cirpublications.com-PVzL-1790880681-b246057097 1790880681 Clinical Intelligence Research Press info@cirpublications.com Clinical Intelligence Research Press Journal of Artificial Intelligence for Healthcare Systems J. Artif. Intell. Healthc. Syst. 3149-8981 2024 3 2 Luis Herrera Daniela Rojas Andres Castro 2024 10.68159/b246057097 https://cirpublications.com/pub/journal/1/article/b246057097 Christiano PF, Leike J, Brown T, Martic M, Legg S, Amodei D. Deep reinforcement learning from human preferences. Adv Neural Inf Process Syst. 2017;30. Stiennon N, Ouyang L, Wu J, Ziegler D, Lowe R, Voss C, et al. Learning to summarize with human feedback. Adv Neural Inf Process Syst. 2020;33:3008-21. Ouyang L, Wu J, Jiang X, Almeida D, Wainwright C, Mishkin P, et al. Training language models to follow instructions with human feedback. Adv Neural Inf Process Syst. 2022;35:27730-44. Sendak M, Elish MC, Gao M, Futoma J, Ratliff W, Nichols M, et al. “The human body is a black box”: supporting clinical decision-making with deep learning. In: Proc 2020 Conf Fairness Accountability Transparency. 2020. p. 99-109. Yu C, Liu J, Zhao H. Inverse reinforcement learning for intelligent mechanical ventilation and sedative dosing in intensive care units. BMC Med Inform Decis Mak. 2019;19(Suppl 2):57. Yu C, Ren G, Dong Y. Supervised-actor-critic reinforcement learning for intelligent mechanical ventilation and sedative dosing in intensive care units. BMC Med Inform Decis Mak. 2020;20(Suppl 3):124. Huang HY, Huang CY, Li LF. Prolonged mechanical ventilation: outcomes and management. J Clin Med. 2022;11(9):2451. den Hengst F, Otten M, Elbers P, van Harmelen F, François-Lavet V, Hoogendoorn M. Guideline-informed reinforcement learning for mechanical ventilation in critical care. Artif Intell Med. 2024;147:102742. Roggeveen LF, Hassouni AE, de Grooth HJ, Girbes AR, Hoogendoorn M, Elbers PW, et al. Reinforcement learning for intensive care medicine: actionable clinical insights from novel approaches to reward shaping and off-policy model evaluation. Intensive Care Med Exp. 2024;12(1):32. Burns KEA, Rochwerg B, Seely AJ. Ventilator weaning and extubation. Crit Care Clin. 2024;40(2):391-408. Lin MY, Li CC, Lin PH, Wang JL, Chan MC, Wu CL, et al. Explainable machine learning to predict successful weaning among patients requiring prolonged mechanical ventilation: a retrospective cohort study in central Taiwan. Front Med (Lausanne). 2021;8:663739. Marshall DC, Komorowski M. Is artificial intelligence ready to solve mechanical ventilation? Computer says blow. Br J Anaesth. 2022;128(2):231-3. Jhou HJ, Chen PH, Ou-Yang LJ, Lin C, Tang SE, Lee CH. Methods of weaning from mechanical ventilation in adult: a network meta-analysis. Front Med (Lausanne). 2021;8:752984. Misseri G, Piattoli M, Cuttone G, Gregoretti C, Bignami EG. Artificial intelligence for mechanical ventilation: a transformative shift in critical care. Ther Adv Pulm Crit Care Med. 2024;19:29768675241298918. Balagopalan A, Baldini I, Celi LA, Gichoya J, McCoy LG, Naumann T, et al. Machine learning for healthcare that matters: reorienting from technical novelty to equitable impact. PLOS Digit Health. 2024;3(4):e0000474. Sblendorio E, Dentamaro V, Cascio AL, Germini F, Piredda M, Cicolini G. Integrating human expertise and automated methods for a dynamic and multi-parametric evaluation of large language models’ feasibility in clinical decision-making. Int J Med Inform. 2024;188:105501. Lee CS, Chen NH, Chuang LP, Chang CH, Li LF, Lin SW, et al. Hypercapnic ventilatory response in the weaning of patients with prolonged mechanical ventilation. Can Respir J. 2017;2017(1):7381424. Ghiani A, Paderewska J, Sainis A, Crispin A, Walcher S, Neurohr C. Variables predicting weaning outcome in prolonged mechanically ventilated tracheotomized patients: a retrospective study. J Intensive Care. 2020;8(1):19. Liao KM, Ko SC, Liu CF, Cheng KC, Chen CM, Sung MI, et al. Development of an interactive AI system for the optimal timing prediction of successful weaning from mechanical ventilation for patients in respiratory care centers. Diagnostics (Basel). 2022;12(4):975. Cheng KH, Tan MC, Chang YJ, Lin CW, Lin YH, Chang TM, et al. The feasibility of a machine learning approach in predicting successful ventilator mode shifting for adult patients in the medical intensive care unit. Medicina (Kaunas). 2022;58(3):360. Park JE, Kim TY, Jung YJ, Han C, Park CM, Park JH, et al. Biosignal-based digital biomarkers for prediction of ventilator weaning success. Int J Environ Res Public Health. 2021;18(17):9229. Park JE, Kim DY, Park JW, Jung YJ, Lee KS, Park JH, et al. Development of a machine learning model for predicting weaning outcomes based solely on continuous ventilator parameters during spontaneous breathing trials. Bioengineering (Basel). 2023;10(10):1163. Pai KC, Su SA, Chan MC, Wu CL, Chao WC. Explainable machine learning approach to predict extubation in critically ill ventilated patients: a retrospective study in central Taiwan. BMC Anesthesiol. 2022;22(1):351. Sheikhalishahi S, Kaspar M, Zaghdoudi S, Sander J, Simon P, Geisler BP, et al. Predicting successful weaning from mechanical ventilation by reduction in positive end-expiratory pressure level using machine learning. PLOS Digit Health. 2024;3(3):e0000478. Huang KY, Hsu YL, Chen HC, Horng MH, Chung CL, Lin CH, et al. Developing a machine-learning model for real-time prediction of successful extubation in mechanically ventilated patients using time-series ventilator-derived parameters. Front Med (Lausanne). 2023;10:1167445. Jia Y, Kaul C, Lawton T, Murray-Smith R, Habli I. Prediction of weaning from mechanical ventilation using convolutional neural networks. Artif Intell Med. 2021;117:102087. Torrini F, Gendreau S, Morel J, Carteaux G, Thille AW, Antonelli M, et al. Prediction of extubation outcome in critically ill patients: a systematic review and meta-analysis. Crit Care. 2021;25(1):391. Leonov Y, Kisil I, Perlov A, Stoichev V, Ginzburg Y, Nazarenko A, et al. Predictors of successful weaning in patients requiring extremely prolonged mechanical ventilation. Adv Respir Med. 2020;88(6):477-84.