Automatic Identification of Biomedical Concepts in Spanish Language Unstructured Clinical Texts

[Poster]. IHI'10 ACM International Health Informatics Symposium Arlington, VA, USA - November 11-12, 2010The processing of health information from medical records and, especially, clinical notes is a complex task due to the nature of the texts themeselves (i.e., hand-written and containing semi-structured or unstructured data) and the diversity of the terminology used. While certain technologies exist to process these types of texts and data in the English language, only a few such initiatives exist for similar texts and data in the Spanish language. This paper presents a new proposal for the semantic annotation of Spanish-language clinical notes, implementing an automated tool similar to the UMLS MetaMap Transfer (MMTx) for the identification of biomedical concepts in the Spanish-language SNOMED CT ontology. Moreover, an assessment of the tool using 100 Spanish-language clinical notes is presented. Using the clinical notes manually annotated by specialists of a Spanish hospital as the gold standard, it is concluded that precision scores are sufficiently good for the several types of matching achieved by the automated tool proposed. The research presented in this contribution offers a launching point for the establishment of semantic relationships between concepts and the application of mining techniques to Spanish-language clinical notes.This study has been partially supported by the MAVIR Consortium (S2009/TIC-1542) and by the TIN2007-67407-C03-01 project BRAVOPublicad

Spanish named entity recognition in the biomedical domain

Named Entity Recognition in the clinical domain and in languages different from English has the difficulty of the absence of complete dictionaries, the informality of texts, the polysemy of terms, the lack of accordance in the boundaries of an entity, the scarcity of corpora and of other resources available. We present a Named Entity Recognition method for poorly resourced languages. The method was tested with Spanish radiology reports and compared with a conditional random fields system.Peer ReviewedPostprint (author's final draft

The biomedical abbreviation recognition and resolution (BARR) track: Benchmarking, evaluation and importance of abbreviation recognition systems applied to Spanish biomedical abstracts

Healthcare professionals are generating a substantial volume of clinical data in narrative form. As healthcare providers are confronted with serious time constraints, they frequently use telegraphic phrases, domain-specific abbreviations and shorthand notes. Efficient clinical text processing tools need to cope with the recognition and resolution of abbreviations, a task that has been extensively studied for English documents. Despite the outstanding number of clinical documents written worldwide in Spanish, only a marginal amount of studies has been published on this subject. In clinical texts, as opposed to the medical literature, abbreviations are generally used without their definitions or expanded forms. The aim of the first Biomedical Abbreviation Recognition and Resolution (BARR) track, posed at the IberEval 2017 evaluation campaign, was to assess and promote the development of systems for generating a sense inventory of medical abbreviations. The BARR track required the detection of mentions of abbreviations or short forms and their corresponding long forms or definitions from Spanish medical abstracts. For this track, the organizers provided the BARR medical document collection, the BARR corpus of manually annotated abstracts labelled by domain experts and the BARR-Markyt evaluation platform. A total of 7 teams submitted 25 runs for the two BARR subtasks: (a) the identification of mentions of abbreviations and their definitions and (b) the correct detection of short form-long form pairs. Here we describe the BARR track setting, the obtained results and the methodologies used by participating systems. The BARR task summary, corpus, resources and evaluation tool for testing systems beyond this campaign are available at: http://temu.inab.org .We acknowledge the Encomienda MINETAD-CNIO/OTG Sanidad Plan TL and Open-Minted (654021) H2020 project for funding.Postprint (published version

Clinical narrative analytics challenges

Precision medicine or evidence based medicine is based on the extraction of knowledge from medical records to provide individuals with the appropriate treatment in the appropriate moment according to the patient features. Despite the efforts of using clinical narratives for clinical decision support, many challenges have to be faced still today such as multilinguarity, diversity of terms and formats in different services, acronyms, negation, to name but a few. The same problems exist when one wants to analyze narratives in literature whose analysis would provide physicians and researchers with highlights. In this talk we will analyze challenges, solutions and open problems and will analyze several frameworks and tools that are able to perform NLP over free text to extract medical entities by means of Named Entity Recognition process. We will also analyze a framework we have developed to extract and validate medical terms. In particular we present two uses cases: (i) medical entities extraction of a set of infectious diseases description texts provided by MedlinePlus and (ii) scales of stroke identification in clinical narratives written in Spanish

Contributions to information extraction for spanish written biomedical text

285 p.Healthcare practice and clinical research produce vast amounts of digitised, unstructured data in multiple languages that are currently underexploited, despite their potential applications in improving healthcare experiences, supporting trainee education, or enabling biomedical research, for example. To automatically transform those contents into relevant, structured information, advanced Natural Language Processing (NLP) mechanisms are required. In NLP, this task is known as Information Extraction. Our work takes place within this growing field of clinical NLP for the Spanish language, as we tackle three distinct problems. First, we compare several supervised machine learning approaches to the problem of sensitive data detection and classification. Specifically, we study the different approaches and their transferability in two corpora, one synthetic and the other authentic. Second, we present and evaluate UMLSmapper, a knowledge-intensive system for biomedical term identification based on the UMLS Metathesaurus. This system recognises and codifies terms without relying on annotated data nor external Named Entity Recognition tools. Although technically naive, it performs on par with more evolved systems, and does not exhibit a considerable deviation from other approaches that rely on oracle terms. Finally, we present and exploit a new corpus of real health records manually annotated with negation and uncertainty information: NUBes. This corpus is the basis for two sets of experiments, one on cue andscope detection, and the other on assertion classification. Throughout the thesis, we apply and compare techniques of varying levels of sophistication and novelty, which reflects the rapid advancement of the field

PadChest: A large chest x-ray image dataset with multi-label annotated reports

We present a labeled large-scale, high resolution chest x-ray dataset for the automated exploration of medical images along with their associated reports. This dataset includes more than 160,000 images obtained from 67,000 patients that were interpreted and reported by radiologists at Hospital San Juan Hospital (Spain) from 2009 to 2017, covering six different position views and additional information on image acquisition and patient demography. The reports were labeled with 174 different radiographic findings, 19 differential diagnoses and 104 anatomic locations organized as a hierarchical taxonomy and mapped onto standard Unified Medical Language System (UMLS) terminology. Of these reports, 27% were manually annotated by trained physicians and the remaining set was labeled using a supervised method based on a recurrent neural network with attention mechanisms. The labels generated were then validated in an independent test set achieving a 0.93 Micro-F1 score. To the best of our knowledge, this is one of the largest public chest x-ray database suitable for training supervised models concerning radiographs, and the first to contain radiographic reports in Spanish. The PadChest dataset can be downloaded from http://bimcv.cipf.es/bimcv-projects/padchest/