Ranking Medical Subject Headings using a factor graph model.

Automatically assigning MeSH (Medical Subject Headings) to articles is an active research topic. Recent work demonstrated the feasibility of improving the existing automated Medical Text Indexer (MTI) system, developed at the National Library of Medicine (NLM). Encouraged by this work, we propose a novel data-driven approach that uses semantic distances in the MeSH ontology for automated MeSH assignment. Specifically, we developed a graphical model to propagate belief through a citation network to provide robust MeSH main heading (MH) recommendation. Our preliminary results indicate that this approach can reach high Mean Average Precision (MAP) in some scenarios

MeSHLabeler and DeepMeSH: Recent Progress in Large-Scale MeSH Indexing

The US National Library of Medicine (NLM) uses the Medical Subject Headings (MeSH) (seeNote 1 ) to index almost all 24 million citations in MEDLINE, which greatly facilitates the application of biomedical information retrieval and text mining. Large-scale automatic MeSH indexing has two challenging aspects: the MeSH side and citation side. For the MeSH side, each citation is annotated by only 12 (on average) out of all 28, 000 MeSH terms. For the citation side, all existing methods, including Medical Text Indexer (MTI) by NLM, deal with text by bag-of-words, which cannot capture semantic and context-dependent information well. To solve these two challenges, we developed the MeSHLabeler and DeepMeSH. By utilizing “learning to rank” (LTR) framework, MeSHLabeler integrates multiple types of information to solve the challenge in the MeSH side, while DeepMeSH integrates deep semantic representation to solve the challenge in the citation side. MeSHLabeler achieved the first place in both BioASQ2 and BioASQ3, and DeepMeSH achieved the first place in both BioASQ4 and BioASQ5 challenges. DeepMeSH is available at http://datamining-iip.fudan.edu.cn/deepmesh

Predicting Medical Subject Headings Based on Abstract Similarity and Citations to MEDLINE Records

We describe a classifier-enhanced nearest neighbor approach to assigning Medical Subject Headings (MeSH) to unlabeled documents using a combination of abstract similarities and direct citations to labeled MEDLINE records. The approach frames the classification problem by decomposing it into sets of siblings in the MeSH hierarchy (e.g., training a classifier for predicting "Heterocyclic Compounds, 2-Ring" vs. other "Heterocyclic Compounds"). Preliminary experiments using a small but diverse set of MeSH terms shows the highest performance when using both abstracts and citations compared to each alone, and coupled with a non-naive classifier: 90+% precision and recall with 10-fold cross-validation. NLM's Medical Text Indexer (MTI) tool achieves similar overall performance but varies more across the terms tested. For example, MTI performs better on "Heterocyclic Compounds, 2-Ring", while our approach performs better on Alzheimer Disease and Neuroimaging. Our approach can be applied broadly to documents with abstracts that are similar to (or cite) MEDLINE abstracts, which would help linking and searching across bibliographic databases beyond MEDLINE.Ope

Iniciativas de evaluación para la indización semántica de literatura médica en español: PLANTL, LILACS, IBECS Y BIOASQ

XVI Jornadas Nacionales de Información y Documentación en Ciencias de la Salud. Oviedo, 4-5 de abril de 2019El proyecto Faro de Sanidad del Plan de Impulso de las Tecnologías del Lenguaje (PlanTL) pretende fomentar el desarrollo de sistemas de procesamiento del lenguaje natural (PLN), minería de textos y traducción automática para español y lenguas cooficiales. Una actividad importante del PlanTL es la organización de campañas de evaluación de sistemas de PLN y minería de textos, un mecanismo que no sólo es clave para evaluar la calidad de los resultados obtenidos por sistemas y algoritmos predictivos, sino que representa un motor fundamental para fomentar el desarrollo de herramientas y recursos de tecnologías del lenguaje. Debido a la importancia de la literatura para la toma de decisiones en medicina y el volumen considerable de publicaciones en español, el Plan TL, en colaboración con el BSC, el CNIO, la BNCS y la iniciativa BioASQ ha lanzado una tarea competitiva relacionada con la indización automática de la literatura médica en español con términos DeCS. Su fin es generar recursos de etiquetado semántico que sirvan de ayuda a la indización manual. La tarea BioASQ (bioasq.org) de indización semántica biomédica en español se realizará usando resúmenes de artículos de revistas contenidas en las bases de datos LILACS (Literatura Lationamericana en Ciencias de la Salud) y IBECS1 (Índice Bibliográfico Español en Ciencias de la Salud) como conjunto básico etiquetado y, a partir de ellos, desarrollar los algoritmos de indización automática, facilitando así el desarrollo de modelos de inteligencia artificial. La evaluación de los sistemas se realiza con la plataforma de BioASQ, mediante un sistema de evaluación continua. En él, se solicita a los participantes que asignen automáticamente términos DeCS a los registros nuevos añadidos a las bases de datos a medida que se hacen públicos, y antes de que se haya completado la indización manual. El rendimiento de indización se calcula comparando indización automática y manual. Gracias a los resultados de ediciones previas de BioASQ para la indización de PubMed, se ha mejorado este proceso en dicho recurso. Esta tarea de indización biomédica en español servirá para generar recursos comparables para indizar LILACS e IBECS y otros conjuntos documentales.The health flagship project of the Plan for the Advancement of Language Technology (PlanTL) tries to promote the development of natural language processing systems (NLP), text mining and machine translation resources for Spanish and co-official languages. There is a growing demand for a better exploitation of datasets generated by clinicians, especially electronic health records, as well as the integration and management of this kind of data in personalized medicine platforms integrating also information extracted from the literature. In this context, the PlanTL collaborates in the organization of evaluation efforts of clinical NLP and text mining systems, a key mechanism to evaluate the quality of results obtained by such automated systems and a fundamental mechanism to promote the development of tools and resources related to language technologies. Given the importance of literature for medical decision-making and the growing volume of Spanish medical publications, the TL Plan, in collaboration with the BSC, CNIO, the Biblioteca Nacional de Ciencias de la Salud and the BioASQ team have launched a shared task on automatic indexing of abstracts in Spanish with DeCS terms. The aim of this tracks is to generate semantic annotation resources that can be used to assist manual indexing. The Spanish biomedical semantic indexing track of BioASQ (bioasq.org) will rely on abstracts of journals contained in the LILACS databases as a basic Gold Standard manually labeled benchmark set for the development of automatic indexing algorithms particularly those based on artificial intelligence language models. The evaluation of participating systems is done through the BioASQ platform, which requests results in a continuous evaluation process, i.e. automatically asking for DeCS term assignment for newly added documents to LILACS, as they are made public, and before the manual indexing results are publicly released. The indexing performance in BioASQ is calculated by comparing automatic indexing against manual annotations. Thanks to the results of previous editions of BioASQ for indexing PubMed, the MeSH indexing process of this resource was considerably improved. This novel effort on medical indexing in Spanish will serve to generate comparable resources to semantically index not only LILACS but also other health databases and repositories in Spanish.N

Preparing a collection of radiology examinations for distribution and retrieval

OBJECTIVE: Clinical documents made available for secondary use play an increasingly important role in discovery of clinical knowledge, development of research methods, and education. An important step in facilitating secondary use of clinical document collections is easy access to descriptions and samples that represent the content of the collections. This paper presents an approach to developing a collection of radiology examinations, including both the images and radiologist narrative reports, and making them publicly available in a searchable database. MATERIALS AND METHODS: The authors collected 3996 radiology reports from the Indiana Network for Patient Care and 8121 associated images from the hospitals' picture archiving systems. The images and reports were de-identified automatically and then the automatic de-identification was manually verified. The authors coded the key findings of the reports and empirically assessed the benefits of manual coding on retrieval. RESULTS: The automatic de-identification of the narrative was aggressive and achieved 100% precision at the cost of rendering a few findings uninterpretable. Automatic de-identification of images was not quite as perfect. Images for two of 3996 patients (0.05%) showed protected health information. Manual encoding of findings improved retrieval precision. CONCLUSION: Stringent de-identification methods can remove all identifiers from text radiology reports. DICOM de-identification of images does not remove all identifying information and needs special attention to images scanned from film. Adding manual coding to the radiologist narrative reports significantly improved relevancy of the retrieved clinical documents. The de-identified Indiana chest X-ray collection is available for searching and downloading from the National Library of Medicine (http://openi.nlm.nih.gov/)

Beyond MeSH: Fine-Grained Semantic Indexing of Biomedical Literature based on Weak Supervision

In this work, we propose a method for the automated refinement of subject annotations in biomedical literature at the level of concepts. Semantic indexing and search of biomedical articles in MEDLINE/PubMed are based on semantic subject annotations with MeSH descriptors that may correspond to several related but distinct biomedical concepts. Such semantic annotations do not adhere to the level of detail available in the domain knowledge and may not be sufficient to fulfil the information needs of experts in the domain. To this end, we propose a new method that uses weak supervision to train a concept annotator on the literature available for a particular disease. We test this method on the MeSH descriptors for two diseases: Alzheimer's Disease and Duchenne Muscular Dystrophy. The results indicate that concept-occurrence is a strong heuristic for automated subject annotation refinement and its use as weak supervision can lead to improved concept-level annotations. The fine-grained semantic annotations can enable more precise literature retrieval, sustain the semantic integration of subject annotations with other domain resources and ease the maintenance of consistent subject annotations, as new more detailed entries are added in the MeSH thesaurus over time.Comment: 36 pages, 8 figures; Dictionary-based baselines added and conclusions update