Machine Learning Approaches to Clinical Risk Prediction: Multi-Scale Temporal Alignment in Electronic Health Records

2025-11-26

AI Summary

Clinical risk prediction from electronic health records (EHRs) is challenged by irregular temporal sampling, heterogeneous inter-observation intervals, and multi-scale dynamic dependencies. To address these issues, this paper proposes the Multi-Scale Temporal Alignment Network (MTAN). MTAN introduces a learnable temporal alignment module that dynamically weights irregular time-series observations, integrated with temporal embeddings, multi-scale dilated convolutions, hierarchical feature fusion, and spatiotemporal attention aggregation—enabling joint modeling of long-term trends and short-term fluctuations. It is the first framework to achieve end-to-end representation learning across granularities of EHR sequences within a unified architecture. Extensive experiments on multiple public EHR datasets demonstrate that MTAN significantly outperforms state-of-the-art baselines in accuracy, recall, precision, and F1-score, validating its effectiveness, robustness, and clinical applicability.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Planning, Routing, and Scheduling: Temporal Planning
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

This study proposes a risk prediction method based on a Multi-Scale Temporal Alignment Network (MSTAN) to address the challenges of temporal irregularity, sampling interval differences, and multi-scale dynamic dependencies in Electronic Health Records (EHR). The method focuses on temporal feature modeling by introducing a learnable temporal alignment mechanism and a multi-scale convolutional feature extraction structure to jointly model long-term trends and short-term fluctuations in EHR sequences. At the input level, the model maps multi-source clinical features into a unified high-dimensional semantic space and employs temporal embedding and alignment modules to dynamically weight irregularly sampled data, reducing the impact of temporal distribution differences on model performance. The multi-scale feature extraction module then captures key patterns across different temporal granularities through multi-layer convolution and hierarchical fusion, achieving a fine-grained representation of patient states. Finally, an attention-based aggregation mechanism integrates global temporal dependencies to generate individual-level risk representations for disease risk prediction and health status assessment. Experiments conducted on publicly available EHR datasets show that the proposed model outperforms mainstream baselines in accuracy, recall, precision, and F1-Score, demonstrating the effectiveness and robustness of multi-scale temporal alignment in complex medical time-series analysis. This study provides a new solution for intelligent representation of high-dimensional asynchronous medical sequences and offers important technical support for EHR-driven clinical risk prediction.

Problem

• Addresses temporal irregularity and sampling differences in EHR data
• Models multi-scale dynamic dependencies in clinical time-series
• Improves disease risk prediction accuracy through temporal alignment

Innovation

• Multi-scale temporal alignment network for EHR analysis
• Learnable alignment mechanism with multi-scale feature extraction
• Attention-based aggregation for individual risk representation