表1_系统性免疫炎症指数指导机器学习用于快速TTP诊断：一项回顾性队列研究.docx

数据集获取（Figshare）

• 源站标题：Table 1_Systemic immune inflammation index guides machine learning for rapid TTP diagnosis: a retrospective cohort study.docx
• DOI：10.3389/fmed.2025.1599999.s001
• 发布日期：2025-10-16
• 文件数：1 个
• 文件总体积：37.12 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Systemic_immune_inflammation_index_guides_machine_learning_for_rapid_TTP_diagnosis_a_retrospective_cohort_study_docx/30371587

命令行下载

curl -L -o "Table 1_Systemic immune inflammation index guides machine learning for rapid TTP diagnosis: a retrospective cohort study.docx" "https://ndownloader.figshare.com/files/58782982"

文件清单

• Table 1_Systemic immune inflammation index guides machine learning for rapid TTP diagnosis: a retrospective cohort study.docx（37.12 KB）

文件总体积 37.12 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

Thrombotic thrombocytopenic purpura (TTP) is a rare, life threatening thrombotic microangiopathy that requires prompt diagnosis to reduce mortality. However, its early identification is often hindered by delayed ADAMTS13 testing, particularly in low resource settings. In this study, we developed a machine learning–based model using readily available inflammatory markers, including systemic immune inflammation index (SII), platelet to lymphocyte ratio (PLR), and platelet neutrophil product (PPN), to distinguish TTP from immune thrombocytopenia (ITP). A retrospective analysis of 196 hospitalized patients was conducted, and eight machine learning models were trained and compared. Logistic regression achieved the best performance (AUC = 0.78), with SII identified as the most influential predictor. While the PLASMIC score remains a widely used tool with higher diagnostic accuracy (AUC = 0.92), our model relies only on routine blood tests and offers a fast, accessible alternative for early risk stratification. These findings suggest that composite inflammatory markers combined with machine learning can assist in the rapid triage of suspected TTP cases, especially in emergency or resource-limited environments.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fmed.2025.1599999.s001（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Systemic_immune_inflammation_index_guides_machine_learning_for_rapid_TTP_diagnosis_a_retrospective_cohort_study_docx/30371587）。