Published on in Vol 22, No 7 (2020): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/18477, first published .
Reinforcement Learning for Clinical Decision Support in Critical Care: Comprehensive Review

Reinforcement Learning for Clinical Decision Support in Critical Care: Comprehensive Review

Reinforcement Learning for Clinical Decision Support in Critical Care: Comprehensive Review

Journals

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  5. Alamdari N, Lobarinas E, Kehtarnavaz N. Personalization of Hearing Aid Compression by Human-in-the-Loop Deep Reinforcement Learning. IEEE Access 2020;8:203503 View
  6. Falconer N, Abdel‐Hafez A, Scott I, Marxen S, Canaris S, Barras M. Systematic review of machine learning models for personalised dosing of heparin. British Journal of Clinical Pharmacology 2021;87(11):4124 View
  7. Li D, Gao J, Hong N, Wang H, Su L, Liu C, He J, Jiang H, Wang Q, Long Y, Zhu W. A Clinical Prediction Model to Predict Heparin Treatment Outcomes and Provide Dosage Recommendations: Development and Validation Study. Journal of Medical Internet Research 2021;23(5):e27118 View
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  9. Wu X, Li R, He Z, Yu T, Cheng C. A value-based deep reinforcement learning model with human expertise in optimal treatment of sepsis. npj Digital Medicine 2023;6(1) View
  10. Al-Zaiti S, Alghwiri A, Hu X, Clermont G, Peace A, Macfarlane P, Bond R. A clinician’s guide to understanding and critically appraising machine learning studies: a checklist for Ruling Out Bias Using Standard Tools in Machine Learning (ROBUST-ML). European Heart Journal - Digital Health 2022;3(2):125 View
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  18. Shiranthika C, Chen K, Wang C, Yang C, Sudantha B, Li W. Supervised Optimal Chemotherapy Regimen Based on Offline Reinforcement Learning. IEEE Journal of Biomedical and Health Informatics 2022;26(9):4763 View
  19. Feng J, Phillips R, Malenica I, Bishara A, Hubbard A, Celi L, Pirracchio R. Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcare. npj Digital Medicine 2022;5(1) View
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  22. Zheng H, Zhu J, Xie W, Zhong J. Reinforcement learning assisted oxygen therapy for COVID-19 patients under intensive care. BMC Medical Informatics and Decision Making 2021;21(1) View
  23. Siyam N, Abdallah S. Toward automatic motivator selection for autism behavior intervention therapy. Universal Access in the Information Society 2023;22(4):1369 View
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  35. Wang M, Sushil M, Miao B, Butte A. Bottom-up and top-down paradigms of artificial intelligence research approaches to healthcare data science using growing real-world big data. Journal of the American Medical Informatics Association 2023;30(7):1323 View
  36. Wiegand T, Velezmoro L, Jung L, Wimbauer F, Dimitriadis K, Koerte I. Künstliche Intelligenz in der Neurologie. Nervenheilkunde 2023;42(09):591 View
  37. Smith B, Khojandi A, Vasudevan R. Bias in Reinforcement Learning: A Review in Healthcare Applications. ACM Computing Surveys 2024;56(2):1 View
  38. Beeson A, Montana G. Balancing policy constraint and ensemble size in uncertainty-based offline reinforcement learning. Machine Learning 2024;113(1):443 View
  39. Alkhodari M, Xiong Z, Khandoker A, Hadjileontiadis L, Leeson P, Lapidaire W. The role of artificial intelligence in hypertensive disorders of pregnancy: towards personalized healthcare. Expert Review of Cardiovascular Therapy 2023;21(7):531 View
  40. Okada Y, Mertens M, Liu N, Lam S, Ong M. AI and machine learning in resuscitation: Ongoing research, new concepts, and key challenges. Resuscitation Plus 2023;15:100435 View
  41. Otten M, Jagesar A, Dam T, Biesheuvel L, den Hengst F, Ziesemer K, Thoral P, de Grooth H, Girbes A, François-Lavet V, Hoogendoorn M, Elbers P. Does Reinforcement Learning Improve Outcomes for Critically Ill Patients? A Systematic Review and Level-of-Readiness Assessment. Critical Care Medicine 2024;52(2):e79 View
  42. Smit J, Krijthe J, Kant W, Labrecque J, Komorowski M, Gommers D, van Bommel J, Reinders M, van Genderen M. Causal inference using observational intensive care unit data: a scoping review and recommendations for future practice. npj Digital Medicine 2023;6(1) View
  43. Khalatbarisoltani A, Boulon L, Hu X. Integrating Model Predictive Control With Federated Reinforcement Learning for Decentralized Energy Management of Fuel Cell Vehicles. IEEE Transactions on Intelligent Transportation Systems 2023;24(12):13639 View
  44. Liu W, Xu X, Wu J, Jiang J. Federated Meta Reinforcement Learning for Personalized Tasks. Tsinghua Science and Technology 2024;29(3):911 View
  45. Divya K, Kannadasan R. RETRACTED ARTICLE: A systematic review and applications of how AI evolved in healthcare. Optical and Quantum Electronics 2024;56(3) View
  46. NAKAIZUMI D, MIYATA S, UCHIYAMA K, TAKAHASHI I. Development and Validation of a Decision Tree Analysis Model for Predicting Home Discharge in a Convalescent Ward: A Single Institution Study. Physical Therapy Research 2024;27(1):14 View
  47. De Carlo A, Tosca E, Fantozzi M, Magni P. Reinforcement Learning and PK‐PD Models Integration to Personalize the Adaptive Dosing Protocol of Erdafitinib in Patients with Metastatic Urothelial Carcinoma. Clinical Pharmacology & Therapeutics 2024;115(4):825 View
  48. Singh H, Nim D, Randhawa A, Ahluwalia S. Integrating clinical pharmacology and artificial intelligence: potential benefits, challenges, and role of clinical pharmacologists. Expert Review of Clinical Pharmacology 2024;17(4):381 View
  49. Fackler J, Ghobadi K, Gurses A. Algorithms at the Bedside: Moving Past Development and Validation*. Pediatric Critical Care Medicine 2024;25(3):276 View
  50. Sun K, Roy A, Tobin J. Artificial intelligence and machine learning: Definition of terms and current concepts in critical care research. Journal of Critical Care 2024;82:154792 View
  51. Yan Z, Mukherjee A, Varıcı B, Tajer A. Robust Causal Bandits for Linear Models. IEEE Journal on Selected Areas in Information Theory 2024;5:78 View
  52. Zhang Q, Li T, Li D, Lu W. A goal-oriented reinforcement learning for optimal drug dosage control. Annals of Operations Research 2024;338(2-3):1403 View
  53. Yamamoto K, Sakaguchi M, Onishi A, Yokoyama S, Matsui Y, Yamamoto W, Onizawa H, Fujii T, Murata K, Tanaka M, Hashimoto M, Matsuda S, Morinobu A, Kuwana M. Energy landscape analysis and time-series clustering analysis of patient state multistability related to rheumatoid arthritis drug treatment: The KURAMA cohort study. PLOS ONE 2024;19(5):e0302308 View
  54. G. A, K.L. N, M.S. A. Improving sepsis classification performance with artificial intelligence algorithms: A comprehensive overview of healthcare applications. Journal of Critical Care 2024;83:154815 View
  55. Altara R, Basson C, Biondi-Zoccai G, Booz G. Exploring the Promise and Challenges of Artificial Intelligence in Biomedical Research and Clinical Practice. Journal of Cardiovascular Pharmacology 2024;83(5):403 View
  56. Zhao Y, Chaw J, Liu L, Chaw S, Ang M, Ting T. Systematic literature review on reinforcement learning in non-communicable disease interventions. Artificial Intelligence in Medicine 2024;154:102901 View
  57. Olmez S, Birks D, Heppenstall A, Ge J. Learning the rational choice perspective: A reinforcement learning approach to simulating offender behaviours in criminological agent-based models. Computers, Environment and Urban Systems 2024;112:102141 View
  58. Huisman T, Huisman T. Artificial Intelligence in Newborn Medicine. Newborn 2024;3(2):96 View
  59. Olmez S, Heppenstall A, Ge J, Elsenbroich C, Birks D. Mitigating housing market shocks: an agent-based reinforcement learning approach with implications for real-time decision support. Journal of Simulation 2024;18(6):921 View
  60. Salama V, Godinich B, Geng Y, Humbert-Vidan L, Maule L, Wahid K, Naser M, He R, Mohamed A, Fuller C, Moreno A. Artificial Intelligence and Machine Learning in Cancer Pain: A Systematic Review. Journal of Pain and Symptom Management 2024;68(6):e462 View
  61. Figueiredo Prudencio R, Maximo M, Colombini E. A Survey on Offline Reinforcement Learning: Taxonomy, Review, and Open Problems. IEEE Transactions on Neural Networks and Learning Systems 2024;35(8):10237 View
  62. Liu S, Xu Q, Xu Z, Liu Z, Sun X, Xie G, Feng M, See K. Reinforcement Learning to Optimize Ventilator Settings for Patients on Invasive Mechanical Ventilation: Retrospective Study. Journal of Medical Internet Research 2024;26:e44494 View
  63. Liu Y, Wang H, Zhou H, Li M, Hou Y, Zhou S, Wang F, Hoetzlein R, Zhang R. A review of reinforcement learning for natural language processing and applications in healthcare. Journal of the American Medical Informatics Association 2024;31(10):2379 View
  64. Pham T, Teh M, Chatzopoulou D, Holmes S, Coulthard P. Artificial Intelligence in Head and Neck Cancer: Innovations, Applications, and Future Directions. Current Oncology 2024;31(9):5255 View
  65. Tang Z, Su W, Liu T, Lu H, Liu Y, Li H, Han K, Moneruzzaman M, Long J, Liao X, Zhang X, Shan L, Zhang H. Prediction of poststroke independent walking using machine learning: a retrospective study. BMC Neurology 2024;24(1) View
  66. Dénes-Fazakas L, Szilágyi L, Kovács L, De Gaetano A, Eigner G. Reinforcement Learning: A Paradigm Shift in Personalized Blood Glucose Management for Diabetes. Biomedicines 2024;12(9):2143 View
  67. Reali G, Femminella M. Artificial Intelligence to Reshape the Healthcare Ecosystem. Future Internet 2024;16(9):343 View
  68. Pan C, Tian Y, Ma L, Zhou T, Ouyang S, Li J. CISL-PD: A deep learning framework of clinical intervention strategies for Parkinson’s disease based on directional counterfactual Dual GANs. Expert Systems with Applications 2025;261:125506 View
  69. Shirali A, Schubert A, Alaa A. Pruning the Way to Reliable Policies: A Multi-Objective Deep Q-Learning Approach to Critical Care. IEEE Journal of Biomedical and Health Informatics 2024;28(10):6268 View
  70. Periáñez Á, Fernández Del Río A, Nazarov I, Jané E, Hassan M, Rastogi A, Tang D. The Digital Transformation in Health: How AI Can Improve the Performance of Health Systems. Health Systems & Reform 2024;10(2) View
  71. Ali S, Rashid M, Yousuf M, Shams S, Asif M, Rehan M, Ujjan I. Towards the Development of the Clinical Decision Support System for the Identification of Respiration Diseases via Lung Sound Classification Using 1D-CNN. Sensors 2024;24(21):6887 View
  72. Naik N, Roth B, Lundy S. Artificial Intelligence for Clinical Management of Male Infertility, a Scoping Review. Current Urology Reports 2025;26(1) View
  73. Visconte V, Maciejewski J, Guarnera L. The potential promise of machine learning in myelodysplastic syndrome. Seminars in Hematology 2025;62(3):235 View
  74. Drudi C, Mollura M, Lehman L, Barbieri R. A Reinforcement Learning Model for Optimal Treatment Strategies in Intensive Care: Assessment of the Role of Cardiorespiratory Features. IEEE Open Journal of Engineering in Medicine and Biology 2024;5:806 View
  75. Jayaraman P, Desman J, Sabounchi M, Nadkarni G, Sakhuja A. A Primer on Reinforcement Learning in Medicine for Clinicians. npj Digital Medicine 2024;7(1) View
  76. Hu S, Shen L, Zhang Y, Chen Y, Tao D. On Transforming Reinforcement Learning With Transformers: The Development Trajectory. IEEE Transactions on Pattern Analysis and Machine Intelligence 2024;46(12):8580 View
  77. da Silva A, Merolli M, Fini N, Granger C, Gustafson O, Parry S. Digital health interventions in adult intensive care and recovery after critical illness to promote survivorship care. Journal of the Intensive Care Society 2025;26(1):96 View
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  79. Zippo V, Robotti E, Maestri D, Fossati P, Valenza D, Maggi S, Papallo G, Belay M, Cerruti S, Porcu G, Marengo E. Development of a Self-Updating System for the Prediction of Steel Mechanical Properties in a Steel Company by Machine Learning Procedures. Technologies 2025;13(2):75 View
  80. Banerjee I, Honnappa H, Rao V. Off-line Estimation of Controlled Markov Chains: Minimaxity and Sample Complexity. Operations Research 2025;73(4):2281 View
  81. Festor P, Nagendran M, Gordon A, Faisal A, Komorowski M, Frasch, M. Safety of human-AI cooperative decision-making within intensive care: A physical simulation study. PLOS Digital Health 2025;4(2):e0000726 View
  82. Lee H, Kim Y, Kim J, Kim S, Jeong T. Optimizing Initial Vancomycin Dosing in Hospitalized Patients Using Machine Learning Approach for Enhanced Therapeutic Outcomes: Algorithm Development and Validation Study. Journal of Medical Internet Research 2025;27:e63983 View
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  85. De Carlo A, Tosca E, Magni P. Precision Dosing in Presence of Multiobjective Therapies by Integrating Reinforcement Learning and PK‐PD Models: Application to Givinostat Treatment of Polycythemia Vera. CPT: Pharmacometrics & Systems Pharmacology 2025;14(6):1018 View
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Books/Policy Documents

  1. Chouvarda I, Perantoni E, Steiropoulos P. Wearable Sensing and Intelligent Data Analysis for Respiratory Management. View
  2. Sarkar A, Feng J, Vorobeychik Y, Gill C, Zhang N. Decision and Game Theory for Security. View
  3. Gaur N, Dharwadkar R, Thomas J. Deep Learning for Targeted Treatments. View
  4. Aguiar-Pérez J, Pérez-Juárez M, Alonso-Felipe M, Del-Pozo-Velázquez J, Rozada-Raneros S, Barrio-Conde M. Encyclopedia of Data Science and Machine Learning. View
  5. George M, Tolley N. Artificial Intelligence in Medicine. View
  6. Movin M, Junior G, Hollmén J, Papapetrou P. Advances in Intelligent Data Analysis XXI. View
  7. Matheny M, Ohno-Machado L, Davis S, Nemati S. Clinical Decision Support and Beyond. View
  8. Chaudhuri D, Kohli S. AI in Clinical Medicine. View
  9. Levy J, Vaickus L. Diagnostic Molecular Pathology. View
  10. Woodman R, Mangoni A. Gerontechnology. A Clinical Perspective. View
  11. Mundru Y, Yogi M, Chatterjee J, Meduri M, Chaitanya K. Deep Learning in Personalized Healthcare and Decision Support. View
  12. Nailwal K, Durgapal S, Dasauni K, Nailwal T. Concepts in Pharmaceutical Biotechnology and Drug Development. View
  13. Shah N, Nagar J, Desai K, Bhatt N, Bhatt N, Mewada H. Artificial Intelligence‐Enabled Blockchain Technology and Digital Twin for Smart Hospitals. View
  14. Qian C, Ren H. Handbook of Robotic Surgery. View
  15. Vashishth T, Sharma V, Neha , Ahamad S. Machine Learning Models and Architectures for Biomedical Signal Processing. View
  16. Vogt Y, Kalweit M, Alieva M, Ullrich E, Boedecker J, Kalweit G. Natural Killer Cells. View
  17. Beeson A, Couper K, Montana G. Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track. View
  18. Kazi J. Python Essentials for Biomedical Data Analysis: An Introductory Textbook. View

Conference Proceedings

  1. Nia N, Nasab A, Kaplanoglu E. 2022 3rd International Informatics and Software Engineering Conference (IISEC). Reinforcement Learning-Based Grasp Pattern Control of Upper Limb Prosthetics in an AI Platform View
  2. Costandache M, Barsan-Romano A, Asmarandei A, Bibire R, Iftene A. 2022 International Conference on INnovations in Intelligent SysTems and Applications (INISTA). Treatment Guidance using Sentiment Analysis View
  3. Lacerda A, Pappa G. 2021 International Joint Conference on Neural Networks (IJCNN). Deep Thompson Sampling for Length of Stay Prediction View
  4. Schmidt H, Schlotterer J, Bargull M, Nasca E, Aydelott R, Seifert C, Meyer F. 2021 IEEE Third International Conference on Cognitive Machine Intelligence (CogMI). Towards a trustworthy, secure and reliable enclave for machine learning in a hospital setting: The Essen Medical Computing Platform (EMCP) View
  5. Mollura M, Drudi C, Lehman L, Barbieri R. 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). A Reinforcement Learning Application for Optimal Fluid and Vasopressor Interventions in Septic ICU Patients View
  6. Li C, Wu F, Zhao J. 2023 International Joint Conference on Neural Networks (IJCNN). Offline Reinforcement Learning with Uncertainty Critic Regularization Based on Density Estimation View
  7. Costandache M, Balint P, Barat N, Bîrzu C, Bodnariu D, Gabor O, Iftene A. 2023 International Conference on Innovations in Intelligent Systems and Applications (INISTA). TreatMedApp - Diagnosis and Treatment System View
  8. Eghbali N, Alhanai T, Ghassemi M. 2023 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI). Reinforcement Learning Approach to Sedation and Delirium Management in the Intensive Care Unit View
  9. Lehel D, Siket M, Szilágyi L, Eigner G, Kovács L. 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC). Effect of Hyperparameters of Reinforcement Learning in Blood Glucose Control View
  10. Li H, Lin W, Huo J, Luo W. 2023 IEEE 29th International Conference on Parallel and Distributed Systems (ICPADS). OOCL-DDQN: Online Evaluation and Offline Training-Based Clipped Double DQN for Automated Anesthesia Control View
  11. Saadi H, Abdulshahed A. 2024 8th International Conference on Image and Signal Processing and their Applications (ISPA). Reinforcement Learning Applications in Medical Imaging View
  12. Peng C, Zhang D, Mitra U. ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Graph Identification and Upper Confidence Evaluation for Causal Bandits with Linear Models View
  13. Qiao J, Zhang Z, Yue S, Yuan Y, Cai Z, Zhang X, Ren J, Yu D. IEEE INFOCOM 2024 - IEEE Conference on Computer Communications. BR-DeFedRL: Byzantine-Robust Decentralized Federated Reinforcement Learning with Fast Convergence and Communication Efficiency View
  14. Chandel S, Bhattacharya R, Nayak M, Pathak A. 2024 2nd World Conference on Communication & Computing (WCONF). Integrating Eye Gaze Estimation with the Internet of Medical Things (IoMT) for Individualized and Efficient Healthcare View
  15. Kaushik P, Kaushik V, Sharma B, Kirthika A, Rout A, Roy R. 2024 4th International Conference on Advancement in Electronics & Communication Engineering (AECE). Advancing Personalized Diabetes Management: The Transformative Potential of Reinforcement Learning in Precision Medicine View
  16. Liu X, Hu F, Su Z, Yang F. 2024 4th International Symposium on Artificial Intelligence and Intelligent Manufacturing (AIIM). Type 1 Diabetes Blood Glucose Management Based on Fuzzy Logic and Reinforcement Learning View
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  19. Huang B, Wang X, Zhang X, Chen R, Wang S, Tan M, Zhang W. 2025 44th Chinese Control Conference (CCC). TD3R: Stabilizing the Offline-to-Online Reinforcement Learning by Restricting Policy Updates View