Application of Reinforcement Learning for the Development of Precision Medicine Treatment DOI Open Access

R. Meenakshi,

S. Murugan,

R. Sivaranjani

et al.

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2025, Volume and Issue: 11(2)

Published: April 30, 2025

The goal of this presentation is to explain the purpose, target, ambition, and effect employment Reinforcement Learning (RL) methods in process developing tailored treatment plans within application precision medicine. objective improve results for patients by adapting medical procedures specific features requirements each individual patient. It RL options iteratively learning from patient reactions updating suggestions accordance with those learnings. contribution consists expanding area medicine via use algorithms, which provide a framework optimization that both dynamic flexible. data, may include genetic profiles, biomarkers, clinical histories, enables assist production individualized therapies consider variability response patterns. strategy has potential revolutionize practice ushering new age are customized establishes foundation future research decision support systems based on reinforcement settings, will eventually lead improvements outcomes delivery healthcare.

Language: Английский

Application of Reinforcement Learning for the Development of Precision Medicine Treatment DOI Open Access

R. Meenakshi,

S. Murugan,

R. Sivaranjani

et al.

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2025, Volume and Issue: 11(2)

Published: April 30, 2025

The goal of this presentation is to explain the purpose, target, ambition, and effect employment Reinforcement Learning (RL) methods in process developing tailored treatment plans within application precision medicine. objective improve results for patients by adapting medical procedures specific features requirements each individual patient. It RL options iteratively learning from patient reactions updating suggestions accordance with those learnings. contribution consists expanding area medicine via use algorithms, which provide a framework optimization that both dynamic flexible. data, may include genetic profiles, biomarkers, clinical histories, enables assist production individualized therapies consider variability response patterns. strategy has potential revolutionize practice ushering new age are customized establishes foundation future research decision support systems based on reinforcement settings, will eventually lead improvements outcomes delivery healthcare.

Language: Английский

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