40 citations · 62 across the 7 of their papers we have counts for
8 papers · 1 filter
Model-based metrics: Sample-efficient estimates of predictive model subpopulation performance
Andrew C. Miller, Leon A. Gatys, Joseph Futoma +1
Machine learning models now commonly developed to screen, diagnose, or predict health conditions are evaluated with a variety of performance metrics. An important first ste…
Breiman's two cultures: You don't have to choose sides
Andrew C. Miller, Nicholas J. Foti, Emily B. Fox
Breiman's classic paper casts data analysis as a choice between two cultures: data modelers and algorithmic modelers. Stated broadly, data modelers use simple, interpretable models…
Representing and Denoising Wearable ECG Recordings
Jeffrey Chan, Andrew C. Miller, Emily B. Fox
Modern wearable devices are embedded with a range of noninvasive biomarker sensors that hold promise for improving detection and treatment of disease. One such sensor is the single…
Learning Insulin-Glucose Dynamics in the Wild
Andrew C. Miller, Nicholas J. Foti, Emily Fox
We develop a new model of insulin-glucose dynamics for forecasting blood glucose in type 1 diabetics. We augment an existing biomedical model by introducing time-varying dynamics d…
Measuring the Stability of EHR- and EKG-based Predictive Models
Andrew C. Miller, Ziad Obermeyer, Sendhil Mullainathan
Databases of electronic health records (EHRs) are increasingly used to inform clinical decisions. Machine learning methods can find patterns in EHRs that are predictive of future a…
A Probabilistic Model of Cardiac Physiology and Electrocardiograms
Andrew C. Miller, Ziad Obermeyer, David M. Blei +2
An electrocardiogram (EKG) is a common, non-invasive test that measures the electrical activity of a patient's heart. EKGs contain useful diagnostic information about patient healt…