12 papers
AAbAAC: An Annotated Corpus for Autoimmunity Information Extraction
Fabien Maury, Solène Grosdidier, Maud de Dieuleveult +1
Despite advances in information extraction driven by deep learning and large language models, performance gaps remain in highly specialized biomedical fields, where domainspecific…
Shapley Regression for Rare Disease Diagnosis Support: a case study on APDS
Safa Alsaidi, Tomás Brogueira, Nizar Mahlaoui +5
Activated PI3K8 Syndrome (APDS) is a rare genetic immune disorder caused by variants in PIK3CD or PIK3R1, with highly heterogeneous symptoms that often delay diagnosis. Early recog…
Analysing Lightweight Large Language Models for Biomedical Named Entity Recognition on Diverse Ouput Formats
Pierre Epron, Adrien Coulet, Mehwish Alam
Despite their strong linguistic capabilities, Large Language Models (LLMs) are computationally demanding and require substantial resources for fine-tuning, which is unadapted to pr…
Clinical Data Goes MEDS? Let's OWL make sense of it
Alberto Marfoglia, Jong Ho Jhee, Adrien Coulet
The application of machine learning on healthcare data is often hindered by the lack of standardized and semantically explicit representation, leading to limited interoperability a…
Comparing representations of long clinical texts for the task of patient note-identification
Safa Alsaidi, Marc Vincent, Olivia Boyer +3
In this paper, we address the challenge of patient-note identification, which involves accurately matching an anonymized clinical note to its corresponding patient, represented by…
Efficient extraction of medication information from clinical notes: an evaluation in two languages
Thibaut Fabacher, Erik-André Sauleau, Emmanuelle Arcay +6
Objective: To evaluate the accuracy, computational cost and portability of a new Natural Language Processing (NLP) method for extracting medication information from clinical narrat…
Predicting clinical outcomes from patient care pathways represented with temporal knowledge graphs
Jong Ho Jhee, Alberto Megina, Pacôme Constant Dit Beaufils +4
Background: With the increasing availability of healthcare data, predictive modeling finds many applications in the biomedical domain, such as the evaluation of the level of risk f…
Step-by-Step Guidance to Differential Anemia Diagnosis with Real-World Data and Deep Reinforcement Learning
Lillian Muyama, Estelle Lu, Geoffrey Cheminet +5
Clinical diagnostic guidelines outline the key questions to answer to reach a diagnosis. Inspired by guidelines, we aim to develop a model that learns from electronic health record…
Prompting Large Language Models for Supporting the Differential Diagnosis of Anemia
Elisa Castagnari, Lillian Muyama, Adrien Coulet
In practice, clinicians achieve a diagnosis by following a sequence of steps, such as laboratory exams, observations, or imaging. The pathways to reach diagnosis decisions are docu…
Enhancing Clinical Data Warehouses with Provenance and Large File Management: The gitOmmix Approach for Clinical Omics Data
Maxime Wack, Adrien Coulet, Anita Burgun +1
Background. Clinical data warehouses (CDWs) are essential in the reuse of hospital data in observational studies or predictive modeling. However, state of-the-art CDW systems prese…
Facilitating phenotyping from clinical texts: the medkit library
Antoine Neuraz, Ghislain Vaillant, Camila Arias +8
Phenotyping consists in applying algorithms to identify individuals associated with a specific, potentially complex, trait or condition, typically out of a collection of Electronic…
Deep Reinforcement Learning for Personalized Diagnostic Decision Pathways Using Electronic Health Records: A Comparative Study on Anemia and Systemic Lupus Erythematosus
Lillian Muyama, Antoine Neuraz, Adrien Coulet
Background: Clinical diagnosis is typically reached by following a series of steps recommended by guidelines authored by colleges of experts. Accordingly, guidelines play a crucial…