A Primer on Reinforcement Learning in Medicine for Clinicians

IF 12.4 1区 医学 Q1 HEALTH CARE SCIENCES & SERVICES NPJ Digital Medicine Pub Date : 2024-11-26 DOI:10.1038/s41746-024-01316-0
Pushkala Jayaraman, Jacob Desman, Moein Sabounchi, Girish N. Nadkarni, Ankit Sakhuja
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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.

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临床医生医学强化学习入门指南
强化学习(RL)是一种机器学习范式,通过解决不确定性问题和优化连续治疗策略,为医疗保健专业人员提高临床决策能力。强化学习利用患者数据创建个性化治疗方案,从而提高疗效和资源效率。本综述将向临床读者介绍 RL,探讨 RL 的核心概念、潜在应用以及将 RL 融入临床实践的挑战,为高效、个性化和有效的患者护理提供见解。
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来源期刊
CiteScore
25.10
自引率
3.30%
发文量
170
审稿时长
15 weeks
期刊介绍: npj Digital Medicine is an online open-access journal that focuses on publishing peer-reviewed research in the field of digital medicine. The journal covers various aspects of digital medicine, including the application and implementation of digital and mobile technologies in clinical settings, virtual healthcare, and the use of artificial intelligence and informatics. The primary goal of the journal is to support innovation and the advancement of healthcare through the integration of new digital and mobile technologies. When determining if a manuscript is suitable for publication, the journal considers four important criteria: novelty, clinical relevance, scientific rigor, and digital innovation.
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