Search Results for author:
Found 7 papers, 2 papers with code
Deep Reinforcement Learning for Personalized Diagnostic Decision Pathways Using Electronic Health Records: A Comparative Study on Anemia and Systemic Lupus Erythematosus
We illustrate with our two use cases their advantages: they generate step-by-step pathways that are self-explanatory; and their correctness is competitive when compared to state-of-the-art approaches.
The Smart Data Extractor, a Clinician Friendly Solution to Accelerate and Improve the Data Collection During Clinical Trials
The Smart Data Extractor pre-populates clinic research forms by following rules.
Extracting Diagnosis Pathways from Electronic Health Records Using Deep Reinforcement Learning
Clinical diagnosis guidelines aim at specifying the steps that may lead to a diagnosis.
Learning structures of the French clinical language:development and validation of word embedding models using 21 million clinical reports from electronic health records
We used two French annotated medical datasets to compare our language models to the original CamemBERT network, evaluating the statistical significance of improvement with the Wilcoxon test.
Learning the grammar of drug prescription: recurrent neural network grammars for medication information extraction in clinical texts
Similarly, we evaluated seq-RNNG, a hybrid RNNG model that takes as extra-input the output of the BiLSTMs for entities and events.
A Model-Based Reinforcement Learning Approach for a Rare Disease Diagnostic Task
In this work, we present our various contributions to the objective of building a decision support tool for the diagnosis of rare diseases.
Model-based Reinforcement Learning
reinforcement-learning
+1
Natural language understanding for task oriented dialog in the biomedical domain in a low resources context
In the biomedical domain, the lack of sharable datasets often limit the possibility of developing natural language processing systems, especially dialogue applications and natural language understanding models.
