Search Results for author:
Found 6 papers, 0 papers with code
Organizational Governance of Emerging Technologies: AI Adoption in Healthcare
This is one of the most detailed qualitative analyses to date of the current governance structures and processes involved in AI adoption by health systems in the United States.
Development and Validation of ML-DQA -- a Machine Learning Data Quality Assurance Framework for Healthcare
The approaches by which the machine learning and clinical research communities utilize real world data (RWD), including data captured in the electronic health record (EHR), vary dramatically.
"The Human Body is a Black Box": Supporting Clinical Decision-Making with Deep Learning
Our work underscores the limits of model interpretability as a solution to ensure transparency, accuracy, and accountability in practice.
Learning to Treat Sepsis with Multi-Output Gaussian Process Deep Recurrent Q-Networks
We evaluate our approach on a heterogeneous dataset of septic spanning 15 months from our university health system, and find that our learned policy could reduce patient mortality by as much as 8. 2\% from an overall baseline mortality rate of 13. 3\%.
An Improved Multi-Output Gaussian Process RNN with Real-Time Validation for Early Sepsis Detection
Latent function values from the Gaussian process are then fed into a deep recurrent neural network to classify patient encounters as septic or not, and the overall model is trained end-to-end using back-propagation.
Scalable Modeling of Multivariate Longitudinal Data for Prediction of Chronic Kidney Disease Progression
Prediction of the future trajectory of a disease is an important challenge for personalized medicine and population health management.
