相关实验视频

Updated: Jun 20, 2025

Cefoperazone-treated Mouse Model of Clinically-relevant Clostridium difficile Strain R20291
06:51

Cefoperazone-treated Mouse Model of Clinically-relevant Clostridium difficile Strain R20291

Published on: December 10, 2016

12.5K

通过可解释的机器学习预测Clostridioides difficile感染的结果

Gregory R Madden1, Rachel H Boone2, Emmanuel Lee3

  • 1Division of Infectious Diseases & International Health, Department of Medicine, University of Virginia School of Medicine, Charlottesville, VA, USA; Office of Hospital Epidemiology/Infection Prevention & Control, University of Virginia School of Medicine, Charlottesville, VA, USA.

EBioMedicine
|July 17, 2024
PubMed
概括

这项研究开发了一个新的模型来预测Clostridioides difficile感染的严重结果和复发. 该模型比现有方法更准确,有助于诊断时的临床决策.

关键词:
克洛斯特里迪奥伊德困难感染.机器学习 机器学习结果模型的结果模型预测模型的预测模型.

更多相关视频

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K

相关概念视频

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
121
Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
125