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.

翻译：我们提出TRACE（基于Transformer的临床评估风险预测模型），这是一种基于临床数据的新型临床风险评估方法，利用自注意力机制增强特征交互和结果可解释性。该方法能够处理连续型、分类型及多选型（复选框）等不同模态的临床数据。所提出的架构通过集成各数据模态的专用嵌入表示，形成临床数据的共享表征，并借助Transformer编码器层实现高危个体的识别。为评估该方法的有效性，我们引入了基于非负多层感知机（MLP）的强基线模型。实验表明，所提出的方法在有效处理缺失值的同时，其性能优于临床风险评估领域广泛使用的多种基线模型。在可解释性方面，基于Transformer的模型通过注意力权重提供易于解读的结果，进一步辅助临床医生的决策过程。