This study will apply the technique of unsupervised feature clustering to identify and model patient clinical behaviors from their electronic health records. The project will allow medical providers to understand groups and patterns in patient clinical behavior, identify fundamental factors contributing to adverse medical events such as death and cardiac arrest, and even discover unknown clinical behaviors.
Project
Clinical Behavior Analysis via Unsupervised Learning
Unsupervised feature clustering to identify and model patient clinical behaviors from their electronic health records.
