Develop informatics solutions to deliver relevant information for clinical decision making that improve the management of cardiovascular disease risk of breast cancer patients

Abstract

In the realm of breast cancer treatment, balancing the efficacy of therapy against the risk of treatment toxicity particularly cardiotoxicity as one of the more morbid complications of treatment poses a significant clinical challenge. This thesis presents an innovative approach to addressing the issue of breat cancer-related cardiotoxicity by integrating health informatics with real world data from Electronic Health Records (EHRs) and other health information technology (e.g., imaging reports from cardiovascular information systems) to improve cardiovascular disease risk management in breast cancer patients. Our approach leverages deep learning algorithms and a range of natural language processing (NLP) approaches in order to extract, analyze, and interpret complex clinical data to enhance decision-making processes in healthcare settings for this vulnerable group of patients.At the core of this body of research is the development and application of transformer-based deep learning methods, which are specifically tailored to extract targeted information from clinical texts in EHRs. By creating a specialized cancer domain vocabulary, the study demonstrates the enhanced performance of these models in accurately identifying relevant clinical data, such as patient demographics, treatment details, and cancer phenotypes. This approach significantly advances the precision and reliability of extracting important clinical information, which often is a crucial step in developing robust predictive models. The thesis further explores the generalizability of these NLP algorithms across different healthcare institutions via external validations, a vital consideration given the varied nature of EHRs. Through cross-institutional evaluations, the research establishes the portability of these models, ensuring their effectiveness in diverse clinical environments. This aspect of the study is critical in validating the broader applicability of the developed methodologies in various healthcare settings. Central to the thesis is the creation of predictive models for assessing the risk of heart disease in breast cancer patients. Utilizing a deep learning approach, specifically LSTM-D models, the study effectively harnesses longitudinal EHR data to predict cardiovascular risks associated with cancer treatments. We find that these models outperform traditional methods, offering a more nuanced understanding of patient-specific risk factors and temporal patterns in treatment responses. The thesis' findings underscore the potential of integrating advanced data analysis tools in clinical decision-making, particularly in the context of breast cancer treatment. By providing a more detailed and personalized risk assessment, the research contributes significantly to the field of personalized medicine, enhancing the quality of patient care and treatment outcomes. Overall, this thesis bridges a critical gap in healthcare informatics by developing and validating innovative methodologies for extracting and analyzing EHR data. The research marks a significant step towards more informed and personalized breast cancer treatment, highlighting the transformative potential of health informatics in managing complex disease interactions and improving patient outcomes.