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Artificial Intelligence-based Decision Support Systems for Precision and Digital Health

arXiv 2407.16062 ↗First posted 22 Jul 2024stat.ML cs.AI cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 13 of its 133 references that are in the map. First posted on arXiv 22 Jul 2024.

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References · 13

  1. 2019Reinforcement Learning in Healthcare: A Surveycited by 310
  2. 2019Personalized HeartSteps: A Reinforcement Learning Algorithm for Optimizing Physical Activitycited by 70
  3. 2017Continuous State-Space Models for Optimal Sepsis Treatment - a Deep Reinforcement Learning Approachcited by 68
  4. 2016Estimating Dynamic Treatment Regimes in Mobile Health Using V-learningcited by 65
  5. 2019Learning When-to-Treat Policiescited by 49
  6. 2015Residual Weighted Learning for Estimating Individualized Treatment Rulescited by 38
  7. 2019Recovering Banditscited by 29
  8. 2017Action Centered Contextual Banditscited by 23
  9. 2018Deep Reinforcement Learning for Dynamic Treatment Regimes on Medical Registry Datacited by 23
  10. 2019Direct and indirect reinforcement learningcited by 11
  11. 2022Multi-disciplinary fairness considerations in machine learning for clinical trialscited by 8
  12. 2022Reinforcement Learning in Modern Biostatistics: Constructing Optimal Adaptive Interventionscited by 6
1 more
  1. 2017Estimating Individualized Treatment Rules for Ordinal Treatmentscited by 4

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  1. 2024Medical Knowledge Integration into Reinforcement Learning Algorithms for Dynamic Treatment Regimes
  2. 2025Beyond Prediction: Reinforcement Learning as the Defining Leap in Healthcare AI
  3. 2024The Digital Transformation in Health: How AI Can Improve the Performance of Health Systems
  4. 2024Monitoring Fidelity of Online Reinforcement Learning Algorithms in Clinical Trials
  5. 2026Reinforcement Learning in the Real World: A Survey of Statistical Challenges and Future Directions
  6. 2026The Evidence Ladder for Reinforcement Learning in Healthcare: From Retrospective Policies to Trusted Interventions
  7. 2024Reinforcement Learning in Dynamic Treatment Regimes Needs Critical Reexamination
  8. 2025Statistical Inference in Reinforcement Learning: A Selective Survey