| Personalized Glucose Forecasting For Type 2 Diabetes Using Data Assimilation (Vol 13, E1005232, 2017) | 0 | 0.34 | 2021 |
| From Reflection to Action: Combining Machine Learning with Expert Knowledge for Nutrition Goal Recommendations | 1 | 0.37 | 2021 |
| Scaling Up HCI Research: from Clinical Trials to Deployment in the Wild. | 0 | 0.34 | 2021 |
| Lessons learned from assimilating knowledge into machine learning to forecast and control glucose in a critical care setting. | 0 | 0.34 | 2020 |
| Feasibility of a machine learning based method to generate personalized nutrition goals for diabetes self-management. | 0 | 0.34 | 2019 |
| Data-driven modeling and prediction of blood glucose dynamics: Machine learning applications in type 1 diabetes. | 0 | 0.34 | 2019 |
| Using mechanistic machine learning to forecast glucose and infer physiologic phenotypes in the ICU - what is possible and what are the challenges. | 0 | 0.34 | 2018 |
| Mechanistic machine learning: how data assimilation leverages physiologic knowledge using Bayesian inference to forecast the future, infer the present, and phenotype. | 0 | 0.34 | 2018 |
| Methodological variations in lagged regression for detecting physiologic drug effects in EHR data. | 0 | 0.34 | 2018 |
| A visual analytics approach for pattern-recognition in patient-generated data. | 0 | 0.34 | 2018 |
| Pictures Worth a Thousand Words: Reflections on Visualizing Personal Blood Glucose Forecasts for Individuals with Type 2 Diabetes. | 3 | 0.45 | 2018 |
| Reflecting on Diabetes Self-Management Logs with Simulated, Continuous Blood Glucose Curves: A Pilot Study. | 0 | 0.34 | 2017 |
| Why predicting postprandial glucose using self-monitoring data is difficult. | 0 | 0.34 | 2017 |
| Using data assimilation to forecast post-meal glucose for patients with type 2 diabetes. | 0 | 0.34 | 2016 |
| Approaches for using temporal and other filters for next generation phenotype discovery. | 0 | 0.34 | 2016 |
| Model Selection For EHR Laboratory Tests Preserving Healthcare Context and Underlying Physiology. | 0 | 0.34 | 2015 |
| Temporal trends of hemoglobin A1c testing. | 6 | 0.44 | 2014 |
| Model selection for EHR laboratory variables: how physiology and the health care process can influence EHR laboratory data and their model representations. | 0 | 0.34 | 2014 |
| Using patient laboratory measurement values and dynamics to deconvolve EHR bias and define acuity-based phenotypes. | 0 | 0.34 | 2013 |
| Next-generation phenotyping of electronic health records. | 120 | 6.35 | 2013 |
| Using Empirical orthogonal functions to identify temporally important variables to understand time-dependent pathophysiologic and phenotypic differences in patients. | 0 | 0.34 | 2012 |
| Using time-delayed mutual information to discover and interpret temporal correlation structure in complex populations. | 11 | 0.70 | 2011 |
| Exploiting time in electronic health record correlations. | 20 | 1.99 | 2011 |
