Introduction To Reinforcement And Systemic Machine Learning Pdf

introduction to reinforcement and systemic machine learning pdf

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In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. This paper presents the deep reinforcement learning DRL framework to estimate the optimal Dynamic Treatment Regimes from observational medical data. This framework is more flexible and adaptive for high dimensional action and state spaces than existing reinforcement learning methods to model real-life complexity in heterogeneous disease progression and treatment choices, with the goal of providing doctors and patients the data-driven personalized decision recommendations.

The proposed DRL framework comprises i a supervised learning step to predict expert actions, and ii a deep reinforcement learning step to estimate the long-term value function of Dynamic Treatment Regimes.

Both steps depend on deep neural networks. As a key motivational example, we have implemented the proposed framework on a data set from the Center for International Bone Marrow Transplant Research CIBMTR registry database, focusing on the sequence of prevention and treatments for acute and chronic graft versus host disease after transplantation.

Medical treatments often compose a sequence of intervention decisions that are made adaptive to the time-varying clinical status and conditions of a patient, which are coined as Dynamic Treatment Regimes DTRs 1.

More specifically, the scientific question our paper focuses on is the determination of the optimal DTRs to maximize the long-term clinical outcome. When the straightforward rule-based treatment guidelines are difficult to be established, the statistical learning method provides a data-driven tool to explore and examine the best strategies.

These data driven approaches leverage on the technology advances to collect the increasingly abundant medical data e. The problem of identifying the optimal DTRs that maximize the long-term clinical outcome using reinforcement learning 2 has received much attention in the statistics community?

They are difficult to implement using observational data such as electronic medical records, registry data , which exhibit a much higher degree of heterogeneity in decision stages among patients, and the treatment options i. The existing methods can only analyze certain simplification of stage and action spaces among the enormous ways.

Simplification by human experts might not lead to the optimal DTRs and in many cases there is no clear way of simplification.

In addition, the simplification process needs substantial domain knowledge and labor-intensive data mining and feature engineering processes. For example, Krakow 13 used Q-learning 9 in the DTR literature to model a simplified problem of our motivating example. And the actions were taken not only at the time of transplant and days. As a result, there is a call for methods to expand DTR methodology from the limited application of SMART studies to broader, flexible, and practical applications using the registry and other observational medical data.

To make reinforcement learning accessible for more general DTR problems using observational datasets, we need a new framework which i automatically extracts and organizes the discriminative information from the data, and ii can explore high-dimensional action and state spaces and make personalized treatment recommendations. Deep learning is a promising new technique to save the labor-intensive feature engineering processes.

The effective combination of deep learning deep neural networks and reinforcement learning technique, named Deep Reinforcement Learning DRL , is initially invented for intelligent game playing and has later emerged as an effective method to solve complicated control problems with large-scale, high-dimensional state and action spaces 14 , 15 , 16 , 17 , 18 , There is a training set containing retrospective patients and a testing set of 38 patients.

Besides, the framework is proposed in a special application of the adaptive strategy of radiation dose in cancer treatment. In contrast, our framework is proposed for the national or worldwide patient registry database for any disease, where we use actual patient observation data and experience replay to train the DQN.

Preprint 21 proposed to use dueling double-DQN with a prioritized experience replay to learn Sepsis Treatment, where the motivating data is from the EHR database. This working paper 21 was brought to our attention by one reviewer. The two teams independently work on DRL implementation on big observation medical database. The distinction between the two paper is that our work aims at a long-term disease treatment and management problem.

In comparison, the other team is working on the treatment of an acute condition, i. Our work is the first and unique general framework proposed for registry databases with similar structure of the motivating Bone Marrow Transplant registry database, which collect long-term follow-up of each patient national wide, throughout the disease course with standard forms for disease-related patient status and treatments.

To demonstrate the effectiveness of the proposed framework, we implement it using a concrete example: Graft Versus Host Disease GVHD prevention and treatment for Leukemia patients who have undergone allogeneic hematopoietic cell transplantation AHCT. The long-term longitudinal follow-up for almost all US patients and some international patients who have undergone AHCT make the Center for International Blood and Marrow Transplant Research CIBMTR registry database an ideal existing data set to explore the capacity of artificial intelligence in medical decision making.

Once established, GVHD is difficult to treat. It can be prevented by selected methods, but often at the expense of an increased risk of relapse, rejection or delayed immune reconstitution 23 , Hence, no optimal or even satisfactory prevention and treatment methods have been defined.

Reference 22 concluded that the difficulty in composing a standard practice guideline is the lack of solid scientific support for a large portion of procedures used in GVHD prevention and treatment, which calls for further systematic studies to compare different strategies. Such clinical needs for methodological innovations in finding the optimal personalized strategies can be largely resolved in the proposed study. More specifically, in this paper we develop a data-driven DRL framework for the optimal DTR, comprising the prevention and treatment of both acute and chronic GVHDs, as well as the initial conditioning chemotherapy after the transplantation.

The DRL framework, which deals with heterogeneous decision stages states and high-dimensional action space, consists of two steps at each decision stage.

The second step is to estimate the value function of DTRs for strategies composed of the top expert actions with the highest probability from the first step. The state and action spaces, as well as reward function, are carefully selected, and effective dimensionality reduction techniques such as the low variance filter are utilized to mitigate the shortcoming of limited data in the database. The similar states have similar encoded representations. In this section, we present the performance of the DRL framework for optimizing the sequence of treatments in prevention and treatment of both acute and chronic GVHDs.

We also present the accuracy of the feed-forward deep neural networks DNNs in predicting the initial conditioning chemotherapy after the transplantation. The outcome of the two DNNs are the treatment combinations of 14 treatment options for GVHD prophylaxis and treatment combinations of 19 treatment options for initial conditioning. The treatment options for initial conditioning include busulfan, cyclophosphamide, total body irradiation, fludarabine, thiotepa, melphalan l-pam , cytarabine, etc.

First, we demonstrate in Fig. Please note that we utilize the top- N prediction accuracy, i. This top- N accuracy is widely utilized for the image recognition such as the ImageNet contest 25 and other deep learning testings. Furthermore, Fig. From these two figures, we can derive the following observations.

First, the prediction accuracies are in general higher compared with the initial conditioning and GVHD preventions, because the medication for GVHD treatments seems to be more regular compared with the initial treatments.

The prediction accuracies are high enough and this shows a first step towards the ultimate goal of DTR using machine intelligence. Next, for the chronic GVHD treatment, the prediction accuracy will increase when time elapses, i. The reason is that the patients will become more stable and easy for treatment when chronic GVHD occurs or prolongs at a later time.

Details about the DQN notations and models are provided in the next section. And it can be an important predictor in deciding the cGVHD treatment. This state vector is also used to define the immediate outcome of the previous step in the following way. This immediate reward can be viewed as a heuristic quality-of-life measurement.

And the cumulative reward, as the sum of these immediate rewards across stages, is also meaningful as quality-of-life measurements. To evaluate the performance, we first present the reward comparisons on the testing data set. The reward is computed using the average of all patients who actually follow the recommendation from each policy. Results demonstrate that the mean reward of DQN is better than the other off-the-shelf methods. We also provide the confidence intervals, estimated through 5, bootstraps of the testing data.

The DQN has a higher mean reward, but the confidence interval is overlapped with the other strategies. This is due to the limited training and testing sample size of the currently available patient cohort.

In contrast, the one-size-fit-all method suggests all patients have the same treatment without personalization. On one hand, machine learning methods provide the possibility to incorporate information and complex patterns in the database to help make the decision. However, it is still in its early phase for facilitating actual decision making for doctors and patients.

One challenge for using the retrospective dataset to build DQN model is evaluating the DQN performance with the limited number of test data. As a result, we propose a second method to evaluate DQN, which is to separately train a DNN to predict rewards, and then compute the expected reward for the top-1 DQN recommendations. In the actual testing data, the top-1 recommendation from DQN is rarely the actual observed treatment, and the DNN will then provide an estimation of the counterfactual reward.

All rewards were computed as the predictions from DNN. Despite the limited data, we can still observe that the proposed DQN method outperforms the other methods both for acute and chronic GVHD treatments, which illustrates the effectiveness of using the DRL method for making recommendations in DTR. In this work, we present a machine learning strategy from an observational dataset to address the decision-making problem of GVHD prevention and treatment.

It has the nationwide long-term follow-up of each patient throughout the disease course with standard forms. Despite the challenge of identifying meaningful status, treatments and long-term outcomes in the EHR database; one fatal challenge is that current EHR databases are usually regional, the number of patients for one specific disease within the connected EHR databases is very limited for the purpose of learning multiple stage decision rules.

The general fitness of the proposed DRL framework with the registry data structure enables enormous potential problems to be addressed in the motivating registry database. Furthermore, it is not hard to transfer to other disease registry databases with the similar data structure.

Although EHR databases are the current most prevailing existing data sources, the data structure might evolve in the future together with the emerging analytical tools to meet the potential of AI to better facilitate medical decisions. In the clinical fields like our motivating example, there are some pressing sequential decision-making questions.

For example, in the leukemia field, one other example is to decide whether transplant is a beneficial strategy compared to non-transplant, under what condition or time transplant will become a better option, and adapting these decisions to personal features.

Given the constraints on conducting sequential randomized clinical trials on these questions, it is more practical to start from analyzing the observational data at this point. With the improvement of data collection and machine learning techniques in this field, a data-driven decision support system can provide treatment recommendations for doctors based on supervised learning and reinforcement learning.

It is of significant interests to incorporate this machine learned rule to facilitate treatment decision making and how to update the decision rules in an online fashion when new data are collected. There are some current trends in the mobile health field that combines randomized clinical trial with online reinforcement learning through micro-randomized-trials 26 , where the randomization probability can be adopted in an online manner, in analogy to the exploration techniques in reinforcement learning.

The applications can be seen in smoking secession, eating disorder management, and blood glucose management for diabetes patients. However, compared with our motivating example in bone marrow transplant, these existing interventions are easier to perform randomization due to the much fewer number of actions, less profound consequences, fewer treatment options and less complicated feature variables.

To make our proposed framework more accessible and make on-line training practical, we call for innovations in the data collecting, sharing, and analytical infrastructure. To explore the true capacity of machine learning including our method for improving health care, we need a data infrastructure, that can collect data for the whole population in the format friendly to machine learning, can share the data to researchers easily and safely, and can update the data and model in real time.

The Q-learning method has the theoretical guarantee to converge to optimal policy only under the assumptions for Markov decision process. However, Deep Q-learning does not have the theoretical guarantee for convergence to optimal policy even under the Markov decision process because of the sub-optimality of deep neural networks. The disease progression process does not strictly follow Markov process and the state vector we are considering may not fully capture the patients status. However Q-learning and DQN have demonstrated good performance in a lot of application that Markov memoryless property does not hold 18 , For future work we will remedy this problem with a model without Markov assumption e.

RNN , taking the history information into account.

Learning the Dynamic Treatment Regimes from Medical Registry Data through Deep Q-network

Metrics details. Emerging machine learning technologies are beginning to transform medicine and healthcare and could also improve the diagnosis and treatment of rare diseases. Currently, there are no systematic reviews that investigate, from a general perspective, how machine learning is used in a rare disease context. This scoping review aims to address this gap and explores the use of machine learning in rare diseases, investigating, for example, in which rare diseases machine learning is applied, which types of algorithms and input data are used or which medical applications e. Using a complex search string including generic search terms and individual disease names, studies from the past 10 years — that applied machine learning in a rare disease context were identified on PubMed. To systematically map the research activity, eligible studies were categorized along different dimensions e. Two hundred eleven studies from 32 countries investigating 74 different rare diseases were identified.

Thanks to rapid increases in data availability and computing power, machine learning now plays a vital role in both technology and business. Machine learning contributes significantly to credit risk modeling applications. We find the machine learning models deliver similar accuracy ratios as the RiskCalc model. Machine learning methods provide a better fit for the nonlinear relationships between the explanatory variables and default risk. We also find that using a broader set of variables to predict defaults greatly improves the accuracy ratio, regardless of the models used. Machine learning is a method of teaching computers to parse data, learn from it, and then make a determination or prediction regarding new data.

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the concept of systemic learning. The systemic machine‐learning paradigm is discussed along with various concepts and techni Chapter 1. Introduction to Reinforcement and Systemic Machine Learning. Parag Kulkarni.


Pdf Reinforcement And Systemic Machine Learning For Decision Making

June 24, note: If you want to cite an example from the post, please cite the paper which that example came from. If you want to cite the post as a whole, you can use the following BibTeX:. Deep reinforcement learning is surrounded by mountains and mountains of hype.

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Reinforcement and Systemic Machine Learning for Decision Making - Ebook

Machine learning as a field of artificial intelligence is increasingly applied in medicine to assist patients and physicians. Growing datasets provide a sound basis with which to apply machine learning methods that learn from previous experiences. This review explains the basics of machine learning and its subfields of supervised learning, unsupervised learning, reinforcement learning and deep learning.

Reinforcement and Systemic Machine Learning for Decision Making There are always difficulties in making machines that learn from experience. Complete information is not always available or it becomes available in bits and pieces over a period of time. With respect to systemic learning, there is a need to understand the impact of decisions and actions on a system over that period of time. This book takes a holistic approach to addressing that need and presents a new paradigm creating new learning applications and, ultimately, more intelligent machines. The first book of its kind in this new and growing field, Reinforcement and Systemic Machine Learning for Decision Making focuses on the specialized research area of machine learning and systemic machine learning.

Learning the Dynamic Treatment Regimes from Medical Registry Data through Deep Q-network

The private and public sectors are increasingly turning to artificial intelligence AI systems and machine learning algorithms to automate simple and complex decision-making processes. AI is also having an impact on democracy and governance as computerized systems are being deployed to improve accuracy and drive objectivity in government functions. The availability of massive data sets has made it easy to derive new insights through computers.

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