Deep Reinforcement Learning for Dynamic Treatment Regimes on Medical Registry Data

Liu, Ying; Logan, Brent R.; Liu, Ning; Xu, Zhiyuan; Tang, Jian; Wang, Yangzhi

doi:10.1109/ichi.2017.45

Cited by 73 publications

(49 citation statements)

References 35 publications

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“…The lifelong function of dynamic treatment regimes was estimated using reinforcement learning [155]. Testing datasets were retrieved from about 6000 patients of Acute Myeloid Leukemia.…”

Section: Reinforcement Learningmentioning

confidence: 99%

Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications

et al. 2017

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Abstract:To promote sustainable development, the smart city implies a global vision that merges artificial intelligence, big data, decision making, information and communication technology (ICT), and the internet-of-things (IoT). The ageing issue is an aspect that researchers, companies and government should devote efforts in developing smart healthcare innovative technology and applications. In this paper, the topic of disease diagnosis in smart healthcare is reviewed. Typical emerging optimization algorithms and machine learning algorithms are summarized. Evolutionary optimization, stochastic optimization and combinatorial optimization are covered. Owning to the fact that there are plenty of applications in healthcare, four applications in the field of diseases diagnosis (which also list in the top 10 causes of global death in 2015), namely cardiovascular diseases, diabetes mellitus, Alzheimer's disease and other forms of dementia, and tuberculosis, are considered. In addition, challenges in the deployment of disease diagnosis in healthcare have been discussed.

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“…The lifelong function of dynamic treatment regimes was estimated using reinforcement learning [155]. Testing datasets were retrieved from about 6000 patients of Acute Myeloid Leukemia.…”

Section: Reinforcement Learningmentioning

confidence: 99%

Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications

et al. 2017

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“…Developing an optimal ATS requires several steps. The first is to clearly define the research question by specifying: (1) how many treatment decision nodes there are and what treatment options are available at each; (2) what subjectlevel data relevant to the possible decisions are available;…”

Section: Two Approaches To Estimating Optimal Ats For a Single Treatmmentioning

confidence: 99%

“…Many receive hypomethylating therapy, intensive chemotherapy, donor lymphocyte infusions, and/or second transplants in diverse sequences. (2) Immune suppression strategies to prevent and treat acute and chronic GvHD [1][2][3]. (3) Sequencing autotransplant vs. chimeric antigen receptor (CAR)-T-cell therapy in persons with advanced lymphomas or sequencing new therapies for chronic lymphocytic leukemia.…”

Section: Introductionmentioning

confidence: 99%

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Precision medicine: Statistical methods for estimating adaptive treatment strategies

Moodie

Krakow

2020

Bone Marrow Transplant

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The beauty of science is that all the important things are unpredictable. Freeman Dyson In the typescript which follows, Moodie and Krakow tackle the topical issue of precision medicine and statistical methods for estimating adaptive treatment strategies. This may be the most difficult typescript in our series so far for non-statisticians to understand. It even has equations! But please bear with the authors and give it a chance. One needs not to understand the equations to get the thrust of the strategy. Precision medicine as we discuss elsewhere, is misnamed. In statistics and mathematics precision refers to getting the same answer again and again. It does not mean getting the correct answer, the term for which is accuracy, not precision. However, precision is the current buzz word so there's no point trying to get this straight. When we think about precision we need to consider two elements, reproducibility and replicability. Reproducibility means you give me your data and computer code and I come to the same conclusion you did. Replicability is another matter. I try to replicate your experiment and hopefully reach the same conclusion. In medicine, replicability is obviously more important than reproducibility but things which cannot be reproduced are unlikely to be replicated. As the authors discuss, one can think about precision medicine as one does a family vacation. A best vacation depends on several covariates: where you live, your prior travel experiences, advice from family and friends, online reviews, Wikitravel, cost, your travel budget, if you have kids and many other co-variates. Consequently, there is unlikely to be a best vacation for everyone. Yours might be a week at the Ritz Carlton Cancun with dinner at Careyes and ours, a week at the Pfister Hotel in Milwaukee with dinner at Mader's German Restaurant (bring simvastatin). Similarly, it is unlikely there is a best therapy of acute myeloid leukemia, a best donor, a best conditioning regimen, a best posttransplant immune suppressive regimen etc. and certainly no best combination of these co-variates for your patient. The question Moodie and Krakow tackle is how we can determine the best therapy or combination of therapies for someone receiving a haematopoietic cell transplant. Although the default answer is typically: randomized clinical trials are the gold standard, these inform us of the outcome of a cohort of subjects, not individuals. In many instances, although a new therapy may be shown to be better than an old one in a controlled randomized trial the benefit is not uniformly distributed. Some subjects in the experimental cohort may do worse with the new therapy compared with controls, others better. The question is who are the winners and losers? We cannot do a controlled randomized trial of one person. Moodie and Krakow discuss statistical tools to help us sort this out. Again, please do not be put off by the equations; forgetaboutit. The overriding message is not so complex, and important. We are always standing by on twitter @BMTStats t...

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“…RL has been used to develop treatment strategies for epilepsy [8] and lung cancer [9]. An approach based on deep RL was recently proposed for developing treatment strategies based on medical registry data [10]. Deep RL has also been used to learn treatment policies for sepsis [11].…”

Section: Rl In Medicinementioning

confidence: 99%

Deep Reinforcement Learning in Medicine

Jönsson

2018

Kidney Dis

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Reinforcement learning has achieved tremendous success in recent years, notably in complex games such as Atari, Go, and chess. In large part, this success has been made possible by powerful function approximation methods in the form of deep neural networks. The objective of this paper is to introduce the basic concepts of reinforcement learning, explain how reinforcement learning can be effectively combined with deep learning, and explore how deep reinforcement learning could be useful in a medical context.

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Deep Reinforcement Learning for Dynamic Treatment Regimes on Medical Registry Data

Cited by 73 publications

References 35 publications

Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications

Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications

Precision medicine: Statistical methods for estimating adaptive treatment strategies

Deep Reinforcement Learning in Medicine

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