CLEF eHealth 2019 Evaluation Lab

Since 2012 CLEF eHealth has focused on evaluation resource building efforts around the easing and support of patients, their next-of-kins, clinical staff, and health scientists in understanding, accessing, and authoring eHealth information in a multilingual setting. This year’s lab offers three tasks: Task 1 on multilingual information extraction; Task 2 on technology assisted reviews in empirical medicine; and Task 3 on consumer health search in mono- and multilingual settings. Herein, we describe the CLEF eHealth evaluation series to-date and then present the 2019 tasks, evaluation methodology, and resources

Overview of the CLEF eHealth Evaluation Lab 2019

In this paper, we provide an overview of the seventh annual edition of the CLEF eHealth evaluation lab. CLEF eHealth 2019 continues our evaluation resource building efforts around the easing and support of patients, their next-of-kins, clinical staff, and health scientists in understanding, accessing, and authoring electronic health information in a multilingual setting. This year’s lab advertised three tasks: Task 1 on indexing non-technical summaries of German animal experiments with International Classification of Diseases, Version 10 codes; Task 2 on technology assisted reviews in empirical medicine building on 2017 and 2018 tasks in English; and Task 3 on consumer health search in mono- and multilingual settings that builds on the 2013–18 Information Retrieval tasks. In total nine teams took part in these tasks (six in Task 1 and three in Task 2). Herein, we describe the resources created for these tasks and evaluation methodology adopted. We also provide a brief summary of participants of this year’s challenges and results obtained. As in previous years, the organizers have made data and tools associated with the lab tasks available for future research and development

Search strategy formulation for systematic reviews: Issues, challenges and opportunities

Systematic literature reviews play a vital role in identifying the best available evidence for health and social care research, policy, and practice. The resources required to produce systematic reviews can be significant, and a key to the success of any review is the search strategy used to identify relevant literature. However, the methods used to construct search strategies can be complex, time consuming, resource intensive and error prone. In this review, we examine the state of the art in resolving complex structured information needs, focusing primarily on the healthcare context. We analyse the literature to identify key challenges and issues and explore appropriate solutions and workarounds. From this analysis we propose a way forward to facilitate trust and to aid explainability and transparency, reproducibility and replicability through a set of key design principles for tools to support the development of search strategies in systematic literature reviews

Cross-language Information Retrieval

Two key assumptions shape the usual view of ranked retrieval: (1) that the searcher can choose words for their query that might appear in the documents that they wish to see, and (2) that ranking retrieved documents will suffice because the searcher will be able to recognize those which they wished to find. When the documents to be searched are in a language not known by the searcher, neither assumption is true. In such cases, Cross-Language Information Retrieval (CLIR) is needed. This chapter reviews the state of the art for CLIR and outlines some open research questions.Comment: 49 pages, 0 figure

Generating Natural Language Queries for More Effective Systematic Review Screening Prioritisation

Screening prioritisation in medical systematic reviews aims to rank the set of documents retrieved by complex Boolean queries. The goal is to prioritise the most important documents so that subsequent review steps can be carried out more efficiently and effectively. The current state of the art uses the final title of the review to rank documents using BERT-based neural neural rankers. However, the final title is only formulated at the end of the review process, which makes this approach impractical as it relies on ex post facto information. At the time of screening, only a rough working title is available, with which the BERT-based ranker achieves is significantly worse than the final title. In this paper, we explore alternative sources of queries for screening prioritisation, such as the Boolean query used to retrieve the set of documents to be screened, and queries generated by instruction-based generative large language models such as ChatGPT and Alpaca. Our best approach is not only practical based on the information available at screening time, but is similar in effectiveness with the final title.Comment: Preprints for Accepted paper in SIGIR-AP-202

Extreme multi-label deep neural classification of Spanish health records according to the International Classification of Diseases

111 p.Este trabajo trata sobre la minería de textos clínicos, un campo del Procesamiento del Lenguaje Natural aplicado al dominio biomédico. El objetivo es automatizar la tarea de codificación médica. Los registros electrónicos de salud (EHR) son documentos que contienen información clínica sobre la salud de unpaciente. Los diagnósticos y procedimientos médicos plasmados en la Historia Clínica Electrónica están codificados con respecto a la Clasificación Internacional de Enfermedades (CIE). De hecho, la CIE es la base para identificar estadísticas de salud internacionales y el estándar para informar enfermedades y condiciones de salud. Desde la perspectiva del aprendizaje automático, el objetivo es resolver un problema extremo de clasificación de texto de múltiples etiquetas, ya que a cada registro de salud se le asignan múltiples códigos ICD de un conjunto de más de 70 000 términos de diagnóstico. Una cantidad importante de recursos se dedican a la codificación médica, una laboriosa tarea que actualmente se realiza de forma manual. Los EHR son narraciones extensas, y los codificadores médicos revisan los registros escritos por los médicos y asignan los códigos ICD correspondientes. Los textos son técnicos ya que los médicos emplean una jerga médica especializada, aunque rica en abreviaturas, acrónimos y errores ortográficos, ya que los médicos documentan los registros mientras realizan la práctica clínica real. Paraabordar la clasificación automática de registros de salud, investigamos y desarrollamos un conjunto de técnicas de clasificación de texto de aprendizaje profundo

A study of Machine Learning models for Clinical Coding of Medical Reports at CodiEsp 2020

The task of identifying one or more diseases associated with a patient’s clinical condition is often very complex, even for doctors and specialists. This process is usually time-consuming and has to take into account different aspects of what has occurred, including symptoms elicited and previous healthcare situations. The medical diagnosis is often provided to patients in the form of written paper without any correlation with a national or international standard. Even if the WHO (World Health Organization) released the ICD10 international glossary of diseases, almost no doctor has enough time to manually associate the patient’s clinical history with international codes. The CodiEsp task at CLEF 2020 addressed this issue by proposing the development of an automatic system to deal with this task. Our solution investigated different machine learning strategies in order to identify an approach to face that challenge. The main outcomes of the experiments showed that a strategy based on BERT for pre-filtering and one based on BiLSTMCNN-SelfAttention for classification provide valuable results. We carried out several experiments on a subset of the training set for tuning the final model submitted to the challenge. In particular, we analyzed the impact of the algorithm, the input encoding strategy, and the thresholds for multi-label classification. A set of experiments has been carried out also during a post hoc analysis. The experiments confirmed that the strategy submitted to the CodiEsp task is the best performing one among those evaluated, and it allowed us to obtain a final mean average error value on the test set equal to 0.202. To support future developments of the proposed approach and the replicability of the experiments we decided to make the source code publicly accessible