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LongitProgression: A Python Tool for Studying Factors of Disease Progression through Multivariate Longitudinal Clustering Cover

LongitProgression: A Python Tool for Studying Factors of Disease Progression through Multivariate Longitudinal Clustering

Open Access
|Nov 2025

References

  1. 1Genolini C, Pingault JB, Driss T, Côté S, Tremblay RE, Vitaro F, Arnaud C, Falissard B. Kml3d: a non-parametric algorithm for clustering joint trajectories. Computer methods and programs in biomedicine. 2013;109(1):104111. DOI: 10.1016/j.cmpb.2012.08.016
  2. 2Genolini B, Jabot F, Cédé AS, Dray S. kml3d: Clustering Longitudinal Data in 3 Dimensions. R package version 2.5.0; 2023. https://CRAN.R-project.org/package=kml3d
  3. 3Berndt DJ, Clifford J. Using dynamic time warping to find patterns in time series. In: Proceedings of the 3rd international conference on knowledge discovery and data mining, 1994. pp. 359370.
  4. 4Cuturi M, Blondel M. Soft-DTW: a Differentiable Loss Function for Time-Series; 2018. https://arxiv.org/abs/1703.01541
  5. 5Fraley C, Raftery AE, Scrucca L, Murphy TB, Fop M. mclust: Gaussian mixture modelling for model-based clustering, classification, and density estimation; 2025. DOI: 10.32614/cran.package.mclust (Accessed 22 April).
  6. 6Zhou Y. fdapace: functional data analysis and empirical dynamics; 2025. DOI: 10.32614/cran.package.fdapace. (Accessed 22 April).
  7. 7Magrini A. gbmt: group-based multivariate trajectory modeling; 2025. DOI: 10.32614/cran.package.gbmt (Accessed 22 April).
  8. 8Ribino P, Mannone M, Di Napoli C, Paragliola G, Chicco D, Gasparini F. Temporal phenotyping and prognostic stratification of patients with sepsis through longitudinal clustering. BioData Mining. 2025;18(1):127. DOI: 10.1186/s13040-025-00480-7
  9. 9Ribino P, Paragliola G, Di Napoli C, Mannone M, Chicco D, Gasparini F. Clustering of longitudinal clinical dementia rating data to identify predictors of alzheimer’s disease progression. Procedia Computer Science. 2024;251:326333. DOI: 10.1016/j.procs.2024.11.117
  10. 10Ribino P, Di Napoli C, Paragliola G, Serino L, Chicco D, Gasparini F. Longitudinal clustering on electronic mental health records reveals meaningful groups of disease trajectories. In: 19th conference on Computational Intelligence methods for Bioinformatics and Biostatistics; 2024.
  11. 11Ribino P, Mannone M, Di Napoli C, Giovanni P, Chicco D, Gasparini F. Analyzing the trajectories of clinical markers in septic patients through multivariate longitudinal clustering. In: 3rd AIxIA Workshop on Artificial Intelligence For Healthcare; 2024.
  12. 12Age-It: Ageing Well in an Ageing Society. https://ageit.eu/wp/ (Accessed: 03 October 2025).
  13. 13Johnson A, Pollard T, Mark R III. Mimic-iii clinical database demo (version 1.4). physionet; 2019.
  14. 14Johnson AE, Pollard TJ, Shen L, Lehman LwH, Feng M, Ghassemi M, Moody B, Szolovits P, Anthony Celi L, Mark RG. Mimic-iii, a freely accessible critical care database. Scientific data. 2016;3(1):19. DOI: 10.1038/sdata.2016.35
  15. 15Ribino P, Di Napoli C, Paragliola G, Chicco D, Gasparini F. Multivariate longitudinal clustering reveals neuropsychological factors as dementia predictors in an alzheimer’s disease progression study. BioData Mining. 2025;18(1):26. DOI: 10.1186/s13040-025-00441-0
  16. 16Ribino P, Paragliola G, Di Napoli C, Serino L, Chicco D, Gasparini F. Longitudinal analysis of disease progression in the elderly: An approach to mitigate the burden of frailty, functional and cognitive decline. In: Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: Scale-IT-up. INSTICC, SciTePress; 2025. pp. 10831091. DOI: 10.5220/0013396800003911
  17. 17Disher T, Gotera K, Ellis J. Identifying longitudinal treatment effect trajectories using flexible outcome modeling and clustering. Value in Health. 2023;26(6):S286. DOI: 10.1016/j.jval.2023.03.1581
  18. 18Bhagwat N, Viviano JD, Voineskos AN, Chakravarty MM, Initiative ADN, et al. Modeling and prediction of clinical symptom trajectories in Alzheimer’s disease using longitudinal data. PLoS computational biology. 2018;14(9):e1006376. DOI: 10.1371/journal.pcbi.1006376
  19. 19Salmanpour MR, Shamsaei M, Hajianfar G, Soltanian-Zadeh H, Rahmim A. Longitudinal clustering analysis and prediction of Parkinson’s disease progression using radiomics and hybrid machine learning. Quantitative Imaging in Medicine and Surgery. 2022;12(2):906. DOI: 10.21037/qims-21-425
DOI: https://doi.org/10.5334/jors.603 | Journal eISSN: 2049-9647
Language: English
Submitted on: Jul 25, 2025
Accepted on: Sep 5, 2025
Published on: Nov 19, 2025
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2025 Patrizia Ribino, Giovanni Paragliola, Claudia Di Napoli, Maria Mannone, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.