7 citations · 24 across the 9 of their papers we have counts for
4 papers · 1 filter
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…
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…
Extracting Diagnosis Pathways from Electronic Health Records Using Deep Reinforcement Learning
Lillian Muyama, Antoine Neuraz, Adrien Coulet
Clinical diagnosis guidelines aim at specifying the steps that may lead to a diagnosis. Inspired by guidelines, we aim to learn the optimal sequence of actions to perform in order…
A Model-Based Reinforcement Learning Approach for a Rare Disease Diagnostic Task
Rémi Besson, Erwan Le Pennec, Stéphanie Allassonnière +3
In this work, we present our various contributions to the objective of building a decision support tool for the diagnosis of rare diseases. Our goal is to achieve a state of knowle…