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首页> 外文期刊>BMC Medical Informatics and Decision Making >A novel deep learning approach to extract Chinese clinical entities for lung cancer screening and staging
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A novel deep learning approach to extract Chinese clinical entities for lung cancer screening and staging

机译：一种新的深入学习方法，提取中国肺癌筛查和分期的临床实体

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Computed tomography (CT) reports record a large volume of valuable information about patients’ conditions and the interpretations of radiology images from radiologists, which can be used for clinical decision-making and further academic study. However, the free-text nature of clinical reports is a critical barrier to use this data more effectively. In this study, we investigate a novel deep learning method to extract entities from Chinese CT reports for lung cancer screening and TNM staging. The proposed approach presents a new named entity recognition algorithm, namely the BERT-based-BiLSTM-Transformer network (BERT-BTN) with pre-training, to extract clinical entities for lung cancer screening and staging. Specifically, instead of traditional word embedding methods, BERT is applied to learn the deep semantic representations of characters. Following the long short-term memory layer, a Transformer layer is added to capture the global dependencies between characters. Besides, pre-training technique is employed to alleviate the problem of insufficient labeled data. We verify the effectiveness of the proposed approach on a clinical dataset containing 359 CT reports collected from the Department of Thoracic Surgery II of Peking University Cancer Hospital. The experimental results show that the proposed approach achieves an 85.96% macro-F1 score under exact match scheme, which improves the performance by 1.38%, 1.84%, 3.81%,4.29%,5.12%,5.29% and 8.84% compared to BERT-BTN, BERT-LSTM, BERT-fine-tune, BERT-Transformer, FastText-BTN, FastText-BiLSTM and FastText-Transformer, respectively. In this study, we developed a novel deep learning method, i.e., BERT-BTN with pre-training, to extract the clinical entities from Chinese CT reports. The experimental results indicate that the proposed approach can efficiently recognize various clinical entities about lung cancer screening and staging, which shows the potential for further clinical decision-making and academic research.
机译：计算机断层扫描（CT）报告记录了大量有关患者病症的有价值信息和放射科学家的放射学图像的解释，可用于临床决策和进一步的学术研究。然而，临床报告的自由文本性质是更有效地使用此数据的关键障碍。在这项研究中，我们研究了一种新的深入学习方法，以提取来自中国CT报告的肺癌筛选和TNM分期的实体。该方法提出了一种新的命名实体识别算法，即具有预训练的BERT基Bilstm变压器网络（BERT-BTN），以提取肺癌筛选和分期的临床实体。具体而言，代替传统的单词嵌入方法，伯特被应用于学习字符的深度语义表示。在长期内记忆层之后，添加变压器层以捕获字符之间的全局依赖关系。此外，采用预训练技术来缓解标记数据不足的问题。我们核实所提出的方法在含有从北京大学癌症医院胸部外科II系收集的359个CT报告的临床数据集中的有效性。实验结果表明，与BERT-相比，该方法在精确匹配方案下实现了85.96％的宏观F1分数，从而提高了1.38％，1.84％，3.81％，4.29％，5.12％，5.29％和8.84％。 BTN，BERT-LSTM，BERT-FINE-TUNE，BERT变压器，FastText-BTN，FastText-Bilstm和FastText-Transformer分别。在这项研究中，我们开发了一种新颖的深度学习方法，即具有预培训的BERT-BTN，提取来自中国CT报告的临床实体。实验结果表明，该方法可以有效地认识到各种关于肺癌筛查和分期的临床实体，这表明了进一步临床决策和学术研究的潜力。

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