RA患者英夫利昔单抗治疗反应预测数据集

数据集获取（Figshare）

• 源站标题：A novel method predicting clinical response using only background clinical data in RA patients before treatment with infliximab
• DOI：10.6084/m9.figshare.3249346.v2
• 发布日期：2016-10-26
• 文件数：4 个
• 文件总体积：3.77 MB
• 源站页面：https://tandf.figshare.com/articles/dataset/A_novel_method_predicting_clinical_response_using_only_background_clinical_data_in_RA_patients_before_treatment_with_infliximab/3249346

命令行下载

curl -L -o "imor_a_1168536_sm0953.docx" "https://ndownloader.figshare.com/files/6589782"
curl -L -o "imor_a_1168536_sm0952.pdf" "https://ndownloader.figshare.com/files/6589788"
curl -L -o "imor_a_1168536_sm0950.csv" "https://ndownloader.figshare.com/files/6589794"
curl -L -o "imor_a_1168536_sm0948.csv" "https://ndownloader.figshare.com/files/6589797"

文件清单

• imor_a_1168536_sm0953.docx（2.46 MB）
• imor_a_1168536_sm0952.pdf（1.29 MB）
• imor_a_1168536_sm0950.csv（15.60 KB）
• imor_a_1168536_sm0948.csv（15.20 KB）

文件总体积 3.77 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

Objectives: The aim of the present study was to generate a novel method for predicting the clinical response to infliximab (IFX), using a machine-learning algorithm with only clinical data obtained before the treatment in rheumatoid arthritis (RA) patients.
Methods: We obtained 32 variables out of the clinical data on the patients from two independent hospitals. Next, we selected both clinical parameters and machine-learning algorithms and decided the candidates of prediction method. These candidates were verified by clinical variables on different patients from two other hospitals. Finally, we decided the prediction method to achieve the highest score.
Results: The combination of multilayer perceptron algorithm (neural network) and nine clinical parameters shows the best accuracy performance. This method could predict the good or moderate response to IFX with 92% accuracy. The sensitivity of this method was 96.7%, while the specificity was 75%.
Conclusions: We have developed a novel method for predicting the clinical response using only background clinical data in RA patients before treatment with IFX. Our method for predicting the response to IFX in RA patients may have advantages over the other previous methods in several points including easy usability, cost-effectiveness and accuracy.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.3249346.v2（source: https://tandf.figshare.com/articles/dataset/A_novel_method_predicting_clinical_response_using_only_background_clinical_data_in_RA_patients_before_treatment_with_infliximab/3249346）。