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.
Copyright: © 2023 Herzeel et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.