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Reinforcement learning for intelligent healthcare applications: A survey.
Coronato, Antonio; Naeem, Muddasar; De Pietro, Giuseppe; Paragliola, Giovanni.
Afiliación
  • Coronato A; ICAR-CNR, Naples, Italy. Electronic address: antonio.coronato@icar.cnr.it.
  • Naeem M; University Parthenope, Naples, Italy. Electronic address: muddasar.naeem@icar.cnr.it.
  • De Pietro G; ICAR-CNR, Naples, Italy. Electronic address: giuseppe.depietro@icar.cnr.it.
  • Paragliola G; ICAR-CNR, Naples, Italy. Electronic address: giovanni.paragliola@icar.cnr.it.
Artif Intell Med ; 109: 101964, 2020 09.
Article en En | MEDLINE | ID: mdl-34756216
Discovering new treatments and personalizing existing ones is one of the major goals of modern clinical research. In the last decade, Artificial Intelligence (AI) has enabled the realization of advanced intelligent systems able to learn about clinical treatments and discover new medical knowledge from the huge amount of data collected. Reinforcement Learning (RL), which is a branch of Machine Learning (ML), has received significant attention in the medical community since it has the potentiality to support the development of personalized treatments in accordance with the more general precision medicine vision. This report presents a review of the role of RL in healthcare by investigating past work, and highlighting any limitations and possible future contributions.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Inteligencia Artificial / Aprendizaje Automático Idioma: En Revista: Artif Intell Med Asunto de la revista: INFORMATICA MEDICA Año: 2020 Tipo del documento: Article Pais de publicación: Países Bajos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Inteligencia Artificial / Aprendizaje Automático Idioma: En Revista: Artif Intell Med Asunto de la revista: INFORMATICA MEDICA Año: 2020 Tipo del documento: Article Pais de publicación: Países Bajos