基于可解释人工智能的临床决策支持系统预测横纹肌溶解症不良结局

数据集获取（Figshare）

• 源站标题：Toward an explainable AI-Based clinical decision support system for predicting adverse outcomes in Rhabdomyolysis
• DOI：10.6084/m9.figshare.31344159.v1
• 发布日期：2026-02-16
• 文件数：2 个
• 文件总体积：360.19 KB
• 源站页面：https://tandf.figshare.com/articles/dataset/Toward_an_explainable_AI-Based_clinical_decision_support_system_for_predicting_adverse_outcomes_in_Rhabdomyolysis/31344159

命令行下载

curl -L -o "imif_a_2628838_sm7755.docx" "https://ndownloader.figshare.com/files/61923378"
curl -L -o "imif_a_2628838_sm7754.docx" "https://ndownloader.figshare.com/files/61923381"

文件清单

• imif_a_2628838_sm7755.docx（344.13 KB）
• imif_a_2628838_sm7754.docx（16.05 KB）

文件总体积 360.19 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

Rhabdomyolysis is a severe condition with high morbidity and mortality, driven by complications like acute kidney injury. Early risk stratification remains challenging as traditional scores fail to capture complex data patterns. This study lays the foundation for an explainable AI (XAI)-based clinical decision support system (CDSS) by developing a machine learning model to predict the composite outcome of renal replacement therapy or 90-day mortality. Using routinely available admission data from 1031 adults, we applied multivariate imputation, Boruta feature selection, and ADASYN for class imbalance. The CatBoost model achieved the highest discrimination (AUC = 0.942, 95% CI: 0.904–0.980), accuracy (0.913), and maintained good calibration (Brier score = 0.080). Shapley additive explanations (SHAP) identified creatinine, troponin T, and albumin as key predictors, validating clinical plausibility and enabling instance-level explanations for CDSS deployment. Decision-curve analysis confirmed superior net benefit against treat-all or treat-none strategies across clinically relevant thresholds. We propose a framework for integrating this interpretable model into electronic health records to provide real-time risk scores at the point of care. Calibration drift in older adults highlights the need for age-specific refinement, underscoring the value of transparent, evaluable AI in clinical informatics.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.31344159.v1（source: https://tandf.figshare.com/articles/dataset/Toward_an_explainable_AI-Based_clinical_decision_support_system_for_predicting_adverse_outcomes_in_Rhabdomyolysis/31344159）。