Vital Sign Forecasting for Sepsis Patients in ICUs
Anubhav Bhatti Yuwei Liu Chen Dan Bingjie Shen San Lee Yonghwan Kim Jang Yong Kim
Sepsis and septic shock are a critical medical condition affecting millions
globally, with a substantial mortality rate. This paper uses state-of-the-art
deep learning (DL) architectures to introduce a multi-step forecasting system
to predict vital signs indicative of septic shock progression in Intensive Care
Units (ICUs). Our approach utilizes a short window of historical vital sign
data to forecast future physiological conditions. We introduce a DL-based vital
sign forecasting system that predicts up to 3 hours of future vital signs from
6 hours of past data. We further adopt the DILATE loss function to capture
better the shape and temporal dynamics of vital signs, which are critical for
clinical decision-making. We compare three DL models, N-BEATS, N-HiTS, and
Temporal Fusion Transformer (TFT), using the publicly available eICU
Collaborative Research Database (eICU-CRD), highlighting their forecasting
capabilities in a critical care setting. We evaluate the performance of our
models using mean squared error (MSE) and dynamic time warping (DTW) metrics.
Our findings show that while TFT excels in capturing overall trends, N-HiTS is
superior in retaining short-term fluctuations within a predefined range. This
paper demonstrates the potential of deep learning in transforming the
monitoring systems in ICUs, potentially leading to significant improvements in
patient care and outcomes by accurately forecasting vital signs to assist
healthcare providers in detecting early signs of physiological instability and
anticipating septic shock.