“Advancing Healthcare with AI: A Focus on Reinforcement Learning in Precision and Digital Health”

“Advancing Healthcare with AI: A Focus on Reinforcement Learning in Precision and Digital Health”

by | Jul 24, 2024 | AI

arXiv:2407.16062v1 Announce Type: new
Abstract: Precision health, increasingly supported by digital technologies, is a domain of research that broadens the paradigm of precision medicine, advancing everyday healthcare. This vision goes hand in hand with the groundbreaking advent of artificial intelligence (AI), which is reshaping the way we diagnose, treat, and monitor both clinical subjects and the general population. AI tools powered by machine learning have shown considerable improvements in a variety of healthcare domains. In particular, reinforcement learning (RL) holds great promise for sequential and dynamic problems such as dynamic treatment regimes and just-in-time adaptive interventions in digital health. In this work, we discuss the opportunity offered by AI, more specifically RL, to current trends in healthcare, providing a methodological survey of RL methods in the context of precision and digital health. Focusing on the area of adaptive interventions, we expand the methodological survey with illustrative case studies that used RL in real practice.
This invited article has undergone anonymous review and is intended as a book chapter for the volume “Frontiers of Statistics and Data Science” edited by Subhashis Ghoshal and Anindya Roy for the International Indian Statistical Association Series on Statistics and Data Science, published by Springer. It covers the material from a short course titled “Artificial Intelligence in Precision and Digital Health” taught by the author Bibhas Chakraborty at the IISA 2022 Conference, December 26-30 2022, at the Indian Institute of Science, Bengaluru.

The Intersection of Artificial Intelligence and Precision Health

Precision health, supported by digital technologies, is a rapidly evolving field that aims to revolutionize healthcare by individualizing treatment and preventive strategies. This paradigm shift is made possible by the advances in artificial intelligence (AI) and machine learning, which have the potential to transform the way we approach healthcare.

Artificial intelligence, particularly reinforcement learning (RL), has shown immense promise in solving complex and dynamic problems in the healthcare domain. RL is a subfield of machine learning that focuses on teaching an agent to make a sequence of decisions to maximize long-term rewards. In the context of healthcare, RL can be used to develop adaptive interventions and just-in-time interventions, making it a valuable tool in precision and digital health.

This article, authored by Bibhas Chakraborty, provides a comprehensive overview of the application of RL in the context of precision and digital health. It delves into the methodological aspects of RL and explores its potential in solving real-world healthcare problems. The article also includes case studies that highlight the successful implementation of RL in healthcare practice.

One of the key strengths of this article is its interdisciplinary nature. The intersection of AI, statistics, and healthcare is a multi-disciplinary field that requires expertise in various domains. The author, with his background in artificial intelligence and extensive experience in teaching and research, is well-equipped to bridge these domains and provide valuable insights.

By focusing on adaptive interventions, the article sheds light on the potential of RL in dynamically adjusting treatment strategies based on individual patient responses. This personalized approach has the potential to greatly improve patient outcomes and reduce healthcare costs. Moreover, the article goes beyond theoretical discussions by presenting real-world applications of RL in healthcare, offering tangible evidence of its efficacy.

The Implications for Future Research

The integration of AI and precision health is still in its early stages, and there is much scope for further research and development. One area that warrants further exploration is the ethical implications of using AI in healthcare. As AI algorithms make decisions that impact human lives, ensuring fairness, transparency, and accountability becomes paramount.

Additionally, the scalability and generalizability of RL algorithms need to be addressed. While RL has shown promise in small-scale studies and specific domains, its application in larger healthcare systems still poses challenges. Developing robust and scalable RL algorithms that can be readily deployed in diverse healthcare settings is an important avenue for future research.

Furthermore, the integration of RL with other emerging technologies such as wearable devices and electronic health records holds immense potential. By leveraging data from these sources, RL algorithms can gain a more comprehensive understanding of individual patient’s needs and preferences, leading to more effective interventions.

In conclusion, this article serves as a valuable resource for researchers, practitioners, and policymakers in the field of precision and digital health. It highlights the potential of RL in transforming healthcare delivery and provides a roadmap for future research. As the field continues to evolve, it is crucial to adopt a multi-disciplinary approach that brings together expertise from AI, statistics, and healthcare to harness the full potential of AI in precision health.

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“Deep Reinforcement Learning for Robust Job-Shop Scheduling”

“Deep Reinforcement Learning for Robust Job-Shop Scheduling”

by | Apr 3, 2024 | AI

arXiv:2404.01308v1 Announce Type: new
Abstract: Job-Shop Scheduling Problem (JSSP) is a combinatorial optimization problem where tasks need to be scheduled on machines in order to minimize criteria such as makespan or delay. To address more realistic scenarios, we associate a probability distribution with the duration of each task. Our objective is to generate a robust schedule, i.e. that minimizes the average makespan. This paper introduces a new approach that leverages Deep Reinforcement Learning (DRL) techniques to search for robust solutions, emphasizing JSSPs with uncertain durations. Key contributions of this research include: (1) advancements in DRL applications to JSSPs, enhancing generalization and scalability, (2) a novel method for addressing JSSPs with uncertain durations. The Wheatley approach, which integrates Graph Neural Networks (GNNs) and DRL, is made publicly available for further research and applications.

The Job-Shop Scheduling Problem (JSSP) is a complex optimization problem that is applicable in various industries and sectors. It involves scheduling tasks on machines, taking into consideration different criteria such as minimizing the makespan or delay. However, in real-world scenarios, the duration of tasks may not be certain and can be subject to variability.

This research introduces a new approach to tackle JSSPs with uncertain durations by leveraging Deep Reinforcement Learning (DRL) techniques. DRL has gained significant attention in recent years due to its ability to learn from experience and make decisions in complex environments. By associating a probability distribution with the duration of each task, the objective is to generate a robust schedule that minimizes the average makespan.

The key contribution of this research lies in the advancements it brings to the application of DRL to JSSPs. The use of DRL enhances generalization and scalability, making it possible to apply the approach to larger and more complex problem instances. Additionally, this research presents a novel method for addressing JSSPs with uncertain durations, which adds a new dimension to the existing literature on JSSP optimization.

The Wheatley approach, a combination of Graph Neural Networks (GNNs) and DRL, is introduced as the methodology for addressing JSSPs with uncertain durations. GNNs are specialized neural networks that can effectively model and represent complex relationships in graph-like structures. By integrating GNNs with DRL, the Wheatley approach offers a powerful tool for solving JSSPs with uncertain durations.

This research holds significant implications for multiple disciplines. From a computer science perspective, it introduces advancements in the application of DRL techniques to combinatorial optimization problems. The integration of GNNs and DRL opens up new possibilities for solving complex scheduling problems in various domains.

Moreover, from an operations research standpoint, the ability to address JSSPs with uncertain durations is a critical step towards more realistic and robust scheduling solutions. By considering the probability distribution of task durations, decision-makers can make informed and resilient schedules that can adapt to uncertainties in real-world scenarios. This research bridges the gap between theoretical research in JSSP optimization and practical implementation in dynamic environments.

In conclusion, this research demonstrates the potential of Deep Reinforcement Learning in addressing the Job-Shop Scheduling Problem with uncertain durations. By introducing the Wheatley approach that integrates Graph Neural Networks and DRL, the research advances the field by enhancing generalization, scalability, and the ability to handle variability in task durations. This multi-disciplinary approach has the potential to revolutionize scheduling practices in various industries and contribute to more robust and efficient operations.

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