1 citations · 1 across the 3 of their papers we have counts for
4 papers
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…
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…