The Role of Selfies in Creating the Next Generation Computer Vision Infused Outpatient Data Driven Electronic Health Records (EHR)

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Conference Proceeding

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Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018



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Selfies are popular. They embrace and represent social and emotional pulse of the User. We offer, nevertheless, groundbreaking and novel radical view on Selfies, especially Selfies that are taken for medical image purposes. In our view Selfies that are taken for medical image purposes are valuable outpatient healthcare data assets that could provide new clinical insights. Additionally, they could be used as diagnostics markers that could provide prognosis of a potential masked disease and necessitate actions to avert any emergency incidence, thereby saving Billions of dollars. We strongly believe that Interweaving Selfies that are taken for medical image purposes with outpatient Electronic Health Records (EHR) could breed new data driven diagnosis and clinical pathways that could potentially preempt healthcare services rendering decision making process for greater efficiencies and that could potentially save valuable time and attention of healthcare professionals who're already operating on a highly constrained time and shortage of skilled human resources. Putting in simple terms, Selfies could offer new diagnosis & clinical insights that have the potential to improve overall health outcomes of people around the globe in a cost-effective manner that epitomizes the confluence of popularity with curiosity and sharing with accountability.In this research paper, we propose computer vision (CV) based Machine Learning (ML) / Artificial Intelligence(AI) algorithms to classify and stratify Selfies that are captured for medical imaging purposes. Finally, the paper presents a CV - ML/AI prototyping solution as well as its application and certain experimental results.


and Outpatient, Computer Vision, EHR, Electronic Health Records, Feature Extraction, Local Binary Patter (LBP), Machine Learning, Pattern Recognition, Sanjeevani Electronic Health Records


Computer Engineering