# ShortFuse: Biomedical Time Series Representations in the Presence of Structured Information

arXiv: https://arxiv.org/abs/1705.04790 · HTML: https://mlsanity.com/paper/1705.04790/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 May 2017.

## Built on by (most cited first, 2 of 2)

- [Tree-Based Learning on Amperometric Time Series Data Demonstrates High Accuracy for Classification](https://mlsanity.com/paper/2302.02650/) (2023)
- [Forecast-Then-Optimize Deep Learning Methods](https://mlsanity.com/paper/2506.13036/) (2025)

## Abstract (arXiv)

In healthcare applications, temporal variables that encode movement, health status and longitudinal patient evolution are often accompanied by rich structured information such as demographics, diagnostics and medical exam data. However, current methods do not jointly optimize over structured covariates and time series in the feature extraction process. We present ShortFuse, a method that boosts the accuracy of deep learning models for time series by explicitly modeling temporal interactions and dependencies with structured covariates. ShortFuse introduces hybrid convolutional and LSTM cells that incorporate the covariates via weights that are shared across the temporal domain. ShortFuse outperforms competing models by 3% on two biomedical applications, forecasting osteoarthritis-related cartilage degeneration and predicting surgical outcomes for cerebral palsy patients, matching or exceeding the accuracy of models that use features engineered by domain experts.