数据表1：AutoML准确预测急性大血管缺血性卒中血管内机械取栓术

数据集获取（Figshare）

• 源站标题：Data_Sheet_1_AutoML accurately predicts endovascular mechanical thrombectomy in acute large vessel ischemic stroke.PDF
• DOI：10.3389/fneur.2023.1259958.s001
• 发布日期：2023-09-28
• 文件数：1 个
• 文件总体积：833.53 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_AutoML_accurately_predicts_endovascular_mechanical_thrombectomy_in_acute_large_vessel_ischemic_stroke_PDF/24211761

命令行下载

curl -L -o "Data_Sheet_1_AutoML accurately predicts endovascular mechanical thrombectomy in acute large vessel ischemic stroke.PDF" "https://ndownloader.figshare.com/files/42495129"

文件清单

• Data_Sheet_1_AutoML accurately predicts endovascular mechanical thrombectomy in acute large vessel ischemic stroke.PDF（833.53 KB）

文件总体积 833.53 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 and objectiveAutomated machine learning or autoML has been widely deployed in various industries. However, their adoption in healthcare, especially in clinical settings is constrained due to a lack of clear understanding and explainability. The aim of this study is to utilize autoML for the prediction of functional outcomes in patients who underwent mechanical thrombectomy and compare it with traditional ML models with a focus on the explainability of the trained models.
MethodsA total of 156 patients of acute ischemic stroke with Large Vessel Occlusion (LVO) who underwent mechanical thrombectomy within 24 h of stroke onset were included in the study. A total of 34 treatment variables including clinical, demographic, imaging, and procedure-related data were extracted. Various conventional machine learning models such as decision tree classifier, logistic regression, random forest, kNN, and SVM as well as various autoML models such as AutoGluon, MLJAR, Auto-Sklearn, TPOT, and H2O were used to predict the modified Rankin score (mRS) at the time of patient discharge and 3 months follow-up. The sensitivity, specificity, accuracy, and AUC for traditional ML and autoML models were compared.
ResultsThe autoML models outperformed the traditional ML models. For the prediction of mRS at discharge, the highest testing accuracy obtained by traditional ML models for the decision tree classifier was 74.11%, whereas for autoML which was obtained through AutoGluon, it showed an accuracy of 88.23%. Similarly, for mRS at 3 months, the highest testing accuracy of traditional ML was that of the SVM classifier at 76.5%, whereas that of autoML was 85.18% obtained through MLJAR. The 24-h ASPECTS score was the most important predictor for mRS at discharge whereas for prediction of mRS at 3 months, the most important factor was mRS at discharge.
ConclusionAutomated machine learning models based on multiple treatment variables can predict the functional outcome in patients more accurately than traditional ML models. The ease of clinical coding and deployment can assist clinicians in the critical decision-making process. We have developed a demo application which can be accesse

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fneur.2023.1259958.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_AutoML_accurately_predicts_endovascular_mechanical_thrombectomy_in_acute_large_vessel_ischemic_stroke_PDF/24211761）。