Clinical Intelligence: Deep Reinforcement Learning for Healthcare and Biomedical Advancements
- Title
- Clinical Intelligence: Deep Reinforcement Learning for Healthcare and Biomedical Advancements
- Creator
- Keerthika K.; Kannan M.; Saravanan T.
- Description
- 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.
- Source
- Deep Reinforcement Learning and Its Industrial Use Cases: AI for Real-World Applications, pp. 137-150.
- Date
- 2024-01-01
- Publisher
- wiley
- Subject
- biomedical; Deep reinforcement learning; deep-Q-networks; healthcare; Monte Carlo; SARSA
- Coverage
- 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
- Rights
- Restricted Access
- Relation
- ISBN: 978-139427258-7; 978-139427255-6
- Format
- Online
- Language
- English
- Type
- Book chapter
Collection
Citation
Keerthika K.; Kannan M.; Saravanan T., “Clinical Intelligence: Deep Reinforcement Learning for Healthcare and Biomedical Advancements,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 23, 2025, https://archives.christuniversity.in/items/show/17843.