# Interpretable Vital Sign Forecasting with Model Agnostic Attention Maps

arXiv: https://arxiv.org/abs/2405.01714 · HTML: https://mlsanity.com/paper/2405.01714/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 20 references that are in the map.
- First posted on arXiv 2 May 2024.

## Builds on (most cited first, 8 of 8)

- [Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting](https://mlsanity.com/paper/1912.09363/) (2019)
- [N-BEATS: Neural basis expansion analysis for interpretable time series forecasting](https://mlsanity.com/paper/1905.10437/) (2019)
- [RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism](https://mlsanity.com/paper/1608.05745/) (2016)
- [A Transformer Architecture for Stress Detection from ECG](https://mlsanity.com/paper/2108.09737/) (2021)
- [DeepSOFA: A Continuous Acuity Score for Critically Ill Patients using Clinically Interpretable Deep Learning](https://mlsanity.com/paper/1802.10238/) (2018)
- [Interpreting Forecasted Vital Signs Using N-BEATS in Sepsis Patients](https://mlsanity.com/paper/2306.14016/) (2023)
- [Vital Sign Forecasting for Sepsis Patients in ICUs](https://mlsanity.com/paper/2311.04770/) (2023)
- [Extending Machine Learning-Based Early Sepsis Detection to Different Demographics](https://mlsanity.com/paper/2311.04325/) (2023)

## Abstract (arXiv)

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.