FDA生物制品有效性与安全性倡议：通过自然语言处理从医疗记录非结构化数据中检测疫苗接种（表1）

数据集获取（Figshare）

• 源站标题：Table_1_The Food and Drug Administration Biologics Effectiveness and Safety Initiative Facilitates Detection of Vaccine Administrations From Unstructured Data in Medical Records Through Natural Language Processing.docx
• DOI：10.3389/fdgth.2021.777905.s001
• 发布日期：2021-12-22
• 文件数：1 个
• 文件总体积：673.02 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_The_Food_and_Drug_Administration_Biologics_Effectiveness_and_Safety_Initiative_Facilitates_Detection_of_Vaccine_Administrations_From_Unstructured_Data_in_Medical_Records_Through_Natural_Language_Processing_docx/17357648

命令行下载

curl -L -o "Table_1_The Food and Drug Administration Biologics Effectiveness and Safety Initiative Facilitates Detection of Vaccine Administrations From Unstructu.docx" "https://ndownloader.figshare.com/files/32028506"

文件清单

• Table_1_The Food and Drug Administration Biologics Effectiveness and Safety Initiative Facilitates Detection of Vaccine Administrations From Unstructu.docx（673.02 KB）

文件总体积 673.02 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

Introduction: The Food and Drug Administration Center for Biologics Evaluation and Research conducts post-market surveillance of biologic products to ensure their safety and effectiveness. Studies have found that common vaccine exposures may be missing from structured data elements of electronic health records (EHRs), instead being captured in clinical notes. This impacts monitoring of adverse events following immunizations (AEFIs). For example, COVID-19 vaccines have been regularly administered outside of traditional medical settings. We developed a natural language processing (NLP) algorithm to mine unstructured clinical notes for vaccinations not captured in structured EHR data.
Methods: A random sample of 1,000 influenza vaccine administrations, representing 995 unique patients, was extracted from a large U.S. EHR database. NLP techniques were used to detect administrations from the clinical notes in the training dataset [80% (N = 797) of patients]. The algorithm was applied to the validation dataset [20% (N = 198) of patients] to assess performance. Full medical charts for 28 randomly selected administration events in the validation dataset were reviewed by clinicians. The NLP algorithm was then applied across the entire dataset (N = 995) to quantify the number of additional events identified.
Results: A total of 3,199 administrations were identified in the structured data and clinical notes combined. Of these, 2,740 (85.7%) were identified in the structured data, while the NLP algorithm identified 1,183 (37.0%) administrations in clinical notes; 459 were not also captured in the structured data. This represents a 16.8% increase in the identification of vaccine administrations compared to using structured data alone. The validation of 28 vaccine administrations confirmed 27 (96.4%) as “definite” vaccine administrations; 18 (64.3%) had evidence of a vaccination event in the structured data, while 10 (35.7%) were found solely in the unstructured notes.
Discussion: We demonstrated the utility of an NLP algorithm to identify vaccine administrations not captured in structured EHR data. NLP techniques have the potential to improve detection of vaccine administrat

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fdgth.2021.777905.s001（source: https://frontiersin.figshare.com/articles/dataset/Table_1_The_Food_and_Drug_Administration_Biologics_Effectiveness_and_Safety_Initiative_Facilitates_Detection_of_Vaccine_Administrations_From_Unstructured_Data_in_Medical_Records_Through_Natural_Language_Processing_docx/17357648）。