TRACE: Transformer-based Risk Assessment for Clinical Evaluation

2024-11-13

arXiv.org

AI Summary

Clinical risk assessment faces challenges in integrating heterogeneous data (continuous, categorical, and multi-label), handling pervasive missing values, and ensuring model interpretability. To address these, we propose the first unified embedding-and-Transformer joint modeling framework specifically designed for heterogeneous clinical data. Our method introduces modality-specific embedding layers to achieve homogeneous representation across data types, incorporates a robust missing-value imputation mechanism, and leverages self-attention weights to generate clinically interpretable risk attributions. Evaluated on multiple real-world clinical risk prediction tasks, our approach significantly outperforms baseline models—including non-negative MLP—in predictive accuracy. Crucially, it provides clinicians with transparent, human-understandable decision rationales, thereby achieving both high performance and high trustworthiness.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Computer Vision: Multi-modal Vision

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• Develops TRACE for clinical risk assessment using Transformers
• Handles diverse clinical data modalities effectively
• Improves interpretability via attention weights for decision-making

Innovation

• Transformer-based clinical risk assessment method
• Handles multiple data modalities effectively
• Provides interpretable results via attention weights