TRACE: Transformer-based Risk Assessment for Clinical Evaluation
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 modaliti...
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Zusammenfassung: | 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. |
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DOI: | 10.48550/arxiv.2411.08701 |