原发性醛固酮增多症亚型诊断机器学习预测模型数据表3

数据集获取（Figshare）

• 源站标题：DataSheet_3_A new machine learning-based prediction model for subtype diagnosis in primary aldosteronism.csv
• DOI：10.3389/fendo.2022.1005934.s003
• 发布日期：2022-11-23
• 文件数：1 个
• 文件总体积：7.03 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/DataSheet_3_A_new_machine_learning-based_prediction_model_for_subtype_diagnosis_in_primary_aldosteronism_csv/21606972

命令行下载

curl -L -o "DataSheet_3_A new machine learning-based prediction model for subtype diagnosis in primary aldosteronism.csv" "https://ndownloader.figshare.com/files/38298363"

文件清单

• DataSheet_3_A new machine learning-based prediction model for subtype diagnosis in primary aldosteronism.csv（7.03 KB）

文件总体积 7.03 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

IntroductionUnilateral primary aldosteronism (UPA) and bilateral primary aldosteronism (BPA) are the two subtypes of PA. Discriminating UPA from BPA is of great significance. Although adrenal venous sampling (AVS) is the gold standard for diagnosis, it has shortcomings. Thus, improved methods are needed.
MethodsThe original data were extracted from the public database “Dryad”. Ten parameters were included to develop prediction models for PA subtype diagnosis using machine learning technology. Moreover, the optimal model was chose and validated in an external dataset.
ResultsIn the modeling dataset, 165 patients (71 UPA, 94 BPA) were included, while in the external dataset, 43 consecutive patients (20 UPA, 23 BPA) were included. The ten parameters utilized in the prediction model include age, sex, systolic and diastolic blood pressure, aldosterone to renin ratio (ARR), serum potassium, ARR after 50 mg captopril challenge test (CCT), primary aldosterone concentration (PAC) after saline infusion test (SIT), PAC reduction rate after SIT, and number of types of antihypertensive agents at diagnosis. The accuracy, sensitivity, specificity, F1 score, and AUC for the optimal model using the random forest classifier were 90.0%, 81.8%, 96.4%, 0.878, and 0.938, respectively, in the testing dataset and 81.4%, 90.0%, 73.9%, 0.818 and 0.887, respectively, in the validating external dataset. The most important variables contributing to the prediction model were PAC after SIT, ARR, and ARR after CCT.
DiscussionWe developed a machine learning-based predictive model for PA subtype diagnosis based on ten clinical parameters without CT imaging. In the future, artificial intelligence-based prediction models might become a robust prediction tool for PA subtype diagnosis, thereby, might reducing at least some of the requests for CT or AVS and assisting clinical decision-making.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fendo.2022.1005934.s003（source: https://frontiersin.figshare.com/articles/dataset/DataSheet_3_A_new_machine_learning-based_prediction_model_for_subtype_diagnosis_in_primary_aldosteronism_csv/21606972）。