Early Prediction of Type 2 Diabetes Using Multimodal data and Tabular Transformers

2026-01-19

AI Summary

This study addresses the inadequate modeling of multimodal longitudinal data in early risk prediction for type 2 diabetes mellitus (T2DM) by leveraging the Qatar Biobank cohort. It pioneers the application of the Tabular Transformer to integrate electronic health records (EHR) with dual-energy X-ray absorptiometry (DXA) measurements for T2DM prediction. By explicitly modeling temporal dependencies among long-term health indicators and employing SMOTE/SMOTE-ENN to mitigate class imbalance, the model achieves a ROC AUC of at least 79.7% across 1,382 participants, significantly outperforming conventional machine learning and state-of-the-art generative AI approaches. Key predictive features identified include visceral adipose tissue mass and lumbar spine bone mineral density, offering interpretable and personalized insights for early T2DM intervention in the Qatari population.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

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

Problem

• Type 2 Diabetes
• early prediction
• multimodal data
• tabular data
• longitudinal health records

Innovation

• Tabular Transformers
• Multimodal Data
• Early T2DM Prediction
• DXA Integration
• Longitudinal Health Records