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Reinforcement learning design for cancer clinical trials
2009
Statistics in Medicine
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We develop reinforcement learning trials for discovering individualized treatment regimens for life threatening diseases such as cancer. ...
SUMMARY We develop reinforcement learning trials for discovering individualized treatment regimens for lifethreatening diseases such as cancer. ...
Mark Socinski for helpful discussions on non-small cell lung cancer treatment and the Reinforcement Learning Group at the University of North Carolina for many stimulating exchanges. ...
doi:10.1002/sim.3720
pmid:19750510
pmcid:PMC2767418
fatcat:y3fyxid5dnhdpktul67fc4ll34
Reinforcement Learning for Precision Oncology
2021
Cancers
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Still largely overlooked is reinforcement learning (RL) that addresses sequential tasks by exploring the underlying dynamics of an environment and shaping it by taking actions in order to maximize cumulative ...
rewards over time, thereby achieving optimal long-term outcomes. ...
clinical trial
of stage
IIIB/IV
non-small
cell lung
cancer
patients
X
X
X
X
[37]
Deep RL-guided dosing
regimens with
temozolomide or
procarbazine, CCNU
and vincristine using
action-derived ...
doi:10.3390/cancers13184624
pmid:34572853
fatcat:psrib4gwbvgkhgmypbwv53aemu
Reinforcement Learning Strategies for Clinical Trials in Nonsmall Cell Lung Cancer
2011
Biometrics
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A reinforcement learning method called Q-learning is utilized which involves learning an optimal regimen from patient data generated from the clinical reinforcement trial. ...
We present an adaptive reinforcement learning approach to discover optimal individualized treatment regimens from a specially designed clinical trial (a "clinical reinforcement trial") of an experimental ...
National Cancer Institute and from pilot funding provided by the Center for Innovative Clinical Trials at the UNC Gillings School of Global Public Health. ...
doi:10.1111/j.1541-0420.2011.01572.x
pmid:21385164
pmcid:PMC3138840
fatcat:t6gsyskwuvbv3a7kx2q2hrn6bu
Reinforcement Learning in Healthcare: A Survey
[article]
2020
arXiv
pre-print
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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 ...
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 ...
For example, the ε-greedy strategy selects the greedy action, arg max a Q t (s, a), with a high probability, and, occasionally, with a small probability selects an action uniformly at random. ...
arXiv:1908.08796v4
fatcat:iqqe3jifqvfntmxr6cakl4p2fy
Cannabidiol inhibits paclitaxel-induced neuropathic pain through 5-HT1Areceptors without diminishing nervous system function or chemotherapy efficacy
2014
British Journal of Pharmacology
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CBD produced no conditioned rewarding effects and did not affect conditioned learning and memory. ...
Hence, adjunct treatment with CBD during PAC chemotherapy may be safe and effective in the prevention or attenuation of CIPN. ...
Acknowledgements We acknowledge Khristina Pavlenko and Mak Sarich.
Conflict of interest There are no conflicts of interest present. ...
doi:10.1111/bph.12439
pmid:24117398
pmcid:PMC3969077
fatcat:xq5v7io6mzeizlzkbdsvxikvkm
Reinforcement learning and Bayesian data assimilation for model-informed precision dosing in oncology
[article]
2020
arXiv
pre-print
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We propose three novel approaches for MIPD employing Bayesian data assimilation (DA) and/or reinforcement learning (RL) to control neutropenia, the major dose-limiting side effect in anticancer chemotherapy ...
Model-informed precision dosing (MIPD) using therapeutic drug/biomarker monitoring offers the opportunity to significantly improve the efficacy and safety of drug therapies. ...
Reinforcement learning (RL) has been applied to various fields in health care, however, mainly focusing on clinical trial design [22, 23] , and only few studies relate to optimal dosing in a PK/PD context ...
arXiv:2006.01061v1
fatcat:xfknpe4hg5cvbnulg2verho3aa
Targeting strategies for oxaliplatin-induced peripheral neuropathy: clinical syndrome, molecular basis, and drug development
2021
Journal of Experimental & Clinical Cancer Research
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Furthermore, mechanisms of OIPN can reinforce each other, and combination therapies may be required for effective management. ...
The molecular mechanisms underlying OIPN are complex, with multi-targets and various cells causing neuropathy. ...
Acknowledgements Figures 1, 2, 3 and 4 in this review were created using BioRender.com (https:// biore nder. com). ...
doi:10.1186/s13046-021-02141-z
pmid:34686205
pmcid:PMC8532307
fatcat:md5ne37dbrd2bbv5g3eqaih4oi
Cannabinoids: Drug or Medication?
[chapter]
2016
Cannabinoids in Health and Disease
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Scientific advances are confronted with the adverse health effects that are demonstrated in preclinical and clinical studies based on the psychotic and addictive properties of this compound. ...
Finally, through alternative strategies to current treatments with both phyto-and synthetic cannabinoids, we try to reconcile the beneficial aspects of the use of cannabinoids for medication and the aspects ...
Acknowledgements We would like to thank Dominique Massotte for constant support and fruitful discussions and Emma Stephens for English proof reading of the manuscript. ...
doi:10.5772/63172
fatcat:cm2jwqhefrcurlbexokxcgw4gm
Reinforcement Learning for Intelligent Healthcare Systems: A Comprehensive Survey
[article]
2021
arXiv
pre-print
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Reinforcement Learning (RL) has witnessed an intrinsic breakthrough in solving a variety of complex problems for diverse applications and services. ...
The rapid increase in the percentage of chronic disease patients along with the recent pandemic pose immediate threats on healthcare expenditure and elevate causes of death. ...
strategies directly from chemotherapy treatment regimens clinical data without identifying any accurate mathematical models Hospital-based Drug dose Cancer Drug dose is fed as an input to the RL scheme ...
arXiv:2108.04087v1
fatcat:ifdpiqwunrawbmpfy6ftjk43g4
Animal models of chemotherapy-induced cognitive decline in preclinical drug development
2021
Psychopharmacology
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Rationale Chemotherapy-induced cognitive impairment (CICI), chemobrain, and chemofog are the common terms for mental dysfunction in a cancer patient/survivor under the influence of chemotherapeutics. ...
Due to differing mechanisms of chemotherapeutic agents with differing tendencies to alter behavioral and biochemical parameters, chemotherapy may present a significant risk in resulting memory impairments ...
Acknowledgements The authors acknowledge the Department of Pharmacology, Manipal College of Pharmaceutical Sciences, Manipal for the constant support and also thank the Manipal Academy of Higher Education ...
doi:10.1007/s00213-021-05977-7
pmid:34643772
pmcid:PMC8605973
fatcat:5qwcpuvdp5c2tafi4ehnljpudm
Pharmacotherapeutic targeting of the endocannabinoid signaling system: Drugs for obesity and the metabolic syndrome
2008
Physiology and Behavior
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To this intent, several selective CB1 receptor antagonists with varied chemical structures are currently in advanced preclinical or clinical trials, and one (rimonabant) has been approved as a weightmanagement ...
with significant therapeutic impact against overweight, obesity, obesity-related cardiometabolic dysregulation, and, more generally, maladies having a reward-supported appetitive component. ...
Acknowledgments The authors thank the National Institute on Drug Abuse (NIDA), National Institutes of Health (NIH), and Northeastern University for support. ...
doi:10.1016/j.physbeh.2007.11.012
pmid:18155257
pmcid:PMC3681125
fatcat:iao3jyakifbmhozoghtnotcssa
Deep Q-networks with web-based survey data for simulating lung cancer intervention prediction and assessment in the elderly: a quantitative study
2022
BMC Medical Informatics and Decision Making
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intervention effect for preventing lung cancer. ...
Methods We screened lung cancer high risk with web-based survey data and conducted simulative intervention. ...
When receiving input samples, reinforcement learning uses the current model to guide next action, updates the model after getting a reward form next action, and iteratively repeats until the model converging ...
doi:10.1186/s12911-021-01695-4
pmid:34983500
pmcid:PMC8725301
fatcat:4ai4skpfdjbj3m5y25r3b3iva4
Neuroscience of Psychoactive Substance Use and Dependence
2004
Addiction
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For this reason, rewards are reinforcers. ...
Ethical issues in clinical trials of pharmacological treatments for substance dependence Clinical trials of new therapeutic drugs are required for drug registration in most developed countries and are ...
Heath AC et al. (2002) Estimating two-stage models for genetic influences on alcohol, tobacco or drug use initiation and dependence vulnerability in twin and family data. ...
doi:10.1111/j.1360-0443.2004.00906.x
fatcat:eyujrmtzbrbrlmr2x4rgkgdiem
Artificial intelligence-enhanced electrocardiography in cardiovascular disease management
2021
Nature Reviews Cardiology
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The clinical and population-level implications of AI-based ECG phenotyping continue to emerge, particularly with the rapid rise in the availability of mobile and wearable ECG technologies. ...
In this Review, we summarize the current and future state of the AI-enhanced ECG in the detection of cardiovascular disease in at-risk populations, discuss its implications for clinical decision-making ...
Machine-learning approaches, including supervised, unsupervised and reinforcement learning, have also been used to determine the optimal dosing regimen during dofetilide treatment 51 . ...
doi:10.1038/s41569-020-00503-2
pmid:33526938
pmcid:PMC7848866
fatcat:jc2b2lb7qjcs3nnk5c5t5t2gry
Are women with breast cancer receiving sufficient education and information about their treatment?
2007
Nature Clinical Practice Oncology
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(52) and in clinical trials (53) . ...
The drug selected should be available, affordable, have a simple dosing regimen, and ideally, should not interfere with the quality of life of the patient. ...
Intended for health managers, policy-makers and clinical practitioners this report provides a concise summary of the consequences of poor adherence for health and economics. ...
doi:10.1038/ncponc1020
pmid:18073721
fatcat:3qsequlgcjd67nenzjrquscl4m
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