METHODS: Considering the uniqueness of the MR imaging protocols at each facility and the particularity of the test order text, we considered that the use of large datasets and pre-training models would be unsuitable. We focused on LSTM, which has been used for natural language processing, and built a 4-layer bi-LSTM model in combination with our own morphological preprocessing to predict MR imaging instructions.
RESULTS: The proposed method achieved macro-average precision, recall, and F1-score of 70.6%, 69.5%, and 68.9%, respectively. Compared to the previous studies, the proposed method achieved satisfactory performance in the natural language analysis task for Japanese. It is considered that the proposed method improved the prediction accuracy of the minority class through direct and indirect effects of vocabulary reduction, optimization, and similarity learning.
CONCLUSIONS: It is suggested that the proposed method is effective and that the prediction of MR imaging instructions using natural language analysis in combination with the proposed method is feasible.
方法:考虑到每个设施的MR成像协议的唯一性以及测试订单文本的特殊性,我们认为使用大型数据集和预训练模型是不合适的。我们专注于LSTM,用于自然语言处理,并结合我们自己的形态学预处理构建了4层双LSTM模型来预测MR成像指令。
结果:所提出的方法实现了宏观平均精度,召回,F1得分为70.9%,65.4%,和66.6%,分别。与以前的研究相比,该方法在日语自然语言分析任务中取得了令人满意的性能。认为该方法通过词汇量减少的直接和间接效应提高了少数民族类的预测精度。优化,相似性学习
结论:建议所提出的方法是有效的,并且结合所提出的方法使用自然语言分析来预测MR成像指令是可行的。