尸检报告自动ICD-10死因分类数据集

数据集获取（Figshare）

• 源站标题：Automatic ICD-10 multi-class classification of cause of death from plaintext autopsy reports through expert-driven feature selection
• DOI：10.1371/journal.pone.0170242
• 发布日期：2017-02-06
• 文件数：2 个
• 文件总体积：372.85 KB
• 源站页面：https://plos.figshare.com/articles/dataset/Automatic_ICD-10_multi-class_classification_of_cause_of_death_from_plaintext_autopsy_reports_through_expert-driven_feature_selection/4623670

命令行下载

curl -L -o "S1 Appendix.pdf" "https://ndownloader.figshare.com/files/7528186"
curl -L -o "S2 Appendix.pdf" "https://ndownloader.figshare.com/files/7528204"

文件清单

• S1 Appendix.pdf（126.77 KB）
• S2 Appendix.pdf（246.08 KB）

文件总体积 372.85 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

Objectives
Widespread implementation of electronic databases has improved the accessibility of plaintext clinical information for supplementary use. Numerous machine learning techniques, such as supervised machine learning approaches or ontology-based approaches, have been employed to obtain useful information from plaintext clinical data. This study proposes an automatic multi-class classification system to predict accident-related causes of death from plaintext autopsy reports through expert-driven feature selection with supervised automatic text classification decision models.
Methods
Accident-related autopsy reports were obtained from one of the largest hospital in Kuala Lumpur. These reports belong to nine different accident-related causes of death. Master feature vector was prepared by extracting features from the collected autopsy reports by using unigram with lexical categorization. This master feature vector was used to detect cause of death [according to internal classification of disease version 10 (ICD-10) classification system] through five automated feature selection schemes, proposed expert-driven approach, five subset sizes of features, and five machine learning classifiers. Model performance was evaluated using precisionM, recallM, F-measureM, accuracy, and area under ROC curve. Four baselines were used to compare the results with the proposed system.
Results
Random forest and J48 decision models parameterized using expert-driven feature selection yielded the highest evaluation measure approaching (85% to 90%) for most metrics by using a feature subset size of 30. The proposed system also showed approximately 14% to 16% improvement in the overall accuracy compared with the existing techniques and four baselines.
Conclusion
The proposed system is feasible and practical to use for automatic classification of ICD-10-related cause of death from autopsy reports. The proposed system assists pathologists to accurately and rapidly determine underlying cause of death based on autopsy findings. Furthermore, the proposed expert-driven feature selection approach and the findings are generally applicable to other kinds of plaintext clinical reports.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0170242（source: https://plos.figshare.com/articles/dataset/Automatic_ICD-10_multi-class_classification_of_cause_of_death_from_plaintext_autopsy_reports_through_expert-driven_feature_selection/4623670）。