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Since the creation of The International Classification of Diseases (ICD), new versions have been released to keep updated with the current medical knowledge. Migrations of Electronic Health Records (EHR) from ICD-9 to ICD-10-PCS as clinical procedure codification system, has been a significant challenge and involved large resources. In addition, it created new barriers for integrated access to legacy medical procedure data (frequently ICD-9 coded) with current data (frequently ICD-10-PCS coded). This work proposes a solution based on extending ICD-10-PCS with a subgroup of ICD-9-CM concepts to facilitate such integrated access. The General Equivalence Mappings (GEMs) has been used as foundation to set the terminology relations of these inserted concepts in ICD-10-PCS hierarchy, but due to the existence of 1-to-many mappings, advanced rules are required to seamlessly integrate both terminologies. With the generation of rules based on GEMs relationships, 2014 ICD-9 concepts were included within the ICD-10-PCS hierarchy. For the rest of the concepts, a new method is also proposed to increase 1-to-1 mappings. As results, with the suggested approach, the percentage of ICD-9-CM procedure concepts that can be mapped accurately (avoiding mappings to a large number of concepts) rise from 11.56% to 69.01% of ICD-9-Proc, through the extended ICD-10-PCS hierarchy.

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.

Este Trabajo de Fin de Grado ha sido realizado por Enrique Alonso Oset, alumno de Grado en Ingeniería Informática en la Escuela Técnica Superior de Ingenieros Informáticos de la Universidad Politécnica de Madrid bajo la supervisión del tutor del proyecto, Raúl Alonso Calvo. El trabajo se ha desarrollado durante el segundo semestre del curso académico 2013/2014. El trabajo ha sido realizado dentro del marco de los proyectos EURECA (Enabling information re-Use by linking clinical REsearch and Care) e INTEGRATE (Integrative Cancer Research Through Innovative Biomedical Infrastructures), en los que colabora el Grupo de Informática Biomédica de la UPM junto a otras universidades e instituciones sanitarias europeas. En ambos proyectos se desarrollan servicios e infraestructuras con el objetivo principal de almacenar información clínica, procedente de fuentes diversas (como por ejemplo de historiales clínicos electrónicos de hospitales, de ensayos clínicos o artículos de investigación biomédica), de una forma común y fácilmente accesible y consultable para facilitar al máximo la investigación de estos ámbitos, de manera colaborativa entre instituciones. Esta es la idea principal de la interoperabilidad semántica en la que se concentran ambos proyectos, siendo clave para el correcto funcionamiento del software del que se componen. El intercambio de datos con un modelo de representación compartido, común y sin ambigüedades, en el que cada concepto, término o dato clínico tendrá una única forma de representación. Lo cual permite la inferencia de conocimiento, y encaja perfectamente en el contexto de la investigación médica. En concreto, la herramienta a desarrollar en este trabajo también está orientada a la idea de maximizar la interoperabilidad semántica, pues se ocupa de la carga de información clínica con un formato estandarizado en un modelo común de almacenamiento de datos, implementado en bases de datos relacionales. El trabajo ha sido desarrollado en el periodo compre