Deep Learning for Healthcare
Deep Learning for Healthcare

Abstract: 

In this tutorial, we present deep learning methods and their applications in computational healthcare, specifically focusing on clinical representation learning, predictive modeling, clinical trial modeling and drug development.
We will also introduce different types of data in healthcare including structured electronic health records, unstructured clinical notes, medical images, clinical trial description, chemical compounds and medical knowledge base.
This tutorial is intended for data scientists, engineers and researchers who are interested in applying deep learning methods to healthcare, and prerequisite knowledge include basic machine knowledge. The first half will be spent on introducing the nature of health data, basic deep learning methods and their application in healthcare. In the second half, we will focus on challenges specific to computational healthcare, and introduce advanced deep learning methods ranging from deep phenotyping, chronic disease prediction, rare disease detection, patient-trial matching, and molecule generation and drug property prediction. The tutorial will be concluded with open problems and a Q&A session.

Bio: 

Cao (Danica) Xiao is the Director of Machine Learning at Analytics Center of Excellence of IQVIA. She is leading IQVIA’s North America machine learning team to drive next generation healthcare AI. Her team works on various projects on disease prediction, clinical trial enrollment modeling, and in silico drug modeling (e.g., adverse drug reaction detection, drug repositioning and de novo design). Her research focuses on using machine learning and data mining approaches to solve diverse real world healthcare challenges. Particularly, she is interested in phenotyping on electronic health records, data mining for in-silico drug modeling, biomarker discovery and patient segmentation for neuro-degenerative diseases. Her research has been published in leading AI conferences including KDD, NIPS, ICLR, AAAI, IJCAI, SDM, ICDM, WWW and top health informatics journals such as Nature Scientific Reports and JAMIA. Prior to IQVIA, she was a research staff member in the AI for Healthcare team at IBM Research from 2017 to 2019 and served as member of the IBM Global Technology Outlook Committee from 2018 to 2019. She acquired her Ph.D. degree from University of Washington, Seattle in 2016.