D. Albers

Suzanne Bakken, Sarah A Collins, Kenrick Cato, 2013, American journal of critical care : an official publication, American Association of Critical-Care Nurses.

Development of a Decision Support System to Generate Data-Powered Personalized Nutrition Recommendations for Diabetes Self-Management

Daniel J. Feller, D. Albers, L. Mamykina, 2017 .

Using Data Assimilation of Mechanistic Models to Estimate Glucose and Insulin Metabolism

A. Sherman, G. Hripcsak, D. Albers, 2020, 2003.06541.

Nonconvulsive seizures after subarachnoid hemorrhage: Multimodal detection and outcomes

George Hripcsak, Adler Perotte, Jan Claassen, 2013, Annals of neurology.

Delay-induced uncertainty for a paradigmatic glucose-insulin model.

G. Hripcsak, D. Albers, W. Ott, 2020, Chaos.

Delay-induced uncertainty for a paradigmatic glucose-insulin model.

George Hripcsak, Bhargav Karamched, David Albers, 2020, Chaos.

Causal Structure of Brain Physiology after Brain Injury from Subarachnoid Hemorrhage

Shah Atiqur Rahman, Soojin Park, D. Albers, 2016, PloS one.

Multi-Task Gaussian Processes and Dilated Convolutional Networks for Reconstruction of Reproductive Hormonal Dynamics pdf

David J. Albers, Inigo Urteaga, No'emie Elhadad, 2019, MLHC.

A damaged-informed lung model for ventilator waveforms

Bradford J. Smith, Deepak K. Agrawal, D. Albers, 2020, bioRxiv.

Ventilator dyssynchrony – Detection, pathophysiology, and clinical relevance: A Narrative review

Bradford J. Smith, D. Albers, M. Moss, 2020, Annals of thoracic medicine.

Behavioral-clinical phenotyping with type 2 diabetes self-monitoring data

D. Albers, L. Mamykina, M. Levine, 2018, 1802.08761.

Real-Time Electronic Health Record Mortality Prediction During the COVID-19 Pandemic: A Prospective Cohort Study

J. Kutner, D. Albers, M. Levine, 2021, medRxiv.

Heart Rate Variability for Preclinical Detection of Secondary Complications After Subarachnoid Hemorrhage

George Hripcsak, Daby Sow, Jan Claassen, 2014, Neurocritical Care.

Intracortical electrophysiological correlates of blood flow after severe SAH: A multimodality monitoring study

D. Albers, J. Claassen, J. Schmidt, 2017, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

Using Empirical orthogonal functions to identify temporally important variables to understand time-dependent pathophysiologic and phenotypic differences in patients

George Hripcsak, David J. Albers, Jan Claassen, 2012, AMIA.

EDMON - A Wireless Communication Platform for a Real-Time Infectious Disease Outbreak De- tection System Using Self-Recorded Data from People with Type 1 Diabetes

Ashenafi Zebene Woldaregay, G. Hartvigsen, E. Årsand, 2018 .

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

B. Gluckman, G. Hripcsak, D. Albers, 2018 .

Evaluating Prediction of Continuous Clinical Values: A Glucose Case Study

G. Hripcsak, D. Albers, 2021, Methods of information in medicine.

Hypothesis-driven modeling of the human lung–ventilator system: A characterization tool for Acute Respiratory Distress Syndrome research

Bradford J. Smith, J. N. Stroh, G. Hripcsak, 2022, bioRxiv.

Neuromuscular blockade is associated with the attenuation of biomarkers of epithelial and endothelial injury in patients with moderate-to-severe acute respiratory distress syndrome

D. Albers, M. Moss, P. Sottile, 2018, Critical Care.

Data assimilation on mechanistic models of glucose metabolism predicts glycemic states in adolescents following bariatric surgery

G. Hripcsak, D. Albers, Linying Zhang, 2022, Frontiers in Physiology.

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

Lena Mamykina, George Hripcsak, David J. Albers, 2018, J. Am. Medical Informatics Assoc..

Estimating summary statistics for electronic health record laboratory data for use in high-throughput phenotyping algorithms

George Hripcsak, Noémie Elhadad, David J. Albers, 2018, J. Biomed. Informatics.

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

George Hripcsak, David J. Albers, Matthew E. Levine, 2016, AMIA.

Personalized glucose forecasting for type 2 diabetes using data assimilation

Lena Mamykina, George Hripcsak, David J. Albers, 2017, PLoS Comput. Biol..

Reduced model for female endocrine dynamics: Validation and functional variations

Erica J. Graham, D. Albers, N. Elhadad, 2020, Mathematical Biosciences.

Development and validation of early warning score system: A systematic literature review

Patricia C. Dykes, Haomiao Jia, Christopher Knaplund, 2020, J. Biomed. Informatics.

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

George Hripcsak, David J. Albers, Matthew E. Levine, 2018, J. Biomed. Informatics.

Exploiting time in electronic health record correlations

George Hripcsak, Adler J. Perotte, David J. Albers, 2011, J. Am. Medical Informatics Assoc..

A Novel Approach for Continuous Health Status Monitoring and Automatic Detection of Infection Incidences in People With Type 1 Diabetes Using Machine Learning Algorithms (Part 2): A Personalized Digital Infectious Disease Detection Mechanism

Gunnar Hartvigsen, Eirik Årsand, Jorge Igual, 2020, Journal of medical Internet research.

Ashenafi Zebene Woldaregay, J. Igual, G. Hartvigsen, 2020 .

Data-driven modeling and prediction of blood glucose dynamics: Machine learning applications in type 1 diabetes

Lena Mamykina, Gunnar Hartvigsen, Eirik Årsand, 2019, Artif. Intell. Medicine.

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

Lena Mamykina, David J. Albers, Matthew E. Levine, 2018, CHI.

Personalization and Pragmatism: Pediatric Intracranial Pressure and Cerebral Perfusion Pressure Treatment Thresholds.

D. Albers, T. Bennett, J. Stroh, 2021, Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies.

Next-generation phenotyping of electronic health records

George Hripcsak, David J. Albers, G. Hripcsak, 2012, J. Am. Medical Informatics Assoc..

Delay-Induced Uncertainty in Physiological Systems

Bhargav Karamched, George Hripcsak, Dave Albers, 2020, bioRxiv.

Offline and online data assimilation for real-time blood glucose forecasting in type 2 diabetes

Lena Mamykina, George Hripcsak, Matthew E Levine, 2017, 1709.00163.

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

Lena Mamykina, Chunhua Weng, Marissa Burgermaster, 2020, Int. J. Medical Informatics.

Healthcare Process Modeling to Phenotype Clinician Behaviors for Exploiting the Signal Gain of Clinical Expertise (HPM-ExpertSignals): Development and evaluation of a conceptual framework

Li Zhou, Patricia C Dykes, Jeffrey G Klann, 2021, J. Am. Medical Informatics Assoc..

High-fidelity phenotyping: richness and freedom from bias

George Hripcsak, David J. Albers, G. Hripcsak, 2017, J. Am. Medical Informatics Assoc..

Delay-induced uncertainty in the glucose-insulin system: Pathogenicity for obesity and type-2 diabetes mellitus

G. Hripcsak, D. Albers, W. Ott, 2022, Frontiers in Physiology.

Estimating intracranial pressure via low-dimensional models: toward a practical tool for clinical decision support at multi-hour timescales

J.N. Stroh, T. Bennett, V. Kheyfets, 2020, bioRxiv.

The Association Between Ventilator Dyssynchrony, Delivered Tidal Volume, and Sedation Using a Novel Automated Ventilator Dyssynchrony Detection Algorithm*

David Albers, D. Albers, M. Moss, 2018, Critical care medicine.

Temporal trends of hemoglobin A1c testing

George Hripcsak, Noémie Elhadad, David J. Albers, 2014, J. Am. Medical Informatics Assoc..

Identifying nursing documentation patterns associated with patient deterioration and recovery from deterioration in critical and acute care settings

Patricia C. Dykes, Frank Y. Chang, David J. Albers, 2021, Int. J. Medical Informatics.

Utilizing timestamps of longitudinal electronic health record data to classify clinical deterioration events

Patricia C. Dykes, Adler J. Perotte, David J. Albers, 2021, J. Am. Medical Informatics Assoc..

Data-Driven Blood Glucose Pattern Classification and Anomalies Detection: Machine-Learning Applications in Type 1 Diabetes

Lena Mamykina, Gunnar Hartvigsen, Eirik Årsand, 2019, Journal of medical Internet research.

A Damaged-Informed Lung Ventilator Model for Ventilator Waveforms

Bradford J. Smith, Deepak K. Agrawal, D. Albers, 2021, Frontiers in Physiology.

Interpretable Forecasting of Physiology in the ICU Using Constrained Data Assimilation and Electronic Health Record Data

G. Hripcsak, D. Albers, J. Claassen, 2023, 2305.06513.

Toward Detecting Infection Incidence in People With Type 1 Diabetes Using Self-Recorded Data (Part 1): A Novel Framework for a Personalized Digital Infectious Disease Detection System (Preprint)

Ashenafi Zebene Woldaregay, G. Hartvigsen, E. Årsand, 2020 .

Ensemble Kalman methods with constraints

Andrew Stuart, David J. Albers, Matthew E. Levine, 2019, Inverse problems.

An Interoperable Similarity-based Cohort Identification Method Using the OMOP Common Data Model Version 5.0

Chunhua Weng, Vojtech Huser, Anando Sen, 2017, J. Heal. Informatics Res..

Inferring Insulin Secretion Rate from Sparse Patient Glucose and Insulin Measures

Rammah M. Abohtyra, D. Albers, Christine L. Chan, 2022, Frontiers in Physiology.

Low hemoglobin and hematoma expansion after intracerebral hemorrhage

Soojin Park, D. Albers, J. Claassen, 2019, Neurology.

Clinical Decision Support for Traumatic Brain Injury: Identifying a Framework for Practical Model-Based Intracranial Pressure Estimation at Multihour Timescales

D. Albers, V. Kheyfets, T. Bennett, 2021, JMIR medical informatics.

Clinical Decision Support for Traumatic Brain Injury: Identifying a Framework for Practical Model-Based Intracranial Pressure Estimation at Multihour Timescales (Preprint)

D. Albers, V. Kheyfets, T. Bennett, 2020 .

Survival analysis with electronic health record data: Experiments with chronic kidney disease

Noémie Elhadad, David J. Albers, Herbert S. Chase, 2014, Stat. Anal. Data Min..

Using time-delayed mutual information to discover and interpret temporal correlation structure in complex populations

George Hripcsak, David J. Albers, G. Hripcsak, 2011, Chaos.

Behavioral-clinical phenotyping with type 2 diabetes self-monitoring data

D. Albers, L. Mamykina, M. Levine, 2018, 1802.08761.

Leveraging Clinical Expertise as a Feature - not an Outcome - of Predictive Models: Evaluation of an Early Warning System Use Case

Patricia C Dykes, Jeffrey G Klann, Kumiko O. Schnock, 2020, AMIA.

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

Elizabeth M. Heitkemper, Elliot G. Mitchell, Meghan Reading Turchioe, 2023, J. Biomed. Informatics.

A simple modeling framework for prediction in the human glucose-insulin system.

D. J. Albers, M. E. Levine, M. Sirlanci, 2019, Chaos.

Data assimilation of glucose dynamics for use in the intensive care unit

Bruce J. Gluckman, Madineh Sedigh-Sarvestani, David J. Albers, 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

Towards Personalized Modeling of the Female Hormonal Cycle: Experiments with Mechanistic Models and Gaussian Processes pdf

Noémie Elhadad, Iñigo Urteaga, David J. Albers, 2017, ArXiv.

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

David J. Albers, Matthew E. Levine, David P. Kao, 2021, J. Am. Medical Informatics Assoc..

Differential effects of two-hit models of acute and ventilator-induced lung injury on lung structure, function, and inflammation

D. Albers, L. Knudsen, P. Sottile, 2023, Frontiers in physiology.

A Damaged-Informed Lung Ventilator Model for Ventilator Waveforms

Bradford J. Smith, Deepak K. Agrawal, D. Albers, 2021, Frontiers in Physiology.

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

Lena Mamykina, Arlene M. Smaldone, David J. Albers, 2018, J. Am. Medical Informatics Assoc..

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

G. Hripcsak, D. Albers, J. Claassen, 2023, Journal of Biomedical Informatics.