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Related Experiment Video

Updated: Jul 10, 2025

An Orthotopic Bladder Cancer Model for Gene Delivery Studies
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A software package for efficient patient trajectory analysis applied to analyzing bladder cancer development.

Charlotte Herzeel1, Ellie D'Hondt1, Valerie Vandeweerd2

  • 1ExaScience Lab, imec, Leuven, Belgium.

PLOS Digital Health
|November 22, 2023
PubMed
Summary
This summary is machine-generated.

The Patient Trajectory Analysis Library (PTRA) software extracts patient development trajectories from medical data. This open-source tool aids researchers in identifying key events and understanding disease progression, like bladder cancer.

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Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Health Data Science

Background:

  • Analyzing patient medical event histories is crucial for understanding disease progression.
  • Existing methods for trajectory analysis may lack practical implementation or scalability.
  • Identifying statistically relevant patient trajectories aids in clinical research and personalized medicine.

Purpose of the Study:

  • To introduce the Patient Trajectory Analysis Library (PTRA), an open-source software package for explorative analysis of patient development.
  • To provide tools for extracting, clustering, and visualizing statistically relevant trajectories from large-scale medical event histories.
  • To enable researchers to identify key events and cohorts in patient progression.

Main Methods:

  • Development of the Patient Trajectory Analysis Library (PTRA) based on Jensen et al.'s statistical method.
  • Implementation of novel clustering strategies, filter mechanisms for cohort selection, and trajectory output control.
  • Creation of a parallel implementation for single-server execution, avoiding high-performance computing (HPC) cluster requirements.
  • Utilizing the TriNetX Dataworks database for analysis of bladder cancer development trajectories.

Main Results:

  • PTRA successfully extracts statistically relevant patient trajectories from complex medical event histories.
  • The software facilitates the identification of medically sound trajectories, as demonstrated in the bladder cancer development analysis.
  • The clustering and filtering mechanisms allow for detailed inspection and cohort-specific analysis of patient progression.

Conclusions:

  • PTRA offers a practical, open-source, and reusable framework for analyzing patient trajectories.
  • The developed algorithms and software extensions enhance the capability to explore and understand patient development.
  • This tool has the potential to significantly advance research in various medical fields by uncovering insights into disease progression.