María Teresa Romá-Ferri

Evaluation of an integrated system for classification, assessment and comparison of services for long-term care in Europe: the eDESDE-LTC study

BackgroundThe harmonization of European health systems brings with it a need for tools to allow the standardized collection of information about medical care. A common coding system and standards for the description of services are needed to allow local data to be incorporated into evidence-informed policy, and to permit equity and mobility to be assessed. The aim of this project has been to design such a classification and a related tool for the coding of services for Long Term Care (DESDE-LTC), based on the European Service Mapping Schedule (ESMS).MethodsThe development of DESDE-LTC followed an iterative process using nominal groups in 6 European countries. 54 researchers and stakeholders in health and social services contributed to this process. In order to classify services, we use the minimal organization unit or “Basic Stable Input of Care” (BSIC), coded by its principal function or “Main Type of Care” (MTC). The evaluation of the tool included an analysis of feasibility, consistency, ontology, inter-rater reliability, Boolean Factor Analysis, and a preliminary impact analysis (screening, scoping and appraisal).ResultsDESDE-LTC includes an alpha-numerical coding system, a glossary and an assessment instrument for mapping and counting LTC. It shows high feasibility, consistency, inter-rater reliability and face, content and construct validity. DESDE-LTC is ontologically consistent. It is regarded by experts as useful and relevant for evidence-informed decision making.ConclusionDESDE-LTC contributes to establishing a common terminology, taxonomy and coding of LTC services in a European context, and a standard procedure for data collection and international comparison.

Análisis de terminologías de salud para su utilización como ontologías computacionales en los sistemas de información clínicos

Objetivos: Las ontologias son un recurso que permite trabajar informaticamente con la conceptualizacion del significado y evitar la limitacion impuesta por los terminos normalizados. El objetivo de este estudio es establecer el grado de usabilidad de las terminologias para el diseno de ontologias, que contribuyan a resolver los problemas de interoperabilidad semantica, y de reutilizacion de conocimiento en los sistemas de informacion clinicos. Metodos: Se han analizado 6 de las terminologias mas relevantes para el ambito clinico, epidemiologico, documental y administrativo-economico. Se valoraron las siguientes cualidades: cobertura conceptual, estructura jerarquica, granularidad conceptual, relaciones conceptuales y grado de formalismo utilizado en la representacion conceptual, para establecer el grado de usabilidad. Resultados: Se consideran como ontologias ligeras los MeSH, los DeCS y el UMLS, aunque con diferencias entre ellas, al explicitar los conceptos, el tipo de relacion y las restricciones entre los conceptos asociados. SNOMED y GALEN, con su formalismo declarativo basado en descripciones logicas, incluyen la explicitacion de las cualidades, una mayor restriccion para relacionar conceptos y las reglas de combinacion entre ellos, por lo que se consideran como ontologias pesadas. Conclusiones: El analisis de la representacion declarada de las terminologias muestra las posibilidades de su reutilizacion como ontologias. Su grado de usabilidad dependera de si se pretende que los sistemas de informacion clinicos resuelvan los problemas de interoperabilidad semantica (ontologias ligeras) o ademas reutilizar su conocimiento para sistemas de ayuda a la toma de decisiones (ontologias pesadas) y para tareas de recuperacion, extraccion y clasificacion de informacion no estructurada.

COMPENDIUM: a text summarization system for generating abstracts of research papers

This paper presents COMPENDIUM, a text summarization system, which has achieved good results in extractive summarization. Therefore, our main goal in this research is to extend it, suggesting a new approach for generating abstractive-oriented summaries of research papers. We conduct a preliminary analysis where we compare the extractive version of COMPENDIUM (COMPENDIUME) with the new abstractiveoriented approach (COMPENDIUME-A). The final summaries are evaluated according to three criteria (content, topic, and user satisfaction) and, from the results obtained, we can conclude that the use of COMPENDIUM is appropriate for producing summaries of research papers automatically, going beyond the simple selection of sentences.

MaNER: A MedicAl Named Entity Recogniser

This paper describes a medicinal products and active ingredients named entity recogniser (MaNER) for Spanish technical documents. This rule-based system uses high quality and low-maintenance lexicons. Our results (F-measure 90 %) proves that dictionary-based approaches, without any deep natural language processing (e.g. POS tagging), can achieve a high performance in this task. Our system obtains better results when compared to similar systems.

An Active Ingredients Entity Recogniser System Based on Profiles

This paper describes an active ingredients named entity recogniser. Our machine learning system, which is language and domain independent, employs unsupervised feature generation and weighting from the training data. The proposed automatic feature extraction process is based on generating a profile for the given entity without traditional knowledge resources (such as dictionaries). Our results (F1 87.3 % [95 %CI: 82.07–92.53]) proves that unsupervised feature generation can achieve a high performance for this task.

Named Entity Classification Based on Profiles: A Domain Independent Approach

This paper presents a Named Entity Classification system, which uses profiles and machine learning based on [6]. Aiming at confirming its domain independence, it is tested on two domains: general - CONLL2002 corpus, and medical - DrugSemantics gold standard. Given our overall results (CONLL2002, F1 = 67.06; DrugSemantics, F1 = 71.49), our methodology has proven to be domain independent.

A Domain and Language Independent Named Entity Classification Approach Based on Profiles and Local Information.

This paper presents a Named Entity Classification system, which employs machine learning. Our methodology employs local entity information and profiles as feature set. All features are generated in an unsupervised manner. It is tested on two different data sets: (i) DrugSemantics Spanish corpus (Overall F1 = 74.92), whose results are in-line with the state of the art without employing external domain-specific resources. And, (ii) English CONLL2003 dataset (Overall F1 = 81.40), although our results are lower than previous work, these are reached without external knowledge or complex linguistic analysis. Last, using the same configuration for the two corpora, the difference of overall F1 is only 6.48 points (DrugSemantics = 74.92 versus CoNLL2003 = 81.40). Thus, this result supports our hypothesis that our approach is language and domain independent and does not require any external knowledge or complex linguistic analysis.

OntoFIS as a NLP resource in the drug-therapy domain: design issues and solutions applied

In the Health domain, and specifically in the drug-therapy domain, in order to improve the access to the information of different types of users, several informational resources, semantically annotated, are under development. One of the existing development lines is oriented to reusing the effort spent on the design of the existing resources on the Web and obtaining knowledge-based resources for natural language processing (NLP) tasks. In this line, OntoFIS was designed as a NLP resource aimed at filling the gap of multilingual knowledgebased resources within the domain. The design process used for building OntoFIS merges the best approaches of several ontology design methodologies. However, given the characteristics of the drug-therapy domain, whose needs of knowledge are very precise, the process of formalisation of the domain knowledge led to a set of issues. Thus, this paper discusses the main issues found and the solutions analysed and applied in each case.