Matthew E. Levine

According to our database, Matthew E. Levine authored at least 32 papers between 2015 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Learning about structural errors in models of complex dynamical systems.

Jinlong Wu, Matthew E. Levine, Tapio Schneider, Andrew M. Stuart

J. Comput. Phys., 2024

Continuum Attention for Neural Operators.

Edoardo Calvello, Nikola B. Kovachki, Matthew E. Levine, Andrew M. Stuart

CoRR, 2024

Hybrid Square Neural ODE Causal Modeling.

Bob Junyi Zou, Matthew E. Levine, Dessi P. Zaharieva, Ramesh Johari, Emily B. Fox

CoRR, 2024

Hybrid2 Neural ODE Causal Modeling and an Application to Glycemic Response.

Bob Junyi Zou, Matthew E. Levine, Dessi P. Zaharieva, Ramesh Johari, Emily B. Fox

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Interpretable physiological forecasting in the ICU using constrained data assimilation and electronic health record data.

David J. Albers, Melike Sirlanci, Matthew E. Levine, Jan Claassen, Caroline Der Nigoghossian, George Hripcsak

J. Biomed. Informatics, September, 2023

Who needs what (features) when? Personalizing engagement with data-driven self-management to improve health equity.

Marissa Burgermaster, Pooja M. Desai, Elizabeth M. Heitkemper, Filippa Juul, Elliot G. Mitchell, Meghan Reading Turchioe, David J. Albers, Matthew E. Levine, Dagny Larson, Lena Mamykina

J. Biomed. Informatics, August, 2023

Learning Absorption Rates in Glucose-Insulin Dynamics from Meal Covariates.

Ke Alexander Wang, Matthew E. Levine, Jiaxin Shi, Emily B. Fox

CoRR, 2023

2021

Correction: Personalized glucose forecasting for type 2 diabetes using data assimilation.

David J. Albers, Matthew E. Levine, Bruce J. Gluckman, Henry N. Ginsberg, George Hripcsak, Lena Mamykina

PLoS Comput. Biol., 2021

Enabling personalized decision support with patient-generated data and attributable components.

Elliot G. Mitchell, Esteban G. Tabak, Matthew E. Levine, Lena Mamykina, David J. Albers

J. Biomed. Informatics, 2021

Real-time electronic health record mortality prediction during the COVID-19 pandemic: a prospective cohort study.

Peter D. Sottile, David J. Albers, Peter E. Dewitt, Seth Russell, J. N. Stroh, David P. Kao, Bonnie Adrian, Matthew E. Levine, Ryan Mooney, Lenny Larchick, Jean S. Kutner, Matthew K. Wynia, Jeffrey J. Glasheen, Tellen D. Bennett

J. Am. Medical Informatics Assoc., 2021

A Framework for Machine Learning of Model Error in Dynamical Systems.

Matthew E. Levine, Andrew M. Stuart

CoRR, 2021

From Reflection to Action: Combining Machine Learning with Expert Knowledge for Nutrition Goal Recommendations.

Elliot G. Mitchell, Elizabeth M. Heitkemper, Marissa Burgermaster, Matthew E. Levine, Yishen Miao, Maria L. Hwang, Pooja M. Desai, Andrea Cassells, Jonathan N. Tobin, Esteban G. Tabak, David J. Albers, Arlene M. Smaldone, Lena Mamykina

Proceedings of the CHI '21: CHI Conference on Human Factors in Computing Systems, 2021

Scaling Up HCI Research: from Clinical Trials to Deployment in the Wild.

Lena Mamykina, Arlene M. Smaldone, Suzanne R. Bakken, Noemie Elhadad, Elliot G. Mitchell, Pooja M. Desai, Matthew E. Levine, Jonathan N. Tobin, Andrea Cassells, Patricia G. Davidson, David J. Albers, George Hripcsak

Proceedings of the CHI '21: CHI Conference on Human Factors in Computing Systems, 2021

2020

A new approach to integrating patient-generated data with expert knowledge for personalized goal setting: A pilot study.

Marissa Burgermaster, Jung Hoon Son, Patricia G. Davidson, Arlene M. Smaldone, Gilad Kuperman, Daniel J. Feller, Katherine Gardner Burt, Matthew E. Levine, David J. Albers, Chunhua Weng, Lena Mamykina

Int. J. Medical Informatics, 2020

Lessons learned from assimilating knowledge into machine learning to forecast and control glucose in a critical care setting.

David J. Albers, Melike Sirlanci Tuysuzoglu, Matthew E. Levine, Caroline Der Nigoghossian, Andrew M. Stuart, Jan Claassen, Bruce J. Gluckman, George Hripcsak

Proceedings of the AMIA 2020, 2020

2019

Personal Health Oracle: Explorations of Personalized Predictions in Diabetes Self-Management.

Pooja M. Desai, Elliot G. Mitchell, Maria L. Hwang, Matthew E. Levine, David J. Albers, Lena Mamykina

Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 2019

Machine learning for personalized decision support with patient-generated health data.

Elliot G. Mitchell, Lena Mamykina, Matthew E. Levine, Esteban G. Tabak, David J. Albers

Proceedings of the AMIA 2019, 2019

Feasibility of a machine learning based method to generate personalized nutrition goals for diabetes self-management.

Elliot G. Mitchell, Marissa Burgermaster, Elizabeth M. Heitkemper, Matthew E. Levine, Yishen Miao, Esteban G. Tabak, Arlene M. Smaldone, David J. Albers, Lena Mamykina

Proceedings of the AMIA 2019, 2019

2018

Methodological variations in lagged regression for detecting physiologic drug effects in EHR data.

Matthew E. Levine, David J. Albers, George Hripcsak

J. Biomed. Informatics, 2018

Effect of vocabulary mapping for conditions on phenotype cohorts.

George Hripcsak, Matthew E. Levine, Ning Shang, Patrick B. Ryan

J. Am. Medical Informatics Assoc., 2018

A visual analytics approach for pattern-recognition in patient-generated data.

Daniel J. Feller, Marissa Burgermaster, Matthew E. Levine, Arlene M. Smaldone, Patricia G. Davidson, David J. Albers, Lena Mamykina

J. Am. Medical Informatics Assoc., 2018

Mechanistic machine learning: how data assimilation leverages physiologic knowledge using Bayesian inference to forecast the future, infer the present, and phenotype.

David J. Albers, Matthew E. Levine, Andrew M. Stuart, Lena Mamykina, Bruce J. Gluckman, George Hripcsak

J. Am. Medical Informatics Assoc., 2018

Pictures Worth a Thousand Words: Reflections on Visualizing Personal Blood Glucose Forecasts for Individuals with Type 2 Diabetes.

Pooja M. Desai, Matthew E. Levine, David J. Albers, Lena Mamykina

Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, 2018

An Intelligent Voice Assistant for Diabetes Self-Management: T2D2 - Taming Type 2 Diabetes, Together.

Elliot G. Mitchell, Marissa Burgermaster, Elizabeth M. Heitkemper, Meghan J. Reading, Matthew E. Levine, Yishen Miao, Pooja M. Desai, Lena Mamykina

Proceedings of the AMIA 2018, 2018

Using mechanistic machine learning to forecast glucose and infer physiologic phenotypes in the ICU: what is possible and what are the challenges.

David J. Albers, Matthew E. Levine, Andrew M. Stuart, Jan Claassen, Bruce J. Gluckman, George Hripcsak

Proceedings of the AMIA 2018, 2018

2017

Personalized glucose forecasting for type 2 diabetes using data assimilation.

David J. Albers, Matthew E. Levine, Bruce J. Gluckman, Henry N. Ginsberg, George Hripcsak, Lena Mamykina

PLoS Comput. Biol., 2017

Reflecting on Diabetes Self-Management Logs with Simulated, Continuous Blood Glucose Curves: A Pilot Study.

Elliot G. Mitchell, Matthew E. Levine, David J. Albers, Lena Mamykina

Proceedings of the AMIA 2017, 2017

Why predicting postprandial glucose using self-monitoring data is difficult.

David J. Albers, Matthew E. Levine, Andrew M. Stuart, Bruce J. Gluckman, George Hripcsak

Proceedings of the AMIA 2017, 2017

2016

Data-driven health management: reasoning about personally generated data in diabetes with information technologies.

Lena Mamykina, Matthew E. Levine, Patricia G. Davidson, Arlene M. Smaldone, Noémie Elhadad, David J. Albers

J. Am. Medical Informatics Assoc., 2016

Comparing Lagged Linear Correlation, Lagged Regression, Granger Causality, and Vector Autoregression for Uncovering Associations in EHR Data.

Matthew E. Levine, David J. Albers, George Hripcsak

Proceedings of the AMIA 2016, 2016

Using data assimilation to forecast post-meal glucose for patients with type 2 diabetes.

David J. Albers, Matthew E. Levine, Andrew M. Stuart, George Hripcsak, Lena Mamykina

Proceedings of the AMIA 2016, 2016

2015

Personalized medicine beyond genetics: using personalized model-based forecasting to help type 2 diabetics understand and predict their post-meal glucose.

David J. Albers, Matthew E. Levine, Bruce J. Gluckman, George Hripcsak, Lena Mamykina

Proceedings of the AMIA 2015, 2015