通过使用主动学习和深度学习分析图像测试报告来检测和分类静脉血栓栓塞
Eunseon Jeong1, Youngje Woo1, Hong Joo Lee2
1Department of Surgery, Division of Vascular and Transplant Surgery, Seoul St. Mary's Hospital, The Catholic University of Korea College of Medicine, Seoul, Republic of Korea.
PloS one
|November 10, 2025
概括
深度学习模型在分类静脉血栓栓塞 (VTE) 报告方面显著优于传统机器学习,即使数据有限. 这一进步有助于识别未被编码的VTE病例,以改善临床工作流程.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
- 机器学习 机器学习
背景情况:
- 静脉血栓塞栓症 (VTE) 在临床报告中往往没有充分记录,阻碍了准确的分类和患者护理.
- 传统的机器学习 (ML) 模型在现实世界临床数据集中常见的小样本大小和类不平衡方面扎.
- 积极学习策略旨在提高使用最小标记数据的分类效率.
研究的目的:
- 用最小的标记数据评估VTE报告分类活动学习的效率.
- 为了比较深度学习 (DL) 模型与VTE报告分类的传统ML模型的性能.
- 评估DL模型克服小样本大小和临床数据集中类不平衡的局限性的能力.
主要方法:
- 使用了5839份成像报告的数据集,其中18.6%为VTE阳性.
- 传统的ML模型 (RF,SVM,GBM) 与主动学习策略 (随机,不确定性,单词,TF-IDF相似性) 结合起来.
- 包括LSTM,1D-CNN与GloVe在内的深度学习模型和基于BERT的模型使用F1分数进行了评估.
主要成果:
- 深度学习模型在7类VTE报告分类中获得F1分数≥0.94,显著优于ML模型 (F1:0.70-0.80).
- DL模型表现出对严重类失衡的稳定性,这是临床数据中常见的问题.
- 推断时间很快 (0.0014-0.024秒),表明实时临床部署的可行性.
结论:
- 与传统的ML相比,深度学习模型在VTE报告分类方面提供了卓越的性能,准确性和稳定性.
- 这些DL模型可以有效地解决VTE病例的记录不足问题,提高临床工作流程.
- DL模型的效率和准确性使它们成为增强VTE检测临床决策的有希望的工具.
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