Applied Mathematics Colloquium: Dr. Sanjay Purushotham
UMBC
Friday, October 26, 2018 · 2 - 3 PM
Title: Deep learning models for Healthcare
Speaker: Sanjay Purushotham, UMBC
Abstract: Exponential growth in Electronic Healthcare Records has resulted in new opportunities for discovery of meaningful data-driven representations and patterns of diseases in Health care research. Deep Learning models have emerged as promising solutions for tackling many health care research problems since they can be automatically trained end-to-end without the need for hand-crafted features. While results from deep learning models are encouraging, there is still a major gap before it can be adopted as the mainstream method for practical healthcare applications. In this talk, I will first discuss the unique challenges that the healthcare data pose for machine learning and data driven analytical systems. Then, I will show the benchmarking performance of the deep learning models with respect to the state-of-the-art machine learning models and prognostic scoring systems on several clinical prediction tasks such as mortality prediction, length of stay prediction, and ICD-9 code group prediction. Finally, I will present our recently proposed novel deep learning models to address the challenges of the healthcare data, and discuss one use-case study where our deep learning model can be used to transfer knowledge across multi-cohort populations.
