使用医生账单索赔识别儿童感染的数据集

数据集获取（Figshare）

• 源站标题：Use of physician billing claims to identify infections in children
• DOI：10.1371/journal.pone.0207468
• 发布日期：2018-11-12
• 文件数：3 个
• 文件总体积：136.15 KB
• 源站页面：https://plos.figshare.com/articles/dataset/Use_of_physician_billing_claims_to_identify_infections_in_children/7329893

命令行下载

curl -L -o "pone.0207468.s001.doc" "https://ndownloader.figshare.com/files/13538714"
curl -L -o "pone.0207468.s002.docx" "https://ndownloader.figshare.com/files/13538717"
curl -L -o "pone.0207468.s003.docx" "https://ndownloader.figshare.com/files/13538720"

文件清单

• pone.0207468.s001.doc（91.14 KB）
• pone.0207468.s002.docx（16.49 KB）
• pone.0207468.s003.docx（28.52 KB）

文件总体积 136.15 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

While medical records have detailed information, they are limited in reach to the availability and accessibility of those records. On the other hand, administrative data while limited in scope, have a much further reach in coverage of an entire population. However, few studies have validated the use of administrative data for identifying infections in pediatric populations. Pediatric patients from Ontario, Canada aged <18 years were randomly sampled from the Electronic Medical Record Administrative data Linked Database (EMRALD). Using physician diagnoses from the electronic medical record (EMR) as the reference standard, we determined the criterion validity of physician billing claims in administrative data for identifying infectious disease syndromes from 2012 to 2014. Diagnosis codes were assessed by infection category (respiratory, skin and soft tissue, gastrointestinal, urinary tract and otitis externa) and for all infections combined. Sensitivity analyses assessed the performance if patients had more than one reason to visit the physician. We analysed 2,139 patients and found 33.3% of all visits were for an infection, and respiratory infections accounted for 67.6% of the infections. When we combined all infection categories, sensitivity was 0.74 (95% CI 0.70–0.77), specificity was 0.95 (95% CI 0.93–0.96), positive predictive value (PPV) was 0.87 (95% CI 0.84–0.90), and negative predictive value (NPV) was 0.88 (95% CI 0.86–0.89). For respiratory infections, sensitivity was 0.77 (95% CI 0.73–0.81), specificity was 0.96 (95% CI 0.95–0.97), PPV was 0.85 (95% CI 0.81–0.88), and NPV was 0.94 (95% CI 0.92–0.95). Similar performance was observed for skin and soft tissue, gastrointestinal, urinary tract, and otitis externa infections, but with lower sensitivity. Performance measures were highest when the patient visited the physician with only one health complaint. We found when using linked EMR data as the reference standard, administrative billing codes are reasonably accurate in identifying infections in a pediatric population.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0207468（source: https://plos.figshare.com/articles/dataset/Use_of_physician_billing_claims_to_identify_infections_in_children/7329893）。