Sergio Paraiso-Medina

Semantic Normalization and Query Abstraction Based on SNOMED-CT and HL7: Supporting Multicentric Clinical Trials

Advances in the use of omic data and other biomarkers are increasing the number of variables in clinical research. Additional data have stratified the population of patients and require that current studies be performed among multiple institutions. Semantic interoperability and standardized data representation are a crucial task in the management of modern clinical trials. In the past few years, different efforts have focused on integrating biomedical information. Due to the complexity of this domain and the specific requirements of clinical research, the majority of data integration tasks are still performed manually. This paper presents a semantic normalization process and a query abstraction mechanism to facilitate data integration and retrieval. A process based on well-established standards from the biomedical domain and the latest semantic web technologies has been developed. Methods proposed in this paper have been tested within the EURECA EU research project, where clinical scenarios require the extraction of semantic knowledge from biomedical vocabularies. The aim of this paper is to provide a novel method to abstract from the data model and query syntax. The proposed approach has been compared with other initiatives in the field by storing the same dataset with each of those solutions. Results show an extended functionality and query capabilities at the cost of slightly worse performance in query execution. Implementations in real settings have shown that following this approach, usable interfaces can be developed to exploit clinical trial data outcomes.

Enabling semantic interoperability in multi-centric clinical trials on breast cancer

BACKGROUND AND OBJECTIVES Post-genomic clinical trials require the participation of multiple institutions, and collecting data from several hospitals, laboratories and research facilities. This paper presents a standard-based solution to provide a uniform access endpoint to patient data involved in current clinical research. METHODS The proposed approach exploits well-established standards such as HL7 v3 or SPARQL and medical vocabularies such as SNOMED CT, LOINC and HGNC. A novel mechanism to exploit semantic normalization among HL7-based data models and biomedical ontologies has been created by using Semantic Web technologies. RESULTS Different types of queries have been used for testing the semantic interoperability solution described in this paper. The execution times obtained in the tests enable the development of end user tools within a framework that requires efficient retrieval of integrated data. CONCLUSIONS The proposed approach has been successfully tested by applications within the INTEGRATE and EURECA EU projects. These applications have been deployed and tested for: (i) patient screening, (ii) trial recruitment, and (iii) retrospective analysis; exploiting semantically interoperable access to clinical patient data from heterogeneous data sources.

A semantic interoperability approach to support integration of gene expression and clinical data in breast cancer

INTRODUCTION The introduction of omics data and advances in technologies involved in clinical treatment has led to a broad range of approaches to represent clinical information. Within this context, patient stratification across health institutions due to omic profiling presents a complex scenario to carry out multi-center clinical trials. METHODS This paper presents a standards-based approach to ensure semantic integration required to facilitate the analysis of clinico-genomic clinical trials. To ensure interoperability across different institutions, we have developed a Semantic Interoperability Layer (SIL) to facilitate homogeneous access to clinical and genetic information, based on different well-established biomedical standards and following International Health (IHE) recommendations. RESULTS The SIL has shown suitability for integrating biomedical knowledge and technologies to match the latest clinical advances in healthcare and the use of genomic information. This genomic data integration in the SIL has been tested with a diagnostic classifier tool that takes advantage of harmonized multi-center clinico-genomic data for training statistical predictive models. CONCLUSIONS The SIL has been adopted in national and international research initiatives, such as the EURECA-EU research project and the CIMED collaborative Spanish project, where the proposed solution has been applied and evaluated by clinical experts focused on clinico-genomic studies.

SNOMED2HL7: A tool to normalize and bind SNOMED CT concepts to the HL7 Reference Information Model

BACKGROUND Current clinical research and practice requires interoperability among systems in a complex and highly dynamic domain. There has been a significant effort in recent years to develop integrative common data models and domain terminologies. Such efforts have not completely solved the challenges associated with clinical data that are distributed among different and heterogeneous institutions with different systems to encode the information. Currently, when providing homogeneous interfaces to exploit clinical data, certain transformations still involve manual and time-consuming processes that could be automated. OBJECTIVES There is a lack of tools to support data experts adopting clinical standards. This absence is especially significant when links between data model and vocabulary are required. The objective of this work is to present SNOMED2HL7, a novel tool to automatically link biomedical concepts from widely used terminologies, and the corresponding clinical context, to the HL7 Reference Information Model (RIM). METHODS Based on the recommendations of the International Health Terminology Standards Development Organisation (IHTSDO), the SNOMED Normal Form has been implemented within SNOMED2HL7 to decompose and provide a method to reduce the number of options to store the same information. The binding of clinical terminologies to HL7 RIM components is the core of SNOMED2HL7, where terminology concepts have been annotated with the corresponding options within the interoperability standard. A web-based tool has been developed to automatically provide information from the normalization mechanisms and the terminology binding. RESULTS SNOMED2HL7 binding coverage includes the majority of the concepts used to annotate legacy systems. It follows HL7 recommendations to solve binding overlaps and provides the binding of the normalized version of the concepts. The first version of the tool, available at http://kandel.dia.fi.upm.es:8078, has been validated in EU funded projects to integrate real world data for clinical research with an 88.47% of accuracy. CONCLUSIONS This paper presents the first initiative to automatically retrieve concept-centered information required to transform legacy data into widely adopted interoperability standards. Although additional functionality will extend capabilities to automate data transformations, SNOMED2HL7 already provides the functionality required for the clinical interoperability community.

Querying clinical data in HL7 RIM based relational model with morph-RDB

BackgroundSemantic interoperability is essential when carrying out post-genomic clinical trials where several institutions collaborate, since researchers and developers need to have an integrated view and access to heterogeneous data sources. One possible approach to accommodate this need is to use RDB2RDF systems that provide RDF datasets as the unified view. These RDF datasets may be materialized and stored in a triple store, or transformed into RDF in real time, as virtual RDF data sources. Our previous efforts involved materialized RDF datasets, hence losing data freshness.ResultsIn this paper we present a solution that uses an ontology based on the HL7 v3 Reference Information Model and a set of R2RML mappings that relate this ontology to an underlying relational database implementation, and where morph-RDB is used to expose a virtual, non-materialized SPARQL endpoint over the data.ConclusionsBy applying a set of optimization techniques on the SPARQL-to-SQL query translation algorithm, we can now issue SPARQL queries to the underlying relational data with generally acceptable performance.

Carbon Nanotubes’ Effect on Mitochondrial Oxygen Flux Dynamics: Polarography Experimental Study and Machine Learning Models using Star Graph Trace Invariants of Raman Spectra

This study presents the impact of carbon nanotubes (CNTs) on mitochondrial oxygen mass flux (Jm) under three experimental conditions. New experimental results and a new methodology are reported for the first time and they are based on CNT Raman spectra star graph transform (spectral moments) and perturbation theory. The experimental measures of Jm showed that no tested CNT family can inhibit the oxygen consumption profiles of mitochondria. The best model for the prediction of Jm for other CNTs was provided by random forest using eight features, obtaining test R-squared (R2) of 0.863 and test root-mean-square error (RMSE) of 0.0461. The results demonstrate the capability of encoding CNT information into spectral moments of the Raman star graphs (SG) transform with a potential applicability as predictive tools in nanotechnology and material risk assessments.

Translational Bioinformatics: Informatics, Medicine, and -Omics

This article reviews some recent achievements reported in the area of Translational Bioinformatics (TBI), which has evolved rapidly as result of the Human Genome Project and subsequent -omic projects. Our goal is to support the understanding and enhancement of informatics research and applications at the intersection between medicine and the -omics fields. We discuss current progress and directions in the road ahead for this field, which already involves a significant number of dedicated professionals in research projects and conferences. Through a literature review, a list of topics of informatics research in TBI has been created, including decision support systems, natural language processing, standards, information retrieval, data, text and opinion mining, electronic health records (EHRs), and data integration. We also describe examples of the most challenging categories for research, such as discovery in EHRs, pharmacogenomics, drug repurposing, and genomic testing for individuals. We conclude with an overview of some of the challenges and opportunities presented by this field for research and education, particularly from the perspective of precision medicine.