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

[Abstract] 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.Instituto de Salud Carlos III; PI13/0202

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

[Abstract] 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 assessmentsInstituto de Salud Carlos III; PI13/02020Instituto de Salud Carlos III; PI13/00280Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; R2014/025Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; GRC2014/049Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; R2014/039Ministerio de Economía y Competitividad; UNLC08-1E-002Ministerio de Economía y Competitividad ; UNLC13-13-3503Ministerio de Economía y Competitividad; CTQ2016-74881-PPaís Vasco.Gobierno; IT1045-16Brasil. Conselho Nacional de Desenvolvimento Científico e Tecnológico; 308539/2016-8Brasil. Conselho Nacional de Desenvolvimento Científico e Tecnológico; 454332/2014-

Método de normalización de datos y abstracción de consultas basado en estándares médicos

Los avances producidos durante las últimas décadas en la investigación clínica han provocado un aumento en la cantidad de información y en los recursos informáticos disponibles. La introducción de nuevos marcadores y pruebas moleculares han aumentado las posibilidades diagnósticas y terapéuticas, aunque a costa de incrementar los requisitos necesarios para la realización de ensayos clínicos. Los cambios han sido tan relevantes que actualmente la mayor parte de los ensayos y estudios clínicos tienen que ser realizados en distintas ubicaciones o regiones mediante la colaboración de diversos centros que puedan intercambiar sus datos. Ante la necesidad de intercambio de datos entre distintos centros surge la oportunidad de investigar nuevos métodos de integración que faciliten la recuperación de la información e interoperabilidad entre distintos sistemas y aplicaciones. En este contexto de interoperabilidad clínica, actualmente destacan propuestas de distintas tecnologías y estándares biomédicos que sirvan como punto común en distintos ámbitos. Hasta el momento presente, esta cuestión no ha sido resuelta. Por ello, se plantea la hipótesis de si es posible enriquecer y corregir la representación de datos clínicos, explotando las ventajas y el conocimiento de terminologías y estándares biomédicos, que mejoren su homogeneización. El desarrollo de métodos basados en estándares clínicos puede facilitar el desarrollo de soluciones adaptables a otros sistemas y contextos de la práctica clínica, lo que permitiría un avance en el campo de la integración e interoperabilidad de datos clínicos. Como respuesta a esta hipótesis, el presente trabajo plantea la tesis de que es posible crear un nuevo método de interoperabilidad entre sistemas clínicos, que combina el diseño de un método de normalización semántica y un método de abstracción de consultas. Para evaluar experimentalmente esta propuesta, se ha realizado una implementación sobre un conjunto relevante de estándares clínicos. Dicha implementación se ha integrado dentro de una capa de interoperabilidad semántica utilizada para almacenar conjuntos de datos clínicos reales de diversas instituciones en entornos de investigación. La evaluación de los métodos propuestos ha constado de un análisis de la representación y acceso a los datos mediante los métodos diseñados, así como de su comparación con otros sistemas similares. El trabajo propuesto en la presente tesis doctoral se enmarca en el área de la informática médica y se ha evaluado finalmente en el marco de varios proyectos de investigación europeos, que han servido como entorno de pruebas y evaluación de los métodos propuestos. Además, la presente tesis doctoral ha generado diversas publicaciones en revistas científicas de impacto y en congresos internacionales durante los años en los que se ha realizado el trabajo. ----------ABSTRACT---------- Clinical research advances during the last decades have led to an increase in the amount of information and the available resources and repositories. The introduction of new biomarkers and molecular tests have greatly increased diagnostic and therapeutic capabilities, although at the expense of increasing the requirement complexity for carrying out clinical trials. Most of the clinical trials and studies have to be currently executed in collaboration among different institutions exchanging data. Given the need for data exchange between different institutions, there is an opportunity to study new integration methods focused on enabling information retrieval and interoperability between different systems and applications. In the clinical interoperability context, there are different biomedical standards and technologies used as a common point in different areas. However, this question has not been yet solved at this moment. The present work raises the hypothesis of whether it is possible to enrich and correct clinical data representation, exploiting the advantages and knowledge of biomedical terminologies and standards for improving their homogenization. Novel methods based on well-established standards can facilitate the development of adaptable solutions for other systems and clinical contexts. It will facilitate advances in the field of clinical data integration and interoperability. In response to this hypothesis, this work proposes a new method of interoperability between clinical systems, which combines the design of a semantic normalization method and a query abstraction method. To experimentally evaluate this proposal, an implementation based on a relevant set of clinical standards has been developed. This implementation has been integrated into a semantic interoperability layer used to store real clinical datasets from various institutions in research projects. The evaluation of the proposed methods has been carried out through an analysis of representation and data access using such methods, as well as a functional comparison with similar systems. The work proposed in this doctoral dissertation is placed in the area of biomedical informatics. It was finally evaluated within the framework of two European Research projects, which served as an ideal environment for testing and evaluating the proposed methods. In addition, the present doctoral dissertation has already led to several publications in high impact scientific journals and International conferences