# Deep Learning Approach for Clinical Risk Identification Using Transformer Modeling of Heterogeneous EHR Data

arXiv: https://arxiv.org/abs/2511.04158 · HTML: https://mlsanity.com/paper/2511.04158/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 29 references that are in the map.
- First posted on arXiv 6 Nov 2025.

## Builds on (most cited first, 12 of 12)

- [Survival Prediction Across Diverse Cancer Types Using Neural Networks](https://mlsanity.com/paper/2404.08713/) (2024)
- [Hi-BEHRT: Hierarchical Transformer-based model for accurate prediction of clinical events using multimodal longitudinal electronic health records](https://mlsanity.com/paper/2106.11360/) (2021)
- [Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Mining](https://mlsanity.com/paper/2409.14327/) (2024)
- [Self-Supervised Graph Neural Networks for Enhanced Feature Extraction in Heterogeneous Information Networks](https://mlsanity.com/paper/2410.17617/) (2024)
- [ChronoFormer: Time-Aware Transformer Architectures for Structured Clinical Event Modeling](https://mlsanity.com/paper/2504.07373/) (2025)
- [Multi-Agent Reinforcement Learning for Adaptive Resource Orchestration in Cloud-Native Clusters](https://mlsanity.com/paper/2508.10253/) (2025)
- [SCEHR: Supervised Contrastive Learning for Clinical Risk Prediction using Electronic Health Records](https://mlsanity.com/paper/2110.04943/) (2021)
- [Structure-Learnable Adapter Fine-Tuning for Parameter-Efficient Large Language Models](https://mlsanity.com/paper/2509.03057/) (2025)
- [Dynamic Prompt Fusion for Multi-Task and Cross-Domain Adaptation in LLMs](https://mlsanity.com/paper/2509.18113/) (2025)
- [Structure-Aware Temporal Modeling for Chronic Disease Progression Prediction](https://mlsanity.com/paper/2508.14942/) (2025)
- [Artificial Intelligence-Based Multiscale Temporal Modeling for Anomaly Detection in Cloud Services](https://mlsanity.com/paper/2508.14503/) (2025)
- [Deep Skin Lesion Segmentation with Transformer-CNN Fusion: Toward Intelligent Skin Cancer Analysis](https://mlsanity.com/paper/2508.14509/) (2025)

## Built on by (most cited first, 5 of 5)

- [Parameter-Efficient Fine-Tuning with Differential Privacy for Robust Instruction Adaptation in Large Language Models](https://mlsanity.com/paper/2512.06711/) (2025)
- [Deep Q-Learning-Based Intelligent Scheduling for ETL Optimization in Heterogeneous Data Environments](https://mlsanity.com/paper/2512.13060/) (2025)
- [Structure-Aware Decoding Mechanisms for Complex Entity Extraction with Large-Scale Language Models](https://mlsanity.com/paper/2512.13980/) (2025)
- [Improving Pattern Recognition of Scheduling Anomalies through Structure-Aware and Semantically-Enhanced Graphs](https://mlsanity.com/paper/2512.18673/) (2025)
- [CaliCausalRank: Calibrated Multi-Objective Ad Ranking with Robust Counterfactual Utility Optimization](https://mlsanity.com/paper/2602.18786/) (2026)

## Abstract (arXiv)

This study proposes a Transformer-based longitudinal modeling method to address challenges in clinical risk classification with heterogeneous Electronic Health Record (EHR) data, including irregular temporal patterns, large modality differences, and complex semantic structures. The method takes multi-source medical features as input and employs a feature embedding layer to achieve a unified representation of structured and unstructured data. A learnable temporal encoding mechanism is introduced to capture dynamic evolution under uneven sampling intervals. The core model adopts a multi-head self-attention structure to perform global dependency modeling on longitudinal sequences, enabling the aggregation of long-term trends and short-term fluctuations across different temporal scales. To enhance semantic representation, a semantic-weighted pooling module is designed to assign adaptive importance to key medical events, improving the discriminative ability of risk-related features. Finally, a linear mapping layer generates individual-level risk scores. Experimental results show that the proposed model outperforms traditional machine learning and temporal deep learning models in accuracy, recall, precision, and F1-Score, achieving stable and precise risk identification in multi-source heterogeneous EHR environments and providing an efficient and reliable framework for clinical intelligent decision-making.