cirpublications.com-TOUq-1790880683-c765515721 1790880683 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 2022 1 1 Lucas Meyer Anna Schmid Stefan Braun 2022 10.68159/c765515721 https://cirpublications.com/pub/journal/1/article/c765515721 Reyna MA, Josef CS, Jeter R, Shashikumar SP, Westover MB, et al. Early prediction of sepsis from clinical data: the PhysioNet/Computing in Cardiology Challenge 2019. Crit Care Med. 2020;48(2):210-7. Nemati S, Holder A, Razmi F, Stanley MD, Clifford GD, et al. An interpretable machine learning model for accurate prediction of sepsis in the ICU. Crit Care Med. 2018;46(4):547-53. 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. Zhou A, Beyah R, Kamaleswaran R. OnAI-comp: an online AI experts competing framework for early sepsis detection. IEEE/ACM Trans Comput Biol Bioinform. 2022;19(6):3595-603. Al-Mualemi BY, Lu L. A deep learning-based sepsis estimation scheme. IEEE Access. 2020;9:5442-52. Rosnati M, Fortuin V. MGP-AttTCN: an interpretable machine learning model for the prediction of sepsis. PLoS One. 2021;16(5):e0251248. Zhang D, Yin C, Hunold KM, Jiang X, Caterino JM, et al. An interpretable deep-learning model for early prediction of sepsis in the emergency department. Patterns. 2021;2(2):100196. Shashikumar SP, Josef CS, Sharma A, Nemati S. DeepAISE – an interpretable and recurrent neural survival model for early prediction of sepsis. Artif Intell Med. 2021;113:102036. Aşuroğlu T, Oğul H. A deep learning approach for sepsis monitoring via severity score estimation. Comput Methods Programs Biomed. 2021;198:105816. Oei SP, van Sloun RJ, van der Ven M, Korsten HH, Mischi M. Towards early sepsis detection from measurements at the general ward through deep learning. Intell Based Med. 2021;5:100042. 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. Goh KH, Wang L, Yeow AY, Poh H, Li K, et al. Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare. Nat Commun. 2021;12(1):711. Bedoya AD, Futoma J, Clement ME, Corey K, Brajer N, et al. Machine learning for early detection of sepsis: an internal and temporal validation study. JAMIA Open. 2020;3(2):252-60. Yang M, Liu C, Wang X, Li Y, Gao H, et al. An explainable artificial intelligence predictor for early detection of sepsis. Crit Care Med. 2020;48(11):e1091-6. Yuan KC, Tsai LW, Lee KH, Cheng YW, Hsu SC, et al. The development an artificial intelligence algorithm for early sepsis diagnosis in the intensive care unit. Int J Med Inform. 2020;141:104176. Kok C, Jahmunah V, Oh SL, Zhou X, Gururajan R, et al. Automated prediction of sepsis using temporal convolutional network. Comput Biol Med. 2020;127:103957. Ackerman MH, Ahrens T, Kelly J, Pontillo A. Sepsis. Crit Care Nurs Clin. 2021;33(4):407-18. Kim J, Chang H, Kim D, Jang DH, Park I, et al. Machine learning for prediction of septic shock at initial triage in emergency department. J Crit Care. 2020;55:163-70. Ibrahim ZM, Wu H, Hamoud A, Stappen L, Dobson RJ, et al. On classifying sepsis heterogeneity in the ICU: insight using machine learning. J Am Med Inform Assoc. 2020;27(3):437-43. Fagerström J, Bång M, Wilhelms D, Chew MS. LiSep LSTM: a machine learning algorithm for early detection of septic shock. Sci Rep. 2019;9(1):15132. Kaji DA, Zech JR, Kim JS, Cho SK, Dangayach NS, et al. An attention based deep learning model of clinical events in the intensive care unit. PLoS One. 2019;14(2):e0211057. Giannini HM, Ginestra JC, Chivers C, Draugelis M, Hanish A, et al. A machine learning algorithm to predict severe sepsis and septic shock: development, implementation, and impact on clinical practice. Crit Care Med. 2019;47(11):1485-92. Ginestra JC, Giannini HM, Schweickert WD, Meadows L, Lynch MJ, et al. Clinician perception of a machine learning–based early warning system designed to predict severe sepsis and septic shock. Crit Care Med. 2019;47(11):1477-84. 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. 2021;8:607952. 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. Wang D, Li J, Sun Y, Ding X, Zhang X, et al. A machine learning model for accurate prediction of sepsis in ICU patients. Front Public Health. 2021;9:754348. Qiu X, Li J, Bonenfant J, Jaroszewski L, Mittal A, et al. Dynamic changes in human single-cell transcriptional signatures during fatal sepsis. J Leukoc Biol. 2021;110(6):1253-68. Khojandi A, Tansakul V, Li X, Koszalinski RS, Paiva W. Prediction of sepsis and in-hospital mortality using electronic health records. Methods Inf Med. 2018;57(4):185-93. Wong A, Otles E, Donnelly JP, Krumm A, McCullough J, et al. External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients. JAMA Intern Med. 2021;181(8):1065-70. Burdick H, Pino E, Gabel-Comeau D, Gu C, Roberts J, et al. Validation of a machine learning algorithm for early severe sepsis prediction: a retrospective study predicting severe sepsis up to 48 h in advance using a diverse dataset from 461 US hospitals. BMC Med Inform Decis Mak. 2020;20(1):276. Kam HJ, Kim HY. Learning representations for the early detection of sepsis with deep neural networks. Comput Biol Med. 2017;89:248-55. Bauer M, Gerlach H, Vogelmann T, Preissing F, Stiefel J, et al. Mortality in sepsis and septic shock in Europe, North America and Australia between 2009 and 2019—results from a systematic review and meta-analysis. Crit Care. 2020;24(1):239. Napolitano LM. Sepsis 2018: definitions and guideline changes. Surg Infect. 2018;19(2):117-25. Fleuren LM, Klausch TL, Zwager CL, Schoonmade LJ, Guo T, 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.