Related Projects¶
Explore other initiatives tackling longitudinal analysis, automation, or clinical decision support. Projects are listed to help you navigate complementary tooling around Scikit-Longitudinal.
Auto-Sklong
- Open-source:
- Authors: Simon Provost & Alex Freitas
- GitHub Link: Auto-Sklong
- Original Paper Link: Auto-Sklong: A New AutoML System for Longitudinal Classification
- Description: Auto-Sklong - An automated machine learning pipeline for longitudinal data. Leveraging Scikit-Longitudinal and Auto-Sklearn with a novel Search Space tailored to the Longitudinal ML classification task.
- Note: Sklong authors are Auto-Sklong authors.
TIDAL
- Open-source:
- Authors: Alex Siu Fung Kwong et al. (See Project Contributors)
- Maintainer: Amelia Edmondson-Stait, Eileen Xu, Alex Siu Fung Kwong
- GitHub Link: TIDAL
- Original Paper Link: Software Application Profile: TIDAL—Tool to Implement Developmental Analysis of Longitudinal data
- Description: Tool to Implement Developmental Analyses of Longitudinal data (TIDAL) is an R Shiny application that guides users through growth-curve modelling workflows for trajectories of symptoms and risk factors. It emphasises accessible visualisations and interpretation for clinicians, public health officials, and other stakeholders while keeping reproducible modelling logic front-and-centre.
- Project Contributors:
- Division of Psychiatry, University of Edinburgh: Alex S. F. Kwong (Project Lead), Heather C. Whalley (Project Lead), Amelia Edmondson-Stait, Eileen Y. Xu, Liana Romaniuk, Iona Beange, Andrew M. McIntosh
- Social, Genetic and Developmental Psychiatry Centre, King’s College London: Thalia C. Eley, Ellen J. Thompson (also University of Sussex)
- Population Health Sciences, Bristol Medical School, University of Bristol: Kate Tilling, Richard M. A. Parker, Ahmed Elhakeem
- Department of Psychology, Manchester Metropolitan University: Rebecca M. Pearson
Auto-prognosis
- Open-source:
- Authors (Both papers' versions): Fergus Imrie, Bogdan Cebere, Eoin F. McKinney, Mihaela van der Schaar, Ahmed M. Alaa
- GitHub Link: Auto-prognosis
- Original Paper Link (Version 1.0): Auto-AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning
- Follow-up Paper Link (Version 2.0): AutoPrognosis 2.0: Democratizing diagnostic and prognostic modeling in healthcare with automated machine learning
- Description: AutoPrognosis automatically searches and ensembles pipelines for classification, regression, and survival analysis with plugin-based components for preprocessing, modelling, calibration, and interpretation. Version 2.0 expands clinical-prognostic tooling with uncertainty quantification, imputation via HyperImpute, and Streamlit demonstrators while keeping Python and R entry points.
- Note: Auto-prognosis is highly correlated with the StepWise Model Selection Via Deep Kernel Learning (SMS-DKL) and the Clairvoyance projects. The original Auto-Prognosis documentation suggested longitudinal support via SMS-DKL, but longitudinal workflows do not appear to be available in the codebase today.
LongiTools
- Open-source:
- Authors: Justiina Ronkainen et al. on behalf of the LongITools Project Group
- Official Website: LongiTools
- Description: LongITools is a European research consortium investigating how environmental exposures, lifestyle, and biology interact across the life course to shape cardiometabolic disease risk. The project curates a longitudinal datasets catalogue (via Molgenis Catalogue) and an exposome toolbox to make cohort harmonisation, risk-factor tracking, and early-life intervention research more reproducible.
Atlas of Longitudinal Datasets
- Open-source: (free-to-use web platform)
- Authors: Louise Arseneault et al. on behalf of the Atlas of Longitudinal Datasets team
- Official Website: Atlas of Longitudinal Datasets
- Reference: Arseneault, L. (2025). Atlas of Longitudinal Datasets. King's College London.
- Description: A free, searchable platform cataloguing thousands of longitudinal datasets worldwide. The Atlas provides rich metadata (sample, design, data types, data access, lived-experience involvement) plus map and list views, filters, and comparison tools to help researchers, funders, policymakers and lived-experience communities discover longitudinal resources—especially those relevant to mental health research.
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