# ISeeU: Visually interpretable deep learning for mortality prediction inside the ICU

arXiv: https://arxiv.org/abs/1901.08201 · HTML: https://mlsanity.com/paper/1901.08201/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2019.

## Built on by (most cited first, 7 of 7)

- [Time Series Prediction using Deep Learning Methods in Healthcare](https://mlsanity.com/paper/2108.13461/) (2021)
- [Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review](https://mlsanity.com/paper/2112.12705/) (2021)
- [Leveraging MIMIC Datasets for Better Digital Health: A Review on Open Problems, Progress Highlights, and Future Promises](https://mlsanity.com/paper/2506.12808/) (2025)
- [A New Approach for Interpretability and Reliability in Clinical Risk Prediction: Acute Coronary Syndrome Scenario](https://mlsanity.com/paper/2110.08331/) (2021)
- [Stronger Baseline Models -- A Key Requirement for Aligning Machine Learning Research with Clinical Utility](https://mlsanity.com/paper/2409.12116/) (2024)
- [Think as a Doctor: An Interpretable AI Approach for ICU Mortality Prediction](https://mlsanity.com/paper/2510.11745/) (2025)
- [TCKAN:A Novel Integrated Network Model for Predicting Mortality Risk in Sepsis Patients](https://mlsanity.com/paper/2407.06560/) (2024)

## Abstract (arXiv)

To improve the performance of Intensive Care Units (ICUs), the field of bio-statistics has developed scores which try to predict the likelihood of negative outcomes. These help evaluate the effectiveness of treatments and clinical practice, and also help to identify patients with unexpected outcomes. However, they have been shown by several studies to offer sub-optimal performance. Alternatively, Deep Learning offers state of the art capabilities in certain prediction tasks and research suggests deep neural networks are able to outperform traditional techniques. Nevertheless, a main impediment for the adoption of Deep Learning in healthcare is its reduced interpretability, for in this field it is crucial to gain insight on the why of predictions, to assure that models are actually learning relevant features instead of spurious correlations. To address this, we propose a deep multi-scale convolutional architecture trained on the Medical Information Mart for Intensive Care III (MIMIC-III) for mortality prediction, and the use of concepts from coalitional game theory to construct visual explanations aimed to show how important these inputs are deemed by the network. Our results show our model attains state of the art performance while remaining interpretable. Supporting code can be found at https://github.com/williamcaicedo/ISeeU.