Semantic Interoperability Research Articles

Abstract 6576: Gabriella Miller Kids First Data Resource Center (KFDRC): Empowering discovery across germline and somatic variation in pediatric cancer

Abstract The Gabriella Miller Kids First Pediatric Research Program (Kids First) has enabled genomic and transcriptomic characterization across a multitude of pediatric diseases at an unprecedented scale. This includes both pediatric cancer cohorts and structure birth defect cohorts, as co-occurrences of these suggest a shared context of developmental biology alterations. Making this quantity of data findable, accessible, interoperable and reusable (FAIR) to researchers around the world has been enabled via the Gabriella Miller Kids First Data Resource Center (KFDRC). Twenty-six Kids First studies are released on the Kids First Data Resource Portal, representing more than 21,000 participants and more than 1.25 PB of data, with additional datasets being released yearly. Additionally, via a partnership between NCI Childhood Cancer Data Initiative and Kids First, large studies currently undergoing sequencing on childhood sarcomas and brain tumors will also be made available via the KFDRC. The KFDRC Portal (https://portal.kidsfirstdrc.org/) provides an interactive cohort building interface as well as powerful search capabilities across 61 billion germline variants in real-time. The somatic variants are made available on the open-access PedcBioPortal (https://pedcbioportal.kidsfirstdrc.org/). Search features include leveraging ontologies to enable different granularity across studies to be cross-querable. The use of these ontologies enable semantic interoperability, while also leveraging FHIR as an interoperable standard for exchange of clinical and other relevant metadata. GA4GH DRS and Passport services via Gen3 and NIH’s Researcher Authentication Service (RAS) enable streamlined access to controlled access data within researchers’ cloud platform of choice, including CAVATICA (https://cavatica.org) which is directly integrated with the KFDRC Portal. The KFDRC is part of the NIH Cloud-Based Platform Interoperability efforts, which includes the NCI Cancer Research Data Commons. This enables discovery and analysis across combined cancer datasets and data modalities and further potential for the cross analysis between cancers and structural birth defects to accelerate the discovery process to ultimately lead to improved knowledge and outcomes for childhood cancer and more generally pediatric disease from genetic causes. Citation Format: David Higgins, Jean-Philippe Thibert, Michele Mattioni, Jack DiGiovanna, Robert L. Grossman, Bailey K. Farrow, Eric Wenger, Samuel Volchenboum, Robert J. Carroll, Melissa A. Haendel, Deanne M. Taylor, Yuankun Zhu, Vincent Ferretti, Adam C. Resnick, Allison P. Heath. Gabriella Miller Kids First Data Resource Center (KFDRC): Empowering discovery across germline and somatic variation in pediatric cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6576.

Modelling with AAS and RDF in Industry 4.0

Industry 4.0 has proposed the Asset Administration Shell (AAS) model for digital twins. This model should help to solve interoperability issues, a topic that is also addressed by the Semantic Web and its Resource Description Framework (RDF). AAS and RDF-based models have their own strengths. AAS models are easier to integrate with operational technologies in a production environment, whereas RDF-based models offer more semantic expressiveness and advanced querying. In the Horizon MAS4AI project we found that both modelling paradigms can complement each other to develop agentbased digital twins for modular production environments. In this work we propose two different approaches to bridge both modelling paradigms. First we define a set of mapping rules to generate an AAS model from a given RDF-based model, supporting model development. Secondly, we propose to use RDF-based models to generate a digital shadow of AASs to improve semantic discoverability. Preliminary results demonstrate that heterogeneity of metamodels does not exclude achieving semantic interoperability, as well as that greater functionality can be obtained compared to using both models in isolation. The solutions will be further developed in collaboration with pilot lines in the MAS4AI project.

Health Systems Need to Transform Data Collection to Advance Health Equity.

Health Systems Need to Transform Data Collection to Advance Health Equity.

A Standardized Clinical Data Harmonization Pipeline for Scalable AI Application Deployment (FHIR-DHP): Validation and Usability Study

Increasing digitalization in the medical domain gives rise to large amounts of health care data, which has the potential to expand clinical knowledge and transform patient care if leveraged through artificial intelligence (AI). Yet, big data and AI oftentimes cannot unlock their full potential at scale, owing to nonstandardized data formats, lack of technical and semantic data interoperability, and limited cooperation between stakeholders in the health care system. Despite the existence of standardized data formats for the medical domain, such as Fast Healthcare Interoperability Resources (FHIR), their prevalence and usability for AI remain limited. In this paper, we developed a data harmonization pipeline (DHP) for clinical data sets relying on the common FHIR data standard. We validated the performance and usability of our FHIR-DHP with data from the Medical Information Mart for Intensive Care IV database. We present the FHIR-DHP workflow in respect of the transformation of "raw" hospital records into a harmonized, AI-friendly data representation. The pipeline consists of the following 5 key preprocessing steps: querying of data from hospital database, FHIR mapping, syntactic validation, transfer of harmonized data into the patient-model database, and export of data in an AI-friendly format for further medical applications. A detailed example of FHIR-DHP execution was presented for clinical diagnoses records. Our approach enables the scalable and needs-driven data modeling of large and heterogenous clinical data sets. The FHIR-DHP is a pivotal step toward increasing cooperation, interoperability, and quality of patient care in the clinical routine and for medical research.

Realizing health data interoperability in low connectivity settings: The case of VODAN-Africa

VODAN Africa has produced FAIR data in low resource settings. Federated machine actionable data is available in a triple store for visiting. Interoperability facets (I1–I3) were followed to achieve semantic interoperability. Vertical interoperability was also realized with DHIS2.

Automating Case Reporting of Chlamydia and Gonorrhea to Public Health Authorities in Illinois Clinics: Implementation and Evaluation of Findings

Chlamydia and gonorrhea cases continue to rise in Illinois, increasing by 16.4% and 70.9% in 2019, respectively, compared with 2015. Providers are required to report both chlamydia and gonorrhea, as mandated by public health laws. Manual reporting remains a huge burden; 90%-93% of cases were reported to Illinois Department of Public Health (IDPH) via electronic laboratory reporting (ELR), and the remaining were reported through web-based data entry platforms, faxes, and phone calls. However, cases reported via ELRs only contain information available to a laboratory facility and do not contain additional data needed for public health. Such data are typically found in an electronic health record (EHR). Electronic case reports (eCRs) were developed and automated the generation of case reports from EHRs to be reported to public health agencies. Prior studies consolidated trigger criteria for eCRs, and compared with manual reporting, found it to be more complete. The goal of this project is to pilot standards-based eCR for chlamydia and gonorrhea. We evaluated the throughput, completeness, and timeliness of eCR compared to ELR, as well as the implementation experience at a large health center-controlled network in Illinois. For this study, we selected 8 clinics located on the north, west, and south sides of Chicago to implement the eCRs; these cases were reported to IDPH. The study period was 52 days. The centralized EHR used by these clinics leveraged 2 of the 3 case detection scenarios, which were previously defined as the trigger, to generate an eCR. These messages were successfully transmitted via Health Level 7 electronic initial case report standard. Upon receipt by IDPH, these eCRs were parsed and housed in a staging database. During the study period, 183 eCRs representing 135 unique patients were received by IDPH. eCR reported 95% (n=113 cases) of all the chlamydia cases and 97% (n=70 cases) of all the gonorrhea cases reported from the participating clinical sites. eCR found an additional 14 (19%) cases of gonorrhea that were not reported via ELR. However, ELR reported an additional 6 cases of chlamydia and 2 cases of gonorrhea, which were not reported via eCR. ELR reported 100% of chlamydia cases but only 81% of gonorrhea cases. While key elements such as patient and provider names were complete in both eCR and ELR, eCR was found to report additional clinical data, including history of present illness, reason for visit, symptoms, diagnosis, and medications. eCR successfully identified and created automated reports for chlamydia and gonorrhea cases in the implementing clinics in Illinois. eCR demonstrated a more complete case report and represents a promising future of reducing provider burden for reporting cases while achieving greater semantic interoperability between health care systems and public health.

An Ostensive Information Architecture to Enhance Semantic Interoperability for Healthcare Information Systems

Semantic interoperability establishes intercommunications and enables data sharing across disparate systems. In this study, we propose an ostensive information architecture for healthcare information systems to decrease ambiguity caused by using signs in different contexts for different purposes. The ostensive information architecture adopts a consensus-based approach initiated from the perspective of information systems re-design and can be applied to other domains where information exchange is required between heterogeneous systems. Driven by the issues in FHIR (Fast Health Interoperability Resources) implementation, an ostensive approach that supplements the current lexical approach in semantic exchange is proposed. A Semantic Engine with an FHIR knowledge graph as the core is constructed using Neo4j to provide semantic interpretation and examples. The MIMIC III (Medical Information Mart for Intensive Care) datasets and diabetes datasets have been employed to demonstrate the effectiveness of the proposed information architecture. We further discuss the benefits of the separation of semantic interpretation and data storage from the perspective of information system design, and the semantic reasoning towards patient-centric care underpinned by the Semantic Engine.

Investigating interoperable event corpora: limitations of reusability of resources and portability of models

Studies on the applicability of heterogeneous semantically interoperable corpora are rare. We investigate to what extent reusability (both of systems and of annotations) is entailed by corpora whose interoperability is based on compliance to standards. In particular, we look at event detection in English texts, supported by the ISO-TimeML annotation scheme. We run two sets of experiments using a common neural network architecture and extensively evaluate our results on both in-distribution and out-of-distribution settings. In all experimental settings, systems obtain state-of-the-art results on the in-distribution data and underperform out-of-distribution ones, setting limits to the benefits of semantically interoperable corpora. By means of a detailed error analysis, we show that while being compliant to a standard guarantees semantic interoperability, this becomes only a necessary condition for reusability, with factors such as differences in the quality of the annotations having a much stronger impact.

Enhancing IoT Data and Semantic Interoperability Based on Entity Tree Embedding Under an Edge–Cloud Framework

Internet of Things (IoT) devices and services have become increasingly ubiquitous in recent years as they greatly facilitate our daily life. To promote IoT data and semantic interoperability, we propose an edge–cloud framework. The edge end is responsible for handling customized data processing tasks and transferring the processed data, while the cloud end deals with semantic information processing. Additionally, we present an entity tree embedding algorithm at the cloud end to convert IoT entities and attributes into embedding vectors in a tree-structured way. Consequently, entity embeddings could reflect the semantic information at both Class and Property levels, which ameliorates our previous entity embedding method, leading to better embedding results and clustering effects. Finally, the entity tree embedding algorithm and the corresponding compression algorithm are evaluated. Results indicate that the Whitening algorithm is the best method to compress embedding vectors. More importantly, the entity tree embedding algorithm captures both the semantic and structural information of entities and attributes. Additionally, the clustering experiments show that the proposed embedding algorithm achieves better clustering results compared with the original entity embeddings and the uncompressed averaging method.

COMPATIBILITY PROVISIONING OF SENSOR SYSTEMS AND INDUSTRIAL INTERNET OF THINGS PLATFORMS

The article relates to the issue of ensuring the compatibility of different sensors types, their measurements results and the industrial Internet of things platforms. The solution offered to ensure semantic interoperability is based on semantic technology with the architecture development and further creation of a common ontological model. The approach offered allows integrating both semantic content of sensor information and methods for arrangement and normalization of measurement results, which is relevant for subsequent analysis and use of industrial Internet of things data on various platforms, including data depersonalization for computer-assisted learning. A study of regulatory documentation and technical solutions to ensure the compatibility of the industrial Internet and sensor systems was carried out, existing similar solutions were considered, shortcomings in comparison with the proposed method were identified. Ontology concepts for the model of semantic compatibility of sensor systems and the industrial Internet of things platform were developed. The article gives an example of the developed logical inference procedure in the framework of the ontology implementation with use of the free software tool Protégé. Using the proposed approach allows to proceed to knowledge-based interoperability.

Aligning Semantic Interoperability Frameworks with the FOXS Stack for FAIR Health Data.

FAIR Guiding Principles present a synergy with the use cases for digital health records, in that clinical data need to be found, accessible within a range of environments, and data must interoperate between systems and subsequently reused. The use of HL7 FHIR, openEHR, IHE XDS, and SNOMED CT (FOXS) together represents a specification to create an open digital health platform for modern health care applications. To describe where logical FOXS components align to the European Open Science Cloud Interoperability Framework (EOSC-IF) reference architecture for semantic interoperability. This should provide a means of defining if FOXS aligns to FAIR principles and to establish the data models and structures that support longitudinal care records as being fit to underpin scientific research. The EOSC-IF Semantic View is a representation of semantic interoperability where meaning is preserved between systems and users. This was analyzed and cross-referenced with FOXS architectural components, mapping concepts, and objects that describe content such as catalogues and semantic artifacts. Majority of conceptual Semantic View components were featured within the FOXS architecture. Semantic Business Objects are composed of a range of elements such as openEHR archetypes and templates, FHIR resources and profiles, SNOMED CT concepts, and XDS document identifiers. Semantic Functional Content comprises catalogues of metadata that were also supported by openEHR and FHIR tools. Despite some elements of EOSC-IF being vague (e.g., FAIR Digital Object), there was a broad conformance to the framework concepts and the components of a FOXS platform. This work supports a health-domain-specific view of semantic interoperability and how this may be achieved to support FAIR data for health research via a standardized framework.

A Framework for Automatic Clustering of EHR Messages Using a Spatial Clustering Approach.

Although Health Level Seven (HL 7) message standards (v2, v3, Clinical Document Architecture (CDA)) have been commonly adopted, there are still issues associated with them, especially the semantic interoperability issues and lack of support for smart devices (e.g., smartphones, fitness trackers, and smartwatches), etc. In addition, healthcare organizations in many countries are still using proprietary electronic health record (EHR) message formats, making it challenging to convert to other data formats-particularly the latest HL7 Fast Health Interoperability Resources (FHIR) data standard. The FHIR is based on modern web technologies such as HTTP, XML, and JSON and would be capable of overcoming the shortcomings of the previous standards and supporting modern smart devices. Therefore, the FHIR standard could help the healthcare industry to avail the latest technologies benefits and improve data interoperability. The data representation and mapping from the legacy data standards (i.e., HL7 v2 and EHR) to the FHIR is necessary for the healthcare sector. However, direct data mapping or conversion from the traditional data standards to the FHIR data standard is challenging because of the nature and formats of the data. Therefore, in this article, we propose a framework that aims to convert proprietary EHR messages into the HL7 v2 format and apply an unsupervised clustering approach using the DBSCAN (density-based spatial clustering of applications with noise) algorithm to automatically group a variety of these HL7 v2 messages regardless of their semantic origins. The proposed framework's implementation lays the groundwork to provide a generic mapping model with multi-point and multi-format data conversion input into the FHIR. Our experimental results show the proposed framework's ability to automatically cluster various HL7 v2 message formats and provide analytic insight behind them.

Self-care nursing interventions: A qualitative study into electronic health records' contents.

This study aims to (1) analyse all self-care-related interventions Portuguese nurses documented, (2) determine potential issues that may impair semantic interoperability and (3) propose a new set of interventions representing nursing actions regarding self-care that may integrate any HER application. As populations age and chronic diseases increase, self-care concerns rise. Individuals who seek healthcare, regardless of context, need prompt access to accurate health information. Healthcare professionals need to understand the information in all placeswhere care is provided, creating the need for semantic interoperability within electronic health records. A qualitative descriptive and exploratory study was conducted in two phases: (1) a content analysis of nursing interventions e-documentation and (2) a focus group with fifteen registered nurses exploring latent criteria or insights gleaned from the findings of content analysis. The COREQ statement was used to guide research reporting. We extracted 1529 nursing intervention sentences from the electronic health records and created 209 intervention categories. We identified the main issues with semantic interoperability in nursing intervention identification. According to the findings, nurses cooperate with clients, offering physical aid and encouraging them to overcome functional limitations to self-care tasks hampered by their conditions. This article provides evidence to warn policy makers against decisions to use locally customised electronic health records, as well as evidence on the importance of policy promoting the adoption of a nursing ontology for electronic health records. And, as a result, the harmonisation and effective provision of high-quality nursing care and the reduction of healthcare costs across nations.

Implementing HL7 FHIR

Fast Healthcare Interoperability Resources (FHIR), created by HL7 more than a decade ago, has become a global standard for exchanging data. Communities of interest have organized under the HL7 FHIR Accelerator Program to develop implementation guides and applications for patient care, health payment systems, public health, patient engagement, clinical and basic research, and social determinants of health. Despite efforts to coordinate the creation of applications in an ever-increasing diversity of use cases, there have been challenges to ensure semantic interoperability, security, privacy, consistent interpretation of the specifications, and the workforce capacity across the industry. In response to these challenges, HL7 created the Standards Implementation Division with the aims of developing a unique ecosystem for continuous testing and discovery, building a global education and certification program, and facilitating advancement across the diverse FHIR implementation communi

The Epilepsy Ontology: a community-based ontology tailored for semantic interoperability and text mining.

Epilepsy is a multifaceted complex disorder that requires a precise understanding of the classification, diagnosis, treatment and disease mechanism governing it. Although scattered resources are available on epilepsy, comprehensive and structured knowledge is missing. In contemplation to promote multidisciplinary knowledge exchange and facilitate advancement in clinical management, especially in pre-clinical research, a disease-specific ontology is necessary. The presented ontology is designed to enable better interconnection between scientific community members in the epilepsy domain. The Epilepsy Ontology (EPIO) is an assembly of structured knowledge on various aspects of epilepsy, developed according to Basic Formal Ontology (BFO) and Open Biological and Biomedical Ontology (OBO) Foundry principles. Concepts and definitions are collected from the latest International League against Epilepsy (ILAE) classification, domain-specific ontologies and scientific literature. This ontology consists of 1879 classes and 28 151 axioms (2171 declaration axioms, 2219 logical axioms) from several aspects of epilepsy. This ontology is intended to be used for data management and text mining purposes. The current release of the ontology is publicly available under a Creative Commons 4.0 License and shared via http://purl.obolibrary.org/obo/epso.owl and is a community-based effort assembling various facets of the complex disease. The ontology is also deposited in BioPortal at https://bioportal.bioontology.org/ontologies/EPIO. Supplementary data are available at Bioinformatics Advances online.

Effective approaches for enhancing data management and semantic interoperability within the healthcare sector

Effective approaches for enhancing data management and semantic interoperability within the healthcare sector

DynO-IoT: a dynamic ontology for provisioning semantic interoperability in internet of things

DynO-IoT: a dynamic ontology for provisioning semantic interoperability in internet of things

Semantic interoperability model for learning object repositories

Semantic interoperability model for learning object repositories

Semantic interoperability model for learning object repositories

Semantic interoperability model for learning object repositories

Toward the Multilingual Semantic Web: Multilingual Ontology Matching and Assessment

The amount of multilingual data on the Web proliferates; therefore, developing ontologies in various natural languages is attracting considerable attention. In order to achieve semantic interoperability for the multilingual Web, cross-lingual ontology matching techniques are highly required. This paper proposes a Multilingual Ontology Matching (MoMatch) approach for matching ontologies in different natural languages. MoMatch uses machine translation and various string similarity techniques to identify correspondences across different ontologies. Furthermore, we propose a Quality Assessment Suite for Ontologies (QASO) that comprises 14 metrics, out of which seven metrics are used to assess the quality of the matching process and seven metrics are used to evaluate the quality of the ontology. We present an in-depth comparison of different string similarity techniques across various languages to get the most effective similarity measure(s) between multilingual terms. To illustrate the applicability of our approach and how it can be used in different domains, we present two use cases. MoMatch has been implemented using Scala and Apache Spark under an open-source license. We have compared our results with the results from the Ontology Alignment Evaluation Initiative (OAEI 2020). MoMatch has achieved significantly high precision, recall, and F-measure compared to five state-of-the-art matching approaches.