预测退伍军人丙型肝炎病毒疾病进展的机器学习模型数据集

数据集获取（Figshare）

• 源站标题：Machine learning models to predict disease progression among veterans with hepatitis C virus
• DOI：10.1371/journal.pone.0208141
• 发布日期：2019-01-04
• 文件数：2 个
• 文件总体积：78.99 KB
• 源站页面：https://plos.figshare.com/articles/dataset/Machine_learning_models_to_predict_disease_progression_among_veterans_with_hepatitis_C_virus/7548302

命令行下载

curl -L -o "pone.0208141.s001.docx" "https://ndownloader.figshare.com/files/14028686"
curl -L -o "pone.0208141.s002.docx" "https://ndownloader.figshare.com/files/14028689"

文件清单

• pone.0208141.s001.docx（39.50 KB）
• pone.0208141.s002.docx（39.50 KB）

文件总体积 78.99 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
Machine learning (ML) algorithms provide effective ways to build prediction models using longitudinal information given their capacity to incorporate numerous predictor variables without compromising the accuracy of the risk prediction. Clinical risk prediction models in chronic hepatitis C virus (CHC) can be challenging due to non-linear nature of disease progression. We developed and compared two ML algorithms to predict cirrhosis development in a large CHC-infected cohort using longitudinal data.
Methods and findings
We used national Veterans Health Administration (VHA) data to identify CHC patients in care between 2000–2016. The primary outcome was cirrhosis development ascertained by two consecutive aspartate aminotransferase (AST)-to-platelet ratio indexes (APRIs) > 2 after time zero given the infrequency of liver biopsy in clinical practice and that APRI is a validated non-invasive biomarker of fibrosis in CHC. We excluded those with initial APRI > 2 or pre-existing diagnosis of cirrhosis, hepatocellular carcinoma or hepatic decompensation. Enrollment was defined as the date of the first APRI. Time zero was defined as 2 years after enrollment. Cross-sectional (CS) models used predictors at or closest before time zero as a comparison. Longitudinal models used CS predictors plus longitudinal summary variables (maximum, minimum, maximum of slope, minimum of slope and total variation) between enrollment and time zero. Covariates included demographics, labs, and body mass index. Model performance was evaluated using concordance and area under the receiver operating curve (AuROC). A total of 72,683 individuals with CHC were analyzed with the cohort having a mean age of 52.8, 96.8% male and 53% white. There are 11,616 individuals (16%) who met the primary outcome over a mean follow-up of 7 years. We found superior predictive performance for the longitudinal Cox model compared to the CS Cox model (concordance 0.764 vs 0.746), and for the longitudinal boosted-survival-tree model compared to the linear Cox model (concordance 0.774 vs 0.764). The accuracy of the longitudinal models at 1,3,5 years after time zero also showed superior performance compared t

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0208141（source: https://plos.figshare.com/articles/dataset/Machine_learning_models_to_predict_disease_progression_among_veterans_with_hepatitis_C_virus/7548302）。