Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Book Chapter Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Clinical Intelligence: Deep Reinforcement Learning for Healthcare and Biomedical Advancements Subject The topic of the resource biomedical; Deep reinforcement learning; deep-Q-networks; healthcare; Monte Carlo; SARSA Description An account of the resource Deep reinforcement learning (DRL) is showing a remarkable impact in the healthcare and biomedical domains, leveraging its ability to learn complex decision-making policies from raw data through trial-and-error interactions. DRL can effectively extract the characteristic information in the environment, propose effective behavior strategies, and correct errors that occurred during the training process. Targeted toward healthcare professionals, researchers, and technology enthusiasts, this chapter begins with notable applications of DRL in healthcare, including personalized treatment recommendations, clinical trial optimization, disease diagnosis, robotic surgery and assistance, mental health support systems, chronic disease management and scheduling, and a few more. It also delves on challenges such as data privacy, interpretability, regulatory compliance, validation, and the need for domain expertise to ensure safe and effective deployment. Next, the chapter seamlessly transitions into DRL algorithms contributing to the biomedical field which are gaining traction due to their potential to provide timely and personalized interventions. Over time, the research community has proposed several methods and algorithms within the field of deep reinforcement learning that help agents learn optimal policies from rich data. Healthcare data is often complex, high-dimensional, and unstructured, such as medical images, genomics data, and patient records. The healthcare-suitable DRL algorithms such as Q-learning, SARSA, Bayesian, actor-critic, reinforcement learning (RL), Deep-Q-Networks (DQN), and Monte Carlo Tree Search (MCTS) are highlighted. In addition, the section offers guidelines for the application of DRL to healthcare and biomedical problems, aiming at providing indications to the designer of new applications in order to choose among different RL methods. Furthermore, a case study is included to fully realize the revolutionary benefits of DRL in healthcare environments, aiming to bridge the gap between theory and practice. The case study presents a remarkable impact on categories such as precision medicine, dynamic treatment regime, medical imaging, diagnostic systems, control systems, chat-bots and advanced interfaces, and healthcare management systems. 2024 Scrivener Publishing LLC. Creator An entity primarily responsible for making the resource Keerthika K.; Kannan M.; Saravanan T. Source A related resource from which the described resource is derived Deep Reinforcement Learning and Its Industrial Use Cases: AI for Real-World Applications, pp. 137-150. Publisher An entity responsible for making the resource available wiley Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1002/9781394272587.ch7" target="_blank" rel="noreferrer noopener">https://doi.org/10.1002/9781394272587.ch7</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215942193&amp;doi=10.1002%2F9781394272587.ch7&amp;partnerID=40&amp;md5=533c0902e9cf82ceffaa7407314c878e" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215942193&amp;doi=10.1002%2f9781394272587.ch7&amp;partnerID=40&amp;md5=533c0902e9cf82ceffaa7407314c878e</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-139427258-7; 978-139427255-6 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Book chapter Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Keerthika K., Department of Computer Science, School of Computing, Amrita Vishwa Vidyapeetham, Mysuru Campus, India; Kannan M., Department of Computer Science, School of Sciences, CHRIST University, Bengaluru, India; Saravanan T., Department of Computer Science and Engineering, GITAM School of Technology, Bengaluru, India