日期:
2020 年 — 2026 年
2020
2021
2022
2023
2024
2025
2026
影响因子:

Translating predictive models into clinical practice: fast-and-frugal trees for postoperative delirium using routine data

将预测模型转化为临床实践:利用常规数据构建用于术后谵妄的快速简便决策树

Wegwarth, Odette; Balzer, Felix; Boie, Sebastian D; Giesa, Niklas; Müller, Anika; Woike, Jan K; Spies, Claudia; Giese, Helge

Multimodal data for predictive medicine: algorithmic fusion of clinical data in anesthesiology and intensive care

用于预测医学的多模态数据:麻醉学和重症监护中临床数据的算法融合

Boie, Sebastian Daniel; Giesa, Niklas; Sekutowicz, Maria; Zhumagambetov, Rustam; Haufe, Stefan; Grünewald, Elias; Balzer, Felix

Fusion of clinical magnet resonance images and electronic health records promotes multimodal predictions of postoperative delirium

临床磁共振图像与电子健康记录的融合促进了术后谵妄的多模态预测

Giesa, Niklas; Dell'Orco, Andrea; Scheel, Michael; Finke, Carsten; Balzer, Felix; Spies, Claudia Doris; Sekutowicz, Maria

Severe Hyponatremia and Syndrome of Inappropriate Antidiuretic Hormone Secretion After Kambô Ritual

卡姆布仪式后出现严重低钠血症和抗利尿激素分泌异常综合征

Kelly, Thomas; Saponaro, Angela; Longenbach, James; Finkelberg, Tomer; Giesa, Christine; Kleiman, Eric; Syed, Hyra; Frederique, Pierre; Hamilton, Richard J

Predicting postoperative delirium assessed by the Nursing Screening Delirium Scale in the recovery room for non-cardiac surgeries without craniotomy: A retrospective study using a machine learning approach

利用机器学习方法预测非心脏手术(非开颅手术)患者在恢复室中通过护理谵妄筛查量表评估的术后谵妄:一项回顾性研究

Giesa, Niklas; Haufe, Stefan; Menk, Mario; Weiß, Björn; Spies, Claudia D; Piper, Sophie K; Balzer, Felix; Boie, Sebastian D

Applying a transformer architecture to intraoperative temporal dynamics improves the prediction of postoperative delirium

将Transformer架构应用于术中时间动态分析可提高术后谵妄的预测准确性

Giesa, Niklas; Sekutowicz, Maria; Rubarth, Kerstin; Spies, Claudia Doris; Balzer, Felix; Haufe, Stefan; Boie, Sebastian Daniel

An evaluation of materials co-created to support access to primary care in the COVID 19 pandemic: Presenter(s): Lynsey Brown, University of St Andrews, United Kingdom

对共同创建的、旨在支持在新冠疫情期间获得初级医疗保健服务的材料进行评估:报告人:林赛·布朗,英国圣安德鲁斯大学

Gronau, Greta; Krishnaji, Sreevidhya T; Kinahan, Michelle E; Giesa, Tristan; Wong, Joyce Y; Kaplan, David L; Buehler, Markus J; Williams, Andrew James; Ozakinci, Gozde; van Beusekom, Mara

The influence of patient characteristics on the alarm rate in intensive care units: a retrospective cohort study

患者特征对重症监护病房报警率的影响:一项回顾性队列研究

Sinno, Zeena-Carola; Shay, Denys; Kruppa, Jochen; Klopfenstein, Sophie A I; Giesa, Niklas; Flint, Anne Rike; Herren, Patrick; Scheibe, Franziska; Spies, Claudia; Hinrichs, Carl; Winter, Axel; Balzer, Felix; Poncette, Akira-Sebastian

A Recurrent Neural Network Model for Predicting Activated Partial Thromboplastin Time After Treatment With Heparin: Retrospective Study

基于循环神经网络模型预测肝素治疗后活化部分凝血酶原时间:回顾性研究

Boie, Sebastian Daniel; Engelhardt, Lilian Jo; Coenen, Nicolas; Giesa, Niklas; Rubarth, Kerstin; Menk, Mario; Balzer, Felix

Predicting lethal courses in critically ill COVID-19 patients using a machine learning model trained on patients with non-COVID-19 viral pneumonia

利用基于非新冠病毒肺炎患者训练的机器学习模型预测新冠肺炎危重患者的死亡进程

Lichtner, Gregor; Balzer, Felix; Haufe, Stefan; Giesa, Niklas; Schiefenhövel, Fridtjof; Schmieding, Malte; Jurth, Carlo; Kopp, Wolfgang; Akalin, Altuna; Schaller, Stefan J; Weber-Carstens, Steffen; Spies, Claudia; von Dincklage, Falk