Events Calendar

CS Colloquium: Joyce Ho (UT-Austin) - Extracting medically interpretable concepts from complex health data

Speaker: Joyce Ho , UT-Austin

Talk Title: Extracting medically interpretable concepts from complex health data

Series: CS Colloquium

Abstract: Electronic health records (EHRs) are an increasingly important source of patient information. However, a major challenge is how to transform EHR into meaningful concepts so domain experts can act on the information in an appropriate manner. In this talk, I will discuss two approaches to extract concise, meaningful concepts from certain types of health datasets. First, I will describe a dynamic time series model that tracks a patient's cardiac arrest risk based on physiological measurements. Our algorithm is inspired by financial econometric and yields interpretability and predictability of a cardiac arrest event. Next, I will present sparse, nonnegative tensor factorization models to obtain multiple medical concepts with minimal human supervision. Tensor factorization utilizes information in the multiway structure to derive concise latent factors even with limited observations. Experimental results on real EHRs demonstrate the effectiveness of our models to extract medically interpretable concepts from complex health data.


Biography: Joyce Ho is a PhD Candidate in the Electrical and Computer Engineering Department at the University of Texas at Austin co-advised by Dr. Joydeep Ghosh and Dr. Sriram Vishwanath. Her research involves the development of novel data mining and machine learning algorithms to address problems in healthcare. Joyce has also co-founded a healthcare data analytics company, Accordion Health, which was awarded an NSF Small Business Innovation Research (SBIR) grant.


Host: Prof. Yan Liu

Location: Henry Salvatori Computer Science Center (SAL) - 322

Audiences: Everyone Is Invited

Contact: Assistant to CS chair