基于机器学习的脓毒症相关急性肾损伤预测模型数据集

数据集获取（Figshare）

• 源站标题：Predictive models of sepsis-associated acute kidney injury based on machine learning: a scoping review
• DOI：10.6084/m9.figshare.26413406.v1
• 发布日期：2024-07-31
• 文件数：3 个
• 文件总体积：1.45 MB
• 源站页面：https://tandf.figshare.com/articles/dataset/Predictive_models_of_sepsis-associated_acute_kidney_injury_based_on_machine_learning_a_scoping_review/26413406

命令行下载

curl -L -o "irnf_a_2380748_sm2800.xlsx" "https://ndownloader.figshare.com/files/48042599"
curl -L -o "irnf_a_2380748_sm2799.docx" "https://ndownloader.figshare.com/files/48042602"
curl -L -o "irnf_a_2380748_sm2798.docx" "https://ndownloader.figshare.com/files/48042605"

文件清单

• irnf_a_2380748_sm2800.xlsx（337.35 KB）
• irnf_a_2380748_sm2799.docx（328.12 KB）
• irnf_a_2380748_sm2798.docx（785.33 KB）

文件总体积 1.45 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

With the development of artificial intelligence, the application of machine learning to develop predictive models for sepsis-associated acute kidney injury has made potential breakthroughs in early identification, grading, diagnosis, and prognosis determination.
Here, we conducted a systematic search of the PubMed, Cochrane Library, Embase (Ovid), Web of Science, and Scopus databases on April 28, 2023, and screened relevant literature. Then, we comprehensively extracted relevant data related to machine learning algorithms, predictors, and predicted objectives. We subsequently performed a critical evaluation of research quality, data aggregation, and analyses.
We screened 25 studies on predictive models for sepsis-associated acute kidney injury from a total of originally identified 2898 studies. The most commonly used machine learning algorithm is traditional logistic regression, followed by eXtreme gradient boosting. We categorized these predictive models into early identification models (60%), prognostic prediction models (32%), and subtype identification models (8%) according to their predictive purpose. The five most commonly used predictors were serum creatinine levels, lactate levels, age, blood urea nitrogen concentration, and diabetes mellitus. In addition, a single data source, insufficient assessment of clinical utility, lack of model bias assessment, and hyperparameter adjustment may be the main reasons for the low quality of the current research.
However, studies on the nondeath prognostic outcomes, the long-term clinical outcomes, and the subtype identification models are insufficient. Additionally, the poor quality of the research and the insufficient practicality of the model are problems that need to be addressed urgently.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.26413406.v1（source: https://tandf.figshare.com/articles/dataset/Predictive_models_of_sepsis-associated_acute_kidney_injury_based_on_machine_learning_a_scoping_review/26413406）。