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 (AMIA, 2015)

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 (AMIA, 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 (AMIA, 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)

Why predicting postprandial glucose using self-monitoring data is difficult. - David J. Albers; Matthew E. Levine; Andrew M. Stuart; Bruce J. Gluckman; George Hripcsak (AMIA, 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 (AMIA, 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)

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 (AMIA, 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 (AMIA, 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 (CHI, 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)

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)

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)

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)

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 (AMIA, 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 (AMIA, 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 (CHI, 2019)

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 (AMIA, 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)

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 (CHI Extended Abstracts, 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; et al. (CHI, 2021)

A Framework for Machine Learning of Model Error in Dynamical Systems. - Matthew E. Levine; Andrew M. Stuart (CoRR, 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; et al. (J. Am. Medical Informatics Assoc., 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)

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)

Learning Absorption Rates in Glucose-Insulin Dynamics from Meal Covariates. - Ke Alexander Wang; Matthew E. Levine; Jiaxin Shi; Emily B. Fox (CoRR, 2023)

Machine Learning and Data Assimilation for Blending Incomplete Models and Noisy Data. - Matthew E. Levine (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, 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, 2023)

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 (ICML, 2024)

Hybrid Square Neural ODE Causal Modeling. - Bob Junyi Zou; Matthew E. Levine; Dessi P. Zaharieva; Ramesh Johari; Emily B. Fox (CoRR, 2024)

Continuum Attention for Neural Operators. - Edoardo Calvello; Nikola B. Kovachki; Matthew E. Levine; Andrew M. Stuart (CoRR, 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)

T2 Coach: A Qualitative Study of an Automated Health Coach for Diabetes Self-Management. - Elliot G. Mitchell; Pooja M. Desai; Arlene M. Smaldone; Andrea Cassells; Jonathan N. Tobin; David J. Albers; Matthew E. Levine; Lena Mamykina (CHI, 2025)

Canonical Bayesian Linear System Identification. - Andrey Bryutkin; Matthew E. Levine; Iñigo Urteaga; Youssef Marzouk 001 (CoRR, 2025)