analiza aiqbench
This research addresses a critical bottleneck in medical AI by using conditional variational autoencoders to synthesize clinical vital sign patterns from healthy data, effectively overcoming the scarcity of representative datasets for rare conditions. The study demonstrates that this generative approach successfully aligns synthetic outputs with specific clinical labels, offering a scalable method to augment training data for complex signal processing tasks without relying on sensitive patient records.
Representing Clinical Conditions on Vital Signs from Healthy Individuals using Latent Modeling
arXiv:2609.15379v1 Announce Type: new 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.
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.
Accepted to the 2025 25th International Conference on Digital Signal Processing. 5 pages, 4 figures
Machine Learning (cs.LG)
arXiv:2609.15379 [cs.LG]
(or arXiv:2609.15379v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.15379 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
https://doi.org/10.1109/DSP65409.2025.11074965 Focus to learn more DOI(s) linking to related resources
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Text integral sub licența deschisă a publicației · 301 cuvinte
