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Mohamed bin Zayed University of Artificial Intelligence Digital Biostatistics Marcos Matabuena’s research group

Research

The ongoing future

Imagine a person experiencing an acute coronary syndrome alone at home, wearing a device that continuously monitors vital signs. As the device detects critical changes—such as an abnormal electrocardiogram or a drop in oxygen levels—it automatically triggers an emergency response. Within seconds, the system locates and dispatches the nearest available ambulance, adapting in real-time to factors like traffic conditions and hospital capacity.

While en route, the device continuously transmits vital patient data, enabling dynamic adjustments to medical and logistical decisions. If the patient’s condition worsens, the hospital is alerted to prepare a critical care unit. Upon arrival, predictive models guide decision-making, ensuring the best possible outcome while maximising healthcare resource efficiency.

Our mission and vision

Our long-term goal is to build a healthcare decision-making framework that supports better clinical and public health decisions at both the individual and population levels by combining biostatistics, digital health technologies, and operations research.

Animation illustrating missing smartphone data

We are currently developing statistical and artificial intelligence methods for complex clinical problems involving continuous, high-frequency, and real-time health data. We want these methods not only to answer existing medical questions, but also to help identify the questions that matter most. What diet is best for a particular person? How much physical activity does someone need to support healthy aging and longevity? How should these recommendations change according to an individual’s clinical profile, biology, environment, and lifestyle?

To address these questions, our research brings together modern statistical AI methods for causal inference, survival analysis, uncertainty quantification, and random-effects generative modeling to advance statistical learning for non-Euclidean random objects. Careful study design and computational scalability are essential to this vision. Our goal is to develop methods that are mathematically rigorous yet computationally efficient at population scale, targeting biomedical resources such as the UK Biobank, the All of Us Research Program, the Human Phenotype Project, and NHANES.

Human Phenotype Project

Although many methods published in top methodological journals are mathematically rigorous and technically sound, they are not always designed with the complexities and hyphotesis of real-world clinical data in mind. Our research is therefore grounded in concrete biomedical questions and practical challenges. We develop methods that reflect the structure of the data, the realities of clinical practice, and the critical decisions facing patients, clinicians, and healthcare systems to progress the field.

Seven pillars of our research program in digital health

Abstract of Glucodensities: A new representation of glucose profiles using distributional data analysis

Glucodensities: A new representation of glucose profiles using distributional data analysis

Marcos Matabuena, Alexander Petersen, Juan C Vidal, Francisco Gude

Our seven papers represent a substantial methodological and conceptual advance for the field of digital health.

The introduction of the notion of glucodensity represents a first step toward the creation of more complex digital phenotypes through functional and distributional data analysis. This framework opens new directions for biological discovery and allows us to use the continuous information generated by new medical devices more efficiently, instead of reducing these data to a reduced number of scalar measures.

The introduction of multivariate glucodensity represents a further conceptual step in the analysis of human physiology. Clinical data are no longer analyzed only as individual numbers, but as complex objects that are closer to medical images. We can now integrate the dynamics of the underlying physiological processes within a unified distributional glucodensity framework.

The joint work with Ciprian Crainiceanu on random-effects models for glucodensity data extends these ideas to longitudinal studies conducted over weeks, months, or years. This work creates a new framework for using digital information in clinical trials, with the possibility of increasing statistical power and discovering new and more relevant longitudinal biomarkers.

With Mateo Cámara, we propose the first framework for random-effects modeling in general metric spaces, providing a unified approach for analyzing different types of statistical objects in clinical studies. Most of the information collected in digital health is nearly continuous over time, is measured at multiple time scales, and naturally exhibits a multilevel structure. Our framework provides, for the first time, a general analytical way to model these complex sources of variation within a broader mathematical generality.

The framework for uncertainty quantification in metric spaces, developed together with Gábor Lugosi, is methodologically important because it provides a formal method for calibrating and evaluating conditional coverage without restrictive smoothness conditions. At the same time, it opens a more general framework for uncertainty quantification for the biomarkers of the future, which will increasingly be represented as distributions, functions, images, networks, or other complex non-Euclidean random objects.

The work with my Chinese colleagues, Mingyang Yi, Zhiming Ma, and Ruoyu Wang, on diffusion models and measurement error opens the possibility of incorporating measurement uncertainty into biomedical decision-making. The main idea is to use general generative artificial intelligence methods within classical measurement error statistical pipelines, allowing the error generated by sensors, clinical measurements, and other biomedical technologies to be incorporated directly into the analysis.

Finally, the success of the new data-driven era will also depend on the development of new experimental and sampling designs. In joint work with Yating Zou and Michael Kosorok, we propose a new random forest methodology for complex survey data. The method has relevant inferential implications because the bootstrap procedure is applied at the node level. This opens new theoretical opportunities about how we can improve the performance of machine learning methods for complex sampling designs and, at the same time, provides a principled way to use large health datasets while preserving valid inference for the target population.

Together, these works define the main pillars of our research program: the new representation of digital health data, longitudinal and multilevel modeling, uncertainty quantification, generative AI-driven measurement-error correction, and statistically valid inference from complex biomedical studies.

Research output from previous years

2026

Denoising Data with Measurement Error Using a Reproducing Kernel-based Diffusion Model
Denoising Data with Measurement Error Using a Reproducing Kernel-based Diffusion Model
Mingyang Yi, Marcos Matabuena, Zhiming Ma, Ruoyu Wang
arXiv  ·  14 Jul 2026  ·  arxiv:2501.00212
EpiBinder: a multimodal framework for cell-type-specific prediction and interpretation of transcription factor binding
EpiBinder: a multimodal framework for cell-type-specific prediction and interpretation of transcription factor binding
Ruben Solozabal, Albert Baichorov, Irina Miodownik, Tamir Avioz, Le Song, Marcos Matabuena, Martin Takáč, Ariel Afek
openRxiv  ·  08 Jul 2026  ·  10.64898/2026.07.06.736502
Pre-activity glycemic prediction prioritizes post-meal movement
Pre-activity glycemic prediction prioritizes post-meal movement
Smadar Shilo, Gal Sapir, Guy Lutsker, Yeela Talmor-Barkan, Anastasia Godneva, Alon Diament, Marcos Matabuena, Eran Segal, Hagai Rossman
openRxiv  ·  24 Jun 2026  ·  doi:10.64898/2026.06.22.26356272
Conformal and kNN Predictive Uncertainty Quantification Algorithms in Metric Spaces
Conformal and kNN Predictive Uncertainty Quantification Algorithms in Metric Spaces
Gábor Lugosi, Marcos Matabuena
arXiv  ·  23 Jun 2026  ·  arxiv:2507.15741
A Deep Learning-Based Predictive Algorithm for Metabolic Syndrome Detection in the U.S. Population
A Deep Learning-Based Predictive Algorithm for Metabolic Syndrome Detection in the U.S. Population
Cristina Correa, Ruben Solozabal, Ziad Akram Ali Hammouri, Fernando Gómez-Peralta, Hagai Rossman, Juan C. Vidal, David C. Klonoff, Eran Segal, Marcos Matabuena
openRxiv  ·  02 Jun 2026  ·  doi:10.64898/2026.05.24.26354007
Capturing the Curve: Functional Data Analysis for Validated Digital Outcome Measures
Capturing the Curve: Functional Data Analysis for Validated Digital Outcome Measures
Mia S. Tackney, Marcos Matabuena, Marco Palma, Michael Wester, Claire Maassen, Thomas Krammer, Julian Mustroph, Peter H. Charlton, James Carpenter, Sofia S. Villar
arXiv  ·  28 May 2026  ·  arxiv:2605.28344
Gaussian mixture models in Hilbert spaces via kernel methods
Gaussian mixture models in Hilbert spaces via kernel methods
Daniel López-Montero, Antonio Álvarez-López, Marcos Matabuena
arXiv  ·  08 May 2026  ·  arxiv:2605.05996
Screening for diabetes mellitus in the US population using neural network-based modeling and complex survey designs
Screening for diabetes mellitus in the US population using neural network-based modeling and complex survey designs
Marcos Matabuena, Juan C Vidal, Rahul Ghosal, Jukka-Pekka Onnela
Statistical Methods in Medical Research  ·  07 May 2026  ·  doi:10.1177/09622802261442893
Random-Effects Algorithm for Random Objects in Metric Spaces
Random-Effects Algorithm for Random Objects in Metric Spaces
Marcos Matabuena, Mateo Cámara
arXiv  ·  05 May 2026  ·  arxiv:2605.02693
Continuous-Time Learning of Probability Distributions: A Case Study in a Digital Trial of Young Children with Type 1 Diabetes
Continuous-Time Learning of Probability Distributions: A Case Study in a Digital Trial of Young Children with Type 1 Diabetes
Antonio Álvarez-López, Marcos Matabuena
arXiv  ·  26 Mar 2026  ·  arxiv:2603.24427
Multilevel functional distributional models with applications to continuous glucose monitoring in diabetes clinical trials
Multilevel functional distributional models with applications to continuous glucose monitoring in diabetes clinical trials
Marcos Matabuena, Ciprian M. Crainiceanu
The Annals of Applied Statistics  ·  01 Mar 2026  ·  doi:10.1214/26-AOAS2139
Glucodensity-Based Models Outperform Time in Range and Glycemia Risk Index in Prediction Models
Glucodensity-Based Models Outperform Time in Range and Glycemia Risk Index in Prediction Models
Mandy M. Shao, Agatha F. Scheideman, David C. Klonoff, Francisco Gude, Marcos Matabuena
Journal of Diabetes Science and Technology  ·  23 Feb 2026  ·  doi:10.1177/19322968261421954
Survival on Image Regression With Application to Partially Functional Distributional Representation of Physical Activity
Survival on Image Regression With Application to Partially Functional Distributional Representation of Physical Activity
Rahul Ghosal, Sunwoo Emma Cho, Marcos Matabuena
Statistical Analysis and Data Mining: An ASA Data Science Journal  ·  01 Feb 2026  ·  doi:10.1002/sam.70068
Exploratory analysis of smartphone-based step counts as a digital biomarker for survival in ALS patients
Exploratory analysis of smartphone-based step counts as a digital biomarker for survival in ALS patients
Marcos Matabuena, Marcin Straczkiewicz, Narghes Calcagno, Katherine M. Burke, Timothy B. Royse, Amrita Iyer, Kendall T. Carney, Sydney Hall, James D. Berry, Jukka-Pekka Onnela
Frontiers in Digital Health  ·  29 Jan 2026  ·  doi:10.3389/fdgth.2025.1705368
Beyond scalar metrics: functional data analysis of postprandial continuous glucose monitoring in the AEGIS study
Beyond scalar metrics: functional data analysis of postprandial continuous glucose monitoring in the AEGIS study
Marcos Matabuena, Joseph Sartini, Francisco Gude
BMC Medical Research Methodology  ·  24 Jan 2026  ·  doi:10.1186/s12874-025-02748-2
Distributional Random Forests for Complex Survey Designs on Reproducing Kernel Hilbert Spaces
Distributional Random Forests for Complex Survey Designs on Reproducing Kernel Hilbert Spaces
Yating Zou, Marcos Matabuena, Michael R. Kosorok
arXiv  ·  07 Jan 2026  ·  arxiv:2512.08179
Daily Physical Activity Patterns Enhance Mortality Prediction in NHANES 2011-2014: A Comparative Study Across Classical, Machine Learning, and Deep Learning Models Using Accelerometer Data
Daily Physical Activity Patterns Enhance Mortality Prediction in NHANES 2011-2014: A Comparative Study Across Classical, Machine Learning, and Deep Learning Models Using Accelerometer Data
Srimanta Ghosh, Sunwoo Emma Cho, Yuanzhen Yue, Marcos Matabuena, Enakshi Saha, Rahul Ghosal
Journal for the Measurement of Physical Behaviour  ·  01 Jan 2026  ·  doi:10.1123/jmpb.2025-0052
From Physical Activity to Spirometry as a Curve : A Combined Model for Mortality Prediction
From Physical Activity to Spirometry (as a Curve): A Combined Model for Mortality Prediction
Marcos Matabuena, Marcos Matabuena@mbzuai Ac Ae, Emma Cho, Rahul Ghosal, Cristina Correa, Mateo Cámara, Manuel Oviedo De La Fuente, Holger Cevallos, Francisco Gude
Unpublished  ·  01 Jan 2026  ·  doi:10.13140/RG.2.2.35806.60486

2025

Continuous Glucose Monitoring as an alternative for Cystic Fibrosis Related diabetes screening and diagnosis
Continuous Glucose Monitoring as an alternative for Cystic Fibrosis Related diabetes screening and diagnosis
I. Aguilera García, P. Vázquez Pérez, B. López Plaza, M. Matabuena, R.M. García Moreno, S.Palma Milla, E. Zamarrón de Lucas, M.C. Prados Sánchez, N. González Pérez de Villar
Diabetes Research and Clinical Practice  ·  01 Nov 2025  ·  doi:10.1016/j.diabres.2025.112952
Unequal strides to longevity. The extra mile to health equity
Unequal strides to longevity. The extra mile to health equity
Borja del Pozo Cruz, Francisco Perales, Marcin Straczkiewicz, Marcos Matabuena, Jesús del Pozo-Cruz, Rubén López-Bueno
Annals of Epidemiology  ·  01 Nov 2025  ·  doi:10.1016/j.annepidem.2025.10.009
kNN Algorithm for Conditional Mean and Variance Estimation with Automated Uncertainty Quantification and Variable Selection
kNN Algorithm for Conditional Mean and Variance Estimation with Automated Uncertainty Quantification and Variable Selection
Marcos Matabuena, Juan C. Vidal, Oscar Hernan Madrid Padilla, Jukka-Pekka Onnela
arXiv  ·  28 Oct 2025  ·  arxiv:2402.01635
Variable Selection for Fixed and Random Effects in Multilevel Functional Mixed Effects Models
Variable Selection for Fixed and Random Effects in Multilevel Functional Mixed Effects Models
Rahul Ghosal, Marcos Matabuena, Enakshi Saha
arXiv  ·  24 Oct 2025  ·  arxiv:2505.05416
ROC Analysis with Covariate Adjustment Using Neural Network Models: Evaluating the Role of Age in the Physical Activity-Mortality Association
ROC Analysis with Covariate Adjustment Using Neural Network Models: Evaluating the Role of Age in the Physical Activity-Mortality Association
Ziad Akram Ali Hammouri, Yating Zou, Rahul Ghosal, Juan C. Vidal, Marcos Matabuena
arXiv  ·  20 Oct 2025  ·  arxiv:2510.14494
Glucodensity functional profiles outperform traditional continuous glucose monitoring metrics
Glucodensity functional profiles outperform traditional continuous glucose monitoring metrics
Marcos Matabuena, Rahul Ghosal, Javier Enrique Aguilar, Ayya Keshet, Robert Wagner, Carmen Fernández Merino, Juan Sánchez Castro, Vadim Zipunnikov, Jukka-Pekka Onnela, Francisco Gude
Scientific Reports  ·  29 Sep 2025  ·  doi:10.1038/s41598-025-18119-2
Age, Sex, BMI, Meal Timing, and Glycemic Response to Meal Glycemic Load
Age, Sex, BMI, Meal Timing, and Glycemic Response to Meal Glycemic Load
Mar Calvo-Malvar, Óscar Lado-Baleato, Ana Cao Ríos, Cristina Porca Fernández, Alfonso Benítez-Calvo, Carmen Fernandez-Merino, Juan Sánchez-Castro, Robert Wagner, Marcos Matabuena, Francisco Gude
JAMA Network Open  ·  23 Sep 2025  ·  doi:10.1001/jamanetworkopen.2025.33193
Continuous Temporal Learning of Probability Distributions via Neural ODEs with Applications in Continuous Glucose Monitoring Data
Continuous Temporal Learning of Probability Distributions via Neural ODEs with Applications in Continuous Glucose Monitoring Data
Antonio Álvarez-López, Marcos Matabuena
arXiv  ·  18 Sep 2025  ·  arxiv:2505.08698
Distributional Data Analysis Uncovers Hundreds of Novel and Heritable Phenomic Features from Temporal Cotton and Maize Drone Imagery
Distributional Data Analysis Uncovers Hundreds of Novel and Heritable Phenomic Features from Temporal Cotton and Maize Drone Imagery
Aaron J. DeSalvio, Marcos Matabuena, Alper Adak, Mustafa A. Arik, Serina M. DeSalvio, …, Shawn M. Kaeppler, Dayane C. Lima, Candice N. Hirsch, Addie Thompson, David M. Stelly
openRxiv  ·  07 Sep 2025  ·  doi:10.1101/2025.09.05.674557
Functional time transformation model with applications to digital health
Functional time transformation model with applications to digital health
Rahul Ghosal, Marcos Matabuena, Sujit K. Ghosh
Computational Statistics & Data Analysis  ·  01 Jul 2025  ·  doi:10.1016/j.csda.2025.108131
Model-Free Kernel Conformal Depth Measures Algorithm for Uncertainty Quantification in Regression Models in Separable Hilbert Spaces
Model-Free Kernel Conformal Depth Measures Algorithm for Uncertainty Quantification in Regression Models in Separable Hilbert Spaces
Marcos Matabuena, Rahul Ghosal, Pavlo Mozharovskyi, Oscar Hernan Madrid Padilla, Jukka-Pekka Onnela
arXiv  ·  11 Jun 2025  ·  arxiv:2506.08325
Conformal prediction for uncertainty quantification in dynamic biological systems
Conformal prediction for uncertainty quantification in dynamic biological systems
Alberto Portela, Julio R. Banga, Marcos Matabuena
PLOS Computational Biology  ·  12 May 2025  ·  doi:10.1371/journal.pcbi.1013098
A Telehealth Program Using Continuous Glucose Monitoring and a Connected Insulin Pen Cap in Nursing Homes for Older Adults with Insulin-Treated Diabetes: The Trescasas Study
A Telehealth Program Using Continuous Glucose Monitoring and a Connected Insulin Pen Cap in Nursing Homes for Older Adults with Insulin-Treated Diabetes: The Trescasas Study
Fernando Gomez-Peralta, Cristina Abreu, Estefanía Santos, Alvaro Da Silva, Ana San Frutos, …, Ana Franco-López, Cristina López Mardomingo, Benito Cañuelo, Guillermo Blazquez, Marcos Matabuena
Diabetes Technology & Therapeutics  ·  01 May 2025  ·  doi:10.1089/dia.2024.0356
Uncertainty quantification for intervals
Uncertainty quantification for intervals
Carlos García Meixide, Michael R. Kosorok, Marcos Matabuena
arXiv  ·  01 Apr 2025  ·  arxiv:2408.16381
Deep Learning Framework with Uncertainty Quantification for Survey Data: Assessing and Predicting Diabetes Mellitus Risk in the American Population
Deep Learning Framework with Uncertainty Quantification for Survey Data: Assessing and Predicting Diabetes Mellitus Risk in the American Population
Marcos Matabuena, Juan C. Vidal, Rahul Ghosal, Jukka-Pekka Onnela
arXiv  ·  27 Mar 2025  ·  arXiv:2403.19752
Optimal Cut-Point Estimation for Functional Digital Biomarkers: Application to Diabetes Risk Stratification via Continuous Glucose Monitoring
Optimal Cut-Point Estimation for Functional Digital Biomarkers: Application to Diabetes Risk Stratification via Continuous Glucose Monitoring
Oscar Lado-Baleato, Carla Díaz-Louza, Francisco Gude, Marcos Matabuena
arXiv  ·  11 Mar 2025  ·  arxiv:2404.09716
Causal survival embeddings: Non-parametric counterfactual inference under right-censoring
Causal survival embeddings: Non-parametric counterfactual inference under right-censoring
Carlos García Meixide, Marcos Matabuena
Statistical Methods in Medical Research  ·  11 Feb 2025  ·  doi:10.1177/09622802241311455
Kernel biclustering algorithm in Hilbert spaces
Kernel biclustering algorithm in Hilbert spaces
Marcos Matabuena, Juan C. Vidal, Oscar Hernan Madrid Padilla, Dino Sejdinovic
Advances in Data Analysis and Classification  ·  21 Jan 2025  ·  doi:10.1007/s11634-024-00619-0
Variable Selection Methods for Multivariate, Functional, and Complex Biomedical Data in the AI Age
Variable Selection Methods for Multivariate, Functional, and Complex Biomedical Data in the AI Age
Marcos Matabuena
arXiv  ·  14 Jan 2025  ·  arxiv:2501.06868

2024

Predicting distributions of physical activity profiles in the National Health and Nutrition Examination Survey database using a partially linear Fr chet single index model
Predicting distributions of physical activity profiles in the National Health and Nutrition Examination Survey database using a partially linear Fréchet single index model
Marcos Matabuena, Aritra Ghosal, Wendy Meiring, Alexander Petersen
Biostatistics  ·  31 Dec 2024  ·  doi:10.1093/biostatistics/kxaf013
Divergent hypoglycemic and hyperglycemic responses to the components of evening meals. A general adult population study in individuals without diabetes AEGIS Study
Divergent hypoglycemic and hyperglycemic responses to the components of evening meals. A general adult population study in individuals without diabetes (AEGIS Study)
Tomás González-Vidal, Mar Calvo-Malvar, Carmen Fernández-Merino, Juan Sánchez-Castro, Óscar Lado-Baleato, Carla Díaz-Louzao, Marcos Pazos-Couselo, Manuela Alonso-Sampedro, Marcos Matabuena, Francisco Gude
Clinical Nutrition  ·  01 Dec 2024  ·  doi:10.1016/j.clnu.2024.11.020
Exploring the Interplay Between Physical Activity, Dietary Patterns, Diabetes, and All-Cause Mortality in the U.S.A. Population, NHANES 2011-2014: A Distributional Data Analysis Perspective
Exploring the Interplay Between Physical Activity, Dietary Patterns, Diabetes, and All-Cause Mortality in the U.S.A. Population, NHANES 2011-2014: A Distributional Data Analysis Perspective
Marcos Matabuena, Anxo Carreira, Robert Wagner, Miguel Sousa, Miguel Saavedra, Rahul Ghosal, JP Onnela, Lucimere Bohn
Springer Science and Business Media LLC  ·  15 Nov 2024  ·  doi:10.21203/rs.3.rs-5314139/v1
Exploring the association between daily distributional patterns of physical activity and cardiovascular mortality risk among older adults in NHANES 2003-2006
Exploring the association between daily distributional patterns of physical activity and cardiovascular mortality risk among older adults in NHANES 2003-2006
Sunwoo Emma Cho, Enakshi Saha, Marcos Matabuena, Jingkai Wei, Rahul Ghosal
Annals of Epidemiology  ·  01 Nov 2024  ·  doi:10.1016/j.annepidem.2024.10.001
Uncertainty Quantification in Dynamic Models of Biological Systems Using Conformal Prediction
Uncertainty Quantification in Dynamic Models of Biological Systems Using Conformal Prediction
Alberto Portela, Julio R. Banga, Marcos Matabuena
[object Object]  ·  01 Oct 2024  ·  doi: 10.5281/zenodo.13838652
Book Review: Functional Data Analysis with R by Ciprian M. Crainiceanu, Jeff Goldsmith, Andrew Leroux, and Erjia Cui
Book Review: Functional Data Analysis with R by Ciprian M. Crainiceanu, Jeff Goldsmith, Andrew Leroux, and Erjia Cui
Marcos Matabuena
Journal of Agricultural, Biological and Environmental Statistics  ·  01 Oct 2024  ·  doi:10.1007/s13253-024-00656-4
Multivariate scalar on multidimensional distribution regression with application to modeling the association between physical activity and cognitive functions
Multivariate scalar on multidimensional distribution regression with application to modeling the association between physical activity and cognitive functions
Rahul Ghosal, Marcos Matabuena
Biometrical Journal  ·  22 Sep 2024  ·  doi:10.1002/bimj.202400042
Neural interval-censored survival regression with feature selection
Neural interval-censored survival regression with feature selection
Carlos García Meixide, Marcos Matabuena, Louis Abraham, Michael R. Kosorok
Statistical Analysis and Data Mining: The ASA Data Science Journal  ·  16 Jul 2024  ·  doi:10.1002/sam.11704
Energy Distance and Kernel Mean Embeddings for Two-Sample Survival Testing with Applications in Immunotherapy Clinical Trials
Energy Distance and Kernel Mean Embeddings for Two-Sample Survival Testing with Applications in Immunotherapy Clinical Trials
Marcos Matabuena, Oscar Hernan Madrid Padilla
REVSTAT-Statistical Journal  ·  14 Jun 2024  ·  doi:10.57805/revstat.vi.572
Conformal uncertainty quantification using kernel depth measures in separable Hilbert spaces
Conformal uncertainty quantification using kernel depth measures in separable Hilbert spaces
Marcos Matabuena, Rahul Ghosal, Pavlo Mozharovskyi, Oscar Hernan Madrid Padilla, Jukka-Pekka Onnela
arXiv  ·  24 May 2024  ·  arxiv:2405.13970
Uncertainty quantification in metric spaces
Uncertainty quantification in metric spaces
Gábor Lugosi, Marcos Matabuena
arXiv  ·  09 May 2024  ·  arxiv:2405.05110
Insulin glargine 300 U ml versus insulin degludec 100 U ml improves nocturnal glycaemic control and variability in type 1 diabetes under routine clinical practice: A glucodensities-based post hoc analysis of the OneCare study
Insulin glargine 300 U/ml versus insulin degludec 100 U/ml improves nocturnal glycaemic control and variability in type 1 diabetes under routine clinical practice: A glucodensities-based post hoc analysis of the OneCare study
Fernando Gomez‐Peralta, Ana Chico Ballesteros, Amparo Marco Martínez, Begoña Pérez Corral, Ignacio Conget Donlo, Paulina Fuentealba Melo, Fernando Zaragozá Arnáez, Marcos Matabuena Rodríguez
Diabetes, Obesity and Metabolism  ·  01 May 2024  ·  doi: 10.1111/dom.15496
Personalized Imputation in metric spaces via conformal prediction: Applications in Predicting Diabetes Development with Continuous Glucose Monitoring Information
Personalized Imputation in metric spaces via conformal prediction: Applications in Predicting Diabetes Development with Continuous Glucose Monitoring Information
Marcos Matabuena, Carla Díaz-Louzao, Rahul Ghosal, Francisco Gude
arXiv  ·  28 Mar 2024  ·  arxiv:2403.18069
Insulin glargine 300 U ml versus insulin degludec 100 U ml improves nocturnal glycaemic control and variability in type 1 diabetes under routine clinical practice: A glucodensities-based post hoc analysis of the OneCare study
Insulin glargine 300 U/ml versus insulin degludec 100 U/ml improves nocturnal glycaemic control and variability in type 1 diabetes under routine clinical practice: A glucodensities-based post hoc analysis of the OneCare study
Fernando Gomez‐Peralta, Ana Chico Ballesteros, Amparo Marco Martínez, Begoña Pérez Corral, Ignacio Conget Donlo, Paulina Fuentealba Melo, Fernando Zaragozá Arnáez, Marcos Matabuena Rodríguez
Diabetes, Obesity and Metabolism  ·  20 Feb 2024  ·  doi:10.1111/dom.15496
Author Correction: Reassessment of fluctuating dental asymmetry in Down syndrome
Author Correction: Reassessment of fluctuating dental asymmetry in Down syndrome
Marcos Matabuena Rodríguez, Pedro Diz Dios, Carmen Cadarso-Suárez, Márcio Diniz-Freitas, Mercedes Outumuro Rial, Maria Teresa Abeleira Pazos, Jacobo Limeres Posse
Scientific Reports  ·  16 Jan 2024  ·  doi:10.1038/s41598-024-51672-w

2021

Glucodensities: A new representation of glucose profiles using distributional data analysis
Glucodensities: A new representation of glucose profiles using distributional data analysis
Marcos Matabuena, Alexander Petersen, Juan C Vidal, Francisco Gude
Statistical Methods in Medical Research  ·  01 Jun 2021  ·  doi: 10.1177/0962280221998064