2.7k citations · 8.5k across the 41 of their papers we have counts for
11 papers · 1 filter
Deep Physiological State Space Model for Clinical Forecasting
Yuan Xue, Denny Zhou, Nan Du +4
Clinical forecasting based on electronic medical records (EMR) can uncover the temporal correlations between patients' conditions and outcomes from sequences of longitudinal clinic…
Flow Contrastive Estimation of Energy-Based Models
Ruiqi Gao, Erik Nijkamp, Diederik P. Kingma +3
This paper studies a training method to jointly estimate an energy-based model and a flow-based model, in which the two models are iteratively updated based on a shared adversarial…
Modelling EHR timeseries by restricting feature interaction
Kun Zhang, Yuan Xue, Gerardo Flores +3
Time series data are prevalent in electronic health records, mostly in the form of physiological parameters such as vital signs and lab tests. The patterns of these values may be s…
Federated and Differentially Private Learning for Electronic Health Records
Stephen R. Pfohl, Andrew M. Dai, Katherine Heller
The use of collaborative and decentralized machine learning techniques such as federated learning have the potential to enable the development and deployment of clinical risk predi…
Capacity, Bandwidth, and Compositionality in Emergent Language Learning
Cinjon Resnick, Abhinav Gupta, Jakob Foerster +2
Many recent works have discussed the propensity, or lack thereof, for emergent languages to exhibit properties of natural languages. A favorite in the literature is learning compos…
Learning an Adaptive Learning Rate Schedule
Zhen Xu, Andrew M. Dai, Jonas Kemp +1
The learning rate is one of the most important hyper-parameters for model training and generalization. However, current hand-designed parametric learning rate schedules offer limit…
Improved Hierarchical Patient Classification with Language Model Pretraining over Clinical Notes
Jonas Kemp, Alvin Rajkomar, Andrew M. Dai
Clinical notes in electronic health records contain highly heterogeneous writing styles, including non-standard terminology or abbreviations. Using these notes in predictive modeli…
Explaining an increase in predicted risk for clinical alerts
Michaela Hardt, Alvin Rajkomar, Gerardo Flores +5
Much work aims to explain a model's prediction on a static input. We consider explanations in a temporal setting where a stateful dynamical model produces a sequence of risk estima…
Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer
Edward Choi, Zhen Xu, Yujia Li +4
Effective modeling of electronic health records (EHR) is rapidly becoming an important topic in both academia and industry. A recent study showed that using the graphical structure…
Analyzing the Role of Model Uncertainty for Electronic Health Records
Michael W. Dusenberry, Dustin Tran, Edward Choi +5
In medicine, both ethical and monetary costs of incorrect predictions can be significant, and the complexity of the problems often necessitates increasingly complex models. Recent…
Gmail Smart Compose: Real-Time Assisted Writing
Mia Xu Chen, Benjamin N Lee, Gagan Bansal +9
In this paper, we present Smart Compose, a novel system for generating interactive, real-time suggestions in Gmail that assists users in writing mails by reducing repetitive typing…