Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

135
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:
135
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.4K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.4K
Prediction Intervals01:03

Prediction Intervals

2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.3K
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

382
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
382
Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

433
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
433
Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

240
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...
240

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

First true blood pressure measurement with micropulse detection of arterial opening achieved.

Scientific reports·2025
Same author

Population Digital Health: Continuous Health Monitoring and Profiling at Scale.

Online journal of public health informatics·2024
Same author

The 2023 wearable photoplethysmography roadmap.

Physiological measurement·2023
Same author

Real-Time Evaluation of Time-Domain Pulse Rate Variability Parameters in Different Postures and Breathing Patterns Using Wireless Photoplethysmography Sensor: Towards Remote Healthcare in Low-Resource Communities.

Sensors (Basel, Switzerland)·2023
Same author

Influence of Sea Level Anomaly on Underwater Gravity Gradient Measurements.

Sensors (Basel, Switzerland)·2022
Same author

MW‑9, a chalcones derivative bearing heterocyclic moieties, attenuates experimental autoimmune encephalomyelitis via suppressing pathogenic T<sub>H</sub>17 cells.

Molecular medicine reports·2022

相关实验视频

Updated: Jul 11, 2025

A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

179

针对COVID-19的可解释机器学习:关于严重性预测任务的实证研究

Han Wu1, Wenjie Ruan1, Jiangtao Wang2

  • 1University of Exeter EX4 4PY Exeter U.K.

IEEE transactions on artificial intelligence
|November 13, 2023
PubMed
概括

机器学习模型可以通过解释生物标志物来预测COVID-19的严重程度. 增加的N-终端前脑尿素,C反应蛋白,乳酸脱酶和淋巴细胞减少表明严重的感染和死亡风险.

关键词:
在健康方面的人工智能.医学中的人工智能可以解释的机器学习.

更多相关视频

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

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

Published on: August 16, 2020

6.8K

相关实验视频

Last Updated: Jul 11, 2025

A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

179
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

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

Published on: August 16, 2020

6.8K

科学领域:

  • 生物医学信息学 生物医学信息学
  • 人工智能在医学中的应用
  • 计算生物学 计算生物学

背景情况:

  • 机器学习 (ML) 模型的"黑子"性质阻碍了对COVID-19诊断等关键医疗应用的信任和部署.
  • 解释ML模型可以揭示关键的生物标志物,帮助临床医生在流行病期间感到压倒.
  • 识别可靠的生物标志物对于预测COVID-19严重程度和患者结果至关重要.

研究的目的:

  • 解释机器学习模型以识别与COVID-19感染严重程度相关的生物标志物.
  • 用独立的数据集验证已识别的生物标志物.
  • 提高高精度ML诊断工具的临床实用性.

主要方法:

  • 使用了四种ML模型:决策树,随机森林,梯度增强树和神经网络.
  • 采用了各种解释技术:排列特征重要性,部分依赖图,个人有条件预期,累积局部效应,LIME和SHAP.
  • 分析了两个数据集:中国珠海的92名患者和Kaggle (以色列阿尔伯特·爱因斯坦医院) 的5644名患者.

主要成果:

  • 鉴定了N-终端前脑尿素,C反应蛋白和乳酸脱酶的增加,以及淋巴细胞的减少,作为严重COVID-19和死亡风险的指标.
  • 在更大的数据集上验证了这些发现,证实了与严重感染的关联.
  • 发现白细胞,乙氨基和血小板作为COVID-19的额外指示生物标志物.

结论:

  • 模型解释技术有效地识别了用于COVID-19严重程度预测的关键生物标志物.
  • 生物标志物,如N终端亲脑尿素,C反应蛋白,乳酸脱酶和淋巴细胞,对于评估COVID-19预后至关重要.
  • 白血球,乙氨基和血小板也作为COVID-19的重要指标,支持在临床环境中使用可解释AI.