Using Medical Big Data to Develop Personalized Medicine for Dry Eye Disease

Area of Science:

• Ophthalmology
• Biomedical Informatics
• Precision Medicine

Background:

• Dry eye disease (DED) is a prevalent, chronic ocular surface disorder with increasing incidence.
• Current DED treatments focus on symptom relief, lacking effective preventative strategies and reliable biomarkers.
• The medical field is ill-equipped to manage the growing DED patient population.

Purpose of the Study:

• To explore the potential of medical big data, including multiomics and mobile health applications, for advancing DED management.
• To highlight the need for personalized and precision medicine approaches in treating ocular surface diseases like DED.

Main Methods:

• Leveraging omics-based data to understand individual physiological status for disease prevention and diagnosis.
• Utilizing mobile health applications for real-world data collection and biosignal monitoring.
• Integrating diverse data sources to build a foundation for personalized ocular surface disease treatments.

Main Results:

• Medical big data analyses offer a promising avenue for managing chronic conditions such as DED.
• Omics data can inform disease prevention, accurate diagnosis, and prognosis improvement.
• Mobile health technology facilitates portable collection of real-world medical data and biosignals.

Conclusions:

• Personalized treatments for DED and other ocular surface diseases are achievable through integrated data approaches.
• Precision medicine requires tailoring treatments based on individual etiology, phenotype, presentation, and symptoms, moving beyond aggregate data.
• Future DED management should incorporate big data analytics for personalized and preventative healthcare strategies.