A New Method to Retrieve, Cluster And Annotate Clinical Literature Related To Electronic Health Records

The access to medical literature collections such as PubMed, MedScape or Cochrane has been increased notably in the last years by the web-based tools that provide instant access to the information. However, more sophisticated methodologies are needed to exploit efficiently all that information. The lack of advanced search methods in clinical domain produce that even using well-defined questions for a particular disease, clinicians receive too many results. Since no information analysis is applied afterwards, some relevant results which are not presented in the top of the resultant collection could be ignored by the expert causing an important loose of information. In this work we present a new method to improve scientific article search using patient information for query generation. Using federated search strategy, it is able to simultaneously search in different resources and present a unique relevant literature collection. And applying NLP techniques it presents semantically similar publications together, facilitating the identification of relevant information to clinicians. This method aims to be the foundation of a collaborative environment for sharing clinical knowledge related to patients and scientific publications.

Service for the pseudonymization of electronic healthcare records based on ISO/EN 13606 for the secondary use of information

The availability of electronic health data favors scientific advance through the creation of repositories for secondary use. Data anonymization is a mandatory step to comply with current legislation. A service for the pseudonymization of electronic healthcare record (EHR) extracts aimed at facilitating the exchange of clinical information for secondary use in compliance with legislation on data protection is presented. According to ISO/TS 25237, pseudonymization is a particular type of anonymization. This tool performs the anonymizations by maintaining three quasi-identifiers (gender, date of birth and place of residence) with a degree of specification selected by the user. The developed system is based on the ISO/EN 13606 norm using its characteristics specifically favorable for anonymization. The service is made up of two independent modules: the demographic server and the pseudonymizing module. The demographic server supports the permanent storage of the demographic entities and the management of the identifiers. The pseudonymizing module anonymizes the ISO/EN 13606 extracts. The pseudonymizing process consists of four phases: the storage of the demographic information included in the extract, the substitution of the identifiers, the elimination of the demographic information of the extract and the elimination of key data in free-text fields. The described pseudonymizing system was used in three Telemedicine research projects with satisfactory results. A problem was detected with the type of data in a demographic data field and a proposal for modification was prepared for the group in charge of the drawing up and revision of the ISO/EN 13606 norm.

PREDIRCAM eHealth platform for individualized telemedical assistance for lifestyle modification in the treatment of obesity, diabetes, and cardiometabolic risk prevention: a pilot study (PREDIRCAM 1)

Background: Healthy diet and regular physical activity are powerful tools in reducing diabetes and cardiometabolic risk. Various international scientific and health organizations have advocated the use of new technologies to solve these problems. The PREDIRCAM project explores the contribution that a technological system could offer for the continuous monitoring of lifestyle habits and individualized treatment of obesity as well as cardiometabolic risk prevention. Methods: PREDIRCAM is a technological platform for patients and professionals designed to improve the effectiveness of lifestyle behavior modifications through the intensive use of the latest information and communication technologies. The platform consists of a web-based application providing communication interface with monitoring devices of physiological variables, application for monitoring dietary intake, ad hoc electronic medical records, different communication channels, and an intelligent notification system. A 2-week feasibility study was conducted in 15 volunteers to assess the viability of the platform. Results: The website received 244 visits (average time/session: 17 min 45 s). A total of 435 dietary intakes were recorded (average time for each intake registration, 4 min 42 s ± 2 min 30 s), 59 exercises were recorded in 20 heart rate monitor downloads, 43 topics were discussed through a forum, and 11 of the 15 volunteers expressed a favorable opinion toward the platform. Food intake recording was reported as the most laborious task. Ten of the volunteers considered long-term use of the platform to be feasible. Conclusions: The PREDIRCAM platform is technically ready for clinical evaluation. Training is required to use the platform and, in particular, for registration of dietary food intake.

CDAPubMed: a browser extension to retrieve EHR-based biomedical literature

Over the last few decades, the ever-increasing output of scientific publications has led to new challenges to keep up to date with the literature. In the biomedical area, this growth has introduced new requirements for professionals, e.g., physicians, who have to locate the exact papers that they need for their clinical and research work amongst a huge number of publications. Against this backdrop, novel information retrieval methods are even more necessary. While web search engines are widespread in many areas, facilitating access to all kinds of information, additional tools are required to automatically link information retrieved from these engines to specific biomedical applications. In the case of clinical environments, this also means considering aspects such as patient data security and confidentiality or structured contents, e.g., electronic health records (EHRs). In this scenario, we have developed a new tool to facilitate query building to retrieve scientific literature related to EHRs. Results: We have developed CDAPubMed, an open-source web browser extension to integrate EHR features in biomedical literature retrieval approaches. Clinical users can use CDAPubMed to: (i) load patient clinical documents, i.e., EHRs based on the Health Level 7-Clinical Document Architecture Standard (HL7-CDA), (ii) identify relevant terms for scientific literature search in these documents, i.e., Medical Subject Headings (MeSH), automatically driven by the CDAPubMed configuration, which advanced users can optimize to adapt to each specific situation, and (iii) generate and launch literature search queries to a major search engine, i.e., PubMed, to retrieve citations related to the EHR under examination. Conclusions: CDAPubMed is a platform-independent tool designed to facilitate literature searching using keywords contained in specific EHRs. CDAPubMed is visually integrated, as an extension of a widespread web browser, within the standard PubMed interface. It has been tested on a public dataset of HL7-CDA documents, returning significantly fewer citations since queries are focused on characteristics identified within the EHR. For instance, compared with more than 200,000 citations retrieved by breast neoplasm, fewer than ten citations were retrieved when ten patient features were added using CDAPubMed. This is an open source tool that can be freely used for non-profit purposes and integrated with other existing systems.

Implementación del modelo RIM de HL7 v3 en orientación a objetos y su uso en procesos de interoperabilidad semántica

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 comprendido entre el 3 de Febrero y el 6 de Junio de 2014. Se ha seguido un ciclo de vida en cascada para la organización del trabajo realizado en las tareas de las que se compone el proyecto, de modo que una fase no puede iniciarse sin que se haya terminado, revisado y aceptado la fase anterior. Exceptuando la tarea de documentación del trabajo (para la elaboración de esta memoria), que se ha desarrollado paralelamente a todas las demás. ----ABSTRACT--- The project has been developed during the second semester of the 2013/2014 academic year. This Project has been done inside EURECA and INTEGRATE European biomedical research projects, where the GIB (Biomedical Informatics Group) of the UPM works as a partner. Both projects aim is to develop platforms and services with the main goal of storing clinical information (e.g. information from hospital electronic health records (EHRs), clinical trials or research articles) in a common way and easy to access and query, in order to support medical research. The whole software environment of these projects is based on the idea of semantic interoperability, which means the ability of computer systems to exchange data with unambiguous and shared meaning. This idea allows knowledge inference, which fits perfectly in medical research context. The tool to develop in this project is also "semantic operability-oriented". Its purpose is to store standardized clinical information in a common data model, implemented in relational databases. The project has been performed during the period between February 3rd and June 6th, of 2014. It has followed a "Waterfall model" of software development, in which progress is seen as flowing steadily downwards through its phases. Each phase starts when its previous phase has been completed and reviewed. The task of documenting the project‟s work is an exception; it has been performed in a parallel way to the rest of the tasks.

Diseño e implementación de un algoritmo para la detección de la negación de textos clínicos en español

Vivimos en una época en la que cada vez existe una mayor cantidad de información. En el dominio de la salud la historia clínica digital ha permitido digitalizar toda la información de los pacientes. Estas historias clínicas digitales contienen una gran cantidad de información valiosa escrita en forma narrativa que sólo podremos extraer recurriendo a técnicas de procesado de lenguaje natural. No obstante, si se quiere realizar búsquedas sobre estos textos es importante analizar que la información relativa a síntomas, enfermedades, tratamientos etc. se puede refererir al propio paciente o a sus antecentes familiares, y que ciertos términos pueden aparecer negados o ser hipotéticos. A pesar de que el español ocupa la segunda posición en el listado de idiomas más hablados con más de 500 millones de hispano hablantes, hasta donde tenemos de detección de la negación, probabilidad e histórico en textos clínicos en español. Por tanto, este Trabajo Fin de Grado presenta una implementación basada en el algoritmo ConText para la detección de la negación, probabilidad e histórico en textos clínicos escritos en español. El algoritmo se ha validado con 454 oraciones que incluían un total de 1897 disparadores obteniendo unos resultado de 83.5 %, 96.1 %, 96.9 %, 99.7% y 93.4% de exactitud con condiciones afirmados, negados, probable, probable negado e histórico respectivamente. ---ABSTRACT---We live in an era in which there is a huge amount of information. In the domain of health, the electronic health record has allowed to digitize all the information of the patients. These electronic health records contain valuable information written in narrative form that can only be extracted using techniques of natural language processing. However, if you want to search on these texts is important to analyze if the relative information about symptoms, diseases, treatments, etc. are referred to the patient or family casework, and that certain terms may appear negated or be hypothesis. Although Spanish is the second spoken language with more than 500 million speakers, there seems to be no method of detection of negation, hypothesis or historical in medical texts written in Spanish. Thus, this bachelor’s final degree presents an implementation based on the ConText algorithm for the detection of negation, hypothesis and historical in medical texts written in Spanish. The algorithm has been validated with 454 sentences that included a total of 1897 triggers getting a result of 83.5 %, 96.1 %, 96.9 %, 99.7% and 93.4% accuracy with affirmed, negated, hypothesis, negated hypothesis and historical respectively.

Diseño e implementación de un módulo para la estructuración de notas clínicas

El presente Trabajo Fin de Grado (TFG) surge de la necesidad de disponer de tecnologías que faciliten el Procesamiento de Lenguaje Natural (NLP) en español dentro del sector de la medicina. Centrado concretamente en la extracción de conocimiento de las historias clínicas electrónicas (HCE), que recogen toda la información relacionada con la salud del paciente y en particular, de los documentos recogidos en dichas historias, pretende la obtención de todos los términos relacionados con la medicina. El Procesamiento de Lenguaje Natural permite la obtención de datos estructurados a partir de información no estructurada. Estas técnicas permiten un análisis de texto que genera etiquetas aportando significado semántico a las palabras para la manipulación de información. A partir de la investigación realizada del estado del arte en NLP y de las tecnologías existentes para otras lenguas, se propone como solución un módulo de anotación de términos médicos extraídos de documentos clínicos. Como términos médicos se han considerado síntomas, enfermedades, partes del cuerpo o tratamientos obtenidos de UMLS, una ontología categorizada que agrega distintas fuentes de datos médicos. Se ha realizado el diseño y la implementación del módulo así como el análisis de los resultados obtenidos realizando una evaluación con treinta y dos documentos que contenían 1372 menciones de terminología médica y que han dado un resultado medio de Precisión: 70,4%, Recall: 36,2%, Accuracy: 31,4% y F-Measure: 47,2%.---ABSTRACT---This Final Thesis arises from the need for technologies that facilitate the Natural Language Processing (NLP) in Spanish in the medical sector. Specifically it is focused on extracting knowledge from Electronic Health Records (EHR), which contain all the information related to the patient's health and, in particular, it expects to obtain all the terms related to medicine from the documents contained in these records. Natural Language Processing allows us to obtain structured information from unstructured data. These techniques enable analysis of text generating labels providing semantic meaning to words for handling information. From the investigation of the state of the art in NLP and existing technologies in other languages, an annotation module of medical terms extracted from clinical documents is proposed as a solution. Symptoms, diseases, body parts or treatments are considered part of the medical terms contained in UMLS ontology which is categorized joining different sources of medical data. This project has completed the design and implementation of a module and the analysis of the results have been obtained. Thirty two documents which contain 1372 mentions of medical terminology have been evaluated and the average results obtained are: Precision: 70.4% Recall: 36.2% Accuracy: 31.4% and F-Measure: 47.2%.

Modelo basado en técnicas de procesamiento de lenguaje natural para extraer y anotar información de publicaciones científicas

La rápida evolución experimentada en los últimos años por las tecnologías de Internet ha estimulado la proliferación de recursos software en varias disciplinas científicas, especialmente en bioinformática. En la mayoría de los casos, la tendencia actual es publicar dichos recursos como servicios accesibles libremente a través de Internet, utilizando tecnologías y patrones de diseño definidos para la implementación de Arquitecturas Orientadas a Servicios (SOA). La combinación simultánea de múltiples servicios dentro de un mismo flujo de trabajo abre la posibilidad de crear aplicaciones potencialmente más útiles y complejas. La integración de dichos servicios plantea grandes desafíos, tanto desde un punto de vista teórico como práctico, como por ejemplo, la localización y acceso a los recursos disponibles o la coordinación entre ellos. En esta tesis doctoral se aborda el problema de la identificación, localización, clasificación y acceso a los recursos informáticos disponibles en Internet. Con este fin, se ha definido un modelo genérico para la construcción de índices de recursos software con información extraída automáticamente de artículos de la literatura científica especializada en un área. Este modelo consta de seis fases que abarcan desde la selección de las fuentes de datos hasta el acceso a los índices creados, pasando por la identificación, extracción, clasificación y “curación” de la información relativa a los recursos. Para verificar la viabilidad, idoneidad y eficiencia del modelo propuesto, éste ha sido evaluado en dos dominios científicos diferentes—la BioInformática y la Informática Médica—dando lugar a dos índices de recursos denominados BioInformatics Resource Inventory (BIRI) y electronic-Medical Informatics Repository of Resources(e-MIR2) respectivamente. Los resultados obtenidos de estas aplicaciones son presentados a lo largo de la presente tesis doctoral y han dado lugar a varias publicaciones científicas en diferentes revistas JCR y congresos internacionales. El impacto potencial y la utilidad de esta tesis doctoral podrían resultar muy importantes teniendo en cuenta que, gracias a la generalidad del modelo propuesto, éste podría ser aplicado en cualquier disciplina científica. Algunas de las líneas de investigación futuras más relevantes derivadas de este trabajo son esbozadas al final en el último capítulo de este libro. ABSTRACT The rapid evolution experimented in the last years by the Internet technologies has stimulated the proliferation of heterogeneous software resources in most scientific disciplines, especially in the bioinformatics area. In most cases, current trends aim to publish those resources as services freely available over the Internet, using technologies and design patterns defined for the implementation of Service-Oriented Architectures (SOA). Simultaneous combination of various services into the same workflow opens the opportunity of creating more complex and useful applications. Integration of services raises great challenges, both from a theoretical to a practical point of view such as, for instance, the location and access to the available resources or the orchestration among them. This PhD thesis deals with the problem of identification, location, classification and access to informatics resources available over the Internet. On this regard, a general model has been defined for building indexes of software resources, with information extracted automatically from scientific articles from the literature specialized in the area. Such model consists of six phases ranging from the selection of data sources to the access to the indexes created, covering the identification, extraction, classification and curation of the information related to the software resources. To verify the viability, feasibility and efficiency of the proposed model, it has been evaluated in two different scientific domains—Bioinformatics and Medical Informatics—producing two resources indexes named BioInformatics Resources Inventory (BIRI) and electronic-Medical Informatics Repository of Resources (e-MIR2) respectively. The results and evaluation of those systems are presented along this PhD thesis, and they have produced different scientific publications in several JCR journals and international conferences. The potential impact and utility of this PhD thesis could be of great relevance considering that, thanks to the generality of the proposed model, it could be successfully extended to any scientific discipline. Some of the most relevant future research lines derived from this work are outlined at the end of this book.