Machine Learning for Ophthalmologic Predictions

With the advent of Machine Learning and the existence of Electronic Health Records, with most non-federal acute care hospitals a large number of office-based physicians already having opted for having a certified EHRs, each patient has essentially become a big data problem for medical predictions. This is also true in the field of Ophthalmology with its various specific modalities. Across two projects we explore how electronic health records can be used to make predictive model for various condition using machine learning.

Using patient histories and demographics such as age, gender, and race, body mass index (BMI), medications, biologicals, comorbidities, past medical history, and visual acuities we model a risk classifier for progression of age-related macular degeneration from its dry for to its wet form, which is a much faster progressing form of the disease. We found older age, use of biologicals such as anti-VEGF agents, and lover visual acuity to be associated with increased risk of progression of the disease. Our model gave an indicative tool with accuracy of 0.778±0.045, F1 score of 0.795±0.038 and sensitivity of 0.86±0.068. Also using imaging modalities such as SD-OCTs we model the detection of hydro-chloroquine toxicity related retinopathy, and attempt propose a prediction model. Our Model was able to detect hydro-chloroquine toxicity induced retinopathy with a precision of 0.72, recall of 0.92, F1 score of 0.81, and accuracy of 0.81.

Using the two projects we showed that using data extracted from electronic health records we can make effective models for various tasks using machine learning fairly well.