Researcher ORCID Identifier
Graduation Year
2023
Document Type
Open Access Senior Thesis
Degree Name
Bachelor of Science
Department
Mathematics
Reader 1
Weiqing Gu
Reader 2
Jina Kim
Terms of Use & License Information
Rights Information
© 2023 Justin B Jiang
Abstract
There exist petabytes of data pertaining to medical visits – everything from blood pressure recordings, X-rays, and doctor’s notes. Electronic health records (EHRs) organize this data into databases, providing an exciting opportunity for machine learning researchers to dive deeper into analyzing human health. There already exist machine learning models that aim to expedite the process of hospital visits; for example, summary models can digest a patient’s medical history and highlight certain parts of their past that merit attention. The current frontier of medical machine learning is combining the various formats of data to generate a clinical prediction – much like a medical professional would. Challenges exist in accessing data, ethical AI, and the inequality that is inherent in our medical system.
This thesis explores the frontier of medical machine learning, particularly how graph learning is used to predict diseases using multi-modality data.
Recommended Citation
Jiang, Justin, "Graph Learning on Multi-Modality Medical Data to Generate Clinical Predictions" (2023). HMC Senior Theses. 281.
https://scholarship.claremont.edu/hmc_theses/281
Included in
Artificial Intelligence and Robotics Commons, Data Science Commons, Other Mathematics Commons, Statistical Models Commons, Vital and Health Statistics Commons
