In this talk, I will discuss three real-world Health applications of Machine Learning research, the progress that we have made in deployment to hospitals or directly to individuals, and where we hope to be heading next. In the first part, I will discuss Sepsis Watch, our Sepsis prediction system that has been deployed to the emergency departments of Duke University hospitals. This system performs prediction for incoming patients through a combination of Gaussian Processes, which estimate patient features in continuous time from uneven measurements, and Recurrent Neural Networks. Next I discuss Graph-coupled HMMs, work that we have done making individual-level predictions of disease spread in a social network in influenza, and how this might affect prediction abilities in other diseases, such as Coronavirus. Lastly, I will discuss the iOS app developed to record data on people with Multiple Sclerosis outside of a clinic environment, what collected data and basic analyses imply for our ability to do symptom and subpopulation prediction in this setting, and where we are headed in the future.
- Learning to Detect Sepsis with a Multitask Gaussian Process RNN Classifier
- Real-World Integration of a Sepsis Deep Learning Technology Into Routine Clinical Care: Implementation Study
- Graph-Coupled HMMs for Modeling the Spread of Infection
- Hierarchical Graph-Coupled HMMs for Heterogeneous Personalized Health Data
- Understanding MS Fatigue: Initial Subtype Discovery from the MS Mosaic Project