Predicting near-term glaucoma progression: An artificial intelligence approach using clinical free-text notes and data from electronic health records

ABSTRACT:

Purpose

The purpose of this study was to develop a model to predict whether or not glaucoma will progress to the point of requiring surgery within the following year, using data from electronic health records (EHRs), including both structured data and free-text progress notes.

Methods

A cohort of adult glaucoma patients was identified from the EHR at Stanford University between 2008 and 2020, with data including free-text clinical notes, demographics, diagnosis codes, prior surgeries, and clinical information, including intraocular pressure, visual acuity, and central corneal thickness. Words from patients’ notes were mapped to ophthalmology domain-specific neural word embeddings. Word embeddings and structured clinical data were combined as inputs to deep learning models

SUBMITTER:  

PROVIDER: S-EPMC10133544 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature