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  1. Home
  3. Sequential Pattern Transformer (spt): A Generative And Interpretable Framework For Predicting Disease Trajectories.
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  3. Sequential Pattern Transformer (spt): A Generative And Interpretable Framework For Predicting Disease Trajectories.

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Sequential pattern transformer (SPT): a generative and interpretable framework for predicting disease trajectories.

Mohammad Assadi Shalmani1, Masoud Khani1, Amirsajjad Taleban1

  • 1Health Informatics Program, Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, WI, USA.

Neural Computing & Applications
|March 30, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

We developed a Sequential Pattern Transformer (SPT) to generate explainable disease trajectories from electronic health records. This AI model improves prediction accuracy and provides interpretable insights for clinical applications.

Keywords:
Clinical decision supportDiabetes mellitusDisease trajectory predictionExplainable AI (XAI)Generative AIHealthcare informaticsSequential pattern miningTransformer architecture

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Area of Science:

  • Artificial Intelligence in Medicine
  • Clinical Informatics
  • Machine Learning for Healthcare

Background:

  • Integrating AI into clinical workflows necessitates models offering explainable and actionable disease trajectories.
  • Opaque deep learning models and noisy electronic health records (EHRs) present significant limitations.
  • Existing predictive models often lack the comprehensive, interpretable outputs required for clinical decision-making.

Purpose of the Study:

  • To introduce the Sequential Pattern Transformer (SPT), a novel framework for generating explainable and actionable disease trajectories.
  • To address the limitations of current AI models in clinical settings by enhancing transparency and interpretability.
  • To develop a robust and scalable AI solution for mapping complex disease dynamics.

Main Methods:

  • Synergized sequential pattern mining (PrefixSpan) with generative transformer modeling.
  • Distilled EHR data from 258,460 type 2 diabetes patients into 95,630 validated disease progression patterns.
  • Trained a decoder-only transformer on these patterns to learn temporal disease dynamics, shifting from classification to trajectory generation.

Main Results:

  • Achieved 85.78% Top-5 accuracy, significantly outperforming a standard LSTM baseline (71.47%).
  • Developed a dynamic Disease Atlas visualizing future patient pathways using explainable AI (XAI) techniques.
  • Demonstrated the framework's domain-agnostic nature and efficient fine-tuning capabilities.

Conclusions:

  • The Sequential Pattern Transformer (SPT) provides a transparent, robust, and scalable framework for disease trajectory modeling.
  • SPT bridges the gap between high-performance AI and interpretable clinical applications.
  • The methodology is transferable to diverse clinical conditions and healthcare settings, enhancing AI integration in medicine.