使用电子病历文本特征生成和评估倾向性评分模型

数据集获取（Figshare）

• 源站标题：Generating and evaluating a propensity model using textual features from electronic medical records
• DOI：10.1371/journal.pone.0212999
• 发布日期：2019-03-04
• 文件数：4 个
• 文件总体积：58.64 KB
• 源站页面：https://plos.figshare.com/articles/dataset/Generating_and_evaluating_a_propensity_model_using_textual_features_from_electronic_medical_records/7800014

命令行下载

curl -L -o "pone.0212999.s001.docx" "https://ndownloader.figshare.com/files/14517248"
curl -L -o "pone.0212999.s002.docx" "https://ndownloader.figshare.com/files/14517251"
curl -L -o "pone.0212999.s003.docx" "https://ndownloader.figshare.com/files/14517257"
curl -L -o "pone.0212999.s004.docx" "https://ndownloader.figshare.com/files/14517260"

文件清单

• pone.0212999.s001.docx（13.67 KB）
• pone.0212999.s002.docx（15.82 KB）
• pone.0212999.s003.docx（15.76 KB）
• pone.0212999.s004.docx（13.40 KB）

文件总体积 58.64 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
Propensity score (PS) methods are commonly used to control for confounding in comparative effectiveness studies. Electronic health records (EHRs) contain much unstructured data that could be used as proxies for potential confounding factors. The goal of this study was to assess whether the unstructured information can also be used to construct PS models that would allow to properly deal with confounding. We used an example of coxibs (Cox-2 inhibitors) vs. traditional NSAIDs and the risk of upper gastro-intestinal bleeding as example, since this association is often confounded due to channeling of coxibs to patients at higher risk of upper gastro-intestinal bleeding.
Methods
In a cohort study of new users of nonsteroidal anti-inflammatory drugs (NSAIDs) from the Dutch Integrated Primary Care Information (IPCI) database, we identified all patients who experienced an upper gastrointestinal bleeding (UGIB). We used a large-scale regularized regression to fit two PS models using all structured and unstructured information in the EHR. We calculated hazard ratios (HRs) to estimate the risk of UGIB among selective cyclo-oxygenase-2 (COX-2) inhibitor users compared to nonselective NSAID (nsNSAID) users.
Results
The crude hazard ratio of UGIB for COX-2 inhibitors compared to nsNSAIDs was 0.50 (95% confidence interval 0.18–1.36). Matching only on age resulted in an HR of 0.36 (0.11–1.16), and of 0.35 (0.11–1.11) when further adjusted for sex. Matching on PS only, the first model yielded an HR of 0.42 (0.13–1.38), which reduced to 0.35 (0.96–1.25) when adjusted for age and sex. The second model resulted in an HR of 0.42 (0.13–1.39), which dropped to 0.31 (0.09–1.08) after adjustment for age and sex.
Conclusions
PS models can be created using unstructured information in EHRs. An incremental benefit was observed by matching on PS over traditional matching and adjustment for covariates.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0212999（source: https://plos.figshare.com/articles/dataset/Generating_and_evaluating_a_propensity_model_using_textual_features_from_electronic_medical_records/7800014）。