识别理赔数据库中小细胞肺癌患者算法的数据表1

数据集获取（Figshare）

• 源站标题：DataSheet1_Development of an algorithm to identify small cell lung cancer patients in claims databases.pdf
• DOI：10.3389/fonc.2024.1358562.s001
• 发布日期：2024-08-15
• 文件数：1 个
• 文件总体积：76.34 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/DataSheet1_Development_of_an_algorithm_to_identify_small_cell_lung_cancer_patients_in_claims_databases_pdf/26699530

命令行下载

curl -L -o "DataSheet1_Development of an algorithm to identify small cell lung cancer patients in claims databases.pdf" "https://ndownloader.figshare.com/files/48541585"

文件清单

• DataSheet1_Development of an algorithm to identify small cell lung cancer patients in claims databases.pdf（76.34 KB）

文件总体积 76.34 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

IntroductionThe treatment landscape of small cell lung cancer (SCLC) is evolving. Evidence generated from administrative claims is needed to characterize real-world SCLC patients. However, the current ICD-10 coding system cannot distinguish SCLC from non-small cell lung cancer (NSCLC). We developed and estimated the accuracy of an algorithm to identify SCLC in claims-only databases.
MethodsWe performed a cross-sectional study of lung cancer patients diagnosed from 2016-2017 using the Surveillance, Epidemiology and End Results (SEER), linked with Medicare database. The analysis included two phases – data exploration (utilizing a 25% random sample) and data validation (remaining 75% sample). The SEER definition of SCLC and NSCLC were used as the gold standard. Claims-based algorithms were identified and evaluated for their sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).
ResultsThe eligible cohort included 31,912 lung cancer patients. The mean age was 76.3 years, 44.6% were male, with 9.4% having SCLC and 90.6% identified as NSCLC using SEER. The exploration analysis identified potential algorithms based on treatment data. In the validation analysis of 7,438 lung cancer patients who received systemic treatment in the outpatient setting, an etoposide-based algorithm (etoposide use in 180 days following lung cancer diagnosis) to identify SCLC showed: sensitivity 95%, specificity 95%, PPV 82% and NPV 99%.
DiscussionAn etoposide treatment-based algorithm showed good accuracy in identifying SCLC patients. Such algorithms can facilitate analyses of treatment patterns, outcomes, healthcare resource and costs among treated SCLC patients, thereby bolstering the evidence-base for best patient care.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fonc.2024.1358562.s001（source: https://frontiersin.figshare.com/articles/dataset/DataSheet1_Development_of_an_algorithm_to_identify_small_cell_lung_cancer_patients_in_claims_databases_pdf/26699530）。