使用潜在建模表示健康个体生命体征中的临床状况
Representing Clinical Conditions on Vital Signs from Healthy Individuals using Latent Modeling
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中文总结 AI 辅助
本文提出基于条件变分自编码器的深度生成模型，利用ICU数据训练，以重塑健康个体生命体征模拟临床状况，并通过距离度量验证其优于基线。
中文摘要 AI 辅助
机器学习在医疗保健等场景中，对于帮助扩展复杂的信号处理应用至关重要。然而，这些机器学习模型需要丰富的数据集进行训练，且常常存在无法访问代表性数据集的情况。在本文中，我们提出了一种基于条件变分自编码器的深度生成模型，其目标是增强健康个体的生命体征，使其模拟特定临床状况的模式。更具体地说，我们使用公开的ICU（重症监护室）数据集来训练我们的模型，然后使用我们从健康个体收集的生命数据进行评估。我们的结果表明，所提出的模型不仅能学习ICU数据的潜在动态，更重要的是，能将我们从健康个体收集的数据重塑为与特定临床状况的生命体征相一致的模式。我们提出了一种距离度量，表明与测试的基线相比，我们的模型能够生成与预期临床标签更一致的样本。
英文摘要
Machine learning can be crucial to help scale complex signal processing applications in scenarios such as healthcare. However, these machine learning models need rich datasets to be trained and there are often cases where it is not possible to access representative datasets. In this paper, we propose a deep generative model based on conditional variational autoencoders with the objective of augmenting the vital signs of healthy individuals in a way that mimics the patterns of a certain clinical condition. More specifically, we use a publicly available ICU (Intensive Care Unit) dataset to train our model and then evaluate it using the vital data that we have collected from healthy individuals. Our results demonstrate that the proposed model can not only learn the underlying dynamics of the ICU data but, more importantly, can reshape our collected data from healthy individuals in a way that is aligned with the vital signs of a certain clinical condition. We propose a distance metric that shows how our model can generate samples that are more aligned with the intended clinical labels when compared to the tested baselines.
发表机构
- Institute for Digital Technologies Loughborough University London（拉夫堡大学伦敦数字技术研究所）
机构由 AI 辅助整理，请以论文原文为准。
