纳入研究概述

数据集获取（Figshare）

• 源站标题：Overview of included studies.
• DOI：10.1371/journal.pone.0312968.t001
• 发布日期：2024-12-12
• 文件数：1 个
• 文件总体积：17.92 KB
• 源站页面：https://plos.figshare.com/articles/dataset/Overview_of_included_studies_/28019163

命令行下载

curl -L -o "Table 1.xls" "https://ndownloader.figshare.com/files/51135252"

文件清单

• Table 1.xls（17.92 KB）

文件总体积 17.92 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

Background
Surgical site infections (SSIs) lead to increased mortality and morbidity, as well as increased healthcare costs. Multiple models for the prediction of this serious surgical complication have been developed, with an increasing use of machine learning (ML) tools.
Objective
The aim of this systematic review was to assess the performance as well as the methodological quality of validated ML models for the prediction of SSIs.
Methods
A systematic search in PubMed, Embase and the Cochrane library was performed from inception until July 2023. Exclusion criteria were the absence of reported model validation, SSIs as part of a composite adverse outcome, and pediatric populations. ML performance measures were evaluated, and ML performances were compared to regression-based methods for studies that reported both methods. Risk of bias (ROB) of the studies was assessed using the Prediction model Risk of Bias Assessment Tool.
Results
Of the 4,377 studies screened, 24 were included in this review, describing 85 ML models. Most models were only internally validated (81%). The C-statistic was the most used performance measure (reported in 96% of the studies) and only two studies reported calibration metrics. A total of 116 different predictors were described, of which age, steroid use, sex, diabetes, and smoking were most frequently (100% to 75%) incorporated. Thirteen studies compared ML models to regression-based models and showed a similar performance of both modelling methods. For all included studies, the overall ROB was high or unclear.
Conclusions
A multitude of ML models for the prediction of SSIs are available, with large variability in performance. However, most models lacked external validation, performance was reported limitedly, and the risk of bias was high. In studies describing both ML models and regression-based models, one modelling method did not outperform the other.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0312968.t001（source: https://plos.figshare.com/articles/dataset/Overview_of_included_studies_/28019163）。