# TRACE: Transformer-based Risk Assessment for Clinical Evaluation

arXiv: https://arxiv.org/abs/2411.08701 · HTML: https://mlsanity.com/paper/2411.08701/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 29 references that are in the map.
- First posted on arXiv 13 Nov 2024.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Revisiting Deep Learning Models for Tabular Data](https://mlsanity.com/paper/2106.11959/) (2021)
- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [TabNet: Attentive Interpretable Tabular Learning](https://mlsanity.com/paper/1908.07442/) (2019)
- [TabTransformer: Tabular Data Modeling Using Contextual Embeddings](https://mlsanity.com/paper/2012.06678/) (2020)
- [Calibrating Deep Neural Networks using Focal Loss](https://mlsanity.com/paper/2002.09437/) (2020)
- [DANets: Deep Abstract Networks for Tabular Data Classification and Regression](https://mlsanity.com/paper/2112.02962/) (2021)
- [PyTorch Tabular: A Framework for Deep Learning with Tabular Data](https://mlsanity.com/paper/2104.13638/) (2021)
- [GANDALF: Gated Adaptive Network for Deep Automated Learning of Features](https://mlsanity.com/paper/2207.08548/) (2022)

## Built on by (most cited first, 1 of 1)

- [Skin Lesion Phenotyping via Nested Multi-modal Contrastive Learning](https://mlsanity.com/paper/2505.23709/) (2025)

## Abstract (arXiv)

We present TRACE (Transformer-based Risk Assessment for Clinical Evaluation), a novel method for clinical risk assessment based on clinical data, leveraging the self-attention mechanism for enhanced feature interaction and result interpretation. Our approach is able to handle different data modalities, including continuous, categorical and multiple-choice (checkbox) attributes. The proposed architecture features a shared representation of the clinical data obtained by integrating specialized embeddings of each data modality, enabling the detection of high-risk individuals using Transformer encoder layers. To assess the effectiveness of the proposed method, a strong baseline based on non-negative multi-layer perceptrons (MLPs) is introduced. The proposed method outperforms various baselines widely used in the domain of clinical risk assessment, while effectively handling missing values. In terms of explainability, our Transformer-based method offers easily interpretable results via attention weights, further enhancing the clinicians' decision-making process.