接受超大量输血（UMT）患者的特征

数据集获取（Figshare）

• 源站标题：Characteristics of patients receiving UMT.
• DOI：10.1371/journal.pone.0335151.t002
• 发布日期：2025-10-24
• 文件数：1 个
• 文件总体积：9.73 KB
• 源站页面：https://plos.figshare.com/articles/dataset/Characteristics_of_patients_receiving_UMT_/30439897

命令行下载

curl -L -o "Table 2.xls" "https://ndownloader.figshare.com/files/59029057"

文件清单

• Table 2.xls（9.73 KB）

文件总体积 9.73 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

Early triage of trauma patients requiring massive transfusion (MT) may help to marshal appropriate resources and improve treatment and outcome. Artificial intelligence (AI) and machine learning (ML) offer theoretical advantages compared to conventional prediction algorithms but have not been thoroughly evaluated in this population. We hypothesized that AI/ML techniques incorporating all available data in a patient’s medical record could achieve similar, if not higher, performance in the prediction of mortality in MT patients as compared to existing models. Patients from the American College of Surgeons Trauma Quality Improvement Project database (TQIP) were retrospectively reviewed. Those receiving ≥ 5 units of red blood cells and/or whole blood within the first four hours of arrival were defined as MT patients. Those receiving ≥10 units were identified as ultramassive transfusion (UMT) patients. ML models were created to predict 6-hour mortality using variables available at different time points, including patient arrival. Of 5,481,046 patients in TQIP from 2017 to 2021, 47,744 received MT and 20,337 of these received UMT. Using only variables available on arrival, MT AUROC was 0.901 [95% CI 0.895–0.910] which increased to 0.943 [95% CI 0.938–0.948] with addition of 4-hour variables. For UMT, arrival AUROC was 0.858 [95% CI 0.846–0.872] and increased to 0.922 [95% CI 0.914–0.931] at 4 hours. ML models reliably predict mortality in both MT and UMT patients. These are the only ML models trained on MT and UMT patients. Future work can focus on prospective implementation of these models with potential direct integration into the electronic medical record. Real-time utilization of comprehensive patient data may enhance clinical decision-making regarding which patients should continue receiving massive transfusion, thus optimizing the allocation of this limited resource.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0335151.t002（source: https://plos.figshare.com/articles/dataset/Characteristics_of_patients_receiving_UMT_/30439897）。