健康变化揭示未诊断胰腺癌的可能性？基于医疗索赔数据的风险预测模型开发

数据集获取（Figshare）

• 源站标题：Do changes in health reveal the possibility of undiagnosed pancreatic cancer? Development of a risk-prediction model based on healthcare claims data
• DOI：10.1371/journal.pone.0218580
• 发布日期：2019-06-25
• 文件数：4 个
• 文件总体积：56.78 KB
• 源站页面：https://plos.figshare.com/articles/dataset/Do_changes_in_health_reveal_the_possibility_of_undiagnosed_pancreatic_cancer_Development_of_a_risk-prediction_model_based_on_healthcare_claims_data/8322170

命令行下载

curl -L -o "pone.0218580.s001.docx" "https://ndownloader.figshare.com/files/15595526"
curl -L -o "pone.0218580.s002.docx" "https://ndownloader.figshare.com/files/15595529"
curl -L -o "pone.0218580.s003.docx" "https://ndownloader.figshare.com/files/15595532"
curl -L -o "pone.0218580.s004.docx" "https://ndownloader.figshare.com/files/15595535"

文件清单

• pone.0218580.s001.docx（13.98 KB）
• pone.0218580.s002.docx（13.96 KB）
• pone.0218580.s003.docx（13.94 KB）
• pone.0218580.s004.docx（14.90 KB）

文件总体积 56.78 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 objective
Early detection methods for pancreatic cancer are lacking. We aimed to develop a prediction model for pancreatic cancer based on changes in health captured by healthcare claims data.
Methods
We conducted a case-control study on 29,646 Medicare-enrolled patients aged 68 years and above with pancreatic ductal adenocarcinoma (PDAC) reported to the Surveillance Epidemiology an End Results (SEER) tumor registries program in 2004–2011 and 88,938 age and sex-matched controls. We developed a prediction model using multivariable logistic regression on Medicare claims for 16 risk factors and pre-diagnostic symptoms of PDAC present within 15 months prior to PDAC diagnosis. Claims within 3 months of PDAC diagnosis were excluded in sensitivity analyses. We evaluated the discriminatory power of the model with the area under the receiver operating curve (AUC) and performed cross-validation by bootstrapping.
Results
The prediction model on all cases and controls reached AUC of 0.68. Excluding the final 3 months of claims lowered the AUC to 0.58. Among new-onset diabetes patients, the prediction model reached AUC of 0.73, which decreased to 0.63 when claims from the final 3 months were excluded. Performance measures of the prediction models was confirmed by internal validation using the bootstrap method.
Conclusion
Models based on healthcare claims for clinical risk factors, symptoms and signs of pancreatic cancer are limited in classifying those who go on to diagnosis of pancreatic cancer and those who do not, especially when excluding claims that immediately precede the diagnosis of PDAC.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0218580（source: https://plos.figshare.com/articles/dataset/Do_changes_in_health_reveal_the_possibility_of_undiagnosed_pancreatic_cancer_Development_of_a_risk-prediction_model_based_on_healthcare_claims_data/8322170）。