Semantic representation of reported measurements in radiology

Background In radiology, a vast amount of diverse data is generated, and unstructured reporting is standard. Hence, much useful information is trapped in free-text form, and often lost in translation and transmission. One relevant source of free-text data consists of reports covering the assessment of changes in tumor burden, which are needed for the evaluation of cancer treatment success. Any change of lesion size is a critical factor in follow-up examinations. It is difficult to retrieve specific information from unstructured reports and to compare them over time. Therefore, a prototype was implemented that demonstrates the structured representation of findings, allowing selective review in consecutive examinations and thus more efficient comparison over time. Methods We developed a semantic Model for Clinical Information (MCI) based on existing ontologies from the Open Biological and Biomedical Ontologies (OBO) library. MCI is used for the integrated representation of measured image findings and medical knowledge about the normal size of anatomical entities. An integrated view of the radiology findings is realized by a prototype implementation of a ReportViewer. Further, RECIST (Response Evaluation Criteria In Solid Tumors) guidelines are implemented by SPARQL queries on MCI. The evaluation is based on two data sets of German radiology reports: An oncologic data set consisting of 2584 reports on 377 lymphoma patients and a mixed data set consisting of 6007 reports on diverse medical and surgical patients. All measurement findings were automatically classified as abnormal/normal using formalized medical background knowledge, i.e., knowledge that has been encoded into an ontology. A radiologist evaluated 813 classifications as correct or incorrect. All unclassified findings were evaluated as incorrect. Results The proposed approach allows the automatic classification of findings with an accuracy of 96.4 % for oncologic reports and 92.9 % for mixed reports. The ReportViewer permits efficient comparison of measured findings from consecutive examinations. The implementation of RECIST guidelines with SPARQL enhances the quality of the selection and comparison of target lesions as well as the corresponding treatment response evaluation. Conclusions The developed MCI enables an accurate integrated representation of reported measurements and medical knowledge. Thus, measurements can be automatically classified and integrated in different decision processes. The structured representation is suitable for improved integration of clinical findings during decision-making. The proposed ReportViewer provides a longitudinal overview of the measurements

Clinical Natural Language Processing in languages other than English: opportunities and challenges

Background: Natural language processing applied to clinical text or aimed at a clinical outcome has been thriving in recent years. This paper offers the first broad overview of clinical Natural Language Processing (NLP) for languages other than English. Recent studies are summarized to offer insights and outline opportunities in this area. Main Body We envision three groups of intended readers: (1) NLP researchers leveraging experience gained in other languages, (2) NLP researchers faced with establishing clinical text processing in a language other than English, and (3) clinical informatics researchers and practitioners looking for resources in their languages in order to apply NLP techniques and tools to clinical practice and/or investigation. We review work in clinical NLP in languages other than English. We classify these studies into three groups: (i) studies describing the development of new NLP systems or components de novo, (ii) studies describing the adaptation of NLP architectures developed for English to another language, and (iii) studies focusing on a particular clinical application. Conclusion: We show the advantages and drawbacks of each method, and highlight the appropriate application context. Finally, we identify major challenges and opportunities that will affect the impact of NLP on clinical practice and public health studies in a context that encompasses English as well as other languages

Annotated dataset creation through large language models for non-english medical NLP

Obtaining text datasets with semantic annotations is an effortful process, yet crucial for supervised training in natural language processing (NLP). In general, developing and applying new NLP pipelines in domain-specific contexts for tasks often requires custom-designed datasets to address NLP tasks in a supervised machine learning fashion. When operating in non-English languages for medical data processing, this exposes several minor and major, interconnected problems such as the lack of task-matching datasets as well as task-specific pre-trained models. In our work, we suggest to leverage pre-trained large language models for training data acquisition in order to retrieve sufficiently large datasets for training smaller and more efficient models for use-case-specific tasks. To demonstrate the effectiveness of your approach, we create a custom dataset that we use to train a medical NER model for German texts, GPTNERMED, yet our method remains language-independent in principle. Our obtained dataset as well as our pre-trained models are publicly available at https://github.com/frankkramer-lab/GPTNERMED

Improving Readability of Swedish Electronic Health Records through Lexical Simplification: First Results

Abstract This paper describes part of an ongoing effort to improve the readability of Swedish electronic health records (EHRs). An EHR contains systematic documentation of a single patient's medical history across time, entered by healthcare professionals with the purpose of enabling safe and informed care. Linguistically, medical records exemplify a highly specialised domain, which can be superficially characterised as having telegraphic sentences involving displaced or missing words, abundant abbreviations, spelling variations including misspellings, and terminology. We report results on lexical simplification of Swedish EHRs, by which we mean detecting the unknown, out-ofdictionary words and trying to resolve them either as compounded known words, abbreviations or misspellings

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