CoChat
Sign Up
Cited by 327Artificial Intelligence in Healthcare and EducationReinforcement Learning in RoboticsMachine Learning in Healthcare

The DOI matches its Crossref record; 197 of the 204 references it cites were found in a registry or an index.

Reinforcement learning for intelligent healthcare applications: A survey

Coronato, Antonio, Naeem, Muddasar, De Pietro, Giuseppe, Paragliola, Giovanni·2020·Artificial Intelligence in Medicine
DOI 10.1016/j.artmed.2020.101964
Open this paper in CoChat — free account

Abstract

Discovering new treatments and personalizing existing ones is one of the major goals of modern clinical research. In the last decade, Artificial Intelligence (AI) has enabled the realization of advanced intelligent systems able to learn about clinical treatments and discover new medical knowledge from the huge amount of data collected. Reinforcement Learning (RL), which is a branch of Machine Learning (ML), has received significant attention in the medical community since it has the potentiality to support the development of personalized treatments in accordance with the more general precision medicine vision. This report presents a review of the role of RL in healthcare by investigating past work, and highlighting any limitations and possible future contributions.

Overview

Integrity signals

Registry

Crossref · 10.1016/j.artmed.2020.101964

Cites

197 of 197 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Research works printed but not linked: 29

  1. Liu S, Ngiam KY, Feng M. Deep reinforcement learning for clinical decision support: a brief survey; 2019. · DOI
  2. Salamon J, Bello JP. Dynamic treatment regimes. PMCID: PMC4231831, NIHMSID: NIHMS591555, PMID: 25401119.
  3. Schulte, 2014, Q, Stat Sci, 29, 640
  4. Sariaa S. Individualized sepsis treatment using reinforcement learning. Nat Med 24. https://www.nature.com/articles/s41591-018-0253-x.
  5. Raghu A, Komorowski M, Ahmed I, Celi L, Szolovits P, Ghassemi M. Deep reinforcement learning for sepsis treatment, CoRR abs/1711.09602. arXiv:1711.09602. · arXiv
  6. Weng WH, Gao M, He Z, Yan S, Szolovits P. Representation and reinforcement learning for personalized glycemic control in septic patients, CoRR abs/1712.00654. arXiv:1712.00654. · arXiv
  7. Raghu A, Komorowski M, Celi LA, Szolovits P, Ghassemi M. Continuous state-space models for optimal sepsis treatment – a deep reinforcement learning approach, CoRR abs/1705.08422. arXiv:1705.08422. · arXiv
  8. Futoma J, Lin A, Sendak M, Bedoya A, Clement M, O’Brien C, et al. Learning to treat sepsis with multi-output Gaussian process deep recurrent q-networks. https://openreview.net/forum?id=SyxCqGbRZ.
  9. Humphrey K. Using reinforcement learning to personalize dosing strategies in a simulated cancer trial with high dimensional data. http://hdl.handle.net/10150/625341.
  10. Escandell-Montero, 2014, Optimization of anemia treatment in hemodialysis patients via reinforcement learning, Artif Intell Med, 62, 47, 10.1016/j.artmed.2014.07.004 · DOI
  11. Tang, 2016, Inquire and diagnose: neural symptom checking ensemble using deep reinforcement learning, 29th conference on neural information processing systems (NIPS), Barcelona, Spain
  12. Chen ML, Doddi A, Royer J, Freschi L, Schito M, Ezewudo M, et al. Deep learning predicts tuberculosis drug resistance status from whole-genome sequencing data. BioRxiv; 2018. · DOI
  13. Arora S, Batra K, Singh S. Dialogue system: a brief review, CoRR abs/1306.4134. arXiv:1306.4134. · arXiv
  14. Zhang B, Cai Q, Mao J, Guo B. Planning and acting under uncertainty: a new model for spoken dialogue systems, CoRR abs/1301.2319. arXiv:1301.2319. · arXiv
  15. Kearns M, Litman D, Singh S, Walker M. Optimizing dialogue management with reinforcement learning: Experiments with the njfun system, CoRR abs/1106.0676. arXiv:1106.0676. · arXiv
  16. Tang F, Lin K, Uchendu I, Dodge HH, Zhou J. Improving mild cognitive impairment prediction via reinforcement learning and dialogue simulation, CoRR abs/1802.06428. arXiv:1802.06428. · arXiv
  17. Prasad N, Cheng LF, Chivers C, Draugelis M, Engelhardt BE. A reinforcement learning approach to weaning of mechanical ventilation in intensive care units, CoRR abs/1704.06300. arXiv:1704.06300. · arXiv
  18. Yasini S, Naghibi-Sistani MB, Karimpour A. Agent-based simulation for blood glucose control in diabetic patients. Int J Appl Sci Eng Technol 2009;5(1):40–9.
  19. Paragliola, 2019, Risk management for nuclear medical department using reinforcement learning, Springer J Reliab Intell Environ, 5
  20. Chapelle O, Li L. An empirical evaluation of Thompson sampling. In: Advances in neural information processing.
  21. Clemente AV, Castejón HN, Chandra A. Efficient parallel methods for deep reinforcement learning, CoRR abs/1705.04862. · arXiv
  22. Nair A, Srinivasan P, Blackwell S, Alcicek C, Fearon R, De Maria A, et al. Massively parallel methods for deep reinforcement learning, CoRR abs/1507.04296. · arXiv
  23. Wu Y, Mansimov E, Grosse RB, Liao S, Ba J. Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation; 2017. arXiv:1708.05144. · arXiv
  24. Haarnoja T, Tang H, Abbeel P, Levine S. Reinforcement learning with deep energy-based policies. In: Proceedings of the 34th international conference on machine learning, vol. 70. JMLR. p. 1352–61.
  25. Tseng HH, Luo Y, Cui S, Chien JT, Ten Haken RK, El Naqa I. Deep reinforcement learning for automated radiation adaptation in lung cancer. Medic Phys 2017;44(12):6690–705. · DOI
  26. Coronato, 2018, Engineering high quality medical software: regulations, standards, methodologies and tools for certification, healthcare technologies, Inst Eng Technol
  27. Watkins CJ. Learning from delayed rewards, vol. 3(1).
  28. Russo D, Roy BV, Kazerouni A, Osband I. A tutorial on Thompson sampling. CoRR.
  29. Arora S, Doshi P. A survey of inverse reinforcement learning: challenges, methods and progress, CoRR abs/1806.06877. · arXiv

Cited by

Sorted by citations

See all 327

Related

See all

Metadata

Authors
Coronato, A., Naeem, M., De Pietro, G., & Paragliola, G.
Affiliations
  • National Research Council (IT)
  • Parthenope University of Naples (IT)
Corresponding author
Coronato, Antonio
ORCID
Venue
Artificial Intelligence in Medicine
Publisher
Elsevier BV
Published
2020-09
Volume
109
Pages
101964
ISSN
0933-3657 (ISSN-L)
DOI
10.1016/j.artmed.2020.101964
OpenAlex
W3090832565
Cited by
320
References
204
Citations by year
2026: 43 · 2025: 67 · 2024: 55 · 2023: 73 · 2022: 51 · 2021: 28 · 2020: 3
FWCI
11.57
Type
journal article
Language
en
Open access
closed
Licence
Locations
  • Artificial Intelligence in Medicine (publishedVersion)
  • PubMed (publishedVersion)
Funders
Ministero dell’Istruzione, dell’Università e della Ricerca
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Keywords
reinforcement learning, medical applications, personalized treatment, precision medicine, clinical decision support, medical knowledge discovery
MeSH terms
Machine Learning, Artificial Intelligence, Delivery of Health Care, Reinforcement, Psychology, Precision Medicine

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review