| 1 |
中国医师协会急诊医师分会，中国研究型医院学会休克与脓毒症专业委员会.中国脓毒症/脓毒性休克急诊治疗指南（2018）[J].临床急诊杂志，2018，19（9）：567-588.
|
| 2 |
张锦鑫，沈括，胡大海，等.脓毒症早期诊断和治疗进展[J/CD].中华损伤与修复杂志（电子版），2022，17（1）：76-80.
|
| 3 |
Singer M, Deutschman CS, Seymour CW, et al. The third international consensus definitions for sepsis and septic shock (sepsis-3)[J]. JAMA, 2016, 315 (8): 801-810.
|
| 4 |
McGrath SP, Perreard I, MacKenzie T, et al. Improvement of sepsis identification through multi-year comparison of sepsis and early warning scores[J]. Am J Emerg Med, 2022 (51): 239-247.
|
| 5 |
袁琪茜，陈宇，杨晓玲，等.人工智能决策系统在脓毒症管理中的应用前景[J/CD].中华危重症医学杂志（电子版），2021，14（5）：416-419.
|
| 6 |
Barton C, Chettipally U, Zhou Y, et al. Evaluation of a machine learning algorithm for up to 48-hour advance prediction of sepsis using six vital signs[J]. Comput Biol Med, 2019 (109): 79-84.
|
| 7 |
黄艺龙，秦小林，陈芋文，等.利用LIME对脓毒症预测模型进行可解释性分析[J].计算机应用，2021，41（z1）：332-335.
|
| 8 |
邓洪飞.基于机器学习和深度学习对脓毒症的预警及不良预后预测[D].成都：电子科技大学，2021.
|
| 9 |
Gelbard RB, Hensman H, Schobel S, et al. Random forest modeling can predict infectious complications following trauma laparotomy[J]. J Trauma Acute Care Surg, 2019, 87 (5): 1125-1132.
|
| 10 |
Tran NK, Albahra S, Pham TN, et al. Novel application of an automated-machine learning development tool for predicting burn sepsis: proof of concept[J]. Sci Rep, 2020, 10 (1): 12354.
|
| 11 |
Shankar A, Diwan M, Singh S, et al. Early prediction of sepsis using machine learning[C]. 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence), Noida, India, 2021: 837-842.
|
| 12 |
Rayan Z, Alfonse M, Salem AM. Sepsis prediction model in the intensive care unit (ICU) using support vector machine (SVM)[M]// Magdi DA, Helmy YK, Mamdouh M, et al. Digital transformation technology. Lecture notes in networks and systems. Singapore: Springer, 2021: 539-546.
|
| 13 |
Zhao X, Shen W, Wang G. Early prediction of sepsis based on machine learning algorithm[J]. Comput Intell Neurosci, 2021 (2021): 6522633.
|
| 14 |
Parashar A, Mohan Y, Rathee N. Analysis of various health parameters for early and efficient prediction of sepsis[C]. IOP Conference Series: Materials Science and Engineering, 2021: 1-17.
|
| 15 |
Abromavicius V, Plonis D, Tarasevicius D, et al. Two-stage monitoring of patients in intensive care unit for sepsis prediction using non-overfitted machine learning models[J]. Electronics, 2020, 9 (7): 1133.
|
| 16 |
Zhang D, Yin C, Hunold KM, et al. An interpretable deep-learning model for early prediction of sepsis in the emergency department[J]. Patterns (N Y), 2021, 2 (2): 100196.
|
| 17 |
Yang M, Liu C, Wang X, et al. An explainable artificial intelligence predictor for early detection of sepsis[J]. Crit Care Med, 2020, 48 (11): e1091-e1096.
|
| 18 |
Rosnati M, Fortuin V. MGP-AttTCN: an interpretable machine learning model for the prediction of sepsis[J]. PLoS One, 2021, 16 (5): e0251248.
|
| 19 |
Baghaei KT, Rahimi S. Sepsis prediction: an attention-based interpretable approach[C]. 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), New Orleans, LA, USA, 2019: 1-6.
|
| 20 |
Amrollahi F, Shashikumar SP, Razmi F, et al. Contextual embeddings from clinical notes improves prediction of sepsis[C]. AMIA Annual Symposium Proceedings, 2020: 197-202.
|
| 21 |
Wang RZ, Sun CH, Schroeder PH, et al. Predictive models of sepsis in adult ICU patients[C]. 2018 IEEE International Conference on Healthcare Informatics (ICHI), New York, NY, USA, 2018: 390-391.
|
| 22 |
Scherpf M, Graβer F, Malberg H, et al. Predicting sepsis with a recurrent neural network using the MIMIC III database[J]. Comput Biol Med, 2019 (113):103395.
|
| 23 |
Mao Q, Jay M, Hoffman JL, et al. Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU[J]. BMJ Open, 2018, 8 (1): e017833.
|
| 24 |
Gül F, Arslantas MK, Cinel I, et al. Changing definitions of sepsis[J]. Turk J Anaesthesiol Reanim, 2017, 45 (3): 129-138.
|
| 25 |
Guan Y, Wang X, Chen X, et al. Assessment of the timeliness and robustness for predicting adult sepsis[J]. iScience, 2021, 24 (2): 102106.
|
| 26 |
van Wyk F, Khojandi A, Mohammed A, et al. A minimal set of physiomarkers in continuous high frequency data streams predict adult sepsis onset earlier[J]. Int J Med Inform, 2019 (122): 55-62.
|
| 27 |
Tang G, Luo Y, Lu F, et al. Prediction of sepsis in COVID-19 using laboratory indicators[J]. Front Cell Infect Microbiol, 2021 (10): 586054.
|
| 28 |
El-Rashidy N, Abuhmed T, Alarabi L, et al. Sepsis prediction in intensive care unit based on genetic feature optimization and stacked deep ensemble learning[J]. Neural Comput & Applic, 2022 (34): 3603-3632.
|
| 29 |
Goh KH, Wang L, Yeow AYK, et al. Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare[J]. Nat Commun, 2021, 12 (1): 711.
|
| 30 |
Wong A, Otles E, Donnelly JP, et al. External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients[J]. JAMA Intern Med, 2021, 181 (8): 1065-1070.
|
| 31 |
Bloch E, Rotem T, Cohen J, et al. Machine learning models for analysis of vital signs dynamics: a case for sepsis onset prediction[J]. J Healthc Eng, 2019 (2019): 5930379.
|
| 32 |
Desautels T, Calvert J, Hoffman J, et al. Prediction of sepsis in the intensive care unit with minimal electronic health record data: a machine learning approach[J]. JMIR Med Inform, 2016, 4 (3): e28.
|
| 33 |
Wang D, Li J, Sun Y, et al. A machine learning model for accurate prediction of sepsis in ICU patients[J]. Front Public Health, 2021 (9): 754348.
|
| 34 |
Guillén J, Liu J, Furr M, et al. Predictive models for severe sepsis in adult ICU patients[C]. 2015 Systems and Information Engineering Design Symposium, Charlottesville, VA, USA, 2015: 182-187.
|
| 35 |
He Z, Du L, Zhang P, et al. Early sepsis prediction using ensemble learning with deep features and artificial features extracted from clinical electronic health records[J]. Crit Care Med, 2020, 48 (12): e1337-e1342.
|
| 36 |
Rafiei A, Rezaee A, Hajati F, et al. SSP: early prediction of sepsis using fully connected LSTM-CNN model[J]. Comput Biol Med, 2021 (128): 104110.
|
| 37 |
Li X, Xu X, Xie F, et al. A time-phased machine learning model for real-time prediction of sepsis in critical care[J]. Crit Care Med, 2020, 48 (10): e884-e888.
|
| 38 |
Nesaragi N, Patidar S. Early prediction of sepsis from clinical data using ratio and power-based features[J]. Crit Care Med, 2020, 48 (12): e1343-e1349.
|
| 39 |
Persson I, Ostling A, Arlbrandt M, et al. A machine learning sepsis prediction algorithm for intended intensive care unit use (NAVOY sepsis): proof-of-concept study[J]. JMIR Form Res, 2021, 5 (9): e28000.
|
| 40 |
Kamaleswaran R, Satapathy SK, Mas VR, et al. Artificial intelligence may predict early sepsis after liver transplantation[J]. Front Physiol, 2021 (12): 692667.
|
| 41 |
Zargoush M, Sameh A, Javadi M, et al. The impact of recency and adequacy of historical information on sepsis predictions using machine learning[J]. Sci Rep, 2021, 11 (1): 20869.
|
| 42 |
王涌超.面向心脏疾病和脓毒症的时序生理信号分析[D].重庆：重庆邮电大学，2020.
|
| 43 |
Nesaragi N, Patidar S, Aggarwal V. Tensor learning of pointwise mutual information from EHR data for early prediction of sepsis[J]. Comput Biol Med, 2021 (134): 104430.
|
| 44 |
Baniasadi A, Rezaeirad S, Zare H, et al. Two-step imputation and AdaBoost-based classification for early prediction of sepsis on imbalanced clinical data[J]. Crit Care Med, 2021, 49 (1): e91-e97.
|
| 45 |
Bunn C, Kulshrestha S, Boyda J, et al. Application of machine learning to the prediction of postoperative sepsis after appendectomy[J]. Surgery, 2021, 169 (3): 671-677.
|
| 46 |
Shashikumar SP, Josef CS, Sharma A, et al. DeepAISE-an interpretable and recurrent neural survival model for early prediction of sepsis[J]. Artif Intell Med, 2021 (113): 102036.
|