HIV感染者长期住院持续时间和风险预测的机器学习模型数据集

数据集获取（Figshare）

• 源站标题：Table_2_Supervised machine learning algorithms to predict the duration and risk of long-term hospitalization in HIV-infected individuals: a retrospective study.docx
• DOI：10.3389/fpubh.2023.1282324.s004
• 发布日期：2024-01-05
• 文件数：1 个
• 文件总体积：27.89 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_2_Supervised_machine_learning_algorithms_to_predict_the_duration_and_risk_of_long-term_hospitalization_in_HIV-infected_individuals_a_retrospective_study_docx/24946896

命令行下载

curl -L -o "Table_2_Supervised machine learning algorithms to predict the duration and risk of long-term hospitalization in HIV-infected individuals: a retrospect.docx" "https://ndownloader.figshare.com/files/43919040"

文件清单

• Table_2_Supervised machine learning algorithms to predict the duration and risk of long-term hospitalization in HIV-infected individuals: a retrospect.docx（27.89 KB）

文件总体积 27.89 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

ObjectiveThe study aimed to use supervised machine learning models to predict the length and risk of prolonged hospitalization in PLWHs to help physicians timely clinical intervention and avoid waste of health resources.
MethodsRegression models were established based on RF, KNN, SVM, and XGB to predict the length of hospital stay using RMSE, MAE, MAPE, and R2, while classification models were established based on RF, KNN, SVM, NN, and XGB to predict risk of prolonged hospital stay using accuracy, PPV, NPV, specificity, sensitivity, and kappa, and visualization evaluation based on AUROC, AUPRC, calibration curves and decision curves of all models were used for internally validation.
ResultsIn regression models, XGB model performed best in the internal validation (RMSE = 16.81, MAE = 10.39, MAPE = 0.98, R2 = 0.47) to predict the length of hospital stay, while in classification models, NN model presented good fitting and stable features and performed best in testing sets, with excellent accuracy (0.7623), PPV (0.7853), NPV (0.7092), sensitivity (0.8754), specificity (0.5882), and kappa (0.4672), and further visualization evaluation indicated that the largest AUROC (0.9779), AUPRC (0.773) and well-performed calibration curve and decision curve in the internal validation.
ConclusionThis study showed that XGB model was effective in predicting the length of hospital stay, while NN model was effective in predicting the risk of prolonged hospitalization in PLWH. Based on predictive models, an intelligent medical prediction system may be developed to effectively predict the length of stay and risk of HIV patients according to their medical records, which helped reduce the waste of healthcare resources.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fpubh.2023.1282324.s004（source: https://frontiersin.figshare.com/articles/dataset/Table_2_Supervised_machine_learning_algorithms_to_predict_the_duration_and_risk_of_long-term_hospitalization_in_HIV-infected_individuals_a_retrospective_study_docx/24946896）。