Big Data and Precision Medicine

Haroon Ur Rashid1, Fatma Hussain2, Khalid Masood3

Abstract

This paper focuses on clinical data taken from diversified sources that can be utilized to predict medical conditions. Precision medicine being top priority in medication is main essence to describe treatment based on individual physiology, genetic makeup and other factors. Healthcare information is available from clinics, government hospitals and electronic medical records along with advanced digital resources such as glucometers, insulin injectors, blood pressure monitors, and smart watches. Social media is an excellent source where people share their medical treatment status on Facebook, Twitter, WhatsApp and LinkedIn. Effective statistical models can be created social media to prescribe medicines. Vital architectural components include storage programs (Amazon S3, Google cloud store), data incorporation mechanisms (Kafka, Storm Topology, Sqoop), APIs (Fitbit Web, Apple HealthKit, OneTouch, Facebook, Twitter), processing engine (Spark, Hadoop) and training datasets (Spark ML, Mahout scalable machine learning, data mining techniques, appropriate algorithms). Advantages of precision medicine includes powerful decision making resources (big data), better selection of disease targets, treatment opportunities, reduced medical expenses and timely delivery of healthcare. To optimize the capability of precision medicine, uninterrupted research funding, scientific initiatives, patient involvement in medicinal initiatives. Successful execution of precision medicine with holistic individually tailored approach necessitates the coordinated efforts of all healthcare stakeholders for its recognition, up-gradation of diagnosis and management.