Improving Prediction of Need for Mechanical Ventilation using Cross-Attention
Journal:
arXiv
Published Date:
Jul 21, 2024
Abstract
In the intensive care unit, the capability to predict the need for mechanical
ventilation (MV) facilitates more timely interventions to improve patient
outcomes. Recent works have demonstrated good performance in this task
utilizing machine learning models. This paper explores the novel application of
a deep learning model with multi-head attention (FFNN-MHA) to make more
accurate MV predictions and reduce false positives by learning personalized
contextual information of individual patients. Utilizing the publicly available
MIMIC-IV dataset, FFNN-MHA demonstrates an improvement of 0.0379 in AUC and a
17.8\% decrease in false positives compared to baseline models such as
feed-forward neural networks. Our results highlight the potential of the
FFNN-MHA model as an effective tool for accurate prediction of the need for
mechanical ventilation in critical care settings.
