Hierarchical Reinforcement Learning Framework for Personalized Perioperative Antibiotic Prophylaxis Timing and Intraoperative Redosing

cirpublications.com-YTXM-1790880675-a434092090 1790880675 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 2026 5 1 Hierarchical Reinforcement Learning Framework for Personalized Perioperative Antibiotic Prophylaxis Timing and Intraoperative Redosing Ethan Wright Chloe Bennett Jack Turner 2026 10.68159/a434092090 https://cirpublications.com/pub/journal/1/article/a434092090 Berríos-Torres SI, Umscheid CA, Bratzler DW, Leas B, Stone EC, Kelz RR, et al. Centers for disease control and prevention guideline for the prevention of surgical site infection, 2017. JAMA Surg. 2017;152(8):784-91. de Jonge SW, Gans SL, Atema JJ, Solomkin JS, Dellinger PE, Boermeester MA. Timing of preoperative antibiotic prophylaxis in 54,552 patients and the risk of surgical site infection: a systematic review and meta-analysis. Medicine (Baltimore). 2017;96(29):e6903. Weber WP, Mujagic E, Zwahlen M, Bundi M, Hoffmann H, Soysal SD, et al. Timing of surgical antimicrobial prophylaxis: a phase 3 randomised controlled trial. Lancet Infect Dis. 2017;17(6):605-14. Branch-Elliman W, O’Brien W, Strymish J, Itani K, Wyatt C, Gupta K. Association of duration and type of surgical prophylaxis with antimicrobial-associated adverse events. JAMA Surg. 2019;154(7):590-8. de Jonge SW, Boldingh QJ, Solomkin JS, Dellinger EP, Egger M, Salanti G, et al. Effect of postoperative continuation of antibiotic prophylaxis on the incidence of surgical site infection: a systematic review and meta-analysis. Lancet Infect Dis. 2020;20(10):1182-92. de Jonge SW, Boldingh QJ, Koch AH, Daniels L, de Vries EN, Spijkerman IJ, et al. Timing of preoperative antibiotic prophylaxis and surgical site infection: TAPAS, an observational cohort study. Ann Surg. 2021;274(4):e308-e314. Bertschi D, Weber WP, Zeindler J, Stekhoven D, Mechera R, Salm L, et al. Antimicrobial prophylaxis redosing reduces surgical site infection risk in prolonged duration surgery irrespective of its timing. World J Surg. 2019;43(10):2420-5. O’Hara LM, Thom KA, Preas MA. Update to the Centers for Disease Control and Prevention and the Healthcare Infection Control Practices Advisory Committee Guideline for the Prevention of Surgical Site Infection (2017): a summary, review, and strategies for implementation. Am J Infect Control. 2018;46(6):602-9. Sartelli M, Labricciosa FM, Casini B, Cortese F, Cricca M, Facciolà A, et al. Optimizing surgical antibiotic prophylaxis in the era of antimicrobial resistance: a position paper from the Italian Multidisciplinary Society for the Prevention of Healthcare-Associated Infections (SIMPIOS). Pathogens. 2025;14(10):1031. Al Mamlook RE, Wells LJ, Sawyer R. Machine-learning models for predicting surgical site infections using patient pre-operative risk and surgical procedure factors. Am J Infect Control. 2023;51(5):544-50. Chen KA, Joisa CU, Stem JM, Guillem JG, Gomez SM, Kapadia MR. Improved prediction of surgical-site infection after colorectal surgery using machine learning. Dis Colon Rectum. 2023;66(3):458-66. Xiong C, Zhao R, Xu J, Liang H, Zhang C, Zhao Z, et al. Construct and validate a predictive model for surgical site infection after posterior lumbar interbody fusion based on machine learning algorithm. Comput Math Methods Med. 2022;2022:2697841. McLean KA, Sgrò A, Brown LR, Buijs LF, Mountain KE, Shaw CA, et al. Multimodal machine learning to predict surgical site infection with healthcare workload impact assessment. NPJ Digit Med. 2025;8(1):121. Muaddi H, Choudhary A, Lee F, Anderson SS, Habermann E, Etzioni D, et al. Imaging-based surgical site infection detection using artificial intelligence. Ann Surg. 2025;282(3):419-28. Elhage SA, Deerenberg EB, Ayuso SA, Murphy KJ, Shao JM, Kercher KW, et al. Development and validation of image-based deep learning models to predict surgical complexity and complications in abdominal wall reconstruction. JAMA Surg. 2021;156(10):933-40. Bartek MA, Saxena RC, Solomon S, Fong CT, Behara LD, Venigandla R, et al. Improving operating room efficiency: machine learning approach to predict case-time duration. J Am Coll Surg. 2019;229(4):346-54. Strömblad CT, Baxter-King RG, Meisami A, Yee SJ, Levine MR, Ostrovsky A, et al. Effect of a predictive model on planned surgical duration accuracy, patient wait time, and use of presurgical resources: a randomized clinical trial. JAMA Surg. 2021;156(4):315-21. Jiao Y, Xue B, Lu C, Avidan MS, Kannampallil T. Continuous real-time prediction of surgical case duration using a modular artificial neural network. Br J Anaesth. 2022;128(5):829-37. Spence C, Shah OA, Cebula A, Tucker K, Sochart D, Kader D, et al. Machine learning models to predict surgical case duration compared to current industry standards: scoping review. BJS Open. 2023;7(6):zrad113. Riahi V, Hassanzadeh H, Khanna S, Boyle J, Syed F, Biki B, et al. Improving preoperative prediction of surgery duration. BMC Health Serv Res. 2023;23(1):1343. Rozario D. Can machine learning optimize the efficiency of the operating room in the era of COVID-19? Can J Surg. 2020;63(6):E527. Lex JR, Abbas A, Mosseri J, Toor JS, Simone M, Ravi B, et al. Using machine learning to predict-then-optimize elective orthopedic surgery scheduling to improve operating room utilization: retrospective study. JMIR Med Inform. 2025;13(1):e70857. Zhong C, Liao K, Chen W, Liu Q, Peng B, Huang X, et al. Hierarchical reinforcement learning for automatic disease diagnosis. Bioinformatics. 2022;38(16):3995-4001. Du X, Chen H, Yang B, Long C, Zhao S. HRL4EC: hierarchical reinforcement learning for multi-mode epidemic control. Inf Sci. 2023;640:119065. Tang S, Makar M, Sjoding M, Doshi-Velez F, Wiens J. Leveraging factored action spaces for efficient offline reinforcement learning in healthcare. Adv Neural Inf Process Syst. 2022;35:34272-86. Tang S, Wiens J. Model selection for offline reinforcement learning: practical considerations for healthcare settings. In: Proceedings of Machine Learning for Healthcare Conference. PMLR; 2021. p. 2-35. Komorowski M, Celi LA, Badawi O, Gordon AC, Faisal AA. The artificial intelligence clinician learns optimal treatment strategies for sepsis in intensive care. Nat Med. 2018;24(11):1716-20. Roggeveen L, El Hassouni A, Ahrendt J, Guo T, Fleuren L, Thoral P, et al. Transatlantic transferability of a new reinforcement learning model for optimizing haemodynamic treatment for critically ill patients with sepsis. Artif Intell Med. 2021;112:102003. Wang Y, Liu A, Yang J, Wang L, Xiong N, Cheng Y, et al. Clinical knowledge-guided deep reinforcement learning for sepsis antibiotic dosing recommendations. Artif Intell Med. 2024;150:102811.