Incorporating longitudinal variability in prediction models: A comparison of machine learning and logistic regression in a cohort study with long follow-up Article Swipe

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Lichelle Groot , Jos W. R. Twisk , Almar A. L. Kok , Martijn W. Heymans ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.1016/j.annepidem.2025.07.060

Machine Learning methods did not outperform logistic regression. Nonetheless, incorporating variability in longitudinal predictors enhances prediction, especially with expected changes in predictors, e.g., ageing populations.

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Medicine Logistic regression Regression Cohort Longitudinal study Cohort study Regression analysis Machine learning Statistics Artificial intelligence Internal medicine Pathology Computer science Mathematics

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Type: article
Language: en
Landing Page: https://doi.org/10.1016/j.annepidem.2025.07.060
OA Status: hybrid
Cited By: 1
References: 70
Related Works: 10
OpenAlex ID: https://openalex.org/W4412658528

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DOI: https://doi.org/10.1016/j.annepidem.2025.07.060

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Title: Incorporating longitudinal variability in prediction models: A comparison of machine learning and logistic regression in a cohort study with long follow-up

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Language: en

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Publication year: 2025

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Publication date: 2025-07-26

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Authors: Lichelle Groot, Jos W. R. Twisk, Almar A. L. Kok, Martijn W. Heymans

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Landing page: https://doi.org/10.1016/j.annepidem.2025.07.060

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OA status: hybrid

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OA URL: https://doi.org/10.1016/j.annepidem.2025.07.060

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Concepts: Medicine, Logistic regression, Regression, Cohort, Longitudinal study, Cohort study, Regression analysis, Machine learning, Statistics, Artificial intelligence, Internal medicine, Pathology, Computer science, Mathematics

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Cited by: 1

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referenced_works	https://openalex.org/W3149054887, https://openalex.org/W2019694480, https://openalex.org/W2533691405, https://openalex.org/W1998392635, https://openalex.org/W2611236118, https://openalex.org/W2395172628, https://openalex.org/W2802782110, https://openalex.org/W3021818026, https://openalex.org/W2981367039, https://openalex.org/W2969754783, https://openalex.org/W4310600662, https://openalex.org/W4317388187, https://openalex.org/W4220665572, https://openalex.org/W4321366453, https://openalex.org/W2606105183, https://openalex.org/W2115033264, https://openalex.org/W4283155638, https://openalex.org/W2913997948, https://openalex.org/W6794192535, https://openalex.org/W6804678892, https://openalex.org/W2135046866, https://openalex.org/W6723835939, https://openalex.org/W6864959068, https://openalex.org/W2962937844, https://openalex.org/W2112778345, https://openalex.org/W2093505519, https://openalex.org/W2098307144, https://openalex.org/W4317212040, https://openalex.org/W2747576011, https://openalex.org/W4392782682, https://openalex.org/W2126508852, https://openalex.org/W4313280520, https://openalex.org/W4360600067, https://openalex.org/W2550172236, https://openalex.org/W3118182420, https://openalex.org/W2171661265, https://openalex.org/W2775691173, https://openalex.org/W2796024623, https://openalex.org/W2126340541, https://openalex.org/W1985168785, https://openalex.org/W2549473443, https://openalex.org/W6659742454, https://openalex.org/W2145656642, https://openalex.org/W2718071750, https://openalex.org/W4382051736, https://openalex.org/W2120851774, https://openalex.org/W2160810830, https://openalex.org/W2758198687, https://openalex.org/W3105112072, https://openalex.org/W3140880252, https://openalex.org/W1994682257, https://openalex.org/W6610017368, https://openalex.org/W4294541781, https://openalex.org/W2810511505, https://openalex.org/W2996480032, https://openalex.org/W2108514834, https://openalex.org/W2119168155, https://openalex.org/W2334028018, https://openalex.org/W4399555588, https://openalex.org/W4399271987, https://openalex.org/W4376131992, https://openalex.org/W2498119267, https://openalex.org/W2036870243, https://openalex.org/W4394908947, https://openalex.org/W4220947948, https://openalex.org/W2024623357, https://openalex.org/W4200202177, https://openalex.org/W273955616, https://openalex.org/W3214880663, https://openalex.org/W2496911238
referenced_works_count	70
abstract_inverted_index.in	11, 20
abstract_inverted_index.did	3
abstract_inverted_index.not	4
abstract_inverted_index.with	17
abstract_inverted_index.e.g.,	22
abstract_inverted_index.ageing	23
abstract_inverted_index.Machine	0
abstract_inverted_index.changes	19
abstract_inverted_index.methods	2
abstract_inverted_index.Learning	1
abstract_inverted_index.enhances	14
abstract_inverted_index.expected	18
abstract_inverted_index.logistic	6
abstract_inverted_index.especially	16
abstract_inverted_index.outperform	5
abstract_inverted_index.predictors	13
abstract_inverted_index.prediction,	15
abstract_inverted_index.predictors,	21
abstract_inverted_index.regression.	7
abstract_inverted_index.variability	10
abstract_inverted_index.Nonetheless,	8
abstract_inverted_index.longitudinal	12
abstract_inverted_index.populations.	24
abstract_inverted_index.incorporating	9
cited_by_percentile_year.max	95
cited_by_percentile_year.min	91
countries_distinct_count	1
institutions_distinct_count	4
citation_normalized_percentile.value	0.92239268
citation_normalized_percentile.is_in_top_1_percent	False
citation_normalized_percentile.is_in_top_10_percent	True