Abstract:

Continuous, automated, electronic patient vital
signs data are important to physicians in evaluating traumatic
brain injury (TBI) patients' physiological status and reaching
timely decisions for therapeutic interventions. However, missing
values in the medical data streams hinder applying many
standard statistical or machine learning algorithms and result
in losing some episodes of clinical importance. In this paper, we
present a novel approach to ?lling missing values in streams
of vital signs data. We construct sequences of Hankel matrices
from vital signs data streams, ?nd that these matrices exhibit
low-rank, and utilize low-rank matrix completion methods
from compressible sensing to ?ll in the missing data. We
demonstrate that our approach always substantially outperforms 
other popular fill-in methods, like k-nearest-neighbors
and expectation maximization. Further, we show that our
approach recovers thousands of simulated missing data for
intracranial pressure, a critical stream of measurements for
guiding clinical interventions and monitoring traumatic brain
injuries.

Keywords-low rank; matrix completion; Hankel matrix; vital
signs; missing values; data imputation.