cirpublications.com-fMk4-1790880685-w932931125 1790880685 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 Ahmed Al-Sayed Omar Khalid 2026 10.68159/w932931125 https://cirpublications.com/pub/journal/1/article/w932931125 Nemati S, Holder A, Razmi F, Stanley MD, Clifford GD, Buchman TG, et al. An interpretable machine learning model for accurate prediction of sepsis in the ICU. Crit Care Med. 2018;46(4):547-53. Reyna MA, Josef CS, Jeter R, Shashikumar SP, Westover MB, Nemati S, et al. Early prediction of sepsis from clinical data: the PhysioNet/Computing in Cardiology Challenge 2019. Crit Care Med. 2020;48(2):210-7. Wong A, Otles E, Donnelly JP, Krumm A, McCullough J, DeTroyer-Cooley O, et al. External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients. JAMA Intern Med. 2021;181(8):1065-70. Fleuren LM, Klausch TL, Zwager CL, Schoonmade LJ, Guo T, Roggeveen LF, et al. Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy. Intensive Care Med. 2020;46(3):383-400. Lauritsen SM, Kalør ME, Kongsgaard EL, Lauritsen KM, Jørgensen MJ, Lange J, et al. Early detection of sepsis utilizing deep learning on electronic health record event sequences. Artif Intell Med. 2020;104:101820. Rafiei A, Rezaee A, Hajati F, Gheisari S, Golzan M. SSP: early prediction of sepsis using fully connected LSTM-CNN model. Comput Biol Med. 2021;128:104110. Shashikumar SP, Wardi G, Malhotra A, Nemati S. Artificial intelligence sepsis prediction algorithm learns to say “I don’t know”. NPJ Digit Med. 2021;4(1):134. Islam MM, Nasrin T, Walther BA, Wu CC, Yang HC, Li YC. Prediction of sepsis patients using machine learning approach: a meta-analysis. Comput Methods Programs Biomed. 2019;170:1-9. Yan MY, Gustad LT, Nytrø Ø. Sepsis prediction, early detection, and identification using clinical text for machine learning: a systematic review. J Am Med Inform Assoc. 2022;29(3):559-75. Moor M, Rieck B, Horn M, Jutzeler CR, Borgwardt K. Early prediction of sepsis in the ICU using machine learning: a systematic review. Front Med (Lausanne). 2021;8:607952. Islam KR, Prithula J, Kumar J, Tan TL, Reaz MB, Sumon MS, et al. Machine learning-based early prediction of sepsis using electronic health records: a systematic review. J Clin Med. 2023;12(17):5658. Yao RQ, Jin X, Wang GW, Yu Y, Wu GS, Zhu YB, et al. A machine learning-based prediction of hospital mortality in patients with postoperative sepsis. Front Med (Lausanne). 2020;7:445. Futoma J, Hariharan S, Heller K. Learning to detect sepsis with a multitask Gaussian process RNN classifier. In: International Conference on Machine Learning. Sydney, Australia: PMLR; 2017. p. 1174-82. Valik JK, Ward L, Tanushi H, Johansson AF, Färnert A, Mogensen ML, et al. Predicting sepsis onset using a machine learned causal probabilistic network algorithm based on electronic health records data. Sci Rep. 2023;13:11760. Adams R, Henry KE, Sridharan A, Soleimani H, Zhan A, Rawat N, et al. Prospective, multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis. Nat Med. 2022;28(7):1455-60. Burdick H, Pino E, Gabel-Comeau D, McCoy A, Gu C, Roberts J, et al. Effect of a sepsis prediction algorithm on patient mortality, length of stay and readmission: a prospective multicentre clinical outcomes evaluation of real-world patient data from US hospitals. BMJ Health Care Inform. 2020;27(1):e100109. Shimabukuro DW, Barton CW, Feldman MD, Mataraso SJ, Das R. Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial. BMJ Open Respir Res. 2017;4(1):e000234. Mao Q, Jay M, Hoffman JL, Calvert J, Barton C, Shimabukuro D, et al. Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU. BMJ Open. 2018;8(1):e017833. Kok C, Jahmunah V, Oh SL, Zhou X, Gururajan R, Tao X, et al. Automated prediction of sepsis using temporal convolutional network. Comput Biol Med. 2020;127:103957. Li J, Xi F, Yu W, Sun C, Wang X. Real-time prediction of sepsis in critical trauma patients: machine learning–based modeling study. JMIR Form Res. 2023;7(1):e42452. Taneja I, Damhorst GL, Lopez-Espina C, Zhao SD, Zhu R, Khan S, et al. Diagnostic and prognostic capabilities of a biomarker and EMR based machine learning algorithm for sepsis. Clin Transl Sci. 2021;14(4):1578-89. Tang Y, Zhang Y, Li J. A time series driven model for early sepsis prediction based on transformer module. BMC Med Res Methodol. 2024;24(1):23. Wang Z, Yao B. Multi-branching temporal convolutional network for sepsis prediction. IEEE J Biomed Health Inform. 2021;26(2):876-87. Zhu Y, Cheng Y, Zhang T, Zhang L, Hong X, Wang D, et al. Application of the KA-Transformer model to early sepsis prediction: a hybrid network analysis based on time series data. Discov Appl Sci. 2025;7(3):218. Hu C, Li L, Huang W, Wu T, Xu Q, Liu J, et al. Interpretable machine learning for early prediction of prognosis in sepsis: a discovery and validation study. Infect Dis Ther. 2022;11(3):1117-32. Persson I, Macura A, Becedas D, Sjövall F. Early prediction of sepsis in intensive care patients using the machine learning algorithm NAVOY® Sepsis, a prospective randomized clinical validation study. J Crit Care. 2024;80:154400. Boussina A, Shashikumar SP, Malhotra A, Owens RL, El-Kareh R, Longhurst CA, et al. Impact of a deep learning sepsis prediction model on quality of care and survival. NPJ Digit Med. 2024;7(1):14. Persson I, Östling A, Arlbrandt M, Söderberg J, Becedas D. A machine learning sepsis prediction algorithm for intended intensive care unit use (NAVOY Sepsis): proof-of-concept study. JMIR Form Res. 2021;5(9):e28000.