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通过机器学习增强狼抗凝剂测试:深度神经网络与专家的性能匹配,而不需要特征选择

Jeffrey Wang¹, Rachel Leger², Dong Chen²

¹Biology Department, Carleton College, Northfield, MN, United States.

The journal of applied laboratory medicine

|April 16, 2025

概括

深度神经网络可以自动解释狼抗凝剂 (LAC),这是抗脂抗体综合征的关键诊断标准. 多输出DNN的性能与传统方法相当或优于传统方法,为临床实验室提供了多功能解决方案.

科学领域:

医学诊断医学诊断医学诊断
人工智能在医学中的应用
计算生物学计算生物学

背景情况:

狼抗凝剂 (LAC) 对于诊断抗脂抗体综合征至关重要.
目前的LAC测试涉及医生对多达13项测试进行复杂的解释.
自动化LAC解释是临床实验室的一个重要目标.

研究的目的:

探索使用深度神经网络 (DNN) 架构来对LAC资料进行多标签分类的可行性.
评估DNN在自动化LAC测试解释方面的潜力.
为了比较不同的DNN架构,以提高准确性和效率.

主要方法:

使用了7202个追溯病例的数据集,随机分为培训,验证和测试.
评估了两个DNN架构:具有特征选择的单输出DNN和使用所有13个输入的多输出DNN.
LAC阳性 (DRVVT,APTT) 和抗凝剂存在 (华法林,肝素) 被专家评判.

主要成果:

在所有四个预测任务中,域知识原始的多输出DNN显示了可比或改进的性能.
获得了高的F1分数:LAC-DRVVT的0.977,LAC-APTT的0.954,HEP的0.961和WAR的0.995分.
证明了DNN在没有明确选择的情况下学习特征重要性的能力.

结论:

与传统方法相比,多输出DNN为LAC解释提供了一种多功能且可能更简单的方法.
相似的性能表明DNN可以有效地在临床环境中标准化LAC诊断.
建议实施多输出DNN,以提高LAC测试的效率和一致性.

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