Publications

Abstract : The advancement of the digital world and the growth in cloud computation have led to enormous amounts of data from various sensors and digital devices along with communication models. The data gathered together are growing exponentially day by day. The structure of data is also dynamic and complicated. These data have to be processed and analyzed in a more efficient manner with the exploitation of “big data.” This chapter discusses the necessities of big data in the areas of healthcare and life sciences. Big data analytics have shown effectiveness in handling huge volumes of data and enhancing the outcomes at a cheaper cost. Healthcare systems produce enormous amounts of both structured and unstructured data. That data can be managed and analyzed disparately using the available tools of big data. Disease outbreaks and care delivery are the two major emerging fields of healthcare where big data analytics have been applied recently. The traditional methods of handling data are not suitable in healthcare and life-sciences applications, as the data in those fields are expected to grow exponentially in the forthcoming years. Healthcare industries and organizations should utilize the available infrastructures and methodologies to handle this enormous growing data to ensure the highest care of their patients. This technological overview discusses the employment of big data as an emerging stream in healthcare. It also delves into the business characteristics and optimized benefits of big data techniques within this field. The challenges and imminent predictions of big data in healthcare industries are also mentioned. This chapter reveals patient-centric lifetime evolution analytics using big data. The outpatient medical history along with the sensor data from devices connected to patient’s bodies are collected and processed using big data to instantly predict life-threatening medical conditions. Mobile networks are utilized. Big data is used to solve the problem of resource allocation for networks, thereby increasing the prediction likelihood of critical conditions of outpatients.