用表格Transformer分析多模态数据，提前预测糖尿病风险
Early Prediction of Type 2 Diabetes Using Multimodal data and Tabular Transformers
- 采用表格Transformer模型处理长期健康记录与骨密度数据
- 在1382人队列中实现79.7%以上曲线下面积，优于传统模型
- 发现内脏脂肪和骨密度是关键预测指标，适合临床早筛
本研究提出一种基于表格Transformer（TabTrans）的新方法，用于早期预测2型糖尿病（T2DM）。通过分析患者的纵向健康记录与双能X线吸收检测（DXA）的表格数据，模型捕捉疾病进展中的复杂长程依赖关系。研究在包含1,382名受试者（男性725人，女性657人）的卡塔尔生物银行（QBB）回顾性队列上验证模型，其中146名男性和133名女性患有糖尿病。结合电子健康记录（EHR）与DXA数据，并采用SMOTE和SMOTE-ENN解决类别不平衡问题。模型性能在多种传统机器学习与生成式AI模型（包括Claude 3.5 Sonnet、GPT-4、Gemini Pro）中表现最优，预测准确率超过79.7%的ROC AUC。特征解释分析揭示内脏脂肪组织（VAT）质量与体积、腕部骨密度（BMD）、骨矿含量（BMC）、T和Z得分以及腰椎L1-L4评分是关键风险指标，对卡塔尔成年人群具有重要临床意义。
原文摘要 · Abstract (English)
This study introduces a novel approach for early Type 2 Diabetes Mellitus (T2DM) risk prediction using a tabular transformer (TabTrans) architecture to analyze longitudinal patient data. By processing patients` longitudinal health records and bone-related tabular data, our model captures complex, long-range dependencies in disease progression that conventional methods often overlook. We validated our TabTrans model on a retrospective Qatar BioBank (QBB) cohort of 1,382 subjects, comprising 725 men (146 diabetic, 579 healthy) and 657 women (133 diabetic, 524 healthy). The study integrated electronic health records (EHR) with dual-energy X-ray absorptiometry (DXA) data. To address class imbalance, we employed SMOTE and SMOTE-ENN resampling techniques. The proposed model`s performance is evaluated against conventional machine learning (ML) and generative AI models, including Claude 3.5 Sonnet (Anthropic`s constitutional AI), GPT-4 (OpenAI`s generative pre-trained transformer), and Gemini Pro (Google`s multimodal language model). Our TabTrans model demonstrated superior predictive performance, achieving ROC AUC $\geq$ 79.7 % for T2DM prediction compared to both generative AI models and conventional ML approaches. Feature interpretation analysis identified key risk indicators, with visceral adipose tissue (VAT) mass and volume, ward bone mineral density (BMD) and bone mineral content (BMC), T and Z-scores, and L1-L4 scores emerging as the most important predictors associated with diabetes development in Qatari adults. These findings demonstrate the significant potential of TabTrans for analyzing complex tabular healthcare data, providing a powerful tool for proactive T2DM management and personalized clinical interventions in the Qatari population. Index Terms: tabular transformers, multimodal data, DXA data, diabetes, T2DM, feature interpretation, tabular data
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