Dear all, You are cordially invited to attend Ping Wang’s PhD defense on 13th July (Tomorrow) at 1:00 PM. Title: Automatic Question Answering and Knowledge Discovery from Electronic Health Records Time: Tuesday, July 13, 2021, 1:00 PM Eastern Time Zoom link: https://virginiatech.zoom.us/j/3137821683 Meeting ID: 313 782 1683 Committee: Dr. Chandan K. Reddy, CS, VT, (Chair) Dr. Naren Ramakrishnan, CS, VT Dr. Chang-Tien Lu, CS, VT Dr. Jiepu Jiang, University of Wisconsin-Madison https://www.wisc.edu/ Dr. Sutanay Choudhury, Pacific Northwest National Laboratory Abstract: Healthcare is an important part of our lives. Due to the recent advances in data collection and storing techniques, a large amount of medical information is generated and stored in Electronic Health Records (EHR). By comprehensively documenting the longitudinal medical history information about a large patient cohort, this EHR data forms a fundamental resource in assisting doctors' decision making including optimization of treatments for patients and selection of patients for clinical trials. However, EHR data also presents a number of unique challenges, such as (i) large-scale, heterogeneous, and dynamic data, (ii) medical term abbreviation, and (iii) noisy nature caused by misspelled words. It is difficult for doctors to effectively utilize such complex data collected in a typical clinical practice. Therefore, it is imperative to develop advanced methods that are helpful for the efficient use of EHR and further benefit doctors in their clinical decision-making. This dissertation focuses on automatically retrieving useful medical information, analyzing complex relationships of medical entities, and detecting future medical outcomes from EHR data. In order to retrieve information from EHR efficiently, we develop deep learning based algorithms that can automatically answer various clinical questions on structured and unstructured EHR data. These algorithms can help us understand more about the challenges in retrieving information from different data types in EHR. We also build a clinical knowledge graph based on EHR and link the distributed medical information and further perform the link prediction task, which allows us to analyze the complex underlying relationships of various medical entities. In addition, we propose a temporal multi-task survival analysis method to dynamically predict multiple medical events at the same time and identify the most important factors leading to future medical events. Regards, Chandan. -- Chandan K. Reddy Professor Department of Computer Science Virginia Tech http://www.cs.vt.edu/~reddy/