Today’s healthcare system is not built for the seamless integration of innovations in machine learning. Health data is heavily siloed, inaccessible, and non standardized, making it challenging to work with in machine learning systems. Additionally, deep learning techniques aren’t well suited for many healthcare problems due to the difficulty in interpreting their decisions. At Remedy, we’re building a healthcare system that captures granular data at the point of care, and enables the deployment of machine learning models at the point of care. We’re also developing interpretable model to tackle tasks such as diagnosis, physician education, treatment planning, and triage.
Will is CEO of Remedy Health, a startup focused on building a healthcare system around a strong software backbone to enable the seamless integration of new innovations into care. Will is also a venture partner at Alsop Louie Partners. Prior to this Will worked in R&D on SpaceX’s internet satellite project, and studied physics and computer science at MIT. An Ohio native, Will spent his childhood developing a particle collider in his basement, using the nuclear reactions it carried out to investigate novel methods of medical imaging.
This presentation took place at the Deep Learning Summit in San Francisco on 26-27 January 2017: https://www.re-work.co/events/deep-learning-summit-san-francisco-2017
You can view more videos on Deep Learning here: http://videos.re-work.co/
9. Model DistillationBLACK BOX
DEEP MODEL
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Model Interpretability: Distillation
Source: Che et al.“Interpretable Deep Models for ICU Outcome Prediction”
