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1
Factors Associated With Variability in the Performance of a Proprietary Sepsis Prediction Model Across 9 Networked Hospitals in the US.美国9家联网医院中，与一种专利脓毒症预测模型性能变异性相关的因素
JAMA Intern Med. 2023 Jun 1;183(6):611-612. doi: 10.1001/jamainternmed.2022.7182.
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Use of Sepsis Clinical Prediction Models to Improve Patient Care.使用脓毒症临床预测模型改善患者护理。
JAMA Intern Med. 2023 Jun 1;183(6):612-615. doi: 10.1001/jamainternmed.2022.7164.
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External Validation of a Widely Implemented Proprietary Sepsis Prediction Model in Hospitalized Patients.在住院患者中验证广泛实施的专有脓毒症预测模型的外部有效性。
JAMA Intern Med. 2021 Aug 1;181(8):1065-1070. doi: 10.1001/jamainternmed.2021.2626.
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Sepsis Prediction Model for Determining Sepsis vs SIRS, qSOFA, and SOFA.用于区分脓毒症与全身炎症反应综合征（qSOFA 和 SOFA）的脓毒症预测模型。
JAMA Netw Open. 2023 Aug 1;6(8):e2329729. doi: 10.1001/jamanetworkopen.2023.29729.
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Development and external validation of a new clinical prediction model for early recognition of sepsis.一种用于早期识别脓毒症的新型临床预测模型的开发与外部验证
Br J Gen Pract. 2022 May 26;72(719):260. doi: 10.3399/bjgp22X719561. Print 2022 Jun.
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Assessing the effects of data drift on the performance of machine learning models used in clinical sepsis prediction.评估数据漂移对临床脓毒症预测中使用的机器学习模型性能的影响。
Int J Med Inform. 2023 May;173:104930. doi: 10.1016/j.ijmedinf.2022.104930. Epub 2022 Nov 19.
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Application of regression model combined with computer technology in the construction of early warning model of sepsis infection in children.回归模型结合计算机技术在儿童脓毒症感染预警模型构建中的应用。
J Infect Public Health. 2020 Feb;13(2):253-259. doi: 10.1016/j.jiph.2019.11.012. Epub 2019 Dec 7.
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Early detection of sepsis utilizing deep learning on electronic health record event sequences.利用深度学习对电子健康记录事件序列进行脓毒症的早期检测。
Artif Intell Med. 2020 Apr;104:101820. doi: 10.1016/j.artmed.2020.101820. Epub 2020 Feb 19.
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Clinical Impact of a Sepsis Alert System Plus Electronic Sepsis Navigator Using the Epic Sepsis Prediction Model in the Emergency Department.急诊应用 Epic 脓毒症预测模型的脓毒症预警系统加电子脓毒症导航器的临床影响。
J Emerg Med. 2023 May;64(5):584-595. doi: 10.1016/j.jemermed.2023.02.025. Epub 2023 Mar 8.
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An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU.一种用于 ICU 中脓毒症准确预测的可解释机器学习模型。
Crit Care Med. 2018 Apr;46(4):547-553. doi: 10.1097/CCM.0000000000002936.
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Pragmatic, multicentre, factorial, randomised controlled trial of sepsis electronic prompting for timely intervention and care (SEPTIC trial): a protocol.脓毒症电子提示以促进及时干预和护理的实用、多中心、析因、随机对照试验（SEPTIC试验）：一项方案
BMJ Open. 2025 Aug 11;15(8):e088792. doi: 10.1136/bmjopen-2024-088792.
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External validation of a widely available, localizable sepsis early detection model in the emergency department setting.在急诊科环境中对一种广泛可用、可定位的脓毒症早期检测模型进行外部验证。
Am J Emerg Med. 2025 Jul 24;97:147-151. doi: 10.1016/j.ajem.2025.07.056.
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Rural Medical Centers Struggle to Produce Well-Calibrated Clinical Prediction Models: Data Augmentation Can Help.农村医疗中心难以生成校准良好的临床预测模型：数据增强可提供帮助。
medRxiv. 2025 Jun 17:2025.06.16.25329699. doi: 10.1101/2025.06.16.25329699.
4
Multicenter Validation of a Machine Learning Model for Surgical Transfusion Risk at 45 US Hospitals.美国45家医院手术输血风险机器学习模型的多中心验证
JAMA Netw Open. 2025 Jun 2;8(6):e2517760. doi: 10.1001/jamanetworkopen.2025.17760.
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Evaluating sepsis watch generalizability through multisite external validation of a sepsis machine learning model.通过脓毒症机器学习模型的多中心外部验证评估脓毒症监测的可推广性。
NPJ Digit Med. 2025 Jun 11;8(1):350. doi: 10.1038/s41746-025-01664-5.
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The future of healthcare-associated infection surveillance: Automated surveillance and using the potential of artificial intelligence.医疗保健相关感染监测的未来：自动监测与人工智能潜力的利用
J Intern Med. 2025 Aug;298(2):54-77. doi: 10.1111/joim.20100. Epub 2025 Jun 5.
7
Predictions With a Purpose: Elevating Standards for Clinical Modeling Research.有目的的预测：提升临床建模研究的标准
Crit Care Explor. 2025 May 27;7(6):e1268. doi: 10.1097/CCE.0000000000001268. eCollection 2025 Jun 1.
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Unreasonable effectiveness of training AI models locally.在本地训练人工智能模型的不合理有效性。
BMJ Qual Saf. 2025 Aug 18;34(9):567-569. doi: 10.1136/bmjqs-2025-018543.
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Online database of clinical algorithms with race and ethnicity.包含种族和民族信息的临床算法在线数据库。
Sci Rep. 2025 Mar 29;15(1):10913. doi: 10.1038/s41598-025-94152-5.
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In-Hospital Mortality Prediction among Intensive Care Unit Patients with Acute Ischemic Stroke: A Machine Learning Approach.急性缺血性脑卒中重症监护病房患者的院内死亡率预测：一种机器学习方法
Health Data Sci. 2025 Mar 17;5:0179. doi: 10.34133/hds.0179. eCollection 2025.
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