Robust Methods for Clinical Text Classification and Disease Understanding with NLP Extracted Symptoms from Clinical Notes

Abstract

Electronic Health Records (EHR) contain comprehensive medical and treatment histories of patients and have the potential to be used to provide better healthcare. A significant portion of the EHR is in the form of clinical notes and Natural Language Processing (NLP) methods can help extract hidden information from them. However, applying NLP in healthcare has challenges. Many of the clinical note datasets are scarce and imbalanced, making it difficult to develop generalizable and robust NLP methods. Additionally, effective use of NLP in healthcare requires close collaboration with medical experts to identify and understand meaningful clinical problems. This dissertation addresses these challenges and explores the application of NLP in healthcare. In Chapter 3 and 4, we develop generalizable and robust NLP methods for clinical note classification and female suicide report coding. In Chapter 5 and 6, we apply NLP to extract symptoms from clinical notes and study risk factors associated with out-of-hospital cardiac arrest (OHCA) and Long COVID.