Natural language processing and machine learning algorithm to identify brain MRI reports with acute ischemic stroke
Natural language processing and machine learning algorithm to identify brain MRI reports with acute ischemic stroke
复制标题
DOI：
10.1371/journal.pone.0212778
复制
发表时间：
2019-02-28
期刊：
影响因子：
3.7
通讯作者：
Lenert, Leslie
中科院分区：
文献类型：
--
作者：
Kim, Chulho;Zhu, Vivienne;Lenert, Leslie
背景和目的本项目评估了自然语言处理（NLP）和机器学习（ML）算法的性能，用于将脑MRI放射学报告分类为急性缺血性卒中（AIS）和非AIS phenotypes.Materials和methodsAll脑MRI报告从一个单一的学术机构在两年的时间内被随机分为2组ML：培训（70%）和测试（30%）。使用“quanteda”NLP包，将所有文本数据解析为令牌，以创建数据频率矩阵。应用十重交叉验证对训练集进行偏倚校正。手动进行AIS标签，识别临床记录。我们采用二元逻辑回归，朴素贝叶斯分类，单决策树，支持向量机的二进制分类器，我们评估的性能的算法F1-措施。我们还评估了如何N-克或长期频率逆文件频率加权影响的性能的algorithm.ResultsOf所有3,204脑MRI文件，432（14.3%）被标记为AIS。AIS文件的字符长度长于非AIS文件（中位数[四分位距]; 551 [377-681] vs. 309 [164-396]）。在所有ML算法中，单个决策树具有最高的F1度量（93.2）和准确率（98.0%）。添加bigrams的ML模型改进了F1-mesaure的朴素贝叶斯分类，但不是在其他人，和长期频率逆文件频率加权数据频率matrix并没有表现出任何额外的性能improvementsConclusionsSupervised ML基于NLP算法是有用的脑MRI报告识别AIS患者的自动分类。单决策树分类器是识别AIS的最佳分类器。
Background and purposeThis 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 methodsAll 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, naive 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.ResultsOf 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 naive Bayesian classification, but not in others, and term frequency-inverse document frequency weighting to data frequency matrix did not show any additional performance improvements.ConclusionsSupervised 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.