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

1 code implementation • 3 Aug 2023 • Matthieu Doutreligne, Tristan Struja, Judith Abecassis, Claire Morgand, Leo Anthony Celi, Gaël Varoquaux

We illustrate the various choices in studying the effect of albumin on sepsis mortality in the Medical Information Mart for Intensive Care database (MIMIC-IV).

Decision Making valid

How to select predictive models for causal inference?

no code implementations • 1 Feb 2023 • Matthieu Doutreligne, Gaël Varoquaux

But does computing these nuisance adds noise to model selection?

Causal Inference Model Selection +1

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

no code implementations • 19 Mar 2019 • Xavier Tannier, Nicolas Paris, Hugo Cisneros, Christel Daniel, Matthieu Doutreligne, Catherine Duclos, Nicolas Griffon, Claire Hassen-Khodja, Ivan Lerner, Adrien Parrot, Éric Sadou, Cyrina Saussol, Pascal Vaillant

Materials and Methods: The first method is a weakly supervised method using an unlabeled corpus (MIMIC) to build a silver standard, by producing semi-automatically a small and very precise set of rules to detect some samples of positive and negative patients.

Evaluation of a Sequence Tagging Tool for Biomedical Texts

1 code implementation • WS 2018 • Julien Tourille, Matthieu Doutreligne, Olivier Ferret, Aur{\'e}lie N{\'e}v{\'e}ol, Nicolas Paris, Xavier Tannier

Many applications in biomedical natural language processing rely on sequence tagging as an initial step to perform more complex analysis.

named-entity-recognition Named Entity Recognition +4