Deep Reinforcement Learning for personalized diagnostic decision pathways using Electronic Health Records: A comparative study on anemia and Systemic Lupus Erythematosus
DRL offers the opportunity to learn personalized decision pathways for diagnosis. Our two use cases illustrate the advantages of this approach: they generate step-by-step pathways that are explainable, and their performance is competitive when compared to state-of-the-art methods.
Authors
- Lillian Muyama (ORCID: https://orcid.org/0000-0002-0409-0176)
- Antoine Neuraz (ORCID: https://orcid.org/0000-0001-7142-6728)
- Adrien Coulet (ORCID: https://orcid.org/0000-0002-1466-062X)
Institutions
- Délégation Paris 5 (FR)
- Hôpital Necker-Enfants Malades (FR)
- Inserm (FR)
- Université Paris Cité (FR)
- Sorbonne Université (FR)
- Sorbonne Paris Cité (FR)
- Centre de Recherche des Cordeliers (FR)
- Assistance Publique – Hôpitaux de Paris (FR)
Publication Details
- Journal
- Artificial Intelligence in Medicine
- Published
- 2024-10-02
- DOI
- https://doi.org/10.1016/j.artmed.2024.102994
- Citations
- 11
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 3.82
Funders
- Institut national de recherche en informatique et en automatique (INRIA)
- Agence Nationale de la Recherche