2 research outputs found
Characterization of Hemodynamic Signal by Learning Multi-View Relationships
Multi-view data are increasingly prevalent in practice. It is often relevant
to analyze the relationships between pairs of views by multi-view component
analysis techniques such as Canonical Correlation Analysis (CCA). However, data
may easily exhibit nonlinear relations, which CCA cannot reveal. We aim to
investigate the usefulness of nonlinear multi-view relations to characterize
multi-view data in an explainable manner. To address this challenge, we propose
a method to characterize globally nonlinear multi-view relationships as a
mixture of linear relationships. A clustering method, it identifies partitions
of observations that exhibit the same relationships and learns those
relationships simultaneously. It defines cluster variables by multi-view rather
than spatial relationships, unlike almost all other clustering methods.
Furthermore, we introduce a supervised classification method that builds on our
clustering method by employing multi-view relationships as discriminative
factors. The value of these methods resides in their capability to find useful
structure in the data that single-view or current multi-view methods may
struggle to find. We demonstrate the potential utility of the proposed approach
using an application in clinical informatics to detect and characterize slow
bleeding in patients whose central venous pressure (CVP) is monitored at the
bedside. Presently, CVP is considered an insensitive measure of a subject's
intravascular volume status or its change. However, we reason that features of
CVP during inspiration and expiration should be informative in early
identification of emerging changes of patient status. We empirically show how
the proposed method can help discover and analyze multiple-to-multiple
correlations, which could be nonlinear or vary throughout the population, by
finding explainable structure of operational interest to practitioners
Detecting Patterns of Physiological Response to Hemodynamic Stress via Unsupervised Deep Learning
Monitoring physiological responses to hemodynamic stress can help in
determining appropriate treatment and ensuring good patient outcomes.
Physicians' intuition suggests that the human body has a number of
physiological response patterns to hemorrhage which escalate as blood loss
continues, however the exact etiology and phenotypes of such responses are not
well known or understood only at a coarse level. Although previous research has
shown that machine learning models can perform well in hemorrhage detection and
survival prediction, it is unclear whether machine learning could help to
identify and characterize the underlying physiological responses in raw vital
sign data. We approach this problem by first transforming the high-dimensional
vital sign time series into a tractable, lower-dimensional latent space using a
dilated, causal convolutional encoder model trained purely unsupervised.
Second, we identify informative clusters in the embeddings. By analyzing the
clusters of latent embeddings and visualizing them over time, we hypothesize
that the clusters correspond to the physiological response patterns that match
physicians' intuition. Furthermore, we attempt to evaluate the latent
embeddings using a variety of methods, such as predicting the cluster labels
using explainable features.Comment: Machine Learning for Health (ML4H) at NeurIPS 2019 - Extended
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