溃疡性结肠炎内镜疾病活动预测模型数据表

数据集获取（Figshare）

• 源站标题：Data_Sheet_1_Predictive models for endoscopic disease activity in patients with ulcerative colitis: Practical machine learning-based modeling and interpretation.pdf
• DOI：10.3389/fmed.2022.1043412.s001
• 发布日期：2022-12-21
• 文件数：1 个
• 文件总体积：333.44 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Predictive_models_for_endoscopic_disease_activity_in_patients_with_ulcerative_colitis_Practical_machine_learning-based_modeling_and_interpretation_pdf/21761639

命令行下载

curl -L -o "Data_Sheet_1_Predictive models for endoscopic disease activity in patients with ulcerative colitis: Practical machine learning-based modeling and inte.pdf" "https://ndownloader.figshare.com/files/38617628"

文件清单

• Data_Sheet_1_Predictive models for endoscopic disease activity in patients with ulcerative colitis: Practical machine learning-based modeling and inte.pdf（333.44 KB）

文件总体积 333.44 KB（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

BackgroundEndoscopic disease activity monitoring is important for the long-term management of patients with ulcerative colitis (UC), there is currently no widely accepted non-invasive method that can effectively predict endoscopic disease activity. We aimed to develop and validate machine learning (ML) models for predicting it, which are desired to reduce the frequency of endoscopic examinations and related costs.
MethodsThe patients with a diagnosis of UC in two hospitals from January 2016 to January 2021 were enrolled in this study. Thirty nine clinical and laboratory variables were collected. All patients were divided into four groups based on MES or UCEIS scores. Logistic regression (LR) and four ML algorithms were applied to construct the prediction models. The performance of models was evaluated in terms of accuracy, sensitivity, precision, F1 score, and area under the receiver-operating characteristic curve (AUC). Then Shapley additive explanations (SHAP) was applied to determine the importance of the selected variables and interpret the ML models.
ResultsA total of 420 patients were entered into the study. Twenty four variables showed statistical differences among the groups. After synthetic minority oversampling technique (SMOTE) oversampling and RFE variables selection, the random forests (RF) model with 23 variables in MES and the extreme gradient boosting (XGBoost) model with 21 variables in USEIS, had the greatest discriminatory ability (AUC = 0.8192 in MES and 0.8006 in UCEIS in the test set). The results obtained from SHAP showed that albumin, rectal bleeding, and CRP/ALB contributed the most to the overall model. In addition, the above three variables had a more balanced contribution to each classification under the MES than the UCEIS according to the SHAP values.
ConclusionThis proof-of-concept study demonstrated that the ML model could serve as an effective non-invasive approach to predicting endoscopic disease activity for patients with UC. RF and XGBoost, which were first introduced into data-based endoscopic disease activity prediction, are suitable for the present prediction modeling.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fmed.2022.1043412.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Predictive_models_for_endoscopic_disease_activity_in_patients_with_ulcerative_colitis_Practical_machine_learning-based_modeling_and_interpretation_pdf/21761639）。