Project Summary/Abstract: Glaucoma is the leading cause of irreversible blindness, affecting over 60 million
people worldwide. Glaucoma patients vary widely in their presentation, with some retaining long-term disease
stability, and others progressing quickly to vision loss. If glaucoma patients at highest risk of progression could
be identified early, clinicians could better personalize their treatment approaches. Many clinical factors that
affect glaucoma progression, such as intraocular pressure, treatment history, and medication adherence, are
documented within the free-text notes of the electronic health records (EHR) and are not in large-scale
administrative claims databases. Recent advances in artificial intelligence (AI) and natural language
processing (NLP) have enabled the integration of the rich and complex EHR data into highly accurate
predictive algorithms for health outcomes in medicine and surgery. We hypothesize that we can extend these
AI and NLP techniques to build predictive algorithms for glaucoma progression that outperform traditional
models reliant on only administrative features. The goal of this project is to build and evaluate predictive
algorithms for glaucoma progression using large-scale EHR data, while developing Dr Wang's
expertise in AI and NLP, advancing her career as an independent clinician scientist. Aim 1 focuses on
using the structured clinical data within the EHR, which are numeric or coded and readily machine-readable, to
build baseline machine learning models predicting glaucoma progression requiring surgery. Aim 2 focuses on
using and augmenting clinical named entity recognition tools to integrate information from EHR free text into AI
models predicting glaucoma progression to surgery. Aim 3 focuses on understanding, explaining, and
evaluating the performance of AI algorithms in a real-world prospective setting, by evaluating their performance
on key subpopulations, their reliance on key features, and investigating potential areas of bias in a new cohort
of glaucoma patients. This proposal is innovative in developing AI-based predictive algorithms for
glaucoma progression using numeric and textual clinical data uniquely available in the EHR. The tools
and methods Dr Wang will build and evaluate will substantially impact the ophthalmology field by enabling
evidence-based tailoring of treatment approaches to patients' unique clinical characteristics, a step towards
precision medicine. Furthermore, the careful evaluation of AI predictive algorithms on a new cohort of patients
will provide insights into their performance on key subpopulations and reliance on key features, which is critical
to advancing our understanding of possible limitations of deploying AI in the clinical workflow. Dr. Wang's
career and research will advance under the primary mentorship of Dr. Tina Hernandez-Boussard, a national
leader in informatics and expert in using NLP on EHR to improve patient care. Her outstanding Advisory
Committee, including clinician-investigators Drs. Pershing, Stein, Chang, and Goldberg, will ensure Dr. Wang's
success in becoming an independent clinician-investigator integrating ophthalmology and informatics.