Clinical AI / Published · 2026
Sequential Pattern Transformer
From clinical history to possible futures
A generative and interpretable framework for predicting disease trajectories.
01 / Inside the method
How it works.
Select a stage to explore the workflow.
Conceptual schematic · Simplified method overview
Clinical histories are represented as ordered sequences of disease codes. Order provides context beyond the presence of individual diagnoses.
PrefixSpan extracts recurring sequential patterns to prepare the input used by the framework.
A decoder learns relationships within the sequence and predicts what could follow the observed history.
The model produces candidate next codes. These predictions describe learned associations, not a certain outcome for an individual.
02 / Context & sources
The research.
Modeling health as a sequence
The Sequential Pattern Transformer (SPT) studies sequences of clinical events to predict disease trajectories. The framework combines generative prediction with interpretable sequential patterns, connecting a patient’s recorded history to possible future diagnoses.
Publication
Mohammad Assadi Shalmani, Masoud Khani, Amirsajjad Taleban, Zihao Yi, Jennifer T. Fink, Christopher E. Weber, Qiang Lu, and Jake Luo. Sequential pattern transformer (SPT): a generative and interpretable framework for predicting disease trajectories. Neural Computing and Applications, 38, 28 (2026).