Representing Clinical Conditions on Vital Signs from Healthy Individuals using Latent Modeling

2026-09-14

AI Summary

本文提出一种基于条件变分自编码器的深度生成模型，用于模拟健康个体生命体征在特定临床状况下的变化，以解决医疗领域代表性数据集不足的问题。

Abstract

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.

Problem

• machine learning
• representative datasets
• vital signs
• clinical condition
• healthy individuals

Innovation

• conditional variational autoencoders
• vital signs
• clinical conditions
• data augmentation
• distance metric