Reinforcement learning for clinical decision support in critical care: comprehensive review
Open accessPublished version of a preprint
Abstract
Background: Decision support systems based on reinforcement learning (RL) have been implemented to facilitate the delivery of personalized care. This paper aimed to provide a comprehensive review of RL applications in the critical care setting. Objective: This review aimed to survey the literature on RL applications for clinical decision support in critical care and to provide insight into the challenges of applying various RL models. Methods: We performed an extensive search of the following databases: PubMed, Google Scholar, Institute of Electrical and Electronics Engineers (IEEE), ScienceDirect, Web of Science, Medical Literature Analysis and Retrieval System Online (MEDLINE), and Excerpta Medica Database (EMBASE). Studies published over the past 10 years (2010-2019) that have applied RL for critical care were included. Results: We included 21 papers and found that RL has been used to optimize the choice of medications, drug dosing, and timing of interventions and to target personalized laboratory values. We further compared and contrasted the design of the RL models and the evaluation metrics for each application. Conclusions: RL has great potential for enhancing decision making in critical care. Challenges regarding RL system design, evaluation metrics, and model choice exist. More importantly, further work is required to validate RL in authentic clinical environments.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
Reinforcement learning and deep reinforcement learning have recently been used in clinical settings, including optimal dosing and choice of medications, optimal timing of interventions, and optimal individual target laboratory values.
“Recently, reinforcement learning and deep reinforcement learning have been used in several clinical settings, including optimal dosing and choice of medications, optimal timing of interventions, and optimal individual target laboratory values (34).”
From Accessing Artificial Intelligence for Clinical Decision-Making (2021) · cites this paper as [34]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works
The third international consensus definitions for sepsis and septic shock (Sepsis-3).
Singer, M., Deutschman, C. S., Seymour, C. W., Shankar-Hari, M., Annane, D., Bauer, M., Bellomo, R., Bernard, G. R., Chiche, J.-D., Coopersmith, C. M., et al. (2016) · JAMA · doi:10.1001/jama.2016.0287 · Cited by 28,250 · see citing works
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612 · see citing works
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 · see citing works
Self-citationA Brief Survey of Deep Reinforcement Learning
Arulkumaran, K., Deisenroth, M. P., Brundage, M., & Bharath, A. A. (2017) · arXiv · doi:10.48550/arxiv.1708.05866 · Cited by 4,733 · see citing works
Deep Reinforcement Learning: A Brief Survey
Arulkumaran, K., Deisenroth, M. P., Brundage, M., & Bharath, A. A. (2017) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2743240 · Cited by 4,733 · see citing works
Barr, J. A., Fraser, G. L., Puntillo, K., Ely, E. W., Gélinas, C., Dasta, J. F., Davidson, J. E., Devlin, J. W., Kress, J. P., Joffe, A. M., et al. (2012) · Critical Care Medicine · doi:10.1097/ccm.0b013e3182783b72 · Cited by 3,785 · see citing works
Big data and machine learning algorithms for health-care delivery
Ngiam, K. Y., & Khor, I. W. (2019) · The Lancet Oncology · doi:10.1016/s1470-2045(19)30149-4 · Cited by 1,424 · see citing works
Self-citationA New, Evidence-based Estimate of Patient Harms Associated with Hospital Care
James, J. T. (2013) · Journal of Patient Safety · doi:10.1097/pts.0b013e3182948a69 · Cited by 1,399 · see citing works
Komorowski, M., Celi, L. A., Badawi, O., Gordon, A. C., & Faisal, A. A. (2018) · Nature Medicine · doi:10.1038/s41591-018-0213-5 · Cited by 1,290 · see citing works
Self-citationSaeed, M., Villarroel, M., Reisner, A. T., Clifford, G., Lehman, L.-W., Moody, G., Heldt, T., Kyaw, T. H., Moody, B., & Mark, R. G. (2011) · Critical Care Medicine · doi:10.1097/ccm.0b013e31820a92c6 · Cited by 1,162 · see citing works
Sepsis: a roadmap for future research
Cohen, J., Vincent, J., Adhikari, N. K. J., Machado, F. R., Angus, D. C., Calandra, T., Jaton, K., Giulieri, S. G., Delaloye, J., Opal, S. M., et al. (2015) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(15)70112-x · Cited by 996 · see citing works
Optimal and Autonomous Control Using Reinforcement Learning: A Survey
Kiumarsi, B., Vamvoudakis, K. G., Modares, H., & Lewis, F. L. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2773458 · Cited by 908 · see citing works
Partially observable Markov decision processes for spoken dialog systems
Williams, J. D., & Young, S. J. (2006) · Computer Speech & Language · doi:10.1016/j.csl.2006.06.008 · Cited by 853 · see citing works
Riedmiller, M. (2005) · Lecture notes in computer science · doi:10.1007/11564096_32 · Cited by 771 · see citing works
(2016) · PubMed · Cited by 576 · see citing works
Autonomous Inverted Helicopter Flight via Reinforcement Learning
Ng, A. Y., Coates, A., Diel, M., Ganapathi, V., Schulte, J., Tse, B., Berger, E., & Liang, E. (2006) · Springer tracts in advanced robotics · doi:10.1007/11552246_35 · Cited by 557 · see citing works
Multiple model-based reinforcement learning
Doya, K., Samejima, K., Katagiri, K.-I., & Kawato, M. (2002) · Neural Computation · doi:10.1162/089976602753712972 · Cited by 476 · see citing works
Fan, J., Wang, J., Chen, Z., Hu, C., Zhang, Z., & Hu, W. (2018) · Medical Physics · doi:10.1002/mp.13271 · Cited by 357 · see citing works
Supervised Reinforcement Learning with Recurrent Neural Network for Dynamic Treatment Recommendation
Wang, L., Zhang, W., He, X., & Hongyuan Zha, B. (2018) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3219819.3219961 · Cited by 236 · see citing works
Almirall, D., Compton, S. N., Gunlicks‐Stoessel, M., Duan, N., & Murphy, S. A. (2012) · Statistics in Medicine · doi:10.1002/sim.4512 · Cited by 223 · see citing works
An, G. C. (2004) · Critical Care Medicine · doi:10.1097/01.ccm.0000139707.13729.7d · Cited by 217 · see citing works
Sedation and Analgesia in the Mechanically Ventilated Patient
Patel, S. B., & Kress, J. P. (2011) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201102-0273ci · Cited by 205 · see citing works
Optimal medication dosing from suboptimal clinical examples: a deep reinforcement learning approach
Nemati, S., Ghassemi, M. M., & Clifford, G. D. (2016) · 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2016.7591355 · Cited by 195 · see citing works
Morphine: controlled trial of different methods of administration for postoperative pain relief.
Rutter, P. C., Murphy, F., & Dudley, H. A. F. (1980) · BMJ · doi:10.1136/bmj.280.6206.12 · Cited by 192 · see citing works
Bispectral index for improving intraoperative awareness and early postoperative recovery in adults
Lewis, S. R., Pritchard, M., Fawcett, L. J., & Punjasawadwong, Y. (2019) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd003843.pub4 · Cited by 173 · see citing works
Osborne, M. A., Roberts, S., Rogers, A., et al. (2008) · ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) · doi:10.1109/ipsn.2008.25 · Cited by 145 · see citing works
Instruments for monitoring intensive care unit sedation.
Carrasco, G. (2000) · Critical Care · doi:10.1186/cc697 · Cited by 122 · see citing works
Reinforcement Learning: An Introduction, by Sutton, R.S. and Barto, A.G.
Montague, P. R. (1999) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(99)01331-5 · Cited by 120 · see citing works
Clinical Practice Guideline: Safe Medication Use in the ICU
Kane‐Gill, S. L., Dasta, J. F., Buckley, M. S., Devabhakthuni, S., Liu, M., Cohen, H., GEORGE, E. L., Pohlman, A. S., Agarwal, S., Henneman, E. A., et al. (2017) · Critical Care Medicine · doi:10.1097/ccm.0000000000002533 · Cited by 113 · see citing works
Dagan, N., Elnekave, E., Barda, N., Bregman-Amitai, O., Bar, A., Orlovsky, M., Bachmat, E., & Balicer, R. D. (2020) · Nature Medicine · doi:10.1038/s41591-019-0720-z · Cited by 109 · see citing works
In the Era of Precision Medicine and Big Data, Who Is Normal?
Manrai, A. K., Patel, C. J., & Ioannidis, J. P. A. (2018) · JAMA · doi:10.1001/jama.2018.2009 · Cited by 98 · see citing works
Watanabe, A. T., Lim, V., Vu, H., Chim, C. Y., Weise, E., Liu, J., Bradley, W. G., & Comstock, C. (2019) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-019-00192-5 · Cited by 96 · see citing works
Negative trials in critical care: why most research is probably wrong
Laffey, J. G., & Kavanagh, B. P. (2018) · The Lancet Respiratory Medicine · doi:10.1016/s2213-2600(18)30279-0 · Cited by 89 · see citing works
A Physician Advisory System for Chronic Heart Failure management based on knowledge patterns
CHEN, Z., MARPLE, K., SALAZAR, E., GUPTA, G., & Tamil, L. S. (2016) · Theory and Practice of Logic Programming · doi:10.1017/s1471068416000429 · Cited by 73 · see citing works
Improving Sepsis Treatment Strategies by Combining Deep and Kernel-Based Reinforcement Learning
Peng, X., Ding, Y., Wihl, D., Gottesman, O., Komorowski, M., Lehman, L.-W. H., Ross, A. S., Faisal, A. A., & Doshi‐Velez, F. (2019) · Europe PMC (PubMed Central) · doi:10.48550/arxiv.1901.04670 · Cited by 71 · see citing works
The eICU Research Institute - A Collaboration Between Industry, Health-Care Providers, and Academia
McShea, M. J., Holl, R., Badawi, O., Riker, R. R., & Silfen, E. Z. (2010) · IEEE Engineering in Medicine and Biology Magazine · doi:10.1109/memb.2009.935720 · Cited by 65 · see citing works
Yu, C., Liu, J., & Zhào, H. (2019) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-019-0763-6 · Cited by 61 · see citing works
Emergency reversal of antithrombotic treatment
Levi, M. M. (2008) · Internal and Emergency Medicine · doi:10.1007/s11739-008-0201-8 · Cited by 59 · see citing works
Implementing evidence-based medicine in general practice: a focus group based study
Hannes, K., Leys, M., Vermeire, E., et al. (2005) · BMC Family Practice · doi:10.1186/1471-2296-6-37 · Cited by 58 · see citing works
Lopez-Martinez, D., Eschenfeldt, P. C., Ostvar, S., Ingram, M. A., Hur, C., & Picard, R. W. (2019) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2019.8857295 · Cited by 58 · see citing works
Optimal adaptive control of drug dosing using integral reinforcement learning
Padmanabhan, R., Meskin, N., & Haddad, W. M. (2019) · Mathematical Biosciences · doi:10.1016/j.mbs.2019.01.012 · Cited by 44 · see citing works
A Deep Deterministic Policy Gradient Approach to Medication Dosing and Surveillance in the ICU
Lin, R., Stanley, M. D., Ghassemi, M. M., & Nemati, S. (2018) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2018.8513203 · Cited by 38 · see citing works
Cohen, J. D., Shapiro, M., Grozovski, E., et al. (2002) · CHEST Journal · doi:10.1378/chest.122.3.980 · Cited by 36 · see citing works
TextRay: Mining Clinical Reports to Gain a Broad Understanding of Chest X-Rays
Laserson, J., Lantsman, C. D., Cohen-Sfady, M., et al. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00934-2_62 · Cited by 36 · see citing works
Yearbook of Intensive Care and Emergency Medicine
Vincent, J. (1992) · Yearbook of intensive care and emergency medicine · doi:10.1007/978-3-540-77290-3 · Cited by 35 · see citing works
Automatic Classification of Sedation Levels in ICU Patients Using Heart Rate Variability
Nagaraj, S. B., McClain, L. M., Zhou, D. W., et al. (2016) · Critical Care Medicine · doi:10.1097/ccm.0000000000001708 · Cited by 34 · see citing works
Wilson, A. (1999) · BMJ · doi:10.1136/bmj.319.7216.1078 · Cited by 34 · see citing works
An Optimal Policy for Patient Laboratory Tests in Intensive Care Units
Cheng, L.-F., Prasad, N., & Engelhardt, B. E. (2018) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789813279827_0029 · Cited by 26 · see citing works
Burke, A. E., Thaler, K., Geva, M., et al. (2019) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2019.06.015 · Cited by 25 · see citing works
Information Gain and a General Measure of Correlation
Kent, J. T. (1983) · Biometrika · doi:10.2307/2335954 · Cited by 24 · see citing works
Yu, G., Zhang, F.-Q., Tang, S., Lai, M., Su, R., & Gong, Z. (2014) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2014.08.009 · Cited by 23 · see citing works
Zhang, Z., Hong, Y., & Liu, N. (2018) · Intensive Care Medicine · doi:10.1007/s00134-018-5142-8 · Cited by 21 · see citing works
Truly Batch Apprenticeship Learning with Deep Successor Features
Lee, D., Srinivasan, S., & Doshi‐Velez, F. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/819 · Cited by 17 · see citing works
Borera, E. C., Moore, B. L., Doufas, A. G., et al. (2011) · Proceedings - International Conference on Tools with Artificial Intelligence, TAI · doi:10.1109/ictai.2011.15 · Cited by 15 · see citing works
Reinforcement Learning:State of the Art
Wiering, M. (2012) · Adaptation, learning, and optimization · doi:10.1007/978-3-642-27645-3 · Cited by 14 · see citing works
Petersen, B. K., Yang, J., Grathwohl, W., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.10440 · Cited by 14 · see citing works
Iannuzzi, E., Iannuzzi, M., Viola, G., et al. (2007) · PubMed · Cited by 10 · see citing works
Closed-loop control of anesthesia and mean arterial pressure using reinforcement learning
Padmanabhan, R., Meskin, N., & Haddad, W. M. (2014) · IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning · doi:10.1109/adprl.2014.7010644 · Cited by 9 · see citing works
Reinforcement learning-based control for combined infusion of sedatives and analgesics
Padmanabhan, R., Meskin, N., & Haddad, W. M. (2017) · International Conference on Control, Decision and Information Technologies (CoDIT) · doi:10.1109/codit.2017.8102643 · Cited by 8 · see citing works
An Optimal Policy for Patient Laboratory Tests in Intensive Care UnitsPreprint
Cheng, L., Prasad, N., & Engelhardt, B. E. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.04679 · Cited by 4 · see citing works
Bedside Monitoring of Diaphragm Electrical Activity during Mechanical Ventilation
Sinderby, C. A., Beck, J. C., & Brander, L. (2009) · Springer eBooks · doi:10.1007/978-3-540-92276-6_37 · Cited by 2 · see citing works
Monitoring and Managing Raised Intracranial Pressure after Traumatic Brain Injury
Smith, M. A. (2009) · Intensive Care Medicine · doi:10.1007/978-0-387-92278-2_73 · Cited by 1 · see citing works
Flagged first, then by citations
Research works printed but not linked: 15
- [17]Howard RA Dynamic Programming and Markov Process 1960 New York, USA MIT Press and Wiley
- [18]Mnih V Kavukcuoglu K Silver D Graves A Antonoglou I Wierstra D Riedmiller M Playing atari with deep reinforcement learning arXiv preprint 2013 - epub ahead of print(1312.5602)
- [23]van Hasselt H Guez A Silver D Deep Reinforcement Learning With Double Q-learning Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence 2016 AAAI'16 February 12-17, 2016 Phoenix, Arizona, USA 10.5555/3016100.3016191 · DOI
- [24]Mnih V Puigdomenech A Mirza M Graves A Lillicrap T Harley T Silver D Kavukcuoglu K Asynchronous methods for deep reinforcement learning Arxiv 2016 - epub ahead of print(1602.01783)
- [33]Ghassemi MM Alhanai T Westover MB Mark TG Nemati S Personalized Medication Dosing Using Volatile Data Streams Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence 2018 AAAI'18 February 2-7, 2018 New Orleans, Louisiana, USA
- [35]Raghu A Komorowski M Ahmed I Celi L Szolovits P Ghassemi M Deep reinforcement learning for sepsis treatment arXiv 2017 - epub ahead of print(1711.09602)
- [36]Raghu A Komorowski M Celi L Szolovits P Ghassemi M Continuous state-space models for optimal sepsis treatment-a deep reinforcement learning approach arXiv 2017 - epub ahead of print(1705.08422)
- [37]Raghu A Komorowski M Singh S Model-based reinforcement learning for sepsis treatment arXiv 2018 - epub ahead of print-1811.09602
- [38]Futoma J Lin A Sendak M Bedoya A Clement M O'Brien C Heller K Learning to Treat Sepsis with Multi-Output Gaussian Process Deep Recurrent Q-Networks OpenReview 2018 2020-06-10 https://openreview.net/forum?id=SyxCqGbRZ
- [43]Prasad N Cheng LF Chivers C Draugelis M Engelhardt B A reinforcement learning approach to weaning of mechanical ventilation in intensive care units Arxiv 2017 - epub ahead of print(1704.06300)
- [46]Weng W Gao M He Z Yan S Szolovits P Representation and reinforcement learning for personalized glycemic control in septic patients arXiv 2017 - epub ahead of print(1712.00654)
- [56]Kashyap R Loftsgard TO Clinicians role in reducing lab order frequency in ICU settings J Perioper Crit Intens Care Nurs 2015 2 1 320 31 10.4172/jpcic.1000112 · DOI
- [66]Jeter R Josef C Shashikumar S Nemati S Does the artificial intelligence clinician learn optimal treatment strategies for sepsis in intensive care? arXiv 2019 - epub ahead of print(1902.03271)
- [67]Lillicrap T Hunt J Pritzel A Heess N Erez T Tassa Y Silver D Wierstra D Continuous control with deep reinforcement learning arXiv 2015 - epub ahead of print(1509.02971)
- [76]Maei H Szepesv´ari C Bhatnagar S Sutton R Toward Off-Policy Learning Control with Function Approximation The 27th International Conference on Machine Learning 2010 ICML'10 June 21-24, 2010 Haifa, Israel
Cited by
Sorted by citations
Feng, J., Phillips, R. V., Malenica, I., Bishara, A., Hubbard, A. E., Celi, L. A., & Pirracchio, R. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00611-y · Cited by 423 · see citing works
Self-citationAccessing Artificial Intelligence for Clinical Decision-Making
Giordano, C., Brennan, M., Mohamed, B., Rashidi, P., Modave, F., & Tighe, P. (2021) · Frontiers in Digital Health · doi:10.3389/fdgth.2021.645232 · Cited by 326 · see citing works
Reinforcement learning for intelligent healthcare applications: A survey
Coronato, A., Naeem, M., De Pietro, G., & Paragliola, G. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2020.101964 · Cited by 320 · see citing works
Beldhuis, I. E., Marapin, R. S., Jiang, Y. Y., Simões de Souza, N. F., Georgiou, A., Kaufmann, T., Castela Forte, J., & van der Horst, I. C. C. (2021) · Journal of Critical Care · doi:10.1016/j.jcrc.2021.04.012 · Cited by 58 · see citing works
Artificial Intelligence in the Intensive Care Unit
Greco, M., Caruso, P. F., & Cecconi, M. (2021) · Seminars in Respiratory and Critical Care Medicine · doi:10.1055/s-0040-1719037 · Cited by 59 · see citing works
Sun, K., Roy, A., & Tobin, J. M. (2024) · Journal of Critical Care · doi:10.1016/j.jcrc.2024.154792 · Cited by 28 · see citing works
Alkhodari, M., Xiong, Z., Khandoker, A. H., Hadjileontiadis, L. J., Leeson, P., & Lapidaire, W. (2023) · Expert Review of Cardiovascular Therapy · doi:10.1080/14779072.2023.2223978 · Cited by 23 · see citing works
Catling, F. J., Nagendran, M., Festor, P., Bien, Z., Harris, S., Faisal, A. A., Gordon, A. C., & Komorowski, M. (2023) · doi:10.1101/2023.08.26.23294666 · Cited by 3 · see citing works
Disha, J. F., Dehan, M. F. Z., & Anzum, K. M. T. (2026) · Advanced Intelligent Systems · doi:10.1002/aisy.70487
- Authors
- Liu, S., See, K. C., Ngiam, K. Y., Celi, L. A., Sun, X., & Feng, M.
- Affiliations
- National University of Singapore (SG)
- National University Hospital (SG)
- National University Health System (SG)
- Beth Israel Deaconess Medical Center (US)
- Ping An (China) (CN)
- Corresponding author
- Feng, Mengling
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2020-07-20
- Volume
- 22
- Issue
- 7
- Pages
- e18477
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC7400046
- OpenAlex
- W3025019304
- Cited by
- 225
- References
- 63
- Citations by year
- 2026: 37 · 2025: 47 · 2024: 46 · 2023: 34 · 2022: 32 · 2021: 25 · 2020: 4
- FWCI
- 0.82
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- National University of Singapore (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Research Foundation (AISG-GC-2019-002)
- National University of Singapore
- National Research Foundation Singapore (AISG-GC-2019-002)
- National University Health System (WBS R-608-000-199-733)
- Medical Research Council
- National Medical Research Council (HSRG-OC17nov004)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Keywords
- reinforcement learning, clinical decision support, intensive care, personalized medicine, drug dosing optimization, drug selection, intervention timing, evaluation metrics, clinical validation
- MeSH terms
- Critical Care, Humans, Reinforcement, Psychology, Decision Support Systems, Clinical
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
