2 research outputs found
Similarity based hierarchical clustering of physiological parameters for the identification of health states - a feasibility study
This paper introduces a new unsupervised method for the clustering of
physiological data into health states based on their similarity. We propose an
iterative hierarchical clustering approach that combines health states
according to a similarity constraint to new arbitrary health states. We applied
method to experimental data in which the physical strain of subjects was
systematically varied. We derived health states based on parameters extracted
from ECG data. The occurrence of health states shows a high temporal
correlation to the experimental phases of the physical exercise. We compared
our method to other clustering algorithms and found a significantly higher
accuracy with respect to the identification of health states.Comment: 39th Annual International Conference of the IEEE Engineering in
Medicine and Biology Society (EMBC
Identifying TBI Physiological States by Clustering Multivariate Clinical Time-Series Data
Determining clinically relevant physiological states from multivariate time
series data with missing values is essential for providing appropriate
treatment for acute conditions such as Traumatic Brain Injury (TBI),
respiratory failure, and heart failure. Utilizing non-temporal clustering or
data imputation and aggregation techniques may lead to loss of valuable
information and biased analyses. In our study, we apply the SLAC-Time
algorithm, an innovative self-supervision-based approach that maintains data
integrity by avoiding imputation or aggregation, offering a more useful
representation of acute patient states. By using SLAC-Time to cluster data in a
large research dataset, we identified three distinct TBI physiological states
and their specific feature profiles. We employed various clustering evaluation
metrics and incorporated input from a clinical domain expert to validate and
interpret the identified physiological states. Further, we discovered how
specific clinical events and interventions can influence patient states and
state transitions.Comment: 10 pages, 7 figures, 2 table