自然语言处理和机器学习算法识别急性缺血性卒中脑部MRI报告

数据集获取（Figshare）

• 源站标题：Natural language processing and machine learning algorithm to identify brain MRI reports with acute ischemic stroke
• DOI：10.1371/journal.pone.0212778
• 发布日期：2019-02-28
• 文件数：4 个
• 文件总体积：1.66 MB
• 源站页面：https://plos.figshare.com/articles/dataset/Natural_language_processing_and_machine_learning_algorithm_to_identify_brain_MRI_reports_with_acute_ischemic_stroke/7788185

命令行下载

curl -L -o "pone.0212778.s001.docx" "https://ndownloader.figshare.com/files/14495408"
curl -L -o "pone.0212778.s002.csv" "https://ndownloader.figshare.com/files/14495411"
curl -L -o "pone.0212778.s003.docx" "https://ndownloader.figshare.com/files/14495414"
curl -L -o "pone.0212778.s004.docx" "https://ndownloader.figshare.com/files/14495417"

文件清单

• pone.0212778.s001.docx（210.49 KB）
• pone.0212778.s002.csv（1.10 MB）
• pone.0212778.s003.docx（323.03 KB）
• pone.0212778.s004.docx（18.31 KB）

文件总体积 1.66 MB（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

Background and purpose
This project assessed performance of natural language processing (NLP) and machine learning (ML) algorithms for classification of brain MRI radiology reports into acute ischemic stroke (AIS) and non-AIS phenotypes.
Materials and methods
All brain MRI reports from a single academic institution over a two year period were randomly divided into 2 groups for ML: training (70%) and testing (30%). Using “quanteda” NLP package, all text data were parsed into tokens to create the data frequency matrix. Ten-fold cross-validation was applied for bias correction of the training set. Labeling for AIS was performed manually, identifying clinical notes. We applied binary logistic regression, naïve Bayesian classification, single decision tree, and support vector machine for the binary classifiers, and we assessed performance of the algorithms by F1-measure. We also assessed how n-grams or term frequency-inverse document frequency weighting affected the performance of the algorithms.
Results
Of all 3,204 brain MRI documents, 432 (14.3%) were labeled as AIS. AIS documents were longer in character length than those of non-AIS (median [interquartile range]; 551 [377–681] vs. 309 [164–396]). Of all ML algorithms, single decision tree had the highest F1-measure (93.2) and accuracy (98.0%). Adding bigrams to the ML model improved F1-mesaure of naïve Bayesian classification, but not in others, and term frequency-inverse document frequency weighting to data frequency matrix did not show any additional performance improvements.
Conclusions
Supervised ML based NLP algorithms are useful for automatic classification of brain MRI reports for identification of AIS patients. Single decision tree was the best classifier to identify brain MRI reports with AIS.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0212778（source: https://plos.figshare.com/articles/dataset/Natural_language_processing_and_machine_learning_algorithm_to_identify_brain_MRI_reports_with_acute_ischemic_stroke/7788185）。