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Prediction of depression risk in middle-aged and elderly Cardiovascular-Kidney-Metabolic syndrome patients by social and environmental determinants of health: an interpretable machine learning approach using longitudinal data from China.

• Authors : Xu X; School of Public Health, Southern Medical University, Guangzhou, China.; School of Marxism, Southern Medical University, Guangzhou, China.

Subjects: Metabolic Syndrome*/Metabolic Syndrome*/Metabolic Syndrome*/epidemiology ; Metabolic Syndrome*/Metabolic Syndrome*/Metabolic Syndrome*/psychology ; Metabolic Syndrome*/Metabolic Syndrome*/Metabolic Syndrome*/complications

• Source: Journal of health, population, and nutrition [J Health Popul Nutr] 2025 Jun 04; Vol. 44 (1), pp. 187. Date of Electronic Publication: 2025 Jun 04.Publisher: BioMed Central Country of Publication: Bangladesh NLM ID: 100959228 Publication Model: Electronic Cited Medium: Internet ISSN:

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Non-Linear Dose-Response Relationship for Metformin in Japanese Patients With Type 2 Diabetes: Analysis of Irregular Longitudinal Data by Interpretable Machine Learning Models.

• Authors : Akimoto H; Division of Pharmacology, Department of Biomedical Sciences, Nihon University School of Medicine; Oyaguchi-Kamicho 30-1, Itabashi-ku, Tokyo, Japan.

Subjects: Metformin*/Metformin*/Metformin*/administration & dosage ; Metformin*/Metformin*/Metformin*/therapeutic use ; Diabetes Mellitus, Type 2*/Diabetes Mellitus, Type 2*/Diabetes Mellitus, Type 2*/drug therapy

• Source: Pharmacology research & perspectives [Pharmacol Res Perspect] 2025 Feb; Vol. 13 (1), pp. e70055.Publisher: John Wiley & Sons Ltd, British Pharmacological Society and American Society for Pharmacology and Experimental Therapeutics Country of Publication: United States

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Using latent representations to link disjoint longitudinal data for mixed-effects regression

• Authors : Schächter, Clemens; Hackenberg, Maren; Pfaffenlehner, Michelle

Subjects: Machine Learning; Artificial Intelligence; 68T07

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Improving Virtual Contrast Enhancement using Longitudinal Data

• Authors : Fayolle, Pierre; Bône, Alexandre; Debs, Noëlie

Subjects: Image and Video Processing; Machine Learning

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification

• Authors : Pingi, Sharon Torao; Bashar, Md Abul; Nayak, Richi

Subjects: Machine Learning; I.2.6

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An empirical comparison of some outlier detection methods with longitudinal data

• Authors : D'Orazio, Marcello

Subjects: Methodology; Machine Learning; Applications

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Learning Causally Predictable Outcomes from Psychiatric Longitudinal Data

• Authors : Strobl, Eric V.

Subjects: Machine Learning; Quantitative Methods

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NOCTA: Non-Greedy Objective Cost-Tradeoff Acquisition for Longitudinal Data

• Authors : Dinh, Dzung; Chen, Boqi; Niethammer, Marc

Subjects: Machine Learning; Artificial Intelligence

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Boosting K-nearest neighbor regression performance for longitudinal data through a novel learning approach.

• Authors : Loeloe, Mohammad Sadegh¹ (AUTHOR); Tabatabaei, Seyyed Mohammad^2,3 (AUTHOR); Sefidkar, Reyhane¹ (AUTHOR)

• Source: BMC Bioinformatics. 9/30/2025, Vol. 26 Issue 1, p1-26. 26p.

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Data-driven methods to characterize heterogeneity in Alzheimer’s disease using cross-sectional and longitudinal data

• Authors : Martí Juan, Gerard; University/Department: Universitat Pompeu Fabra. Departament de Tecnologies de la Informació i les Comunicacions; ...

Subjects: Alzheimer’s disease; Dementia; Machine learning

• Source: TDX (Tesis Doctorals en Xarxa)

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