Interpretable Vital Sign Forecasting with Model Agnostic Attention Maps

arXiv:2405.01714v1 Announce Type: new
Abstract: Sepsis is a leading cause of mortality in intensive care units (ICUs), representing a substantial medical challenge. The complexity of analyzing diverse vital signs to predict sepsis further aggravates this issue. While deep learning techniques have been advanced for early sepsis prediction, their 'black-box' nature obscures the internal logic, impairing interpretability in critical settings like ICUs. This paper introduces a framework that combines a deep learning model with an attention mechanism that highlights the critical …

abstract advanced arxiv attention box challenge complexity cs.ai cs.lg deep learning deep learning techniques diverse forecasting issue maps medical mortality nature prediction sepsis type units vital while