表1_迈向个性化预测：奥马珠单抗在中重度常年性过敏性鼻炎中反应持续时间的多中心机器学习模型

数据集获取（Figshare）

• 源站标题：Table 1_Toward personalized prediction: a multicenter machine learning model for omalizumab response duration in moderate-to-severe perennial allergic rhinitis.docx
• DOI：10.3389/falgy.2026.1784032.s001
• 发布日期：2026-04-10
• 文件数：1 个
• 文件总体积：1.23 MB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Toward_personalized_prediction_a_multicenter_machine_learning_model_for_omalizumab_response_duration_in_moderate-to-severe_perennial_allergic_rhinitis_docx/31979115

命令行下载

curl -L -o "Table 1_Toward personalized prediction: a multicenter machine learning model for omalizumab response duration in moderate-to-severe perennial allergic.docx" "https://ndownloader.figshare.com/files/63609960"

文件清单

• Table 1_Toward personalized prediction: a multicenter machine learning model for omalizumab response duration in moderate-to-severe perennial allergic.docx（1.23 MB）

文件总体积 1.23 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

BackgroundOmalizumab effectively improves quality of life in patients with moderate-to-severe perennial allergic rhinitis (PAR) uncontrolled by conventional medications. However, the duration of its efficacy remains unclear, and there is a lack of effective tools for individualized prediction.
ObjectiveThis study aimed to identify predictors of Omalizumab duration of efficacy in moderate-to-severe PAR patients, then develop and validate an interpretable, machine learning-based predictive model to forecast the duration of efficacy following treatment.
MethodsThis multicenter retrospective study included 561 patients with moderate-to-severe PAR treated with Omalizumab at three clinical institutions. The trial was registered at Chinese Clinical Trial Registry, ChiCTR2500112034. Patient characteristics included age, sex, serum total IgE concentration, serum specific IgE (sIgE) concentrations including Dermatophagoides pteronyssinus (D1) and Dermatophagoides farina (D2), comorbid conditions (asthma, urticaria, conjunctivitis, atopic dermatitis), and injection frequency. Univariate and multivariate Cox regression were employed to investigate independent predictors of Omalizumab efficacy, with restricted cubic splines for dose-response analysis. Five survival machine learning models (CPH, XGBoost, RSF, SSVM, CoxBoost) were constructed and compared by comprehensive metrics. The optimal model was interpreted using the SHapley Additive exPlanations (SHAP) and deployed as an online web application.
ResultsUnivariate Cox regression identified age, D1, D2, asthma and injection frequency as independent factors affecting Omalizumab efficacy duration. Multivariate analysis did not confirm D2 as significant. Dose-response analysis demonstrated enhanced protective effects beyond four injections. Among the five models, RSF demonstrated robust predictive performance. SHAP analysis identified injection frequency, age, D1, D2, and coexisting asthma as the most critical factors.
ConclusionThis study developed and validated a machine learning-based model capable of forecasting the duration of Omalizumab efficacy in moderate-to-severe PAR based on readily available clinical variables.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/falgy.2026.1784032.s001（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Toward_personalized_prediction_a_multicenter_machine_learning_model_for_omalizumab_response_duration_in_moderate-to-severe_perennial_allergic_rhinitis_docx/31979115）。