  Detecting and predicting septic shock early is crucial for the best possible
outcome for patients. Accurately forecasting the vital signs of patients with
sepsis provides valuable insights to clinicians for timely interventions, such
as administering stabilizing drugs or optimizing infusion strategies. Our
research examines N-BEATS, an interpretable deep-learning forecasting model
that can forecast 3 hours of vital signs for sepsis patients in intensive care
units (ICUs). In this work, we use the N-BEATS interpretable configuration to
forecast the vital sign trends and compare them with the actual trend to
understand better the patient's changing condition and the effects of infused
drugs on their vital signs. We evaluate our approach using the publicly
available eICU Collaborative Research Database dataset and rigorously evaluate
the vital sign forecasts using out-of-sample evaluation criteria. We present
the performance of our model using error metrics, including mean squared error
(MSE), mean average percentage error (MAPE), and dynamic time warping (DTW),
where the best scores achieved are 18.52e-4, 7.60, and 17.63e-3, respectively.
We analyze the samples where the forecasted trend does not match the actual
trend and study the impact of infused drugs on changing the actual vital signs
compared to the forecasted trend. Additionally, we examined the mortality rates
of patients where the actual trend and the forecasted trend did not match. We
observed that the mortality rate was higher (92%) when the actual and
forecasted trends closely matched, compared to when they were not similar
(84%).
