FRASIMED: a Clinical French Annotated Resource Produced through Crosslingual BERT-Based Annotation Projection

Natural language processing (NLP) applications such as named entity recognition (NER) for low-resource corpora do not benefit from recent advances in the development of large language models (LLMs) where there is still a need for larger annotated datasets. This research article introduces a methodology for generating translated versions of annotated datasets through crosslingual annotation projection. Leveraging a language agnostic BERT-based approach, it is an efficient solution to increase low-resource corpora with few human efforts and by only using already available open data resources. Quantitative and qualitative evaluations are often lacking when it comes to evaluating the quality and effectiveness of semi-automatic data generation strategies. The evaluation of our crosslingual annotation projection approach showed both effectiveness and high accuracy in the resulting dataset. As a practical application of this methodology, we present the creation of French Annotated Resource with Semantic Information for Medical Entities Detection (FRASIMED), an annotated corpus comprising 2'051 synthetic clinical cases in French. The corpus is now available for researchers and practitioners to develop and refine French natural language processing (NLP) applications in the clinical field (https://zenodo.org/record/8355629), making it the largest open annotated corpus with linked medical concepts in French

Real-world patient trajectory prediction from clinical notes using artificial neural networks and UMLS-based extraction of concepts

As more data is generated from medical attendances and as Artificial Neural Networks gain momentum in research and industry, computer-aided medical prognosis has become a promising technology. A common approach to perform automated prognoses relies on textual clinical notes extracted from Electronic Health Records (EHRs). Data from EHRs are fed to neural networks that produce a set with the most probable medical problems to which a patient is subject in her/his clinical future, including clinical conditions, mortality, and readmission. Following this research line, we introduce a methodology that takes advantage of the unstructured text found in clinical notes by applying preprocessing, concepts extraction, and fine-tuned neural networks to predict the most probable medical problems to follow in a patient’s clinical trajectory. Different from former works that focus on word embeddings and raw sets of extracted concepts, we generate a refined set of Unified Medical Language System (UMLS) concepts by applying a similarity threshold filter and a list of acceptable concept types. In our prediction experiments, our method demonstrated AUC-ROC performance of 0.91 for diagnosis codes, 0.93 for mortality, and 0.72 for readmission, determining an efficacy that rivals state-of-the-art works. Our findings contribute to the development of automated prognosis systems in hospitals where text is the main source of clinical history.</p

Caregivers Interactions with Clinical Autocomplete Tool: A Retrospective Study

Hospital caregivers report patient data while being under constant pressure. These records include structured information, with some of them being derived from a restricted list of terms. Finding the right term from a large terminology can be time-consuming, harming the clinician's productivity. To deal with this hurdle, an autocomplete system is employed, providing the closest terms after a prefix is typed. While this software application clearly smoothens the term searching, this paper studies the influences of the tool on caregivers' reporting, inspecting the evolution of their typing conduct over time

FRASIMED: a Clinical French Annotated Resource Produced through Crosslingual BERT-Based Annotation Projection

The French Annotated Resource with Semantic Information for Medical Entities Detection (FRASIMED) contains 2'051 synthetic clinical cases in French, with 24'037 annotated entities. The dataset contains two subsets: CANTEMIST-FR: Originally from CANTEMIST (Miranda-Escalada et al. (2020)), it contains 1'301 oncological notes, with 15'978 annotations linked to an ICD-O-3.1 morphology code. Additionally, 15’457 of them are linked to a SNOMED-CT code. DISTEMIST-FR: Originally from DISTEMIST's training set (Miranda-Escalada et al. (2022)), it contains 750 clinical cases, with 8'059 annotations, with 5'132 of them linked to a SNOMED-CT code. Please, cite us: Zaghir, J., Bjelogrlic, M., Goldman, J.-P., Aananou, S., Gaudet-Blavignac, & Lovis, C. (2023). FRASIMED: a Clinical French Annotated Resource Produced through Crosslingual BERT-Based Annotation Projection. arXiv preprint http://arxiv.org/abs/2309.1077

Deep SNOMED CT Enabled Large Clinical Database About COVID-19

In spring 2020, as the COVID-19 pandemic is in its first wave in Europe, the University hospitals of Geneva (HUG) is tasked to take care of all Covid inpatients of the Geneva canton. It is a crisis with very little tools to support decision-taking authorities, and very little is known about the Covid disease. The need to know more, and fast, highlighted numerous challenges in the whole data pipeline processes. This paper describes the decisions taken and processes developed to build a unified database to support several secondary usages of clinical data, including governance and research. HUG had to answer to 5 major waves of COVID-19 patients since the beginning of 2020. In this context, a database for COVID-19 related data has been created to support the governance of the hospital in their answer to this crisis. The principles about this database were a) a clearly defined cohort; b) a clearly defined dataset and c) a clearly defined semantics. This approach resulted in more than 28 000 variables encoded in SNOMED CT and 1 540 human readable labels. It covers more than 216 000 patients and 590 000 inpatient stays. This database is used daily since the beginning of the pandemic to feed the “Predict” dashboards of HUG and prediction reports as well as several research projects

A Lightweight and Interpretable Model to Classify Bundle Branch Blocks from ECG Signals

Automatic classification of ECG signals has been a longtime research area with large progress having been made recently. However these advances have been achieved with increasingly complex models at the expense of model’s interpretability. In this research, a new model based on multivariate autoregressive model (MAR) coefficients combined with a tree-based model to classify bundle branch blocks is proposed. The advantage of the presented approach is to build a lightweight model which combined with post-hoc interpretability can bring new insights into important cross-lead dependencies which are indicative of the diseases of interest

Performance of Machine Learning Methods to Classify French Medical Publications

Many medical narratives are read by care professionals in their preferred language. These documents can be produced by organizations, authorities or national publishers. However, they are often hardly findable using the usual query engines based on English such as PubMed. This work explores the possibility to automatically categorize medical documents in French following an automatic Natural Language Processing pipeline. The pipeline is used to compare the performance of 6 different machine learning and deep neural network approaches on a large dataset of peer-reviewed weekly published Swiss medical journal in French covering major topics in medicine over the last 15 years. An accuracy of 96% was achieved for 5-topic classification and 81% for 20-topic classification

Classification of Oncology Treatment Responses from French Radiology Reports with Supervised Machine Learning

The present study shows first attempts to automatically classify oncology treatment responses on the basis of the textual conclusion sections of radiology reports according to the RECIST classification. After a robust and extended manual annotation of 543 conclusion sections (5-to-50-word long), and after the training of several machine learning techniques (from traditional machine learning to deep learning), the best results show an accuracy score of 0.90 for a two-class classification (non-progressive vs. progressive disease) and of 0.82 for a four-class classification (complete response, partial response, stable disease, progressive disease) both with Logistic Regression approach. Some innovative solutions are further suggested to improve these scores in the future