基于最大耗氧量的机器学习预测出院后短期死亡风险

数据集获取（Figshare）

• 源站标题：Vo2max based ML to predict risk of near term death post hospital discharge
• DOI：10.6084/m9.figshare.28459049.v1
• 发布日期：2025-02-21
• 文件数：1 个
• 文件总体积：2.14 MB
• 源站页面：https://figshare.com/articles/dataset/Vo2max_based_ML_to_predict_risk_of_near_term_death_post_hospital_discharge/28459049

命令行下载

curl -L -o "Copy_of_JOURNAL_SUBMISSION_2025_Predicting__Risk__of_Near_Term__Mortality_Post_Hosp_Discharge_with_30_and_60_Day_Outcomes_By_Race_.ipynb" "https://ndownloader.figshare.com/files/52529219"

文件清单

• Copy_of_JOURNAL_SUBMISSION_2025_Predicting__Risk__of_Near_Term__Mortality_Post_Hosp_Discharge_with_30_and_60_Day_Outcomes_By_Race_.ipynb（2.14 MB）

文件总体积 2.14 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

Background: Low levels of cardiorespiratory fitness (CRF), as measured by Vo2max, are associated with higher risks of cardiovascular disease and all-cause mortality. Despite this, CRF markers have not been incorporated into common hospital clinical morbidity and mortality risk assessment tools such the Hospital Score and LACE Index. Black patients have lower CRF levels in comparison to their white counterparts, along with higher rates of cardiovascular mortality, sudden cardiac death and excess mortality. Objective: This study’s aim was to develop a scalable machine learning based model using Vo2max to identify patients in this high-risk black patient population at elevated risk for near term mortality post discharge. Methods: We performed a retrospective analysis of electronic health record data from the MIMIC-IV dataset to evaluate model parameters of Vo2max, oxygen saturation, pulse pressure and length of stay. Results: Classification modeling was performed via PyCaret. AdaBoost (accuracy 0.7634/ROC curve AUC 0.86) demonstrated the highest performance in the black patient subset, revealing Vo2max to be of greatest importance in this population. This study supports measuring Vo2max prior to hospital discharge, particularly in the black patient population, and demonstrates the potential of this model as a clinical tool to assess near term mortality risk post discharge.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.28459049.v1（source: https://figshare.com/articles/dataset/Vo2max_based_ML_to_predict_risk_of_near_term_death_post_hospital_discharge/28459049）。