Machine Learning Models For Dry Eyes Diagnosis And Treatment: How Are The Patients Doing?

I have previously reported the machine learning characteristics of this novel software. The purpose of this study was to look at Machine Learning (ML) models capable of using demographic and real-world clinical data to see how it helped us predict better treatment profiles based on patient satisfaction.

Real-world clinical data captured data at one site (N-148)

Clinical data was captured to develop two machine learning diagnostic models for severity
and type of dry eye. The acquired data had 25118 samples, where each sample had dozens of features and was
annotated by domain experts. 495 physicians contributed 21190 questionnaires. Correlation feature selection (CFS)
was used to eliminate redundant and irrelevant features. Using stratified 10-fold cross-validation was shown to be the most
effective ML technique. Support Vector Machines (SVM) was the best technique to fit our data. We developed two SVM models,
namely SM (severity model) and TM (type model).  Outcomes using this software based on the subjective data, objective data, and coconspirators allowed us to formulate treatment profiles to study..

The SM model was successful in predicting different dry eye severity cases at AUC−ROC of 0.79 and AUC−PR of 0.61. Furthermore,
the TM model was successful in predicting different dry eye type cases at AUC−ROC of 0.91 and AUC−PR of 0.94. We also verified
the robustness of both SM and TM models by comparing their performance with nine baseline machine learning methods.  This date has

been presented and published. This paper will show how that software and with those machine learning characteristics made us better diagnosticians in treating

patients (N-148)

As a validated dry eye prediction model, the ML models proposed in this study accurately predicted dry eye severity and type. It also beneficial in terms of patient satisfaction and perceived benefits with varying treatment protocols. In this prospecitve series, it made us better diagnosticians to eliminate previously tried therapies and tailor new options for the patients, as well as, identify coconspirators to address.