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临床医生医学强化学习入门

A Primer on Reinforcement Learning in Medicine for Clinicians.

作者信息

Jayaraman Pushkala, Desman Jacob, Sabounchi Moein, Nadkarni Girish N, Sakhuja Ankit

机构信息

The Charles Bronfman Institute for Personalized Medicine (CBIPM), Icahn School of Medicine at Mount Sinai, New York, NY, USA.

Samuel Bronfman Department of Medicine Division of Data Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA.

出版信息

NPJ Digit Med. 2024 Nov 26;7(1):337. doi: 10.1038/s41746-024-01316-0.

Abstract

Reinforcement Learning (RL) is a machine learning paradigm that enhances clinical decision-making for healthcare professionals by addressing uncertainties and optimizing sequential treatment strategies. RL leverages patient-data to create personalized treatment plans, improving outcomes and resource efficiency. This review introduces RL to a clinical audience, exploring core concepts, potential applications, and challenges in integrating RL into clinical practice, offering insights into efficient, personalized, and effective patient care.

摘要

强化学习(RL)是一种机器学习范式,它通过解决不确定性问题和优化序贯治疗策略来增强医疗保健专业人员的临床决策能力。强化学习利用患者数据来制定个性化治疗方案,从而改善治疗效果并提高资源利用效率。本文将强化学习介绍给临床领域的读者,探讨其核心概念、潜在应用以及将强化学习整合到临床实践中所面临的挑战,为高效、个性化且有效的患者护理提供见解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e59/11599275/0e1414be79be/41746_2024_1316_Fig1_HTML.jpg

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