通过语言模型从放射学报告中改进VTE识别：Mamba、Phi-3 Mini与BERT的比较研究
Improving VTE Identification through Language Models from Radiology Reports: A Comparative Study of Mamba, Phi-3 Mini, and BERT
AI总结：
针对现有VTE放射报告识别方法复杂、需专家人工设计规则的问题，本研究对比Mamba、Phi-3 Mini与BERT模型，发现Mamba分类器无需人工规则，在DVT和PE数据集上均达97%以上准确率与F1值，兼顾性能与效率。
摘要：
静脉血栓栓塞症（VTE）是一种严重的心血管疾病，包含深静脉血栓（DVT）和肺栓塞（PE）。准确及时地识别VTE对有效医疗至关重要。本研究基于我们此前的工作展开，此前的研究采用深度学习方法检测DVT，结合深度学习与规则分类的混合方法检测PE。我们早期的方法虽然有效，但存在两大局限：结构复杂，且需要专家参与规则集的特征工程。为克服这些挑战，我们使用基于Mamba架构的分类器。该模型取得了显著效果：在DVT数据集上准确率和F1分数均达97%，在PE数据集上准确率和F1分数均达98%。与此前用于PE识别的混合方法相比，Mamba分类器无需人工设计规则，在保持性能相当的同时大幅降低了模型复杂度。此外，我们评估了轻量级大语言模型（LLM）Phi-3 Mini在VTE检测中的表现。该模型虽能取得有竞争力的结果，性能优于基线BERT模型，但由于参数量更大，计算成本较高。我们的评估表明，基于Mamba的模型在VTE识别中展现出更优的性能与效率，为解决此前方法的局限提供了有效方案。
英文摘要：
Venous thromboembolism (VTE) is a critical cardiovascular condition, encompassing deep vein thrombosis (DVT) and pulmonary embolism (PE). Accurate and timely identification of VTE is essential for effective medical care. This study builds upon our previous work, which addressed VTE detection using deep learning methods for DVT and a hybrid approach combining deep learning and rule-based classification for PE. Our earlier approaches, while effective, had two major limitations: they were complex and required expert involvement for feature engineering of the rule set. To overcome these challenges, we utilize the Mamba architecture-based classifier. This model achieves remarkable results, with a 97\% accuracy and F1 score on the DVT dataset and a 98\% accuracy and F1 score on the PE dataset. In contrast to the previous hybrid method on PE identification, the Mamba classifier eliminates the need for hand-engineered rules, significantly reducing model complexity while maintaining comparable performance. Additionally, we evaluated a lightweight Large Language Model (LLM), Phi-3 Mini, in detecting VTE. While this model delivers competitive results, outperforming the baseline BERT models, it proves to be computationally intensive due to its larger parameter set. Our evaluation shows that the Mamba-based model demonstrates superior performance and efficiency in VTE identification, offering an effective solution to the limitations of previous approaches.
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