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
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
Cites
Statistics
Verification
Claims the paper makes
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?
In the citing paper
“It has been utilized to develop dynamic treatment regimens and provide a precise insulin dosage to react to the immediate needs of patients with diabetes.23”
From Artificial intelligence in diabetes management: Advancements, opportunities, and challenges (2023) · cites this paper as [23]
In the citing paper
“Analysis of medical data can fine-tune drug dosages for individual patients based on their characteristics and past responses [33].”
From Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions (2023) · cites this paper as [33]
Citations
Cites
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,268
Artificial intelligence: a modern approach
Kumar, D. A., Balasubramanian, S., Byeon, D. H., & Manerkar, P. G. V. (1995) · Choice Reviews Online · doi:10.5860/choice.33-1577 · Cited by 22,191
Information theory and statistical mechanics
Jaynes, E. T. (1957) · Physical Review · doi:10.1103/physrev.106.620 · Cited by 12,931
Artificial intelligence: A modern approach
Nilsson, N. J. (1996) · Artificial Intelligence · doi:10.1016/0004-3702(96)00007-0 · Cited by 10,730
MIMIC-III, a freely accessible critical care database
Johnson, A. E. W., Pollard, T. J., Shen, L., Lehman, L.-W. H., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Anthony Celi, L., & Mark, R. G. (2016) · Scientific Data · doi:10.1038/sdata.2016.35 · Cited by 8,141
Asynchronous Methods for Deep Reinforcement Learning
Mnih, V., Badia, A. P., Mirza, M., Graves, A., Lillicrap, T. P., Harley, T., Silver, D., & Kavukcuoglu, K. (2016) · arXiv · doi:10.48550/arxiv.1602.01783 · Cited by 7,132
Finite-time Analysis of the Multiarmed Bandit Problem
Auer, P., Cesa-Bianchi, N., & Fischer, P. (2002) · Machine Learning · doi:10.1023/a:1013689704352 · Cited by 6,033
Learning to predict by the methods of temporal differences
Sutton, R. S. (1988) · Machine Learning · doi:10.1007/bf00115009 · Cited by 5,148
Speech and language processing
Politt, R., Pollock, J., & Waller, E. (2010) · Taylor & Francis eBooks · doi:10.4324/9780203461891_chapter_3 · Cited by 3,594
Research works printed but not linked: 29
- Liu S, Ngiam KY, Feng M. Deep reinforcement learning for clinical decision support: a brief survey; 2019. · DOI
- Salamon J, Bello JP. Dynamic treatment regimes. PMCID: PMC4231831, NIHMSID: NIHMS591555, PMID: 25401119.
- Schulte, 2014, Q, Stat Sci, 29, 640
- Sariaa S. Individualized sepsis treatment using reinforcement learning. Nat Med 24. https://www.nature.com/articles/s41591-018-0253-x.
- 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
- 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
- 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
- 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.
- Humphrey K. Using reinforcement learning to personalize dosing strategies in a simulated cancer trial with high dimensional data. http://hdl.handle.net/10150/625341.
- 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
- Tang, 2016, Inquire and diagnose: neural symptom checking ensemble using deep reinforcement learning, 29th conference on neural information processing systems (NIPS), Barcelona, Spain
- 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
- Arora S, Batra K, Singh S. Dialogue system: a brief review, CoRR abs/1306.4134. arXiv:1306.4134. · arXiv
- 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
- 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
- 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
- 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
- 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.
- Paragliola, 2019, Risk management for nuclear medical department using reinforcement learning, Springer J Reliab Intell Environ, 5
- Chapelle O, Li L. An empirical evaluation of Thompson sampling. In: Advances in neural information processing.
- Clemente AV, Castejón HN, Chandra A. Efficient parallel methods for deep reinforcement learning, CoRR abs/1705.04862. · arXiv
- 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
- 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
- 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.
- 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
- Coronato, 2018, Engineering high quality medical software: regulations, standards, methodologies and tools for certification, healthcare technologies, Inst Eng Technol
- Watkins CJ. Learning from delayed rewards, vol. 3(1).
- Russo D, Roy BV, Kazerouni A, Osband I. A tutorial on Thompson sampling. CoRR.
- Arora S, Doshi P. A survey of inverse reinforcement learning: challenges, methods and progress, CoRR abs/1806.06877. · arXiv
Cited by
Sorted by citations
The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century
Varnosfaderani, S. M., & Forouzanfar, M. (2024) · Bioengineering · doi:10.3390/bioengineering11040337 · Cited by 1,141
Using machine learning approaches for multi-omics data analysis: A review
Reel, P. S., Reel, S., Pearson, E., Trucco, E., & Jefferson, E. (2021) · Biotechnology Advances · doi:10.1016/j.biotechadv.2021.107739 · Cited by 903
Artificial intelligence in diabetes management: Advancements, opportunities, and challenges
Guan, Z., Li, H., Liu, R., Cai, C., Liu, Y., Li, J., Wang, X., Huang, S., Wu, L., Liu, D., et al. (2023) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.101213 · Cited by 287
“23 Despite the rapid progress of ML methods, there are several potential flaws, including data bias, 27 overfitting, 28 resource-intensive training, 29 and limited transfer learning.”
BackgroundMethodGraph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future
Ahmedt‐Aristizabal, D., Armin, M. A., Denman, S., Fookes, C., & Petersson, L. (2021) · Sensors · doi:10.3390/s21144758 · Cited by 298
Machine Learning (ML) in Medicine: Review, Applications, and Challenges
Rahmani, A. M., Yousefpoor, E., Yousefpoor, M. S., Mehmood, Z., Haider, A., Hosseinzadeh, M., & Naqvi, R. A. (2021) · Mathematics · doi:10.3390/math9222970 · Cited by 289
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
- Venue
- Artificial Intelligence in Medicine
- Publisher
- Elsevier BV
- Published
- 2020-09
- Volume
- 109
- Pages
- 101964
- ISSN
- 0933-3657 (ISSN-L)
- 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
- 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