Mathematical Analysis and Computational Integration of Massive Heterogeneous Data from the Human Retina

TL;DR

This paper proposes a mathematical and computational framework for extracting knowledge from heterogeneous data sources, including images and text, to advance epidemiological research on retinal diseases.

Contribution

It introduces a novel roadmap for integrating diverse data types like images and text into epidemiological analysis of retinal diseases.

Findings

Framework for knowledge extraction from images and text.

Application to large-scale retinal disease data.

Potential to improve epidemiological insights.

Abstract

Modern epidemiology integrates knowledge from heterogeneous collections of data consisting of numerical, descriptive and imaging. Large-scale epidemiological studies use sophisticated statistical analysis, mathematical models using differential equations and versatile analytic tools that handle numerical data. In contrast, knowledge extraction from images and descriptive information in the form of text and diagrams remain a challenge for most fields, in particular, for diseases of the eye. In this article we provide a roadmap towards extraction of knowledge from text and images with focus on forthcoming applications to epidemiological investigation of retinal diseases, especially from existing massive heterogeneous collections of data distributed around the globe.