semantic Web technologies.

Conf Proc IEEE Eng Med Biol Soc. 2015;2015:6788-91

Authors: Thermolia C, Bei ES, Petrakis EG, Kritsotakis V, Tsiknakis M, Sakkalis V

Abstract
The new movement to personalize treatment plans and improve prediction capabilities is greatly facilitated by intelligent remote patient monitoring and risk prevention. This paper focuses on patients suffering from bipolar disorder, a mental illness characterized by severe mood swings. We exploit the advantages of Semantic Web and Electronic Health Record Technologies to develop a patient monitoring platform to support clinicians. Relying on intelligently filtering of clinical evidence-based information and individual-specific knowledge, we aim to provide recommendations for treatment and monitoring at appropriate time or concluding into alerts for serious shifts in mood and patients' non response to treatment.

PMID: 26737852 [PubMed - indexed for MEDLINE]

Dynamic User Interfaces for Service Oriented Architectures in Healthcare.

Stud Health Technol Inform. 2016;228:795-7

Authors: Schweitzer M, Hoerbst A

Abstract
Electronic Health Records (EHRs) play a crucial role in healthcare today. Considering a data-centric view, EHRs are very advanced as they provide and share healthcare data in a cross-institutional and patient-centered way adhering to high syntactic and semantic interoperability. However, the EHR functionalities available for the end users are rare and hence often limited to basic document query functions. Future EHR use necessitates the ability to let the users define their needed data according to a certain situation and how this data should be processed. Workflow and semantic modelling approaches as well as Web services provide means to fulfil such a goal. This thesis develops concepts for dynamic interfaces between EHR end users and a service oriented eHealth infrastructure, which allow the users to design their flexible EHR needs, modeled in a dynamic and formal way. These are used to discover, compose and execute the right Semantic Web services.

PMID: 27577496 [PubMed - in process]

A Digital Health System to Assist Family Physicians to Safely Prescribe NOAC Medications.

Stud Health Technol Inform. 2016;228:519-23

Authors: Abidi SR, Cox J, Abusharekh A, Hashemian N, Abidi SS

Abstract
Atrial Fibrillation (AF) is the most common cardiac arrhythmia. Generally, the therapeutic options for managing AF include the use of anticoagulant drugs that prevent the coagulation of blood. New Oral Anticoagulants (NOACs) are not optimally prescribed to patients, despite their efficacy. In Canada, NOAC medications are not directly available to patients who belong to provincial benefits programs, rather a NOAC special authorization process establishes the eligibility of a patient to receive a NOAC and be paid by the provincial Pharmacare program. This special authorization process is tedious and paper-based which inhibits physicians to prescribe NOAC leading to suboptimal AF care to patients. In this paper, we present a computerized NOAC Authorization Decision Support System (NOAC-ADSS), accessible to physicians to help them (a) determine a patient eligibility for NOAC based on Canadian AF clinical guidelines, and (b) complete the special authorization form. We present a semantic web based system to ontologically model the NOAC eligibility criteria and execute the knowledge to determine a patient NOAC eligibility and dosage.

PMID: 27577437 [PubMed - in process]

Thesaurus-Based Hierarchical Semantic Grouping of Medical Terms in Information Extraction.

Stud Health Technol Inform. 2016;228:446-50

Authors: Lassoued Y, Deleris L

Abstract
In this paper we describe a semantic approach for grouping medical terms into a hierarchy of concepts based on the UMLS meta-thesaurus. The context of this work is Medical Recap, a Web system that automatically extracts risk information from PubMed abstracts, and then aggregates this knowledge into dependence graphs or Bayesian networks.

PMID: 27577422 [PubMed - in process]

An Ontological Model of Behaviour Theory to Generate Personalized Action Plans to Modify Behaviours.

Stud Health Technol Inform. 2016;228:399-403

Authors: Baig W, Abidi S, Abidi SS

Abstract
Behavior change approaches aim to assist patients in achieving self-efficacy in managing their condition. Social cognitive theory (SCT) stipulates self-efficacy as a central element to behavior change and provides constructs to achieve self-efficacy guided by person-specific action plans. In our work, to administer behaviour change in patient with chronic conditions, our approach entails the computerization of SCT-based self-efficacy constructs in order to generate personalized action plans that are suitable to an individual's current care scenario. We have taken a knowledge management approach, whereby we have computerized the SCT-based self-efficacy constructs in terms of a high-level SCT knowledge model that can be operationalized to generate personalized behaviour change action plans. We have collected and computerized behavior change content targeting healthy living and physical activity. Semantic web technologies have been used to develop the SCT knowledge model, represented in terms of an ontology and SWRL rules. The ontological SCT model can inferred to generate personalized self-management action plans for a given patient profile. We present formative evaluation of the clinical correctness and relevance of the generated personalized action plans for a range of test patient profiles.

PMID: 27577412 [PubMed - in process]

Building a Semantic Model to Enhance the User's Perceived Functionality of the EHR.

Stud Health Technol Inform. 2016;228:137-41

Authors: Lasierra N, Schweitzer M, Gorfer T, Toma I, Hoerbst A

Abstract
In order to facilitate and increase the usability of Electronic Health Records (EHRs) for healthcare professional's daily work, we have designed a system that enables functional and flexible EHR access, based on the execution of clinical workflows and the composition of Semantic Web Services (SWS). The backbone of this system is based on an ontology. In this paper we present the strategy that we have followed for its design, and an overview of the resulting model. The designed ontology enables to model patient-centred clinical EHR workflows, the involved sequence of tasks and its associated functionality and managed clinical data. This semantic model constitutes also the main pillar for enabling dynamic service selection in our system.

PMID: 27577358 [PubMed - in process]

A National Medical Information System for Senegal: Architecture and Services.

Stud Health Technol Inform. 2016;228:43-7

Authors: Camara G, Diallo AH, Lo M, Tendeng JN, Lo S

Abstract
In Senegal, great amounts of data are daily generated by medical activities such as consultation, hospitalization, blood test, x-ray, birth, death, etc. These data are still recorded in register, printed images, audios and movies which are manually processed. However, some medical organizations have their own software for non-standardized patient record management, appointment, wages, etc. without any possibility of sharing these data or communicating with other medical structures. This leads to lots of limitations in reusing or sharing these data because of their possible structural and semantic heterogeneity. To overcome these problems we have proposed a National Medical Information System for Senegal (SIMENS). As an integrated platform, SIMENS provides an EHR system that supports healthcare activities, a mobile version and a web portal. The SIMENS architecture proposes also a data and application integration services for supporting interoperability and decision making.

PMID: 27577338 [PubMed - in process]

Semantic Web Ontology and Data Integration: a Case Study in Aiding Psychiatric Drug Repurposing.

AMIA Jt Summits Transl Sci Proc. 2016;2016:132-9

Authors: Liang C, Sun J, Tao C

Abstract
Despite ongoing progress towards treating mental illness, there remain significant difficulties in selecting probable candidate drugs from the existing database. We describe an ontology - oriented approach aims to represent the nexus between genes, drugs, phenotypes, symptoms, and diseases from multiple information sources. Along with this approach, we report a case study in which we attempted to explore the candidate drugs that effective for both bipolar disorder and epilepsy. We constructed an ontology that incorporates the knowledge between the two diseases and performed semantic reasoning task on the ontology. The reasoning results suggested 48 candidate drugs that hold promise for a further breakthrough. The evaluation was performed and demonstrated the validity of the proposed ontology. The overarching goal of this research is to build a framework of ontology - based data integration underpinning psychiatric drug repurposing. This approach prioritizes the candidate drugs that have potential associations among genes, phenotypes and symptoms, and thus facilitates the data integration and drug repurposing in psychiatric disorders.

PMID: 27570661 [PubMed]

Integrating semantic dimension into openEHR archetypes for the management of cerebral palsy electronic medical records.

J Biomed Inform. 2016 Aug 24;

Authors: Ellouze AS, Bouaziz R, Ghorbel H

Abstract
PURPOSE: Integrating semantic dimension into clinical archetypes is necessary once modeling medical records. First, it enables semantic interoperability and, it offers applying semantic activities on clinical data and provides a higher design quality of Electronic Medical Record (EMR) systems. However, to obtain these advantages, designers need to use archetypes that cover semantic features of clinical concepts involved in their specific applications. In fact, most of archetypes filed within open repositories are expressed in the Archetype Definition Language (ALD) which allows defining only the syntactic structure of clinical concepts weakening semantic activities on the EMR content in the semantic web environment. This paper focuses on the modeling of an EMR prototype for infants affected by Cerebral Palsy (CP), using the dual model approach and integrating semantic web technologies. Such a modeling provides a better delivery of quality of care and ensures semantic interoperability between all involved therapies' information systems.
METHODS: First, data to be documented are identified and collected from the involved therapies. Subsequently, data are analyzed and arranged into archetypes expressed in accordance of ADL. During this step, open archetype repositories are explored, in order to find the suitable archetypes. Then, ADL archetypes are transformed into archetypes expressed in OWL-DL (Ontology Web Language - Description Language). Finally, we construct an ontological source related to these archetypes enabling hence their annotation to facilitate data extraction and providing possibility to exercise semantic activities on such archetypes.
RESULTS: Semantic dimension integration into EMR modeled in accordance to the archetype approach. The feasibility of our solution is shown through the development of a prototype, baptized "CP-SMS", which ensures semantic exploitation of CP EMR. This prototype provides the following features: (i) creation of CP EMR instances and their checking by using a knowledge base which we have constructed by interviews with domain experts, (ii) translation of initially CP ADL archetypes into CP OWL-DL archetypes, (iii) creation of an ontological source which we can use to annotate obtained archetypes and (vi) enrichment and supply of the ontological source and integration of semantic relations by providing hence fueling the ontology with new concepts, ensuring consistency and eliminating ambiguity between concepts.
CONCLUSIONS: The degree of semantic interoperability that could be reached between EMR systems depends strongly on the quality of the used archetypes. Thus, the integration of semantic dimension in archetypes modeling process is crucial. By creating an ontological source and annotating archetypes, we create a supportive platform ensuring semantic interoperability between archetypes-based EMR-systems.

PMID: 27568295 [PubMed - as supplied by publisher]