Typical Healthcare Pathways as a Basis for Admixture Modeling of Patient Trajectories
Abstract
Background: Understanding whether patients follow similar or distinct patterns of care is important for characterizing clinical practice, identifying patient subgroups, and supporting quality improvement. However, routine healthcare trajectories are difficult to compare directly because patients may differ in their diagnostic workup, treatment sequencing, timing of clinical events, and documentation practices. Despite this variation, trajectories often contain recurring patterns at the cohort level. Methods: To address this challenge, we present a framework that explicitly separates cohort-level typical pathway identification from patient-level inference. At the cohort level, we derive an interpretable representation of care processes using a rule-based algorithm to identify typical healthcare pathways, resulting in a compact pathway graph. These pathways are then modeled as Markov chains and used as structured components in an admixture model, allowing each patient to be represented as a probabilistic mixture of typical pathways rather than being assigned to a single pathway component. The resulting admixture weights provide a compact representation of patient trajectories for subgroup characterization. We further assess the stability of the identified pathways and inferred admixture representations across multiple train-test splits. Results: Across train-test splits, the framework demonstrated consistent pathway structures and patient-level mixture patterns. Applied to routine care data from prostate cancer patients undergoing radical prostatectomy, the framework identified interpretable care patterns and supported the identification of patient subgroups with similar clinical event patterns. Conclusion: Overall, the proposed framework provides an interpretable and stable approach for summarizing treatment pathways and characterizing patient subgroups in real-world practice.
Related
- Bayesian Recovery Trajectories Following Forced Sexual Experience: Healthcare Access and Social Support Among Women
- Joint Modeling of Conditional Mean and Variability in Multilevel Functional Data: Application to Postprandial Glucose Trajectories in the AEGIS Study
- Hierarchical Bayesian Joint Modeling of Athlete Performance Trajectories and Latent Peak Ages
- Nonlinear trajectories of lung function recovery in patients with pulmonary disease: empirical evaluation of longitudinal modeling approaches
- Online reduced-order modeling from primal solver trajectories for PDE-constrained optimization
BibTeX
@misc{farhadizadeh2026typical,
title = {{Typical Healthcare Pathways as a Basis for Admixture Modeling of Patient Trajectories}},
author = {Maryam Farhadizadeh and Carola S. Heinzel and August Sigle and Harald Binder and Frederik Wenz and Jan Hasenauer and Peter Pfaffelhuber and Nadine Binder},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2606.14587},
eprint = {2606.14587},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2606.14587},
}
RIS
TY - UNPB
TI - Typical Healthcare Pathways as a Basis for Admixture Modeling of Patient Trajectories
AU - Maryam Farhadizadeh
AU - Carola S. Heinzel
AU - August Sigle
AU - Harald Binder
AU - Frederik Wenz
AU - Jan Hasenauer
AU - Peter Pfaffelhuber
AU - Nadine Binder
PY - 2026
DA - 2026/10/09
PB - arXiv
DO - 10.48550/arxiv.2606.14587
UR - https://arxiv.org/abs/2606.14587
AB - Background: Understanding whether patients follow similar or distinct patterns of care is important for characterizing clinical practice, identifying patient subgroups, and supporting quality improvement. However, routine healthcare trajectories are difficult to compare directly because patients may differ in their diagnostic workup, treatment sequencing, timing of clinical events, and documentation practices. Despite this variation, trajectories often contain recurring patterns at the cohort level. Methods: To address this challenge, we present a framework that explicitly separates cohort-level typical pathway identification from patient-level inference. At the cohort level, we derive an interpretable representation of care processes using a rule-based algorithm to identify typical healthcare pathways, resulting in a compact pathway graph. These pathways are then modeled as Markov chains and used as structured components in an admixture model, allowing each patient to be represented as a probabilistic mixture of typical pathways rather than being assigned to a single pathway component. The resulting admixture weights provide a compact representation of patient trajectories for subgroup characterization. We further assess the stability of the identified pathways and inferred admixture representations across multiple train-test splits. Results: Across train-test splits, the framework demonstrated consistent pathway structures and patient-level mixture patterns. Applied to routine care data from prostate cancer patients undergoing radical prostatectomy, the framework identified interpretable care patterns and supported the identification of patient subgroups with similar clinical event patterns. Conclusion: Overall, the proposed framework provides an interpretable and stable approach for summarizing treatment pathways and characterizing patient subgroups in real-world practice.
ER -