The QEXL Consortium :- Developing Machine Learning driven The Cognitive Architecture for the BiG-Data Riddled with Uncertainty
From The QEXL Kitchen
The QEXL Approach - a Multivariate Cognitive Architecture driven Unsupervised Machine Learning, is a Systems Thinking driven technique that has been designed with the intention of developing “Go To Market” solutions for Healthcare Big Data applications requiring "interoperability" between Payor, Provider, Health Management (Hospitals), Pharma etc. As such the HealthCare’s systemic complexities tethering on the “edge of chaos” pose enormous challenges in achieving “interoperability” owing to existence of plethora of healthcare system integration standards and management of the unstructured data in addition to structured data ingested from diverse sources. The motivation of the QEXL Approach as an archiecture is to enable creation of Tacit Knowledge Sets by inductive techniques and probabilistic inference from the diverse sets of data characterized by volume, velocity and variability. And, most importantly Uncertainty. Application areas are diverse in the HealthCare sector. As an inference tool to enable “Evidence based Medicine”, Both a synthesis and analytical tool for Public Health and Patient Health, Analytical tool for Genomics and Pharmacogenomics enabling personalization and Biologics. Tool for advancing Drug Discovery, especially Translational Science.