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Reinforcement learning design for cancer clinical trials

Yufan Zhao, Michael R. Kosorok, Donglin Zeng
2009 Statistics in Medicine  
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

Jan-Niklas Eckardt, Karsten Wendt, Martin Bornhäuser, Jan Moritz Middeke
2021 Cancers  
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

Yufan Zhao, Donglin Zeng, Mark A. Socinski, Michael R. Kosorok
2011 Biometrics  
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]

Chao Yu, Jiming Liu, Shamim Nemati
2020 arXiv   pre-print
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

Sara Jane Ward, Sean D McAllister, Rumi Kawamura, Ryuchi Murase, Harshini Neelakantan, Ellen A Walker
2014 British Journal of Pharmacology  
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]

Corinna Maier, Niklas Hartung, Charlotte Kloft, Wilhelm Huisinga, Jana de Wiljes
2020 arXiv   pre-print
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

Yang Yang, Bing Zhao, Xuejiao Gao, Jinbing Sun, Juan Ye, Jun Li, Peng Cao
2021 Journal of Experimental & Clinical Cancer Research  
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]

Léa Giron, Katia Befort
2016 Cannabinoids in Health and Disease  
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]

Alaa Awad Abdellatif, Naram Mhaisen, Zina Chkirbene, Amr Mohamed, Aiman Erbad, Mohsen Guizani
2021 arXiv   pre-print
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

Jeena John, Manas Kinra, Jayesh Mudgal, G. L. Viswanatha, K. Nandakumar
2021 Psychopharmacology  
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

V. Kiran Vemuri, David R. Janero, Alexandros Makriyannis
2008 Physiology and Behavior  
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

Songjing Chen, Sizhu Wu
2022 BMC Medical Informatics and Decision Making  
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  
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

Konstantinos C. Siontis, Peter A. Noseworthy, Zachi I. Attia, Paul A. Friedman
2021 Nature Reviews Cardiology  
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?

Yvonne Wengström
2007 Nature Clinical Practice Oncology  
(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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