Imagen de Portada

Longitudinal Missing Data Imputation for Predicting Disability Stage of Patients with Multiple Sclerosis

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Vazifehdan, Mahin, Bosoni, Pietro, Pala, Daniele, Tavazzi, Eleonora, Bergamaschi, Roberto, Bellazzi, Riccardo, Dagliati, Arianna
Formato:	Preprint
Publicado:	2025
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Exploring the Impact of Environmental Pollutants on Multiple Sclerosis Progression
por: Marinello, Elena, et al.
Publicado: (2024)

Evaluation of Predictive Reliability to Foster Trust in Artificial Intelligence. A case study in Multiple Sclerosis
por: Peracchio, Lorenzo, et al.
Publicado: (2024)

Sex and Gender Transition of People With Multiple Sclerosis: A Real‐World Perspective
por: Pasquale Paletta, et al.
Publicado: (2025)

Missing Data Multiple Imputation for Tabular Q-Learning in Online RL
por: Chasalow, Kyla, et al.
Publicado: (2025)

Predictive Uncertainty in Short-Term PV Forecasting under Missing Data: A Multiple Imputation Approach
por: Pashmchi, Parastoo, et al.
Publicado: (2026)

Pedestrian Trajectory Prediction with Missing Data: Datasets, Imputation, and Benchmarking
por: Chib, Pranav Singh, et al.
Publicado: (2024)

Early Prediction of Multiple Sclerosis Disability Progression via Multimodal Foundation Model Benchmarks
por: Usdin, Maxime, et al.
Publicado: (2025)

Deep Generative Imputation Model for Missing Not At Random Data
por: Chen, Jialei, et al.
Publicado: (2023)

Missing Data Imputation using Neural Cellular Automata
por: Luu, Tin, et al.
Publicado: (2025)

Simple Imputation Rules for Prediction with Missing Data: Contrasting Theoretical Guarantees with Empirical Performance
por: Bertsimas, Dimitris, et al.
Publicado: (2021)

Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion
por: He, Wenying, et al.
Publicado: (2025)

An Interdisciplinary and Cross-Task Review on Missing Data Imputation
por: Fan, Jicong
Publicado: (2025)

Missing Data Imputation With Granular Semantics and AI-driven Pipeline for Bankruptcy Prediction
por: Chakraborty, Debarati, et al.
Publicado: (2024)

CFMI: Flow Matching for Missing Data Imputation
por: Simkus, Vaidotas, et al.
Publicado: (2025)

Missing Data Imputation by Reducing Mutual Information with Rectified Flows
por: Yu, Jiahao, et al.
Publicado: (2025)

DiffPuter: Empowering Diffusion Models for Missing Data Imputation
por: Zhang, Hengrui, et al.
Publicado: (2024)

Machine Learning for Missing Value Imputation
por: Ahmad, Abu Fuad, et al.
Publicado: (2024)

Generative Conditional Missing Imputation Networks
por: Sun, George, et al.
Publicado: (2026)

Missing Pattern Recognized Diffusion Imputation Model for Missing Not At Random
por: Sim, Gyuwon, et al.
Publicado: (2026)

Evaluation of Missing Data Imputation for Time Series Without Ground Truth
por: Farjallah, Rania, et al.
Publicado: (2025)

Markov Missing Graph: A Graphical Approach for Missing Data Imputation
por: Yang, Yanjiao, et al.
Publicado: (2025)

Recursive Equations For Imputation Of Missing Not At Random Data With Sparse Pattern Support
por: Phung, Trung, et al.
Publicado: (2025)

RefiDiff: Progressive Refinement Diffusion for Efficient Missing Data Imputation
por: Ahamed, Md Atik, et al.
Publicado: (2025)

MissHDD: Hybrid Deterministic Diffusion for Hetrogeneous Incomplete Data Imputation
por: Zhou, Youran, et al.
Publicado: (2025)

Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification
por: Pingi, Sharon Torao, et al.
Publicado: (2025)

Enhancing Missing Data Imputation through Combined Bipartite Graph and Complete Directed Graph
por: Zhang, Zhaoyang, et al.
Publicado: (2024)

Missing Data Imputation Based on Dynamically Adaptable Structural Equation Modeling with Self-Attention
por: Deng, Ou, et al.
Publicado: (2023)

Deep Survival Analysis for Competing Risk Modeling with Functional Covariates and Missing Data Imputation
por: Gao, Penglei, et al.
Publicado: (2025)

DPGAN: A Dual-Path Generative Adversarial Network for Missing Data Imputation in Graphs
por: Zheng, Xindi, et al.
Publicado: (2024)

Statistical Jump Model for Mixed-Type Data with Missing Data Imputation
por: Cortese, Federico P., et al.
Publicado: (2024)

Longitudinal Methods Versus Multiple Imputation to Infer Missing Maternal Data in Registry‐Based Pregnancy Studies
por: Takamasa Sakai, et al.
Publicado: (2025)

Domain knowledge-guided machine learning framework for state of health estimation in Lithium-ion batteries
por: Lanubile, Andrea, et al.
Publicado: (2024)

On the Performance of Imputation Techniques for Missing Values on Healthcare Datasets
por: Joel, Luke Oluwaseye, et al.
Publicado: (2024)

Imputation-free Learning of Tabular Data with Missing Values using Incremental Feature Partitions in Transformer
por: Samad, Manar D., et al.
Publicado: (2025)

Meta-Imputation Balanced (MIB): An Ensemble Approach for Handling Missing Data in Biomedical Machine Learning
por: Azad, Fatemeh, et al.
Publicado: (2025)

ITI-IQA: a Toolbox for Heterogeneous Univariate and Multivariate Missing Data Imputation Quality Assessment
por: Pons-Suñer, Pedro, et al.
Publicado: (2024)

Machine Learning Based Missing Values Imputation in Categorical Datasets
por: Ishaq, Muhammad, et al.
Publicado: (2023)

Claustrum Volume Is Reduced in Multiple Sclerosis and Predicts Disability
por: Nicole Shelley, et al.
Publicado: (2025)

Combination of Weak Learners eXplanations to Improve Random Forest eXplicability Robustness
por: Pala, Riccardo, et al.
Publicado: (2024)

Nonparametric End-to-End Probabilistic Forecasting of Distributed Generation Outputs Considering Missing Data Imputation
por: Chen, Minghui, et al.
Publicado: (2024)