Abstract：
OBJECTIVE: Magnetic resonance (MR) images provide essential diagnostic information; however, it is also a very burdensome examination for patients. At our hospital, radiologists make imaging instructions for all MR examination orders, but this is a time-consuming task. If a natural language processing model can predict the imaging instructions, it will be possible to reduce the burden on radiologists and the instruction quality can be assured. The purpose of this study was to investigate the feasibility of using natural language processing to predict MR imaging instructions with the aim of assisting radiologists.
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.
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.
摘要：
目的：磁共振（MR）图像提供必要的诊断信息；然而，这对患者来说也是一项非常繁重的检查。在我们的医院,放射科医生为所有MR检查命令做出成像指示，但这是一项耗时的任务。如果自然语言处理模型可以预测成像指令，这将有可能减轻放射科医生的负担，并确保教学质量。这项研究的目的是研究使用自然语言处理来预测MR成像指令的可行性，以帮助放射科医生。
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。
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。
