65 citations · 221 across the 19 of their papers we have counts for
18 papers
Interpretable Forecasting of Physiology in the ICU Using Constrained Data Assimilation and Electronic Health Record Data
David Albers, Melike Sirlanci, Matthew Levine +3
Prediction of physiologic states are important in medical practice because interventions are guided by predicted impacts of interventions. But prediction is difficult in medicine b…
A damaged-informed lung model for ventilator waveforms
Deepak. K. Agrawal, Bradford J. Smith, Peter D. Sottile +1
The acute respiratory distress syndrome (ARDS) is characterized by the acute development of diffuse alveolar damage (DAD) resulting in increased vascular permeability and decreased…
Delay-Induced Uncertainty for a Paradigmatic Glucose-Insulin Model
Bhargav Karamched, George Hripcsak, Dave Albers +1
Medical practice in the intensive care unit is based on the supposition that physiological systems such as the human glucose-insulin system are predictable. We demonstrate that del…
Reduced model for female endocrine dynamics: Validation and functional variations
E. J. Graham, N. Elhadad, D. Albers
A normally functioning menstrual cycle requires significant crosstalk between hormones originating in ovarian and brain tissues. Reproductive hormone dysregulation may cause abnorm…
Using Data Assimilation of Mechanistic Models to Estimate Glucose and Insulin Metabolism
Jami J. Mulgrave, Matthew E. Levine, David J. Albers +3
Motivation: There is a growing need to integrate mechanistic models of biological processes with computational methods in healthcare in order to improve prediction. We apply data a…
Enabling Personalized Decision Support with Patient-Generated Data and Attributable Components
Elliot G Mitchell, Esteban G Tabak, Matthew E Levine +2
Decision-making related to health is complex. Machine learning (ML) and patient generated data can identify patterns and insights at the individual level, where human cognition fal…
A Simple Modeling Framework For Prediction In The Human Glucose-Insulin System
M. Sirlanci, M. E. Levine, C. C. Low Wang +2
In this paper, we build a new, simple, and interpretable mathematical model to estimate and forecast physiology related to the human glucose-insulin system, constrained by availabl…
Multi-Task Gaussian Processes and Dilated Convolutional Networks for Reconstruction of Reproductive Hormonal Dynamics
Iñigo Urteaga, Tristan Bertin, Theresa M. Hardy +2
We present an end-to-end statistical framework for personalized, accurate, and minimally invasive modeling of female reproductive hormonal patterns. Reconstructing and forecasting…
The Parameter Houlihan: a solution to high-throughput identifiability indeterminacy for brutally ill-posed problems
DJ Albers, M Levine, L Mamykina +1
One way to interject knowledge into clinically impactful forecasting is to use data assimilation, a nonlinear regression that projects data onto a mechanistic physiologic model, in…
Ensemble Kalman Methods With Constraints
David J. Albers, Paul-Adrien Blancquart, Matthew E. Levine +2
Ensemble Kalman methods constitute an increasingly important tool in both state and parameter estimation problems. Their popularity stems from the derivative-free nature of the met…
Behavioral-clinical phenotyping with type 2 diabetes self-monitoring data
Matthew E. Levine, David J. Albers, Marissa Burgermaster +3
Objective: To evaluate unsupervised clustering methods for identifying individual-level behavioral-clinical phenotypes that relate personal biomarkers and behavioral traits in type…
Methodological variations in lagged regression for detecting physiologic drug effects in EHR data
Matthew E. Levine, David J. Albers, George Hripcsak
We studied how lagged linear regression can be used to detect the physiologic effects of drugs from data in the electronic health record (EHR). We systematically examined the effec…
Towards Personalized Modeling of the Female Hormonal Cycle: Experiments with Mechanistic Models and Gaussian Processes
Iñigo Urteaga, David J. Albers, Marija Vlajic Wheeler +3
In this paper, we introduce a novel task for machine learning in healthcare, namely personalized modeling of the female hormonal cycle. The motivation for this work is to model the…
Offline and online data assimilation for real-time blood glucose forecasting in type 2 diabetes
Matthew E Levine, George Hripcsak, Lena Mamykina +2
We evaluate the benefits of combining different offline and online data assimilation methodologies to improve personalized blood glucose prediction with type 2 diabetes self-monito…
A methodology for detecting and exploring non-convulsive seizures in patients with SAH
D J Albers, J Claassen, M J Schmidt +1
A methodology for understanding and de- tecting nonconvulsive seizures in individuals with sub- arachnoid hemorrhage is introduced. Specifically, begin- ning with an EEG signal, th…
Using time-delayed mutual information to discover and interpret temporal correlation structure in complex populations
D. J. Albers, George Hripcsak
This paper addresses how to calculate and interpret the time-delayed mutual information for a complex, diversely and sparsely measured, possibly non-stationary population of time-s…
Population physiology: leveraging population scale (EHR) data to understand human endocrine dynamics
DJ Albers, George Hripcsak, Michael Schmidt
Studying physiology over a broad population for long periods of time is difficult primarily because collecting human physiologic data is intrusive, dangerous, and expensive. Electr…
Estimation of time-delayed mutual information and bias for irregularly and sparsely sampled time-series
DJ Albers, George Hripcsak
A method to estimate the time-dependent correlation via an empirical bias estimate of the time-delayed mutual information for a time-series is proposed. In particular, the bias of…