As a subfield of machine learning, reinforcement learning (RL) aims at empowering one's capabilities in behavioural decision making by using interaction experience with the world and an evaluative feedback. Unlike traditional supervised learning methods that usually rely on one-shot, exhaustive and supervised reward signals, RL tackles with sequential decision making problems with sampled, evaluative and delayed feedback simultaneously. Such distinctive features make RL technique a suitable candidate for developing powerful solutions in a variety of healthcare domains, where diagnosing decisions or treatment regimes are usually characterized by a prolonged and sequential procedure. This survey discusses the broad applications of RL techniques in healthcare domains, in order to provide the research community with systematic understanding of theoretical foundations, enabling methods and techniques, existing challenges, and new insights of this emerging paradigm. By first briefly examining theoretical foundations and key techniques in RL research from efficient and representational directions, we then provide an overview of RL applications in healthcare domains ranging from dynamic treatment regimes in chronic diseases and critical care, automated medical diagnosis from both unstructured and structured clinical data, as well as many other control or scheduling domains that have infiltrated many aspects of a healthcare system. Finally, we summarize the challenges and open issues in current research, and point out some potential solutions and directions for future research.

Reinforcement Learning in Healthcare: A Survey

The vertebrate control of locomotion involves all levels of the nervous system from cortex to the spinal cord. Here, we aim to cover all main aspects of this complex behavior, from the operation of...

https://journals.physiology.org/doi/pdf/10.1152/physrev.00015.2019

Current Principles of Motor Control, with Special Reference to Vertebrate Locomotion

As a subfield of machine learning, reinforcement learning (RL) aims at optimizing decision making by using interaction samples of an agent with its environment and the potentially delayed feedbacks...

The present prognosis for the recovery of voluntary control of movement in patients diagnosed as motor complete is generally poor. Herein we introduce a novel and noninvasive stimulation strategy of painless transcutaneous electrical enabling motor control and a pharmacological enabling motor control strategy to neuromodulate the physiological state of the spinal cord. This neuromodulation enabled the spinal locomotor networks of individuals with motor complete paralysis for 2–6 years American Spinal Cord Injury Association Impairment Scale (AIS) to be re-engaged and trained. We showed that locomotor-like stepping could be induced without voluntary effort within a single test session using electrical stimulation and training. We also observed significant facilitation of voluntary influence on the stepping movements in the presence of stimulation over a 4-week period in each subject. Using these strategies we transformed brain–spinal neuronal networks from a dormant to a functional state sufficien...

Noninvasive Reactivation of Motor Descending Control after Paralysis

Reinforcement learning for intelligent healthcare applications: A survey.

Neuromodulation of spinal networks can improve motor control after spinal cord injury (SCI). The objectives of this study were to (1) determine whether individuals with chronic paralysis c...

/pdf/self-assisted-standing-enabled-by-non-invasive-spinal-u6pi407pqa.pdf

Self-Assisted Standing Enabled by Non-Invasive Spinal Stimulation after Spinal Cord Injury

Electrical neuromodulation of spinal networks improves the control of movement of the paralyzed limbs after spinal cord injury (SCI). However, the potential of noninvasive spinal stimulation to facilitate postural trunk control during sitting in humans with SCI has not been investigated. We hypothesized that transcutaneous electrical stimulation of the lumbosacral enlargement can improve trunk posture. Eight participants with non-progressive SCI at C3-T9, American Spinal Injury Association Impairment Scale (AIS) A or C, performed different motor tasks during sitting. Electromyography of the trunk muscles, three-dimensional kinematics, and force plate data were acquired. Spinal stimulation improved trunk control during sitting in all tested individuals. Stimulation resulted in elevated activity of the erector spinae, rectus abdominis, and external obliques, contributing to improved trunk control, more natural anterior pelvic tilt and lordotic curve, and greater multi-directional seated stability. During spinal stimulation, the center of pressure (COP) displacements decreased to 1.36 ± 0.98 mm compared with 4.74 ± 5.41 mm without stimulation (p = 0.0156) in quiet sitting, and the limits of stable displacement increased by 46.92 ± 35.66% (p = 0.0156), 36.92 ± 30.48% (p = 0.0156), 54.67 ± 77.99% (p = 0.0234), and 22.70 ± 26.09% (p = 0.0391) in the forward, backward, right, and left directions, respectively. During self-initiated perturbations, the correlation between anteroposterior arm velocity and the COP displacement decreased from r = 0.5821 (p = 0.0007) without to r = 0.5115 (p = 0.0039) with stimulation, indicating improved trunk stability. These data demonstrate that the spinal networks can be modulated transcutaneously with tonic electrical spinal stimulation to physiological states sufficient to generate a more stable, erect sitting posture after chronic paralysis.

Trunk Stability Enabled by Noninvasive Spinal Electrical Stimulation after Spinal Cord Injury

Background: Epidural stimulation of the spinal cord can be used to enable stepping on a treadmill (electrical enabling motor control, eEmc) after a complete mid-thoracic spinal cord transection in adult rats Herein we have studied the effects of eEmc using a sub-threshold intensity of stimulation combined with spontaneous load-bearing proprioception to facilitate hindlimb stepping and standing during daily cage activity in paralyzed rats Methods: We hypothesized that eEmc combined with spontaneous cage activity would greatly increase the frequency and level of activation of the locomotor circuits in paralyzed rats Spontaneous cage activity was recorded using a specially designed swivel connector to record EMG signals and an IR based camcorder to record video Results and conclusion: The spinal rats initially were very lethargic in their cages showing little movement Without eEmc, the rats remained rather inactive with the torso rarely being elevated from the cage floor When the rats used their forelimbs to move, the hindlimbs were extended and dragged behind with little or no flexion In contrast, with eEmc the rats were highly active and the hindlimbs showed robust alternating flexion and extension resulting in step-like movements during forelimb-facilitated locomotion and often would stand using the sides of the cages as support The mean and summed integrated EMG levels in both a hindlimb flexor and extensor muscle were higher with than without eEmc These data suggest that eEmc, in combination with the associated proprioceptive input, can modulate the spinal networks to significantly amplify the amount and robustness of spontaneous motor activity in paralyzed rats

/pdf/sub-threshold-spinal-cord-stimulation-facilitates-3gszm65znx.pdf

Sub-threshold spinal cord stimulation facilitates spontaneous motor activity in spinal rats.

This paper introduces a new method for inverse reinforcement learning in large state spaces, where the learned reward function can be used to control high-dimensional robot systems and analyze complex human movement. To avoid solving the computationally expensive reinforcement learning problems in reward learning, we propose a function approximation method to ensure that the Bellman Optimality Equation always holds, and then estimate a function to maximize the likelihood of the observed motion. The time complexity of the proposed method is linearly proportional to the cardinality of the action set, thus it can handle large state spaces efficiently. We test the proposed method in a simulated environment on reward learning, and show that it is more accurate than existing methods and significantly better in scalability. We also show that the proposed method can extend many existing methods to large state spaces. We then apply the method to evaluating the effect of rehabilitative stimulations on patients with spinal cord injuries based on the observed patient motions.

Inverse Reinforcement Learning via Function Approximation for Clinical Motion Analysis

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Self-Assisted Standing Enabled by Non-Invasive Spinal Stimulation after Spinal Cord Injury

Trunk Stability Enabled by Noninvasive Spinal Electrical Stimulation after Spinal Cord Injury

Sub-threshold spinal cord stimulation facilitates spontaneous motor activity in spinal rats.

Inverse Reinforcement Learning via Function Approximation for Clinical Motion Analysis