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TASC: a time-aware sequence clustering framework with uncertainty quantification for electronic health record
April Yujie Yan1,2, Thomas K M Cudjoe3, Casey Overby Taylor4,5,6
1Department of Biomedical Engineering, Johns Hopkins School of Medicine, 214 Hackerman Hall, 3101 Wyman Park Dr, Baltimore, MD, 21218, USA. yyan67@jhu.edu.
Biodata Mining
|June 5, 2026
Summary
This study introduces a new framework, Time-Aware Sequence Clustering (TASC), to analyze complex patient health data over time. TASC effectively identifies distinct patient subgroups and their unique care journeys before surgery.
Area of Science:
- Health Informatics
- Data Science
- Biomedical Research
Background:
- Longitudinal electronic health record (EHR) data present challenges in pattern discovery due to heterogeneity, sparsity, and irregularity.
- Unsupervised learning methods struggle with quantifying uncertainty in temporal data patterns.
Purpose of the Study:
- To develop and validate a Time-Aware Sequence Clustering (TASC) framework for identifying longitudinal patient trajectory patterns from EHR data.
- To integrate temporal spacing, clinical semantic similarity, and probabilistic characterization for robust clustering.
- To apply TASC to pre-surgical musculoskeletal care trajectories preceding total knee replacement (TKR).
Main Methods:
- Applied TASC to EHR and survey data from 2,052 patients undergoing primary TKR.
- Constructed temporally ordered sequences of musculoskeletal diagnoses and comorbidities.
- Utilized a weighted edit-distance algorithm incorporating clinical similarity and time gaps, followed by K-Medoids clustering and stability analysis.
- Quantified cluster membership uncertainty using probability-based subtype assignments and SoftMax transformation.
Main Results:
- TASC identified stable and interpretable trajectory clusters from heterogeneous EHR data.
- A five-cluster solution showed good stability (Adjusted Rand Index = 0.71) and distinct patient profiles.
- Clusters differed in sociodemographics, diagnoses, time to surgery, and opioid use.
- Fast progression to surgery was linked to younger patients with minimal comorbidity; complex chronic subtypes showed multimorbidity and long delays.
Conclusions:
- TASC effectively captures heterogeneous longitudinal care-utilization patterns in musculoskeletal disease.
- The framework provides uncertainty-aware clustering for longitudinal EHR data.
- Findings support TASC's applicability to other clinical domains and event representations.
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