Reinforcement Learning in Healthcare: A Survey

YuChao,; LiuJiming,; NematiShamim,; YinGuosheng,

doi:10.1145/3477600

Cited by 237 publications

(132 citation statements)

References 187 publications

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“…Gottesman et al (2019) present guidelines for reinforcement learning in healthcare. Yu et al (2023) present a survey about RL in healthcare.…”

Section: Healthcarementioning

confidence: 99%

Reinforcement Learning in Practice: Opportunities and Challenges

Li¹

2022

Preprint

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This article is a gentle discussion about the field of reinforcement learning for real life, about opportunities and challenges, with perspectives and without technical details, touching a broad range of topics. The article is based on both historical and recent research papers, surveys, tutorials, talks, blogs, and books. Various groups of readers, like researchers, engineers, students, managers, investors, officers, and people wanting to know more about the field, may find the article interesting.In this article, we first give a brief introduction to reinforcement learning (RL), and its relationship with deep learning, machine learning and AI. Then we discuss opportunities of RL, in particular, applications in products and services, games, recommender systems, robotics, transportation, economics and finance, healthcare, education, combinatorial optimization, computer systems, and science and engineering. The we discuss challenges, in particular, 1) foundation, 2) representation, 3) reward, 4) model, simulation, planning, and benchmarks, 5) learning to learn a.k.a. meta-learning, 6) off-policy/offline learning, 7) software development and deployment, 8) business perspectives, and 9) more challenges. We conclude with a discussion, attempting to answer: "Why has RL not been widely adopted in practice yet?" and "When is RL helpful?".

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“…Gottesman et al (2019) present guidelines for reinforcement learning in healthcare. Yu et al (2023) present a survey about RL in healthcare.…”

Section: Healthcarementioning

confidence: 99%

Reinforcement Learning in Practice: Opportunities and Challenges

Li¹

2022

Preprint

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“…It is essentially based on associating transitional experiences to actions in such a manner that would maximize a reward signal, and is used in several applications, e.g. healthcare [31], robotics [32], finance [33], and many more [34].…”

Section: Related Work Conventional Pose-graph Optimization Approachesmentioning

confidence: 99%

RL-PGO: Reinforcement Learning-based Planar Pose-Graph Optimization

Kourtzanidis¹,

Saeedi²

2022

Preprint

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The objective of pose SLAM or pose-graph optimization (PGO) is to estimate the trajectory of a robot given odometric and loop closing constraints. State-of-the-art iterative approaches typically involve the linearization of a non-convex objective function and then repeatedly solve a set of normal equations. Furthermore, these methods may converge to a local minima yielding sub-optimal results. In this work, we present to the best of our knowledge the first Deep Reinforcement Learning (DRL) based environment and proposed agent for 2D pose-graph optimization. We demonstrate that the pose-graph optimization problem can be modeled as a partially observable Markov Decision Process and evaluate performance on realworld and synthetic datasets. The proposed agent outperforms state-of-the-art solver g 2 o on challenging instances where traditional nonlinear least-squares techniques may fail or converge to unsatisfactory solutions. Experimental results indicate that iterative-based solvers bootstrapped with the proposed approach allow for significantly higher quality estimations. We believe that reinforcement learning-based PGO is a promising avenue to further accelerate research towards globally optimal algorithms. Thus, our work paves the way to new optimization strategies in the 2D pose SLAM domain.

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“…Healthcare offers a multitude of opportunities and challenges for machine learning; for a recent survey, see (Ghassemi et al, 2020). Specifically, reinforcement learning and control have found numerous applications (Yu et al, 2020a), and recently for weaning patients off mechanical ventilators (Prasad et al, 2017;Yu et al, 2019Yu et al, , 2020b. As far as we know, there is no prior work on improving the control of ventilators using machine learning.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning for Mechanical Ventilation Control

Suo¹,

Agarwal²,

Xia³

et al. 2021

Preprint

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We consider the problem of controlling an invasive mechanical ventilator for pressure-controlled ventilation: a controller must let air in and out of a sedated patient's lungs according to a trajectory of airway pressures specified by a clinician. Hand-tuned PID controllers and similar variants have comprised the industry standard for decades, yet can behave poorly by over-or under-shooting their target or oscillating rapidly. We consider a data-driven machine learning approach: First, we train a simulator based on data we collect from an artificial lung. Then, we train deep neural network controllers on these simulators. We show that our controllers are able to track target pressure waveforms significantly better than PID controllers. We further show that a learned controller generalizes across lungs with varying characteristics much more readily than PID controllers do.

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Reinforcement Learning in Healthcare: A Survey

Cited by 237 publications

References 187 publications

Reinforcement Learning in Practice: Opportunities and Challenges

Reinforcement Learning in Practice: Opportunities and Challenges

RL-PGO: Reinforcement Learning-based Planar Pose-Graph Optimization

Machine Learning for Mechanical Ventilation Control

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