Personalized Predictions for Glaucoma Progression Using Artificial Intelligence for Electronic Health Records
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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.
