Explanipedia

Automating the assessment of door-to-imaging time in stroke management using a clinical data warehouse Open

Olivier Hassanaly, Matthieu Doutreligne, P. Troude, A. Solomiac, Sandrine Morin , et al. · 2025

Assessment of quality and safety indicators (QSI) remains often based on time-consuming manual Electronic Health Record (EHR) review. Through a pilot study in stroke management, we investigated the feasibility of automating the calculation…

Step-by-step causal analysis of EHRs to ground decision-making Open

Matthieu Doutreligne, Tristan Struja, J. Abécassis, Claire Morgand, Leo Anthony Celi , et al. · 2025

Causal inference enables machine learning methods to estimate treatment effects of medical interventions from electronic health records (EHRs). The prevalence of such observational data and the difficulty for randomized controlled trials (…

How to select predictive models for decision-making or causal inference Open

Matthieu Doutreligne, Gaël Varoquaux · 2025

Background We investigate which procedure selects the most trustworthy predictive model to explain the effect of an intervention and support decision-making. Methods We study a large variety of model selection procedures in practical setti…

Représentations et inférence à partir de données de santé temporelles collectées en routine Open

Matthieu Doutreligne · 2023

Les bases de données de vie réelle sont de plus en plus accessibles, exhaustives, avec des détails temporels précis. Contrairement aux données utilisées dans la recherche clinique traditionnelle, elles capturent l'organisation routinière d…

Correction: Good practices for clinical data warehouse implementation: A case study in France Open

Matthieu Doutreligne, Adeline Degrémont, Pierre-Alain Jachiet, Antoine Lamer, Xavier Tannier · 2023

[This corrects the article DOI: 10.1371/journal.pdig.0000298.].

Racial Disparities in Invasive ICU Treatments Among Septic Patients:High Resolution Electronic Health Records Analysis fromMIMIC-IV Open

Sara Mohammed, João Matos, Matthieu Doutreligne, Leo Anthony Celi, Tristan Struja · 2023

Background: Low-resolution administrative databases can give biased results, whereas high-resolution, time-stamped variables from clinical databases like MIMIC-IV might provide nuanced insights. We evaluated racial-ethnic disparitie…

Step-by-step causal analysis of Electronic Health Records to ground decision making Open

Matthieu Doutreligne, Tristan Struja, J. Abécassis, Claire Morgand, Gaël Varoquaux , et al. · 2023

Accurate predictions, as with machine learning, may not suffice to provide optimal healthcare for every patient. Indeed, prediction can be driven by shortcuts in the data, such as racial biases. Causal thinking is needed for data-driven de…

Adjusting for the progressive digitization of health records: working examples on a multi-hospital clinical data warehouse Open

Adam Remaki, Benoît Playe, Paul Bernard, Simon Vittoz, Matthieu Doutreligne , et al. · 2023

A bstract Objectives To propose a new method to account for time-dependent data missingness caused by the increasing digitization of health records in the analysis of large-scale clinical data. Materials and Methods Following a data-driven…

Causal thinking for decision making on Electronic Health Records: why and how Open

Matthieu Doutreligne, Tristan Struja, J. Abécassis, Claire Morgand, Leo Anthony Celi , et al. · 2023

Accurate predictions, as with machine learning, may not suffice to provide optimal healthcare for every patient. Indeed, prediction can be driven by shortcuts in the data, such as racial biases. Causal thinking is needed for data-driven de…

Good practices for clinical data warehouse implementation: A case study in France Open

Matthieu Doutreligne, Adeline Degrémont, Pierre-Alain Jachiet, Antoine Lamer, Xavier Tannier · 2023

Real-world data (RWD) bears great promises to improve the quality of care. However, specific infrastructures and methodologies are required to derive robust knowledge and brings innovations to the patient. Drawing upon the national case st…

Good practices for clinical data warehouse implementation: a case study in France Open

Matthieu Doutreligne, Adeline Degrémont, Pierre-Alain Jachiet, Antoine Lamer, Xavier Tannier · 2023

Real World Data (RWD) bears great promises to improve the quality of care. However, specific infrastructures and methodologies are required to derive robust knowledge and brings innovations to the patient. Drawing upon the national case st…

Standardized Description of the Feature Extraction Process to Transform Raw Data Into Meaningful Information for Enhancing Data Reuse: Consensus Study Open

Antoine Lamer, Mathilde Fruchart, Nicolás Paris, Benjamin Popoff, Anaïs Payen , et al. · 2022

Background Despite the many opportunities data reuse offers, its implementation presents many difficulties, and raw data cannot be reused directly. Information is not always directly available in the source database and needs to be compute…

Enhancing Data Reuse through Feature Extraction: Proposal of Concepts, Methods and Data Model (Preprint) Open

Antoine Lamer, Mathilde Fruchart, Nicolás Paris, Benjamin Popoff, Anaïs Payen , et al. · 2022

BACKGROUND Despite the many opportunities data reuse offers, its implementation presents many difficulties and raw data cannot be reused directly. Information are not always directly available in the source database, and have to be comput…

Erratum: Transforming French Electronic Health Records into the Observational Medical Outcome Partnership's Common Data Model: A Feasibility Study Open

Antoine Lamer, Nicolas Depas, Matthieu Doutreligne, A. Parrot, David Verloop , et al. · 2020

Correction to: Transforming French Electronic Health Records into the Observational Medical Outcome Partnership's Common Data Model: A Feasibility StudyAppl Clin Inform 2020; 11(01): 013-022DOI: 10.1055/s-0039-3402754

Transforming French Electronic Health Records into the Observational Medical Outcome Partnership's Common Data Model: A Feasibility Study Open

Antoine Lamer, Nicolas Depas, Matthieu Doutreligne, A. Parrot, David Verloop , et al. · 2020

Background Common data models (CDMs) enable data to be standardized, and facilitate data exchange, sharing, and storage, particularly when the data have been collected via distinct, heterogeneous systems. Moreover, CDMs provide tools for d…

Désidentification de comptes-rendus hospitaliers dans une base de données OMOP Open

Nicolás Paris, Matthieu Doutreligne, A. Parrot, Xavier Tannier · 2019

International audience

Hybrid Approaches for our Participation to the n2c2 Challenge on Cohort\n Selection for Clinical Trials Open

Xavier Tannier, Nicolás Paris, Hugo Cisneros, Christel Daniel, Matthieu Doutreligne , et al. · 2019

Objective: Natural language processing can help minimize human intervention\nin identifying patients meeting eligibility criteria for clinical trials, but\nthere is still a long way to go to obtain a general and systematic approach\nthat i…

Hybrid Approaches for our Participation to the n2c2 Challenge on Cohort Selection for Clinical Trials Open

Xavier Tannier, Nicolás Paris, Hugo Cisneros, Christel Daniel, Matthieu Doutreligne , et al. · 2019

Objective: Natural language processing can help minimize human intervention in identifying patients meeting eligibility criteria for clinical trials, but there is still a long way to go to obtain a general and systematic approach that is u…

Evaluation of a Sequence Tagging Tool for Biomedical Texts Open

Julien Tourille, Matthieu Doutreligne, Olivier Ferret, Aurélie Névéol, Nicolás Paris , et al. · 2018

Julien Tourille, Matthieu Doutreligne, Olivier Ferret, Aurélie Névéol, Nicolas Paris, Xavier Tannier. Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis. 2018.

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