670881 event 1699041936 1699338360 <![CDATA[Ph.D. Dissertation Defense - Yuanda Zhu]]> Title:  Developing Novel NLP-Enabled Timely and Accurate Decision Making for Precision Medicine

Committee:

Dr. May Dongmei Wang, BME, Chair, Advisor

Dr. David Anderson, ECE

Dr. Faramarz Fekri, ECE

Dr. Blake Anderson, Emory

Dr. David Wright, Emory

]]> In this talk, I will report my PhD research in developing novel Natural Language Processing (NLP)-enabled accurate and timely decision-making systems for Precision Medicine. Precision medicine uses big data capturing “individual differences in patients’ genes, environments, and lifestyles focuses” to make prevention strategies, screening, diagnosis of diseases and treatment therapies. NLP uses Artificial Intelligence and Machine Learning to make sense of large volumes of language or time-series data. I have not only investigated how NLP can extract precise information from electronic health records (EHRs) and free-text clinical notes, but also examined how NLP models such as transformers can be used for analyzing human physiological Electroencephalography (EEG) data for accurate clinical decision making. Specifically, on EHRs, I have shown how NLP is able to decipher and sequentially report death events for individual patients; on free-text clinical notes, I have shown how the pretrained transformer-based NLP model BERT can precisely discern long COVID-19 patients from the rest; and on time-series EEG data, I have illustrated how Long Short-Term Memory (LSTM) and transformer architectures, when combing with convolutional neural networks (CNNs), can extract both spatial and temporal features for accurate seizure detection. My PhD research aims to provide a foundation for advancing NLP for personalized patient care in modern Precision Medicine.

]]> <![CDATA[]]> https://gatech.zoom.us/j/3992022181 434381 1788 100811 1808