A new clustering method for detecting rare senses of abbreviations in clinical notes

AbstractAbbreviations are widely used in clinical documents and they are often ambiguous. Building a list of possible senses (also called sense inventory) for each ambiguous abbreviation is the first step to automatically identify correct meanings of abbreviations in given contexts. Clustering based methods have been used to detect senses of abbreviations from a clinical corpus [1]. However, rare senses remain challenging and existing algorithms are not good enough to detect them. In this study, we developed a new two-phase clustering algorithm called Tight Clustering for Rare Senses (TCRS) and applied it to sense generation of abbreviations in clinical text. Using manually annotated sense inventories from a set of 13 ambiguous clinical abbreviations, we evaluated and compared TCRS with the existing Expectation Maximization (EM) clustering algorithm for sense generation, at two different levels of annotation cost (10 vs. 20 instances for each abbreviation). Our results showed that the TCRS-based method could detect 85% senses on average; while the EM-based method found only 75% senses, when similar annotation effort (about 20 instances) was used. Further analysis demonstrated that the improvement by the TCRS method was mainly from additionally detected rare senses, thus indicating its usefulness for building more complete sense inventories of clinical abbreviations

Word Sense Disambiguation for clinical abbreviations

Abbreviations are extensively used in electronic health records (EHR) of patients as well as medical documentation, reaching 30-50% of the words in clinical narrative. There are more than 197,000 unique medical abbreviations found in the clinical text and their meanings vary depending on the context in which they are used. Since data in electronic health records could be shareable across health information systems (hospitals, primary care centers, etc.) as well as others such as insurance companies information systems, it is essential determining the correct meaning of the abbreviations to avoid misunderstandings. Clinical abbreviations have specific characteristic that do not follow any standard rules for creating them. This makes it complicated to find said abbreviations and corresponding meanings. Furthermore, there is an added difficulty to working with clinical data due to privacy reasons, since it is essential to have them in order to develop and test algorithms. Word sense disambiguation (WSD) is an essential task in natural language processing (NLP) applications such as information extraction, chatbots and summarization systems among others. WSD aims to identify the correct meaning of the ambiguous word which has more than one meaning. Disambiguating clinical abbreviations is a type of lexical sample WSD task. Previous research works adopted supervised, unsupervised and Knowledge-based (KB) approaches to disambiguate clinical abbreviations. This thesis aims to propose a classification model that apart from disambiguating well known abbreviations also disambiguates rare and unseen abbreviations using the most recent deep neural network architectures for language modeling. In clinical abbreviation disambiguation several resources and disambiguation models were encountered. Different classification approaches used to disambiguate the clinical abbreviations were investigated in this thesis. Considering that computers do not directly understand texts, different data representations were implemented to capture the meaning of the words. Since it is also necessary to measure the performance of algorithms, the evaluation measurements used are discussed. As the different solutions proposed to clinical WSD we have explored static word embeddings data representation on 13 English clinical abbreviations of the UMN data set (from University of Minnesota) by testing traditional supervised machine learning algorithms separately for each abbreviation. Moreover, we have utilized a transformer-base pretrained model that was fine-tuned as a multi-classification classifier for the whole data set (75 abbreviations of the UMN data set). The aim of implementing just one multi-class classifier is to predict rare and unseen abbreviations that are most common in clinical narrative. Additionally, other experiments were conducted for a different type of abbreviations (scientific abbreviations and acronyms) by defining a hybrid approach composed of supervised and knowledge-based approaches. Most previous works tend to build a separated classifier for each clinical abbreviation, tending to leverage different data resources to overcome the data acquisition bottleneck. However, those models were restricted to disambiguate terms that have been seen in trained data. Meanwhile, based on our results, transfer learning by fine-tuning a transformer-based model could predict rare and unseen abbreviations. A remaining challenge for future work is to improve the model to automate the disambiguation of clinical abbreviations on run-time systems by implementing self-supervised learning models.Las abreviaturas se utilizan ampliamente en las historias clínicas electrónicas de los pacientes y en mucha documentación médica, llegando a ser un 30-50% de las palabras empleadas en narrativa clínica. Existen más de 197.000 abreviaturas únicas usadas en textos clínicos siendo términos altamente ambiguos El significado de las abreviaturas varía en función del contexto en el que se utilicen. Dado que los datos de las historias clínicas electrónicas pueden compartirse entre servicios, hospitales, centros de atención primaria así como otras organizaciones como por ejemplo, las compañías de seguros es fundamental determinar el significado correcto de las abreviaturas para evitar además eventos adversos relacionados con la seguridad del paciente. Nuevas abreviaturas clínicas aparecen constantemente y tienen la característica específica de que no siguen ningún estándar para su creación. Esto hace que sea muy difícil disponer de un recurso con todas las abreviaturas y todos sus significados. A todo esto hay que añadir la dificultad para trabajar con datos clínicos por cuestiones de privacidad cuando es esencial disponer de ellos para poder desarrollar algoritmos para su tratamiento. La desambiguación del sentido de las palabras (WSD, en inglés) es una tarea esencial en tareas de procesamiento del lenguaje natural (PLN) como extracción de información, chatbots o generadores de resúmenes, entre otros. WSD tiene como objetivo identificar el significado correcto de una palabra ambigua (que tiene más de un significado). Esta tarea se ha abordado previamente utilizando tanto enfoques supervisados, no supervisados así como basados en conocimiento. Esta tesis tiene como objetivo definir un modelo de clasificación que además de desambiguar abreviaturas conocidas desambigüe también abreviaturas menos frecuentes que no han aparecido previamente en los conjuntos de entrenaminto utilizando las arquitecturas de redes neuronales profundas más recientes relacionadas ocn los modelos del lenguaje. En la desambiguación de abreviaturas clínicas se emplean diversos recursos y modelos de desambiguación. Se han investigado los diferentes enfoques de clasificación utilizados para desambiguar las abreviaturas clínicas. Dado que un ordenador no comprende directamente los textos, se han implementado diferentes representaciones de textos para capturar el significado de las palabras. Puesto que también es necesario medir el desempeño de cualquier algoritmo, se describen también las medidas de evaluación utilizadas. La mayoría de los trabajos previos se han basado en la construcción de un clasificador separado para cada abreviatura clínica. De este modo, tienden a aprovechar diferentes recursos de datos para superar el cuello de botella de la adquisición de datos. Sin embargo, estos modelos se limitaban a desambiguar con los datos para los que el sistema había sido entrenado. Se han explorado además representaciones basadas vectores de palabras (word embeddings) estáticos para 13 abreviaturas clínicas en el corpus UMN en inglés (de la University of Minnesota) utilizando algoritmos de clasificación tradicionales de aprendizaje automático supervisados (un clasificador por cada abreviatura). Se ha llevado a cabo un segundo experimento utilizando un modelo multi-clasificador sobre todo el conjunto de las 75 abreviaturas del corpus UMN basado en un modelo Transformer pre-entrenado. El objetivo ha sido implementar un clasificador multiclase para predecir también abreviaturas raras y no vistas. Se realizó un experimento adicional para siglas científicas en documentos de dominio abierto mediante la aplicación de un enfoque híbrido compuesto por enfoques supervisados y basados en el conocimiento. Así, basándonos en los resultados de esta tesis, el aprendizaje por transferencia (transfer learning) mediante el ajuste (fine-tuning) de un modelo de lenguaje preentrenado podría predecir abreviaturas raras y no vistas sin necesidad de entrenarlas previamente. Un reto pendiente para el trabajo futuro es mejorar el modelo para automatizar la desambiguación de las abreviaturas clínicas en tiempo de ejecución mediante la implementación de modelos de aprendizaje autosupervisados.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: Israel González Carrasco.- Secretario: Leonardo Campillos Llanos.- Vocal: Ana María García Serran

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

Doctor of Philosophy

dissertationDomain adaptation of natural language processing systems is challenging because it requires human expertise. While manual e ort is e ective in creating a high quality knowledge base, it is expensive and time consuming. Clinical text adds another layer of complexity to the task due to privacy and con dentiality restrictions that hinder the ability to share training corpora among di erent research groups. Semantic ambiguity is a major barrier for e ective and accurate concept recognition by natural language processing systems. In my research I propose an automated domain adaptation method that utilizes sublanguage semantic schema for all-word word sense disambiguation of clinical narrative. According to the sublanguage theory developed by Zellig Harris, domain-speci c language is characterized by a relatively small set of semantic classes that combine into a small number of sentence types. Previous research relied on manual analysis to create language models that could be used for more e ective natural language processing. Building on previous semantic type disambiguation research, I propose a method of resolving semantic ambiguity utilizing automatically acquired semantic type disambiguation rules applied on clinical text ambiguously mapped to a standard set of concepts. This research aims to provide an automatic method to acquire Sublanguage Semantic Schema (S3) and apply this model to disambiguate terms that map to more than one concept with di erent semantic types. The research is conducted using unmodi ed MetaMap version 2009, a concept recognition system provided by the National Library of Medicine, applied on a large set of clinical text. The project includes creating and comparing models, which are based on unambiguous concept mappings found in seventeen clinical note types. The e ectiveness of the nal application was validated through a manual review of a subset of processed clinical notes using recall, precision and F-score metrics

Information extraction from Spanish radiology reports

En los últimos a˜nos, la cantidad de información clínica disponible en formato digital ha crecido constantemente debido a la adopción del uso de sistemas de informática médica. En la mayoría de los casos, dicha información se encuentra representada en forma textual. La extracción de información contenida en dichos textos puede utilizarse para colaborar en tareas relacionadas con la clínica médica y para la toma de decisiones, y resulta esencial para la mejora de la atención médica. El dominio biomédico tiene vocabulario altamente especializado, local a distintos países, regiones e instituciones. Se utilizan abreviaturas ambiguas y no estándares. Por otro lado, algunos tipos de informes médicos suelen presentar faltas ortográficas y errores gramaticales. Además, la cantidad de datos anotados disponibles es escasa, debido a la dificultad de obtenerlos y a temas relacionados con la confidencialidad de la información. Esta situación dificulta el avance en el área de extracción de información. Pese a ser el segundo idioma con mayor cantidad de hablantes nativos en el mundo, poco trabajo se ha realizado hasta ahora en extracción de información de informes médicos escritos en espa˜nol. A los desafíos anteriormente descriptos se agregan la ausencia de terminologías específicas para ciertos dominios médicos y la menor disponibilidad de recursos linguísticos que los existentes para otros idiomas. En este trabajo contribuimos al dominio de la biomedicina en espa˜nol, proveyendo métodos con resultados competitivos para el desarrollo de componentes fundamentales de un proceso de extracción de información médico, específicamente para informes radiológicos. Con este fin, creamos un corpus anotado de informes radiológicos en espa˜nol para el reconocimiento de entidades, negación y especulación y extracción de relaciones. Publicamos el proceso seguido para la anotación y el esquema desarrollado. Implementamos dos algoritmos de detección de entidades nombradas con el fin de encontrar entidades anatómicas y hallazgos clínicos. El primero está basado en un diccionario especializado del dominio no disponible en espa˜nol y en el uso de reglas basadas en conocimiento morfosintáctico y está pensado para trabajar con lenguajes sin muchos recursos linguísticos. El segundo está basado en campos aleatorios condicionales y arroja mejores resultados. Adicionalmente, estudiamos e implementamos distintas soluciones para la detección de hallazgos clínicos negados. Para esto, adaptamos al espa˜nol un conocido algoritmo de detección de negaciones en textos médicos escritos en inglés y desarrollamos un método basado en reglas creadas a partir de patrones inferidos del análisis de caminos en árboles de dependencias. También adaptamos el primer método, que arrojó los mejores resultados, para la detección de negación y especulación en resúmenes de alta hospitalaria y notas de evolución clínica escritos en alemán. Consideramos que los resultados obtenidos y la publicación de criterios de anotación y evaluación contribuirán a seguir avanzando en la extracción de información de informes clínicos escritos en espa˜nol.In the last years, the number of digitized clinical data has been growing steadily, due to the adoption of clinical information systems. A great amount of this data is in textual format. The extraction of information contained in texts can be used to support clinical tasks and decisions and is essential for improving health care. The biomedical domain uses a highly specialized and local vocabulary, with abundance of non-standard and ambiguous abbreviations. Moreover, some type of medical reports present ill-formed sentences and lack of diacritics. Publicly accessible annotated data is scarce, due to two main reasons: the difficulty of creating it and the confidential nature of the data, that demands de-identification. This situation hinders the advance of information extraction in the biomedical domain area. Although Spanish is the second language in terms of numbers of native speakers in the world, not much work has been done in information extraction from Spanish medical reports. Challenges include the absence of specific terminologies for certain medical domains in Spanish and the availability of linguistic resources, that are less developed than those of high resources languages, such as English. In this thesis, we contribute to the BioNLP domain by providing methods with competitive results to apply a fragment of a medical information extraction pipeline to Spanish radiology reports. Therefore, an annotated dataset for entity recognition, negation and speculation detection, and relation extraction was created. The annotation process followed and the annotation schema developed were shared with the community. Two named entity recognition algorithms were implemented for the detection of anatomical entities and clinical findings. The first algorithm developed is based on a specialized dictionary of the radiology domain not available in Spanish and in the use of rules based on morphosyntactic knowledge and is designed for named entity recognition in medium or low resource languages. The second one, based on conditional random fields, was implemented when we were able to obtain a larger set of annotated data and achieves better results. We also studied and implemented different solutions for negation detection of clinical findings: an adaptation to Spanish of a popular negation detection algorithm for English medical reports and a rule-based method that detects negations based on patterns inferred from the analysis of paths of dependency parse trees. The first method obtained the best results and was also adapted for negation and speculation detection in German clinical notes and discharge summaries. We consider that the results obtained, and the annotation guidelines provided will bring new benefits to further advance in the field of information extraction from Spanish medical reports.Fil: Cotik, Viviana Erica. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina

The longitudinal relationship between compulsive exercise, symptoms of anxiety and depression, and eating psychopathology in an adolescent inpatient sample with anorexia nervosa

Systematic Review Abstract Background: Ambivalence about change is a significant barrier in the treatment of anorexia nervosa (AN). Better understanding of the perceptions that patients hold about AN could help inform interventions to overcome these barriers. The Self-Regulation Model (SRM) of Illness may provide a method with which to better understand illness perceptions in AN, but its use in mental health has been questioned. Objectives: This systematic review summarises and synthesises the qualitative literature investigating the perceptions of AN held by people with the diagnosis and the extent to which these perceptions were explained by the SRM. Method: A search protocol based on PRISMA1 guidelines was developed prior to commencing the review. Four databases were searched (MEDLINE, PsycINFO, Embase, and CINAHL), alongside grey literature sources (Open Grey, UK Clinical Trials Gateway, Ethos, and Grey Literature Report) and forwards and backwards citation chasing. Screening was conducted by one reviewer using predetermined criteria; a sample was checked by a second reviewer. Study quality was assessed using the Critical Appraisal Skills Programme (CASP) qualitative checklist, with a sample checked by a second reviewer. Framework synthesis was used to synthesise the data. Results: The search returned 817 results. 753 were excluded following title and abstract screening, and a further 46 were excluded after full-text screening. The final sample consisted of 18 journal articles and one unpublished Masters thesis. Five themes were identified from the framework synthesis. These were ‘Identity’, ‘Cause’, ‘Consequences’, ‘Timeline’, and ‘Efficacy’. Conclusions: Findings from this review suggest refinements to the SRM, which may improve its utility when working with people with AN. The findings of the review are limited by the under-representation of certain demographic groups, e.g., men. Exploration of the relationships between different aspects of IPs, stigma, clinical outcomes, and stages of change will be an important focus for future research. Empirical Paper Abstract Objective: Compulsive exercise is associated with poor clinical outcomes in anorexia nervosa (AN). The mechanisms underlying this relationship are still not well understood, neither is the prospective longitudinal relationship between compulsive exercise and eating pathology through the course of treatment. This research therefore aimed to test the hypothesis that compulsive exercise at admission would predict eating pathology at discharge in an adolescent inpatient setting, controlling for baseline eating pathology and potential confounding factors. It also aimed to test the hypothesis that change in affect from admission to mid-way through treatment would mediate the relationship between compulsive exercise and residual eating pathology. Methods: Routine outcome measures (the Compulsive Exercise Test, the Eating Disorders Examination Questionnaire, the State Trait Anxiety Inventory, and the Children’s Depression Inventory) were obtained from adolescent inpatients with a diagnosis of anorexia nervosa (N = 50) at admission, 85% ideal body weight (IBW), and discharge. The data was analysed using hierarchical multiple regression. Results: Compulsive exercise at admission significantly predicted residual eating pathology at discharge after controlling for eating pathology at admission, age, and treatment duration. This effect was not mediated by change in affect, as compulsive exercise at admission did not significantly predict change in anxiety and depressive symptoms between admission and 85% IBW. Change in anxiety between admission and 85% IBW did significantly predict residual eating pathology independently of compulsive exercise. Conclusion: The unique predictive ability of the CET provides support for a separate theoretical model of compulsive exercise, as there appear to be factors specific to compulsive exercise that maintain eating pathology. The findings suggest that assessing compulsive exercise at the start of treatment may be helpful in identifying individuals at risk of residual symptoms of AN following treatment. This could offer services a different method of identifying at-risk individuals and tailoring interventions accordingly