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David J. Albers
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2020 – today
- 2024
- [j28]Deepak K. Agrawal
, Bradford J. Smith
Quantifiable identification of flow-limited ventilator dyssynchrony with the deformed lung ventilator model. Comput. Biol. Medicine 173: 108349 (2024) - 2023
- [j27]J. N. Stroh
Hypothesis-driven modeling of the human lung-ventilator system: A characterization tool for Acute Respiratory Distress Syndrome research. J. Biomed. Informatics 137: 104275 (2023) - [j26]Marissa Burgermaster, Pooja M. Desai, Elizabeth M. Heitkemper
Who needs what (features) when? Personalizing engagement with data-driven self-management to improve health equity. J. Biomed. Informatics 144: 104419 (2023) - [j25]David J. Albers
Interpretable physiological forecasting in the ICU using constrained data assimilation and electronic health record data. J. Biomed. Informatics 145: 104477 (2023) - [j24]Yanran Wang
A methodology of phenotyping ICU patients from EHR data: High-fidelity, personalized, and interpretable phenotypes estimation. J. Biomed. Informatics 148: 104547 (2023) - 2022
- [c28]Jennifer K. Briggs, J. N. Stroh, Tellen D. Bennett, Soojin Park, David J. Albers, Brandon Foreman:
Optimizing Strategies of Pressure Reactivity Index and Optimal Cerebral Perfusion Pressure Identification for Cerebral Autoregulatory-Guided Clinical Decision Support. AMIA 2022 - [c27]Patricia C. Dykes, Sarah Collins Rossetti, Patricia Sengstack, Guilherme Del Fiol, David J. Albers:
Informatics Research and Implementation During COVID: Challenges, Opportunities and Recommendations for Building a Sustainable Infrastructure. AMIA 2022 - [c26]Lauren R. Richter, Benjamin Albert, Linying Zhang, Ilene Fennoy, David J. Albers, George Hripcsak:
Using Data Assimilation to Predict Post-Operative Bariatric Surgery Glycemic Status in Adolescents. AMIA 2022 - [c25]J. N. Stroh, Bradford J. Smith, Peter D. Sottile, George Hripcsak, David J. Albers:
Gaining Purchase on Ventilator-Induced Lung Injury: A Interpretable Approach to Describing Complex System Data via Informed Modeling. AMIA 2022 - [c24]Yanran Wang, J. N. Stroh, George Hripcsak, Cecilia C. Low Wang, Julia Wrobel, Caroline Der Nigoghossian, Tellen D. Bennett, David J. Albers:
A methodology of phenotyping ICU patients: high-fidelity, personalized, and interpretable phenotypes estimation. AMIA 2022 - 2021
- [j23]Kumiko O. Schnock, Min-Jeoung Kang, Sarah Collins Rossetti, Jose P. Garcia, Graham Lowenthal, Chris Knaplund, Frank Y. Chang, David J. Albers, Zfania Tom Korach, Li Zhou, Jeffrey G. Klann, Kenrick Cato
Identifying nursing documentation patterns associated with patient deterioration and recovery from deterioration in critical and acute care settings. Int. J. Medical Informatics 153: 104525 (2021) - [j22]Sarah Collins Rossetti
Healthcare Process Modeling to Phenotype Clinician Behaviors for Exploiting the Signal Gain of Clinical Expertise (HPM-ExpertSignals): Development and evaluation of a conceptual framework. J. Am. Medical Informatics Assoc. 28(6): 1242-1251 (2021) - [j21]Li-heng Fu, Chris Knaplund, Kenrick Cato
Utilizing timestamps of longitudinal electronic health record data to classify clinical deterioration events. J. Am. Medical Informatics Assoc. 28(9): 1955-1963 (2021) - [j20]Peter D. Sottile, David J. Albers, Peter E. Dewitt
Real-time electronic health record mortality prediction during the COVID-19 pandemic: a prospective cohort study. J. Am. Medical Informatics Assoc. 28(11): 2354-2365 (2021) - [j19]Elliot G. Mitchell, Esteban G. Tabak, Matthew E. Levine, Lena Mamykina
Enabling personalized decision support with patient-generated data and attributable components. J. Biomed. Informatics 113: 103639 (2021) - [j18]David J. Albers, Matthew E. Levine, Bruce J. Gluckman
Correction: Personalized glucose forecasting for type 2 diabetes using data assimilation. PLoS Comput. Biol. 17(8) (2021) - [c23]David J. Albers, Deepak K. Agrawal, Bradford J. Smith, Peter D. Sottile, Tellen D. Bennett, Jake Stroh, George Hripcsak:
Toward phenotyping of ventilator-induced lung injury with a damage-informed pulmonary model of lung-ventilator interaction. AMIA 2021 - [c22]Lena Mamykina, Arlene M. Smaldone, Suzanne R. Bakken, Noemie Elhadad, Elliot G. Mitchell, Pooja M. Desai, Matthew E. Levine, Jonathan N. Tobin
Scaling Up HCI Research: from Clinical Trials to Deployment in the Wild. CHI Extended Abstracts 2021: 50:1-50:6 - [c21]Elliot G. Mitchell, Elizabeth M. Heitkemper, Marissa Burgermaster, Matthew E. Levine, Yishen Miao
From Reflection to Action: Combining Machine Learning with Expert Knowledge for Nutrition Goal Recommendations. CHI 2021: 206:1-206:17 - 2020
- [j17]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: 104158 (2020) - [j16]Li-heng Fu
Development and validation of early warning score system: A systematic literature review. J. Biomed. Informatics 105: 103410 (2020) - [c20]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 - [c19]Li-heng Fu, Christopher Knaplund, Kenrick Cato, David J. Albers, Sarah Collins Rossetti:
Utilizing Timestamps of Longitudinal Data from Electronic Health Record to Predict Clinical Deterioration Events. AMIA 2020
2010 – 2019
- 2019
- [j15]Ashenafi Zebene Woldaregay, Eirik Årsand, Ståle Walderhaug
Data-driven modeling and prediction of blood glucose dynamics: Machine learning applications in type 1 diabetes. Artif. Intell. Medicine 98: 109-134 (2019) - [c18]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 - [c17]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 - [c16]Sarah Collins Rossetti, Christopher Knaplund, David J. Albers, Abdul A. Tariq, Kui Tang, David K. Vawdrey, Natalie Yip, Patricia C. Dykes, Jeffrey G. Klann, Min-Jeoung Kang, Jose P. Garcia, Li-heng Fu, Kumiko O. Schnock, Kenrick Cato:
Leveraging Clinical Expertise as a Feature - not an Outcome - of Predictive Models: Evaluation of an Early Warning System Use Case. AMIA 2019 - [c15]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: 370 - [c14]Iñigo Urteaga, Tristan Bertin, Theresa M. Hardy, David J. Albers, Noémie Elhadad:
Multi-Task Gaussian Processes and Dilated Convolutional Networks for Reconstruction of Reproductive Hormonal Dynamics. MLHC 2019: 66-90 - [i3]Iñigo Urteaga, Tristan Bertin, Theresa M. Hardy, David J. Albers, Noémie Elhadad:
Multi-Task Gaussian Processes and Dilated Convolutional Networks for Reconstruction of Reproductive Hormonal Dynamics. CoRR abs/1908.10226 (2019) - 2018
- [j14]George Hripcsak, David J. Albers:
High-fidelity phenotyping: richness and freedom from bias. J. Am. Medical Informatics Assoc. 25(3): 289-294 (2018) - [j13]Daniel J. Feller
A visual analytics approach for pattern-recognition in patient-generated data. J. Am. Medical Informatics Assoc. 25(10): 1366-1374 (2018) - [j12]David J. Albers, Matthew E. Levine
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): 1392-1401 (2018) - [j11]David J. Albers, Noémie Elhadad, Jan Claassen, Rimma Perotte
Estimating summary statistics for electronic health record laboratory data for use in high-throughput phenotyping algorithms. J. Biomed. Informatics 78: 87-101 (2018) - [j10]Matthew E. Levine
Methodological variations in lagged regression for detecting physiologic drug effects in EHR data. J. Biomed. Informatics 86: 149-159 (2018) - [c13]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 - [c12]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: 538 - 2017
- [j9]Shreya Chakrabarti, Anando Sen, Vojtech Huser, Gregory William Hruby, Alexander Rusanov, David J. Albers, Chunhua Weng
An Interoperable Similarity-based Cohort Identification Method Using the OMOP Common Data Model Version 5.0. J. Heal. Informatics Res. 1(1): 1-18 (2017) - [j8]David J. Albers, Matthew E. Levine
Personalized glucose forecasting for type 2 diabetes using data assimilation. PLoS Comput. Biol. 13(4) (2017) - [c11]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 - [c10]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 - [i2]Iñigo Urteaga, David J. Albers, Marija Vlajic Wheeler, Anna Druet, Hans Raffauf, Noémie Elhadad:
Towards Personalized Modeling of the Female Hormonal Cycle: Experiments with Mechanistic Models and Gaussian Processes. CoRR abs/1712.00117 (2017) - 2016
- [j7]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): 526-531 (2016) - [c9]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 - [c8]David J. Albers, Adler J. Perotte, George Hripcsak:
Approaches for using temporal and other filters for next generation phenotype discovery. AMIA 2016 - [c7]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 - 2015
- [j6]George Hripcsak, David J. Albers, Adler J. Perotte:
Parameterizing time in electronic health record studies. J. Am. Medical Informatics Assoc. 22(4): 794-804 (2015) - [c6]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 - [c5]David J. Albers, Rimma Pivovarov, J. Michael Schmidt, Noemie Elhadad, George Hripcsak:
Model Selection For EHR Laboratory Tests Preserving Healthcare Context and Underlying Physiology. AMIA 2015 - 2014
- [j5]Rimma Pivovarov
Temporal trends of hemoglobin A1c testing. J. Am. Medical Informatics Assoc. 21(6): 1038-1044 (2014) - [j4]Rimma Pivovarov
Identifying and mitigating biases in EHR laboratory tests. J. Biomed. Informatics 51: 24-34 (2014) - [j3]Yolanda Hagar, David J. Albers, Rimma Pivovarov
Survival analysis with electronic health record data: Experiments with chronic kidney disease. Stat. Anal. Data Min. 7(5): 385-403 (2014) - [c4]David J. Albers, Rimma Pivovarov, Noemie Elhadad, George Hripcsak:
Model selection for EHR laboratory variables: how physiology and the health care process can influence EHR laboratory data and their model representations. AMIA 2014 - 2013
- [j2]George Hripcsak, David J. Albers:
Next-generation phenotyping of electronic health records. J. Am. Medical Informatics Assoc. 20(1): 117-121 (2013) - [c3]David J. Albers, Rimma Pivovarov, George Hripcsak, Noemie Elhadad:
Using patient laboratory measurement values and dynamics to deconvolve EHR bias and define acuity-based phenotypes. AMIA 2013 - 2012
- [c2]David J. Albers, Jan Claassen, George Hripcsak:
Using Empirical orthogonal functions to identify temporally important variables to understand time-dependent pathophysiologic and phenotypic differences in patients. AMIA 2012 - [c1]Madineh Sedigh-Sarvestani
Data assimilation of glucose dynamics for use in the intensive care unit. EMBC 2012: 5437-5440 - 2011
- [j1]George Hripcsak, David J. Albers, Adler J. Perotte:
Exploiting time in electronic health record correlations. J. Am. Medical Informatics Assoc. 18(Supplement): 109-115 (2011) - [i1]David J. Albers, George Hripcsak:
Using time-delayed mutual information to discover and interpret temporal correlation structure in complex populations. CoRR abs/1110.4102 (2011)
Coauthor Index
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