基于贝叶斯网络的麻风反应发生预测数据集（表1）

数据集获取（Figshare）

• 源站标题：Table_1_Prediction of the occurrence of leprosy reactions based on Bayesian networks.DOCX
• DOI：10.3389/fmed.2023.1233220.s001
• 发布日期：2023-07-26
• 文件数：1 个
• 文件总体积：24.41 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Prediction_of_the_occurrence_of_leprosy_reactions_based_on_Bayesian_networks_DOCX/23753058

命令行下载

curl -L -o "Table_1_Prediction of the occurrence of leprosy reactions based on Bayesian networks.DOCX" "https://ndownloader.figshare.com/files/41694564"

文件清单

• Table_1_Prediction of the occurrence of leprosy reactions based on Bayesian networks.DOCX（24.41 KB）

文件总体积 24.41 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

IntroductionLeprosy reactions (LR) are severe episodes of intense activation of the host inflammatory response of uncertain etiology, today the leading cause of permanent nerve damage in leprosy patients. Several genetic and non-genetic risk factors for LR have been described; however, there are limited attempts to combine this information to estimate the risk of a leprosy patient developing LR. Here we present an artificial intelligence (AI)-based system that can assess LR risk using clinical, demographic, and genetic data.
MethodsThe study includes four datasets from different regions of Brazil, totalizing 1,450 leprosy patients followed prospectively for at least 2 years to assess the occurrence of LR. Data mining using WEKA software was performed following a two-step protocol to select the variables included in the AI system, based on Bayesian Networks, and developed using the NETICA software.
ResultsAnalysis of the complete database resulted in a system able to estimate LR risk with 82.7% accuracy, 79.3% sensitivity, and 86.2% specificity. When using only databases for which host genetic information associated with LR was included, the performance increased to 87.7% accuracy, 85.7% sensitivity, and 89.4% specificity.
ConclusionWe produced an easy-to-use, online, free-access system that identifies leprosy patients at risk of developing LR. Risk assessment of LR for individual patients may detect candidates for close monitoring, with a potentially positive impact on the prevention of permanent disabilities, the quality of life of the patients, and upon leprosy control programs.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fmed.2023.1233220.s001（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Prediction_of_the_occurrence_of_leprosy_reactions_based_on_Bayesian_networks_DOCX/23753058）。