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

Abstract

Dry eye disease (DED) is a chronic, multifactorial ocular surface disorder with multiple etiologies that results in tear film instability. Globally, the prevalence of DED is expected to increase with an aging society and daily use of digital devices. Unfortunately, the medical field is currently unprepared to meet the medical needs of patients with DED. Noninvasive, reliable, and readily reproducible biomarkers have not yet been identified, and the current mainstay treatment for DED relies on symptom alleviation using eye drops with no effective preventative therapies available. Medical big data analyses, mining information from multiomics studies and mobile health applications, may offer a solution for managing chronic conditions such as DED. Omics-based data on individual physiologic status may be leveraged to prevent high-risk diseases, accurately diagnose illness, and improve patient prognosis. Mobile health applications enable the portable collection of real-world medical data and biosignals through personal devices. Together, these data lay a robust foundation for personalized treatments for various ocular surface diseases and other pathologies that currently lack the components of precision medicine. To fully implement personalized and precision medicine, traditional aggregate medical data should not be applied directly to individuals without adjustments for personal etiology, phenotype, presentation, and symptoms.

Cross-hierarchical Integrative Research Network for Heterogenetic Eye Disease Toward P4 Medicine: A Narrative Review

“…To effectively resolve this "one-size-fitsall" approach to DED, a previously singular disease must undergo stratification in the context of distinct pathologic pathways, contributing factors, and subjective symptom presentation. This will optimize treatment regimens for each disease stratum 2) .…”

“…In understanding the basis of disease heterogeneity and phenotypes, data-driven biological sciences are at the forefront of medical research. In essence, such an approach starts with collecting robust biological big data, visualizing and extracting essential information, and utilizing the results in solving specific problems 2) . The sheer amount of data and raw computational power needed for big data analysis has created an obstacle in prior generations of research, and implementation of AI technologies has enabled high-speed and high-accuracy data analysis.…”

“…Within the realm of IoMT, mHealth and wearable smart devices have received attention for their capability to provide "invisible medicine" through collecting important physiological data through biosensors and various input data without interrupting activity 2,3) . mHealth is defined as "as medical and public health practice supported by mobile devices, such as mobile phones, patient monitoring devices, personal digital assistants (PDAs), and other wireless devices" by the Global Observatory for eHealth (GOe) of the World Health Organization (WHO) 15) .…”

“…Drawing a parallel to IoT, the Internet of Medical Things (IoMT) acts as a networking platform to communicate and share medical big data. The big data gathered from users, electronic medical records, and environmental/geographical reports from independent sources can be collected and analyzed by AI to implement core values of P4 medicine: predictive, preventive, personalized, and participatory medicine (Figure 1) 2,3) . This model will reduce the role of traditional facility-based care and introduce effective longitudinal patientand population-oriented care based on personalized, lifetime, and preventative interventions within one's daily life.…”

“…From the traditional medical and epidemiological big data, a paradigm shift is occurring. It is moving toward incorporating personalized multiomics data and digital phenotypes sourced from mobile health (mHealth) and wearable smart devices 2,3) . Traditional medical big data, with its merits, is positioned to reflect aggregate data appropriate for population medicine, evidenced by its use in various epidemiological studies.…”