Matthew E. Levine

Publication Activity (10 Years)

Years Active: 2015-2024
Publications (10 Years): 31

Top Topics

Diabetic Patients

Dynamical Systems

Machine Learning

Data Assimilation

Top Venues

J. Am. Medical Informatics Assoc.

J. Biomed. Informatics

Publications

Jinlong Wu, Matthew E. Levine, Tapio Schneider, Andrew M. Stuart
Learning about structural errors in models of complex dynamical systems. J. Comput. Phys. 513 (2024)
Edoardo Calvello, Nikola B. Kovachki, Matthew E. Levine, Andrew M. Stuart
Continuum Attention for Neural Operators. CoRR (2024)
Jin-Long Wu, Matthew E. Levine, Tapio Schneider, Andrew M. Stuart
Learning About Structural Errors in Models of Complex Dynamical Systems. CoRR (2024)
Bob Junyi Zou, Matthew E. Levine, Dessi P. Zaharieva, Ramesh Johari, Emily B. Fox
Hybrid Square Neural ODE Causal Modeling. CoRR (2024)
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
Who needs what (features) when? Personalizing engagement with data-driven self-management to improve health equity. J. Biomed. Informatics 144 (2023)
David J. Albers, Melike Sirlanci, Matthew E. Levine, Jan Claassen, Caroline Der Nigoghossian, George Hripcsak
Interpretable physiological forecasting in the ICU using constrained data assimilation and electronic health record data. J. Biomed. Informatics 145 (2023)
Ke Alexander Wang, Matthew E. Levine, Jiaxin Shi, Emily B. Fox
Learning Absorption Rates in Glucose-Insulin Dynamics from Meal Covariates. CoRR (2023)
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
Real-time electronic health record mortality prediction during the COVID-19 pandemic: a prospective cohort study. J. Am. Medical Informatics Assoc. 28 (11) (2021)
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
Scaling Up HCI Research: from Clinical Trials to Deployment in the Wild. CHI Extended Abstracts (2021)
David J. Albers, Matthew E. Levine, Bruce J. Gluckman, Henry N. Ginsberg, George Hripcsak, Lena Mamykina
Correction: Personalized glucose forecasting for type 2 diabetes using data assimilation. PLoS Comput. Biol. 17 (8) (2021)
Matthew E. Levine, Andrew M. Stuart
A Framework for Machine Learning of Model Error in Dynamical Systems. CoRR (2021)
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
From Reflection to Action: Combining Machine Learning with Expert Knowledge for Nutrition Goal Recommendations. CHI (2021)
Elliot G. Mitchell, Esteban G. Tabak, Matthew E. Levine, Lena Mamykina, David J. Albers
Enabling personalized decision support with patient-generated data and attributable components. J. Biomed. Informatics 113 (2021)
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
A new approach to integrating patient-generated data with expert knowledge for personalized goal setting: A pilot study. Int. J. Medical Informatics 139 (2020)
David J. Albers, Melike Sirlanci Tuysuzoglu, Matthew E. Levine, Caroline Der Nigoghossian, Andrew M. Stuart, Jan Claassen, Bruce J. Gluckman, George Hripcsak
Lessons learned from assimilating knowledge into machine learning to forecast and control glucose in a critical care setting. AMIA (2020)
Pooja M. Desai, Elliot G. Mitchell, Maria L. Hwang, Matthew E. Levine, David J. Albers, Lena Mamykina
Personal Health Oracle: Explorations of Personalized Predictions in Diabetes Self-Management. CHI (2019)
Elliot G. Mitchell, Marissa Burgermaster, Elizabeth M. Heitkemper, Matthew E. Levine, Yishen Miao, Esteban G. Tabak, Arlene M. Smaldone, David J. Albers, Lena Mamykina
Feasibility of a machine learning based method to generate personalized nutrition goals for diabetes self-management. AMIA (2019)
Elliot G. Mitchell, Lena Mamykina, Matthew E. Levine, Esteban G. Tabak, David J. Albers
Machine learning for personalized decision support with patient-generated health data. AMIA (2019)
Matthew E. Levine, David J. Albers, George Hripcsak
Methodological variations in lagged regression for detecting physiologic drug effects in EHR data. J. Biomed. Informatics 86 (2018)
David J. Albers, Matthew E. Levine, Andrew M. Stuart, Lena Mamykina, Bruce J. Gluckman, George Hripcsak
Mechanistic machine learning: how data assimilation leverages physiologic knowledge using Bayesian inference to forecast the future, infer the present, and phenotype. J. Am. Medical Informatics Assoc. 25 (10) (2018)
George Hripcsak, Matthew E. Levine, Ning Shang, Patrick B. Ryan
Effect of vocabulary mapping for conditions on phenotype cohorts. J. Am. Medical Informatics Assoc. 25 (12) (2018)
Pooja M. Desai, Matthew E. Levine, David J. Albers, Lena Mamykina
Pictures Worth a Thousand Words: Reflections on Visualizing Personal Blood Glucose Forecasts for Individuals with Type 2 Diabetes. CHI (2018)
David J. Albers, Matthew E. Levine, Andrew M. Stuart, Jan Claassen, Bruce J. Gluckman, George Hripcsak
Using mechanistic machine learning to forecast glucose and infer physiologic phenotypes in the ICU: what is possible and what are the challenges. AMIA (2018)
Elliot G. Mitchell, Marissa Burgermaster, Elizabeth M. Heitkemper, Meghan J. Reading, Matthew E. Levine, Yishen Miao, Pooja M. Desai, Lena Mamykina
An Intelligent Voice Assistant for Diabetes Self-Management: T2D2 - Taming Type 2 Diabetes, Together. AMIA (2018)
Daniel J. Feller, Marissa Burgermaster, Matthew E. Levine, Arlene M. Smaldone, Patricia G. Davidson, David J. Albers, Lena Mamykina
A visual analytics approach for pattern-recognition in patient-generated data. J. Am. Medical Informatics Assoc. 25 (10) (2018)
David J. Albers, Matthew E. Levine, Andrew M. Stuart, Bruce J. Gluckman, George Hripcsak
Why predicting postprandial glucose using self-monitoring data is difficult. AMIA (2017)
Elliot G. Mitchell, Matthew E. Levine, David J. Albers, Lena Mamykina
Reflecting on Diabetes Self-Management Logs with Simulated, Continuous Blood Glucose Curves: A Pilot Study. AMIA (2017)
David J. Albers, Matthew E. Levine, Bruce J. Gluckman, Henry N. Ginsberg, George Hripcsak, Lena Mamykina
Personalized glucose forecasting for type 2 diabetes using data assimilation. PLoS Comput. Biol. 13 (4) (2017)
Matthew E. Levine, David J. Albers, George Hripcsak
Comparing Lagged Linear Correlation, Lagged Regression, Granger Causality, and Vector Autoregression for Uncovering Associations in EHR Data. AMIA (2016)
Lena Mamykina, Matthew E. Levine, Patricia G. Davidson, Arlene M. Smaldone, Noémie Elhadad, David J. Albers
Data-driven health management: reasoning about personally generated data in diabetes with information technologies. J. Am. Medical Informatics Assoc. 23 (3) (2016)
David J. Albers, Matthew E. Levine, Andrew M. Stuart, George Hripcsak, Lena Mamykina
Using data assimilation to forecast post-meal glucose for patients with type 2 diabetes. AMIA (2016)
David J. Albers, Matthew E. Levine, Bruce J. Gluckman, George Hripcsak, Lena Mamykina
Personalized medicine beyond genetics: using personalized model-based forecasting to help type 2 diabetics understand and predict their post-meal glucose. AMIA (2015)