# Uncertainty-Aware Deep Ensembles for Reliable and Explainable Predictions of Clinical Time Series

arXiv: https://arxiv.org/abs/2010.11310 · HTML: https://mlsanity.com/paper/2010.11310/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2020.

## Built on by (most cited first, 13 of 13)

- [Explainable AI for clinical and remote health applications: a survey on tabular and time series data](https://mlsanity.com/paper/2209.06528/) (2022)
- [The challenge of uncertainty quantification of large language models in medicine](https://mlsanity.com/paper/2504.05278/) (2025)
- [RELAX: Representation Learning Explainability](https://mlsanity.com/paper/2112.10161/) (2021)
- [Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine](https://mlsanity.com/paper/2211.09317/) (2022)
- [Uncertainty Quantification of Surrogate Explanations: an Ordinal Consensus Approach](https://mlsanity.com/paper/2111.09121/) (2021)
- [A clinically motivated self-supervised approach for content-based image retrieval of CT liver images](https://mlsanity.com/paper/2207.04812/) (2022)
- [A Reliable Framework for Human-in-the-Loop Anomaly Detection in Time Series](https://mlsanity.com/paper/2405.03234/) (2024)
- [CATNet: Cross-event Attention-based Time-aware Network for Medical Event Prediction](https://mlsanity.com/paper/2204.13847/) (2022)
- [Uncertainty Quantification for Local Model Explanations Without Model Access](https://mlsanity.com/paper/2301.05761/) (2023)
- [Deep sub-ensembles meets quantile regression: uncertainty-aware imputation for time series](https://mlsanity.com/paper/2312.01294/) (2023)
- [REPEAT: Improving Uncertainty Estimation in Representation Learning Explainability](https://mlsanity.com/paper/2412.08513/) (2024)
- [Easydiagnos: a framework for accurate feature selection for automatic diagnosis in smart healthcare](https://mlsanity.com/paper/2410.00366/) (2024)
- [Fighter: Unveiling the Graph Convolutional Nature of Transformers in Time Series Modeling](https://mlsanity.com/paper/2510.17106/) (2025)

## Abstract (arXiv)

Deep learning-based support systems have demonstrated encouraging results in numerous clinical applications involving the processing of time series data. While such systems often are very accurate, they have no inherent mechanism for explaining what influenced the predictions, which is critical for clinical tasks. However, existing explainability techniques lack an important component for trustworthy and reliable decision support, namely a notion of uncertainty. In this paper, we address this lack of uncertainty by proposing a deep ensemble approach where a collection of DNNs are trained independently. A measure of uncertainty in the relevance scores is computed by taking the standard deviation across the relevance scores produced by each model in the ensemble, which in turn is used to make the explanations more reliable. The class activation mapping method is used to assign a relevance score for each time step in the time series. Results demonstrate that the proposed ensemble is more accurate in locating relevant time steps and is more consistent across random initializations, thus making the model more trustworthy. The proposed methodology paves the way for constructing trustworthy and dependable support systems for processing clinical time series for healthcare related tasks.