ArticlePLOS digital health2023
A software package for efficient patient trajectory analysis applied to analyzing bladder cancer development.
Article in PLOS digital health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Evaluation of trajectory analysis for disease risk assessment: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- DiNetxify-a python package for three‑dimensional disease network analysis based on electronic health record data.European journal of epidemiology · 2026Article
- Data-driven hypothesis discovery from disease trajectories in multiple sclerosis.Frontiers in immunology · 2026Article
- Longitudinal trajectories unravel the complex interplay of medication, cardiovascular events, chronic kidney disease, and mortality.Scientific reports · 2025Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present the Patient Trajectory Analysis Library (PTRA), a software package for explorative analysis of patient development. PTRA provides the tools for extracting statistically relevant trajectories from the medical event histories of a patient population. These trajectories can additionally be clustered for visual inspection and identifying key events in patient progression. The algorithms of PTRA are based on a statistical method developed previously by Jensen et al, but we contribute several modifications and extensions to enable the implementation of a practical tool. This includes a new clustering strategy, filter mechanisms for controlling analysis to specific cohorts and for controlling trajectory output, a parallel implementation that executes on a single server rather than a high-performance computing (HPC) cluster, etc. PTRA is furthermore open source and the code is organized as a framework so researchers can reuse it to analyze new data sets. We illustrate our tool by discussing trajectories extracted from the TriNetX Dataworks database for analyzing bladder cancer development. We show this experiment uncovers medically sound trajectories for bladder cancer.
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Registered trials
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