Table1_评估深度神经网络从自由文本电子健康记录数据中检测浅表手术部位感染的效用

数据集获取（Figshare）

• 源站标题：Table1_Assessing the utility of deep neural networks in detecting superficial surgical site infections from free text electronic health record data.docx
• DOI：10.3389/fdgth.2023.1249835.s003
• 发布日期：2024-01-08
• 文件数：1 个
• 文件总体积：19.32 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table1_Assessing_the_utility_of_deep_neural_networks_in_detecting_superficial_surgical_site_infections_from_free_text_electronic_health_record_data_docx/24958215

命令行下载

curl -L -o "Table1_Assessing the utility of deep neural networks in detecting superficial surgical site infections from free text electronic health record data.docx" "https://ndownloader.figshare.com/files/43950762"

文件清单

• Table1_Assessing the utility of deep neural networks in detecting superficial surgical site infections from free text electronic health record data.docx（19.32 KB）

文件总体积 19.32 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

BackgroundHigh-quality outcomes data is crucial for continued surgical quality improvement. Outcomes are generally captured through structured administrative data or through manual curation of unstructured electronic health record (EHR) data. The aim of this study was to apply natural language processing (NLP) to chart notes in the EHR to accurately capture postoperative superficial surgical site infections (SSSIs).
MethodsDeep Learning (DL) NLP models were trained on data from 389,865 surgical cases across all 11 hospitals in the Capital Region of Denmark. Surgical cases in the training dataset were performed between January 01st, 2017, and October 30th, 2021. We trained a forward reading and a backward reading universal language model on unlabeled postoperative chart notes recorded within 30 days of a surgical procedure. The two language models were subsequently finetuned on labeled data for the classification of SSSIs. Validation and testing were performed on surgical cases performed during the month of November 2021. We propose two different use cases: a stand-alone machine learning (SAM) pipeline and a human-in-the-loop (HITL) pipeline. Performances of both pipelines were compared to administrative data and to manual curation.
ResultsThe models were trained on 3,983,864 unlabeled chart notes and finetuned on 1,231,656 labeled notes. Models had a test area under the receiver operating characteristic curves (ROC AUC) of 0.989 on individual chart notes and 0.980 on an aggregated case level. The SAM pipeline had a sensitivity of 0.604, a specificity of 0.996, a positive predictive value (PPV) of 0.763, and a negative predictive value (NPV) of 0.991. Prior to human review, the HITL pipeline had a sensitivity of 0.854, a specificity of 0.987, a PPV of 0.603, and a NPV of 0.997.
ConclusionThe performance of the SAM pipeline was superior to administrative data, and significantly outperformed previously published results. The performance of the HITL pipeline approached that of manual curation.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fdgth.2023.1249835.s003（source: https://frontiersin.figshare.com/articles/dataset/Table1_Assessing_the_utility_of_deep_neural_networks_in_detecting_superficial_surgical_site_infections_from_free_text_electronic_health_record_data_docx/24958215）。