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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  4. Eghbali N, Alhanai T, Ghassemi M. Patient-Specific Sedation Management via Deep Reinforcement Learning. Frontiers in Digital Health 2021;3 View
  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
  8. Giordano C, Brennan M, Mohamed B, Rashidi P, Modave F, Tighe P. Accessing Artificial Intelligence for Clinical Decision-Making. Frontiers in Digital Health 2021;3 View
  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
  11. Girdler B, Caldbeck W, Bae J. Neural Decoders Using Reinforcement Learning in Brain Machine Interfaces: A Technical Review. Frontiers in Systems Neuroscience 2022;16 View
  12. Ameen S, Wong M, Yee K, Turner P. AI and Clinical Decision Making: The Limitations and Risks of Computational Reductionism in Bowel Cancer Screening. Applied Sciences 2022;12(7):3341 View
  13. Al-Ani O, Das S. Reinforcement Learning: Theory and Applications in HEMS. Energies 2022;15(17):6392 View
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  15. Morris A, Horvat C, Stagg B, Grainger D, Lanspa M, Orme J, Clemmer T, Weaver L, Thomas F, Grissom C, Hirshberg E, East T, Wallace C, Young M, Sittig D, Suchyta M, Pearl J, Pesenti A, Bombino M, Beck E, Sward K, Weir C, Phansalkar S, Bernard G, Thompson B, Brower R, Truwit J, Steingrub J, Hiten R, Willson D, Zimmerman J, Nadkarni V, Randolph A, Curley M, Newth C, Lacroix J, Agus M, Lee K, deBoisblanc B, Moore F, Evans R, Sorenson D, Wong A, Boland M, Dere W, Crandall A, Facelli J, Huff S, Haug P, Pielmeier U, Rees S, Karbing D, Andreassen S, Fan E, Goldring R, Berger K, Oppenheimer B, Ely E, Pickering B, Schoenfeld D, Tocino I, Gonnering R, Pronovost P, Savitz L, Dreyfuss D, Slutsky A, Crapo J, Pinsky M, James B, Berwick D. Computer clinical decision support that automates personalized clinical care: a challenging but needed healthcare delivery strategy. Journal of the American Medical Informatics Association 2022;30(1):178 View
  16. Bologheanu R, Kapral L, Laxar D, Maleczek M, Dibiasi C, Zeiner S, Agibetov A, Ercole A, Thoral P, Elbers P, Heitzinger C, Kimberger O. Development of a Reinforcement Learning Algorithm to Optimize Corticosteroid Therapy in Critically Ill Patients with Sepsis. Journal of Clinical Medicine 2023;12(4):1513 View
  17. Pina R, Tibebu H, Hook J, De Silva V, Kondoz A. Overcoming Challenges of Applying Reinforcement Learning for Intelligent Vehicle Control. Sensors 2021;21(23):7829 View
  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
  20. Couckuyt A, Seurinck R, Emmaneel A, Quintelier K, Novak D, Van Gassen S, Saeys Y. Challenges in translational machine learning. Human Genetics 2022;141(9):1451 View
  21. Liuzzi P, Campagnini S, Fanciullacci C, Arienti C, Patrini M, Carrozza M, Mannini A. Predicting SARS-CoV-2 infection duration at hospital admission:a deep learning solution. Medical & Biological Engineering & Computing 2022;60(2):459 View
  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
  24. Awasthi R, Guliani K, Khan S, Vashishtha A, Gill M, Bhatt A, Nagori A, Gupta A, Kumaraguru P, Sethi T. VacSIM: Learning effective strategies for COVID-19 vaccine distribution using reinforcement learning. Intelligence-Based Medicine 2022;6:100060 View
  25. Liu M, Shen X, Pan W. Deep reinforcement learning for personalized treatment recommendation. Statistics in Medicine 2022;41(20):4034 View
  26. El-Kareh R, Sittig D. Enhancing Diagnosis Through Technology. Critical Care Clinics 2022;38(1):129 View
  27. Oselio B, Singal A, Zhang X, Van T, Liu B, Zhu J, Waljee A. Reinforcement learning evaluation of treatment policies for patients with hepatitis C virus. BMC Medical Informatics and Decision Making 2022;22(1) View
  28. Guo H, Li J, Liu H, He J. Learning dynamic treatment strategies for coronary heart diseases by artificial intelligence: real-world data-driven study. BMC Medical Informatics and Decision Making 2022;22(1) View
  29. Nanayakkara T, Clermont G, Langmead C, Swigon D, Chua Chin Heng M. Unifying cardiovascular modelling with deep reinforcement learning for uncertainty aware control of sepsis treatment. PLOS Digital Health 2022;1(2):e0000012 View
  30. Campagnini S, Arienti C, Patrini M, Liuzzi P, Mannini A, Carrozza M. Machine learning methods for functional recovery prediction and prognosis in post-stroke rehabilitation: a systematic review. Journal of NeuroEngineering and Rehabilitation 2022;19(1) View
  31. Aguiar-Pérez J, Pérez-Juárez M. An Insight of Deep Learning Based Demand Forecasting in Smart Grids. Sensors 2023;23(3):1467 View
  32. Costa J, Silva-Correia J, Reis R, Oliveira J. Deep Learning in Bioengineering and Biofabrication: A Powerful Technology Boosting Translation from Research to Clinics. Journal of 3D Printing in Medicine 2021;5(4):191 View
  33. Caruso P, Greco M, Ebm C, Angelotti G, Cecconi M. Implementing Artificial Intelligence. Critical Care Clinics 2023;39(4):783 View
  34. Levy J, Lu Y, Montivero M, Ramwala O, McFadden J, Miles C, Diamond A, Reddy R, Reddy R, Hudson T, Azher Z, Pamal A, Gabbita S, Cronin T, Ould Ismail A, Goel T, Jacob S, Suvarna A, Ratna S, Zavras J, Vaickus L. Artificial Intelligence, Bioinformatics, and Pathology. Advances in Molecular Pathology 2022;5(1):e1 View
  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. 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 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 2024:1 View

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
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