Approaching adverse event detection utilizing transformers on clinical
  time-series
Helge Fredriksen Per Joel Burman Ashenafi Woldaregay Karl Øyvind Mikalsen Ståle Nymo
Patients being admitted to a hospital will most often be associated with a
certain clinical development during their stay. However, there is always a risk
of patients being subject to the wrong diagnosis or to a certain treatment not
pertaining to the desired effect, potentially leading to adverse events. Our
research aims to develop an anomaly detection system for identifying deviations
from expected clinical trajectories. To address this goal we analyzed 16 months
of vital sign recordings obtained from the Nordland Hospital Trust (NHT). We
employed an self-supervised framework based on the STraTS transformer
architecture to represent the time series data in a latent space. These
representations were then subjected to various clustering techniques to explore
potential patient phenotypes based on their clinical progress. While our
preliminary results from this ongoing research are promising, they underscore
the importance of enhancing the dataset with additional demographic information
from patients. This additional data will be crucial for a more comprehensive
evaluation of the method's performance.