Building a semantically annotated corpus of clinical texts

In this paper, we describe the construction of a semantically annotated corpus of clinical texts for use in the development and evaluation of systems for automatically extracting clinically significant information from the textual component of patient records. The paper details the sampling of textual material from a collection of 20,000 cancer patient records, the development of a semantic annotation scheme, the annotation methodology, the distribution of annotations in the final corpus, and the use of the corpus for development of an adaptive information extraction system. The resulting corpus is the most richly semantically annotated resource for clinical text processing built to date, whose value has been demonstrated through its use in developing an effective information extraction system. The detailed presentation of our corpus construction and annotation methodology will be of value to others seeking to build high-quality semantically annotated corpora in biomedical domains

Text mining processing pipeline for semi structured data D3.3

Unstructured and semi-structured cohort data contain relevant information about the health condition of a patient, e.g., free text describing disease diagnoses, drugs, medication reasons, which are often not available in structured formats. One of the challenges posed by medical free texts is that there can be several ways of mentioning a concept. Therefore, encoding free text into unambiguous descriptors allows us to leverage the value of the cohort data, in particular, by facilitating its findability and interoperability across cohorts in the project.Named entity recognition and normalization enable the automatic conversion of free text into standard medical concepts. Given the volume of available data shared in the CINECA project, the WP3 text mining working group has developed named entity normalization techniques to obtain standard concepts from unstructured and semi-structured fields available in the cohorts. In this deliverable, we present the methodology used to develop the different text mining tools created by the dedicated SFU, UMCG, EBI, and HES-SO/SIB groups for specific CINECA cohorts

Doctor of Philosophy

dissertationManual annotation of clinical texts is often used as a method of generating reference standards that provide data for training and evaluation of Natural Language Processing (NLP) systems. Manually annotating clinical texts is time consuming, expensive, and requires considerable cognitive effort on the part of human reviewers. Furthermore, reference standards must be generated in ways that produce consistent and reliable data but must also be valid in order to adequately evaluate the performance of those systems. The amount of labeled data necessary varies depending on the level of analysis, the complexity of the clinical use case, and the methods that will be used to develop automated machine systems for information extraction and classification. Evaluating methods that potentially reduce cost, manual human workload, introduce task efficiencies, and reduce the amount of labeled data necessary to train NLP tools for specific clinical use cases are active areas of research inquiry in the clinical NLP domain. This dissertation integrates a mixed methods approach using methodologies from cognitive science and artificial intelligence with manual annotation of clinical texts. Aim 1 of this dissertation identifies factors that affect manual annotation of clinical texts. These factors are further explored by evaluating approaches that may introduce efficiencies into manual review tasks applied to two different NLP development areas - semantic annotation of clinical concepts and identification of information representing Protected Health Information (PHI) as defined by HIPAA. Both experiments integrate iv different priming mechanisms using noninteractive and machine-assisted methods. The main hypothesis for this research is that integrating pre-annotation or other machineassisted methods within manual annotation workflows will improve efficiency of manual annotation tasks without diminishing the quality of generated reference standards

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

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