基于血浆总Tau化学发光免疫分析诊断阿尔茨海默病的机器学习多预测因子列线图数据表1

数据集获取（Figshare）

• 源站标题：Data_Sheet_1_Machine Learning Reveals a Multipredictor Nomogram for Diagnosing the Alzheimer’s Disease Based on Chemiluminescence Immunoassay for Total Tau in Plasma.pdf
• DOI：10.3389/fnagi.2022.863673.s001
• 发布日期：2022-05-13
• 文件数：1 个
• 文件总体积：20.06 MB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Machine_Learning_Reveals_a_Multipredictor_Nomogram_for_Diagnosing_the_Alzheimer_s_Disease_Based_on_Chemiluminescence_Immunoassay_for_Total_Tau_in_Plasma_pdf/19760341

命令行下载

curl -L -o "Data_Sheet_1_Machine Learning Reveals a Multipredictor Nomogram for Diagnosing the Alzheimer’s Disease Based on Chemiluminescence Immunoassay for Tota.pdf" "https://ndownloader.figshare.com/files/35108527"

文件清单

• Data_Sheet_1_Machine Learning Reveals a Multipredictor Nomogram for Diagnosing the Alzheimer’s Disease Based on Chemiluminescence Immunoassay for Tota.pdf（20.06 MB）

文件总体积 20.06 MB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

BackgroundPredicting amnestic mild cognitive impairment (aMCI) in conversion and Alzheimer’s disease (AD) remains a daunting task. Standard diagnostic procedures for AD population are reliant on neuroimaging features (positron emission tomography, PET), cerebrospinal fluid (CSF) biomarkers (Aβ1-42, T-tau, P-tau), which are expensive or require invasive sampling. The blood-based biomarkers offer the opportunity to provide an alternative approach for easy diagnosis of AD, which would be a less invasive and cost-effective screening tool than currently approved CSF or amyloid β positron emission tomography (PET) biomarkers.
MethodsWe developed and validated a sensitive and selective immunoassay for total Tau in plasma. Robust signatures were obtained based on several clinical features selected by multiple machine learning algorithms between the three participant groups. Subsequently, a well-fitted nomogram was constructed and validated, integrating clinical factors and total Tau concentration. The predictive performance was evaluated according to the receiver operating characteristic (ROC) curves and area under the curve (AUC) statistics. Decision curve analysis and calibration curves are used to evaluate the net benefit of nomograms in clinical decision-making.
ResultsUnder optimum conditions, chemiluminescence analysis (CLIA) displays a desirable dynamic range within Tau concentration from 7.80 to 250 pg/mL with readily achieved higher performances (LOD: 5.16 pg/mL). In the discovery cohort, the discrimination between the three well-defined participant groups according to Tau concentration was in consistent agreement with clinical diagnosis (AD vs. non-MCI: AUC = 0.799; aMCI vs. non-MCI: AUC = 0.691; AD vs. aMCI: AUC = 0.670). Multiple machine learning algorithms identified Age, Gender, EMPG, Tau, ALB, HCY, VB12, and/or Glu as robust signatures. A nomogram integrated total Tau concentration and clinical factors provided better predictive performance (AD vs. non-MCI: AUC = 0.960, AD vs. aMCI: AUC = 0.813 in discovery cohort; AD vs. non-MCI: AUC = 0.938, AD vs. aMCI: AUC = 0.754 in validation cohort).
ConclusionThe developed assay and a satisfactory nomogram model h

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fnagi.2022.863673.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Machine_Learning_Reveals_a_Multipredictor_Nomogram_for_Diagnosing_the_Alzheimer_s_Disease_Based_on_Chemiluminescence_Immunoassay_for_Total_Tau_in_Plasma_pdf/19760341）。