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A software package for efficient patient trajectory analysis applied to analyzing bladder cancer development
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 c...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10664923/ https://www.ncbi.nlm.nih.gov/pubmed/37992021 http://dx.doi.org/10.1371/journal.pdig.0000384 |
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author | Herzeel, Charlotte D’Hondt, Ellie Vandeweerd, Valerie Botermans, Wouter Akand, Murat Van der Aa, Frank Wuyts, Roel Verachtert, Wilfried |
author_facet | Herzeel, Charlotte D’Hondt, Ellie Vandeweerd, Valerie Botermans, Wouter Akand, Murat Van der Aa, Frank Wuyts, Roel Verachtert, Wilfried |
author_sort | Herzeel, Charlotte |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10664923 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-106649232023-11-22 A software package for efficient patient trajectory analysis applied to analyzing bladder cancer development Herzeel, Charlotte D’Hondt, Ellie Vandeweerd, Valerie Botermans, Wouter Akand, Murat Van der Aa, Frank Wuyts, Roel Verachtert, Wilfried PLOS Digit Health Research Article 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. Public Library of Science 2023-11-22 /pmc/articles/PMC10664923/ /pubmed/37992021 http://dx.doi.org/10.1371/journal.pdig.0000384 Text en © 2023 Herzeel et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Herzeel, Charlotte D’Hondt, Ellie Vandeweerd, Valerie Botermans, Wouter Akand, Murat Van der Aa, Frank Wuyts, Roel Verachtert, Wilfried A software package for efficient patient trajectory analysis applied to analyzing bladder cancer development |
title | A software package for efficient patient trajectory analysis applied to analyzing bladder cancer development |
title_full | A software package for efficient patient trajectory analysis applied to analyzing bladder cancer development |
title_fullStr | A software package for efficient patient trajectory analysis applied to analyzing bladder cancer development |
title_full_unstemmed | A software package for efficient patient trajectory analysis applied to analyzing bladder cancer development |
title_short | A software package for efficient patient trajectory analysis applied to analyzing bladder cancer development |
title_sort | software package for efficient patient trajectory analysis applied to analyzing bladder cancer development |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10664923/ https://www.ncbi.nlm.nih.gov/pubmed/37992021 http://dx.doi.org/10.1371/journal.pdig.0000384 |
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