Predictive, Preventive and Personalized Medicine & Molecular Diagnostics

Biography

Biography: Gauri Naik

Abstract

Healthcare and life sciences are generating BigData. Rising costs of healthcare has presented an opportunity for developing newer, robust healthcare models for efficient treatment, likelihood prediction such as patients presenting with vague symptoms, disease prevention by pre-clinical identification and personalized treatment options for patients. iPhronesis™ is an advanced BigData Analytics platform, built to address specific patient centric functions. iPhronesis™ delivers the true power of biomedical BigData by integrating disparate data sources such as EMR/ EHR/ genomics/ imaging/ scientific literature etc., both structured & unstructured, applying powerful analytics, some which are based on machine learning and Bio Natural Language Processing(Bio-NLP) tools enabling better understanding of data, discovering hidden relationships and presenting results with real evidences. iPhronesis™ allows users to choose from a series of domain specific workflows & processes, customizing each step, and integrating with custom algorithms. Every workflow is publishable as APIs or presented to the user interface. When data such as EMR/ EHR are combined with images or with genomics, it allows for generating patient longitudinal views, representing a complete patient/ cohort profile, identifying patterns, which help identify risk factors, predict disease progression, accurate disease classification and efficacy of treatment such as drug dose modification, adverse side effect identification and effects with comorbidity, to name a few. As a platform, iPhronesis™ also integrates with mobile applications increasing patient engagement, retention and enabling organizations to proactively reach a wider audience with analytics based evidences.