Interpretable Vital Sign Forecasting with Model Agnostic Attention Maps

TL;DR

This paper presents a framework combining deep learning with attention mechanisms to improve interpretability in vital sign forecasting for sepsis prediction, maintaining accuracy while providing visual explanations.

Contribution

It introduces a model-agnostic attention-based interpretability method applicable to various deep learning models for time series forecasting in critical care.

Findings

Attention maps highlight critical time steps in forecasting.

The method preserves the accuracy of original models.

Enhanced interpretability aids clinical decision-making.

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 time steps in the forecasting process, thus improving model interpretability and supporting clinical decision-making. We show that the attention mechanism could be adapted to various black box time series forecasting models such as N-HiTS and N-BEATS. Our method preserves the accuracy of conventional deep learning models while enhancing interpretability through attention-weight-generated heatmaps. We evaluated our model on the eICU-CRD dataset, focusing on forecasting vital signs for sepsis patients. We assessed its performance using mean squared error (MSE) and dynamic time warping (DTW) metrics. We explored the attention maps of N-HiTS and N-BEATS, examining the differences in their performance and identifying crucial factors influencing vital sign forecasting.