用Transformer分析临床数据，精准识别高风险患者。
TRACE: Transformer-based Risk Assessment for Clinical Evaluation
- 融合多模态临床数据的共享表征，通过自注意力机制增强特征交互。
- 在多个基准上优于传统方法，对缺失值处理能力强。
- 通过注意力权重实现结果可解释性，适合临床决策支持场景。
我们提出一种基于Transformer的临床风险评估新方法TRACE（Transformer-based Risk Assessment for Clinical Evaluation），利用自注意力机制提升特征交互与结果可解释性。该方法能处理连续、分类及多选（复选框）等多种数据类型，通过整合各模态专用嵌入获得临床数据的共享表示，并借助Transformer编码器层识别高风险个体。为评估性能，引入基于非负多层感知机（MLPs）的强基线。所提方法在多个临床风险评估基准上表现更优，且具备良好的缺失值处理能力。在可解释性方面，通过注意力权重提供直观的结果解释，进一步辅助临床决策。
原文摘要 · Abstract (English)
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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