148 citations · 299 across the 6 of their papers we have counts for
7 papers
BEDS-Bench: Behavior of EHR-models under Distributional Shift--A Benchmark
Anand Avati, Martin Seneviratne, Emily Xue +3
Machine learning has recently demonstrated impressive progress in predictive accuracy across a wide array of tasks. Most ML approaches focus on generalization performance on unseen…
NGBoost: Natural Gradient Boosting for Probabilistic Prediction
Tony Duan, Anand Avati, Daisy Yi Ding +4
We present Natural Gradient Boosting (NGBoost), an algorithm for generic probabilistic prediction via gradient boosting. Typical regression models return a point estimate, conditio…
Predicting Inpatient Discharge Prioritization With Electronic Health Records
Anand Avati, Stephen Pfohl, Chris Lin +7
The paper develops machine‑learning models using eight years of Stanford Hospital electronic health records to predict which inpatients will be discharged within the next 24 hours,…
Ambulatory Atrial Fibrillation Monitoring Using Wearable Photoplethysmography with Deep Learning
Maxime Voisin, Yichen Shen, Alireza Aliamiri +3
We develop an algorithm that accurately detects Atrial Fibrillation (AF) episodes from photoplethysmograms (PPG) recorded in ambulatory free-living conditions. We collect and annot…
Countdown Regression: Sharp and Calibrated Survival Predictions
Anand Avati, Tony Duan, Sharon Zhou +3
Probabilistic survival predictions from models trained with Maximum Likelihood Estimation (MLE) can have high, and sometimes unacceptably high variance. The field of meteorology, w…
Improving Palliative Care with Deep Learning
Anand Avati, Kenneth Jung, Stephanie Harman +3
Improving the quality of end-of-life care for hospitalized patients is a priority for healthcare organizations. Studies have shown that physicians tend to over-estimate prognoses,…
Neural Language Correction with Character-Based Attention
Ziang Xie, Anand Avati, Naveen Arivazhagan +2
Natural language correction has the potential to help language learners improve their writing skills. While approaches with separate classifiers for different error types have high…