  Background: During the early stages of hospital admission, clinicians must
use limited information to make diagnostic and treatment decisions as patient
acuity evolves. However, it is common that the time series vital sign
information from patients to be both sparse and irregularly collected, which
poses a significant challenge for machine / deep learning techniques to analyze
and facilitate the clinicians to improve the human health outcome. To deal with
this problem, We propose a novel deep interpolation network to extract latent
representations from sparse and irregularly sampled time-series vital signs
measured within six hours of hospital admission. Methods: We created a
single-center longitudinal dataset of electronic health record data for all
(n=75,762) adult patient admissions to a tertiary care center lasting six hours
or longer, using 55% of the dataset for training, 23% for validation, and 22%
for testing. All raw time series within six hours of hospital admission were
extracted for six vital signs (systolic blood pressure, diastolic blood
pressure, heart rate, temperature, blood oxygen saturation, and respiratory
rate). A deep interpolation network is proposed to learn from such irregular
and sparse multivariate time series data to extract the fixed low-dimensional
latent patterns. We use k-means clustering algorithm to clusters the patient
admissions resulting into 7 clusters. Findings: Training, validation, and
testing cohorts had similar age (55-57 years), sex (55% female), and admission
vital signs. Seven distinct clusters were identified. M Interpretation: In a
heterogeneous cohort of hospitalized patients, a deep interpolation network
extracted representations from vital sign data measured within six hours of
hospital admission. This approach may have important implications for clinical
decision-support under time constraints and uncertainty.
