序列自动编码器用于特征工程和预训,用于大抑郁症风险预测和风险预测
Barrett W Jones1, Warren D Taylor2,3, Colin G Walsh1,2,4
1Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States.
JAMIA open
|October 11, 2023
概括
自动编码器,特别是长期短期记忆 (LSTM) 模型,在改善主要抑郁症 (MDD) 风险预测方面表现有前途. 预训练的LSTM模型提高了预测准确度,在关键结果中表现优于基准方法.
科学领域:
- 计算精神病学是一种计算精神病学.
- 机器学习在医疗保健中的应用
- 深度学习用于风险预测和风险预测.
背景情况:
- 大型抑郁症 (MDD) 给健康带来重大负担,需要改善预后风险预测.
- 传统的风险模型通常依赖于聚合特征,在患者数据中可能缺少复杂的时间关系.
- 自动编码器为功能工程和预训练提供了一种新的方法,以捕捉这些时间动态.
研究的目的:
- 评估自动编码器作为功能工程和预训练技术,以提高MDD预后风险预测.
- 为了比较基于自编码器的策略的性能与已建立的基准,如受限制的博尔兹曼机器 (RBM) 和随机森林.
- 评估具有序列结构的自动编码器的时间预测性能,包括注意力和LSTM层.
主要方法:
- 使用了来自范德比尔特大学医学中心的MDD患者数据.
- 包含Attention和LSTM层的自动编码模型被训练来生成潜在数据表示.
- 用随机森林模型暂时评估预测性能,并通过初始化神经网络层与自编码器权重进行预测.
- 在整个患者治疗过程中,评估了精度回忆曲线下的区域 (AUPRC) 趋势.
主要成果:
- 预训练的LSTM模型表现出优于预训练的注意力模型和基准对4个结果中的3个进行了优异的预测性能,包括自伤/自杀尝试 (AUPRCs:LSTM预训练=0.012,注意力预训练=0.010,RBM=0.009,随机森林=0.005).
- 用于特征工程的自动编码器产生了多样化的结果,基准标准在自我伤害/自杀企图结果方面表现优于LSTM和Attention编码.
- 虽然预训练改善了预测,但时间特征编码没有显示附加效益,这表明在编码过程中可能会丢失信息.
结论:
- 使用自编码器权重进行预训练的LSTM模型显示出临床上有用的性能,性能优于MDD的当前最先进的预测器.
- 这些发现表明,预训练的LSTM自编码器权重是未来MDD风险建模研究的宝贵工具.
- 需要进一步研究以了解预测信息保留和自动编码器编码过程中的潜在损失背后的机制.
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