Processing of Electronic Health Records using Deep Learning: A review

Availability of large amount of clinical data is opening up new research avenues in a number of fields. An exciting field in this respect is healthcare, where secondary use of healthcare data is beginning to revolutionize healthcare. Except for availability of Big Data, both medical data from healthcare institutions (such as EMR data) and data generated from health and wellbeing devices (such as personal trackers), a significant contribution to this trend is also being made by recent advances on machine learning, specifically deep learning algorithms

open PDS: Protecting the Privacy of Metadata through SafeAnswers.

The rise of smartphones and web services made possible the large-scale collection of personal metadata. Information about individuals' location, phone call logs, or web-searches, is collected and used intensively by organizations and big data researchers. Metadata has however yet to realize its full potential. Privacy and legal concerns, as well as the lack of technical solutions for personal metadata management is preventing metadata from being shared and reconciled under the control of the individual

Empowering citizens' cognition and decision making in smart sustainable cities

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Advances in Internet technologies have made it possible to gather, store, and process large quantities of data, often in real time. When considering smart and sustainable cities, this big data generates useful information and insights to citizens, service providers, and policy makers. Transforming this data into knowledge allows for empowering citizens' cognition as well as supporting decision-making routines. However, several operational and computing issues need to be taken into account: 1) efficient data description and visualization, 2) forecasting citizens behavior, and 3) supporting decision making with intelligent algorithms. This paper identifies several challenges associated with the use of data analytics in smart sustainable cities and proposes the use of hybrid simulation-optimization and machine learning algorithms as an effective approach to empower citizens' cognition and decision making in such ecosystemsPeer ReviewedPostprint (author's final draft

Fog and Cloud Computing Assisted IoT Model Based Personal Emergency Monitoring and Diseases Prediction Services

Along with the rapid development of modern high-tech and the change of people's awareness of healthy life, the demand for personal healthcare services is gradually increasing. The rapid progress of information and communication technology and medical and bio technology not only improves personal healthcare services, but also brings the fact that the human being has entered the era of longevity. At present, there are many researches focused on various wearable sensing devices and implant devices and Internet of Things in order to capture personal daily life health information more conveniently and effectively, and significant results have been obtained, such as fog computing. To provide personal healthcare services, the fog and cloud computing is an effective solution for sharing health information. The health big data analysis model can provide personal health situation reports on a daily basis, and the gene sequencing can provide hereditary disease prediction. However, the injury mortality and emergency diseases since long ago caused death and great pain for the family. And there are no effective rescue methods to save precious lives and no methods to predict the disease morbidity likelihood. The purpose of this research is to capture personal daily health information based on sensors and monitoring emergency situations with the help of fog computing and mobile applications, and disease prediction based on cloud computing and big data analysis. Through the comparison of test results it was proved that the proposed emergency monitoring based on fog and cloud computing and the diseases prediction model based on big data analysis not only gain more of the rescue time than the traditional emergency treatment method, but they also accumulate lots of different personal healthcare related experience. The Taian 960 hospital of PLA and the Yanbian Hospital as IM testbed were joined to provide emergency monitoring tests, and to ensure the CVD and CVA morbidity likelihood medical big data analysis, the people around Taian city participated in personal health tests. Through the project, the five network layers architecture and integrated MAPE-K Model based EMDPS platform not only made the cooperation between hospitals feasible to deal with emergency situations, but also the Internet medicine for the disease prediction was built

Rewriting Judicial Recusal Rules with Big Data

Big data affects the personal and professional life of every judge. A judge’s travel time to work, creditworthiness, and chances of an IRS audit all depend on predictive algorithms interpreting big data. A client’s choice of counsel, the precise wording of a litigant’s motion, and the composition of the jury may be dictated by analytics. Touted as a means of bringing objectivity to judicial decision-making, judges have employed big data to determine sentences and to set the amount of restitution in class action cases. Unfortunately, the legal profession and big data proponents have ignored one perplexing problem begging for a big data solution—the arbitrary and inconsistent manner in which courts determine judicial recusal issues. Every jurisdiction disqualifies a judge when the fully-informed, reasonable, lay observer concludes that the judge’s “impartiality might reasonably be questioned.” Created by the American Bar Association in 1972 to bring uniformity and consistency to the disqualification process, this “objective” test has been a dismal failure. The ABA’s goal, however, can be realized by infusing data analytics into the disqualification decision-making process. Part I of this Article identifies the serious shortcomings of an appearance-based disqualification standard. Part II explains how analysis of big data can correct the theoretical and practical problems plaguing the “might reasonably be questioned” standard. Part III applies the big data derived model to one type of disqualification motion—motions seeking a judge’s removal from a case because of contributions made to the judge’s election campaign by litigants, lawyers, or others connected with the litigation

Status of Big Data In Internet of Things: A Comprehensive Overview

Abstract: Reports suggests that total amount of data generated everyday reaches 2.5 quintillion bytes [9], annual global IP traffic run rate in 2016 was 1.2 zettabytes and will reach 3.3 zettabytes by 2021 [12]. According to Gartner [25], Internet of Things excluding personal computers, tablets and smartphones will grow to 26 billion units of installed devices in year 2020. This results from penetration of digital applications which highly motivated by smart societies which can be defined as to when a society deploys light and advanced computer technologies to aid provision and or supply chain value of social, cultural, governance and economic utilities for efficiency. Smart society is equipped with mobile, ubiquitous computing facilities, sensors and cyber-physical systems aims at exploring economies of scale; and to large extent it has been made possible with Internet of Things (IoT). This survey paper discusses status of big data in Internet of Things; how IoT generates big data, nature of data generated and dynamics in IoT as influenced by big data

Perlindungan Hukum terhadap Data Pribadi Nasabah Pasca Merger 3 Bank Syariah Menjadi Bank Syariah Indonesia

This research was motivated by the merger of three Sharia Banks namely Bank Syariah Mandiri (BSM), Bank Negara Indonesia (BNI) Syariah, and Bank Rakyat Indonesia (BRI) Syariah into Bank Syariah Indonesia. The merger had legal consequences for the customers because the merger policy led to integrating customer personal data into one Big Data. This had become customers' concern related to the security and monitoring system. Consequently, legal protections were needed to secure this issue. This study aims to find a form of legal protection for Sharia Bank Customers after the merger of the three Sharia banks. This study implemented normative legal research with the statute approach and the conceptual approach. The results found that the legal protection for Sharia Bank Customers as intended consists of 2 (two) forms. First, External Protection: legal protection made by the authorities in the form of laws and regulations. Second, Internal protection: legal protection made by the Sharia Bank to secure the customers' personal data

From Dataveillance to Data Economy: Firm View on Data Protection

The increasing availability of electronic records and the expanded reliance on online communications and services have made available a huge amount of data about people’s behaviours, characteristics, and preferences. Advancements in data processing technology, known as big data, offer opportunities to increase organisational efficiency and competitiveness. Analytically sophisticated companies excel in their ability to extract value from the analysis of digital data. However, in order to exploit the potential economic benefits produced by big data and analytics, issues of data privacy and information security need to be addressed. In Europe, organisations processing personal data are being required to implement basic data protection principles, which are considered difficult to implement in big data environments. Little is known in the privacy studies literature about how companies manage the trade-off between data usage and data protection. This study contributes to explore the corporate data privacy environment, by focusing on the interrelationship between the data protection legal regime, the application of big data analytics to achieve corporate objectives, and the creation of an organisational privacy culture. It also draws insights from surveillance studies, particularly the idea of dataveillance, to identify potential limitations of the current legal privacy regime. The findings from the analysis of survey data show that big data and data protection support each other, but also that some frictions can emerge around data collection and data fusion. The demand for the integration of different data sources poses challenges to the implementation of data protection principles. However, this study finds no evidence that data protection laws prevent data gathering. Implications relevant for the debate on the reform of European data protection law are also drawn from these findings