scholarly journals Systematic Predictive Analysis of Personalized Life Expectancy Using Smart Devices

Technologies ◽  
2018 ◽  
Vol 6 (3) ◽  
pp. 74 ◽  
Author(s):  
James Kang ◽  
Sasan Adibi
Keyword(s):  
Life Expectancy ◽  
Smart Devices ◽  
The Novel ◽  
Sleep Monitoring ◽  
Related Data ◽  
Health Related ◽  
Fitness Tracking ◽  
The Individual ◽  
Calorie Expenditure ◽  
The Internet Of Things

With the emergence of technologies such as electronic health and mobile health (eHealth/mHealth), cloud computing, big data, and the Internet of Things (IoT), health related data are increasing and many applications such as smartphone apps and wearable devices that provide wellness and fitness tracking are entering the market. Some apps provide health related data such as sleep monitoring, heart rate measuring, and calorie expenditure collected and processed by the devices and servers in the cloud. These requirements can be extended to provide a personalized life expectancy (PLE) for the purpose of wellbeing and encouraging lifestyle improvement. No existing works provide this PLE information that is developed and customized for the individual. This article is based on the concurrent models and methodologies to calculate and predict life expectancy (LE) and proposes an idea of using multi-phased approaches to the solution as the project requires an immense and broad range of work to accomplish. As a result, the current prediction of LE, which was found to be up to a maximum of five years could potentially be extended to a lifetime prediction by utilizing generic health data. In this article, the novel idea of the solution proposing a PLE on an individual basis, which can be extended to lifetime is presented in addition to the existing works.

Deep Learning in Early Detection of Alzheimer's

2019 ◽  
pp. 153-162
Author(s):  
Anitha S. Pillai ◽  
Bindu Menon
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Early Detection ◽  
Big Data Analytics ◽  
Vital Role ◽  
Smart Devices ◽  
Brain Images ◽  
Related Data ◽  
Computer Scientists ◽  
Health Related

Advancement in technology has paved the way for the growth of big data. We are able to exploit this data to a great extent as the costs of collecting, storing, and analyzing a large volume of data have plummeted considerably. There is an exponential increase in the amount of health-related data being generated by smart devices. Requisite for proper mining of the data for knowledge discovery and therapeutic product development is very essential. The expanding field of big data analytics is playing a vital role in healthcare practices and research. A large number of people are being affected by Alzheimer's Disease (AD), and as a result, it becomes very challenging for the family members to handle these individuals. The objective of this chapter is to highlight how deep learning can be used for the early diagnosis of AD and present the outcomes of research studies of both neurologists and computer scientists. The chapter gives introduction to big data, deep learning, AD, biomarkers, and brain images and concludes by suggesting blood biomarker as an ideal solution for early detection of AD.

scholarly journals Using Personal Genomic Data within Primary Care: A Bioinformatics Approach to Pharmacogenomics

Genes ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1443
Author(s):  
Rick Overkleeft ◽  
Judith Tommel ◽  
Andrea W. M. Evers ◽  
Johan T. den Dunnen ◽  
Marco Roos ◽  
...  
Keyword(s):  
Primary Care ◽  
Bioinformatics Analysis ◽  
Personal Information ◽  
Genomic Data ◽  
Ethical Standards ◽  
Personal Genomic ◽  
Related Data ◽  
Health Related ◽  
The Individual ◽  
High Degree

One application of personalized medicine is the tailoring of medication to the individual, so that the medication will have the highest chance of success. In order to individualize medication, one must have a complete inventory of all current pharmaceutical compounds (a detailed formulary) combined with pharmacogenetic datasets, the genetic makeup of the patient, their (medical) family history and other health-related data. For healthcare professionals to make the best use of this information, it must be visualized in a way that makes the most medically relevant data accessible for their decision-making. Similarly, to enable bioinformatics analysis of these data, it must be prepared and provided through an interface for controlled computational analysis. Due to the high degree of personal information gathered for such initiatives, privacy-sensitive implementation choices and ethical standards are paramount. The Personal Genetic Locker project provides an approach to enable the use of personal genomic data in primary care. In this paper, we provide a description of the Personal Genetic Locker project and show its utility through a use case based on open standards, which is illustrated by the 4MedBox system.

Deep Learning in Early Detection of Alzheimer's

2021 ◽  
pp. 160-169
Author(s):  
Anitha S. Pillai ◽  
Bindu Menon
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Early Detection ◽  
Big Data Analytics ◽  
Vital Role ◽  
Smart Devices ◽  
Brain Images ◽  
Related Data ◽  
Computer Scientists ◽  
Health Related

Advancement in technology has paved the way for the growth of big data. We are able to exploit this data to a great extent as the costs of collecting, storing, and analyzing a large volume of data have plummeted considerably. There is an exponential increase in the amount of health-related data being generated by smart devices. Requisite for proper mining of the data for knowledge discovery and therapeutic product development is very essential. The expanding field of big data analytics is playing a vital role in healthcare practices and research. A large number of people are being affected by Alzheimer's Disease (AD), and as a result, it becomes very challenging for the family members to handle these individuals. The objective of this chapter is to highlight how deep learning can be used for the early diagnosis of AD and present the outcomes of research studies of both neurologists and computer scientists. The chapter gives introduction to big data, deep learning, AD, biomarkers, and brain images and concludes by suggesting blood biomarker as an ideal solution for early detection of AD.

scholarly journals A Blockchain-Enabled Framework for mHealth Systems

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2828
Author(s):  
Dragos Daniel Taralunga ◽  
Bogdan Cristian Florea
Keyword(s):  
Distributed Storage ◽  
Wearable Sensors ◽  
Healthcare Providers ◽  
Modern Technology ◽  
Security And Privacy ◽  
Smart Device ◽  
Related Data ◽  
Smart Healthcare ◽  
Health Related ◽  
The Internet Of Things

Presently modern technology makes a significant contribution to the transition from traditional healthcare to smart healthcare systems. Mobile health (mHealth) uses advances in wearable sensors, telecommunications and the Internet of Things (IoT) to propose a new healthcare concept centered on the patient. Patients’ real-time remote continuous health monitoring, remote diagnosis, treatment, and therapy is possible in an mHealth system. However, major limitations include the transparency, security, and privacy of health data. One possible solution to this is the use of blockchain technologies, which have found numerous applications in the healthcare domain mainly due to theirs features such as decentralization (no central authority is needed), immutability, traceability, and transparency. We propose an mHealth system that uses a private blockchain based on the Ethereum platform, where wearable sensors can communicate with a smart device (a smartphone or smart tablet) that uses a peer-to-peer hypermedia protocol, the InterPlanetary File System (IPFS), for the distributed storage of health-related data. Smart contracts are used to create data queries, to access patient data by healthcare providers, to record diagnostic, treatment, and therapy, and to send alerts to patients and medical professionals.

Margaret Mahy: An Adlerian Reading

2008 ◽  
Vol 1 (2) ◽  
pp. 187-199
Author(s):  
KATHRYN WALLS
Keyword(s):  
Young Female ◽  
The Other ◽  
Individual Psychology ◽  
The Novel ◽  
Alfred Adler ◽  
Female Protagonists ◽  
Magical Power ◽  
Margaret Mahy ◽  
The Individual

According to the ‘Individual Psychology’ of Alfred Adler (1870–1937), Freud's contemporary and rival, everyone seeks superiority. But only those who can adapt their aspirations to meet the needs of others find fulfilment. Children who are rejected or pampered are so desperate for superiority that they fail to develop social feeling, and endanger themselves and society. This article argues that Mahy's realistic novels invite Adlerian interpretation. It examines the character of Hero, the elective mute who is the narrator-protagonist of The Other Side of Silence (1995) , in terms of her experience of rejection. The novel as a whole, it is suggested, stresses the destructiveness of the neurotically driven quest for superiority. Turning to Mahy's supernatural romances, the article considers novels that might seem to resist the Adlerian template. Focusing, in particular, on the young female protagonists of The Haunting (1982) and The Changeover (1984), it points to the ways in which their magical power is utilised for the sake of others. It concludes with the suggestion that the triumph of Mahy's protagonists lies not so much in their generally celebrated ‘empowerment’, as in their transcendence of the goal of superiority for its own sake.

Novel Visualization of Large Health Related Data Sets

2015 ◽  
Author(s):  
William E. Hammond ◽  
Vivian L. West ◽  
David Borland ◽  
Igor Akushevich ◽  
Eugenia M. Heinz
Keyword(s):  
Data Sets ◽  
Related Data ◽  
Health Related

The Free Responsible Action of the Individual

2018 ◽  
Author(s):  
Michael P. DeJonge
Keyword(s):  
The Novel ◽  
Extreme Situation ◽  
The Third ◽  
Third Phase ◽  
Responsible Action ◽  
Action Type ◽  
Two Phases ◽  
The Individual ◽  
The Church

If, as Chapter 12 argues, much of Bonhoeffer’s resistance thinking remains stable even as he undertakes the novel conspiratorial resistance, what is new in his resistance thinking in the third phase? What receives new theological elaboration is the resistance activity of the individual, which in the first two phases was overshadowed by the resistance role played by the church. Indeed, as this chapter shows, Bonhoeffer’s conspiratorial activity is associated with what he calls free responsible action (type 6), and this is the action of the individual, not the church, in the exercise of vocation. As such, the conspiratorial activity is most closely related to the previously developed type 1 resistance, which includes individual vocational action in response to state injustice. But the conspiratorial activity differs from type 1 resistance as individual vocational action in the extreme situation.

scholarly journals IoT Traffic: Modeling and Measurement Experiments

IoT ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 140-162
Author(s):  
Hung Nguyen-An ◽  
Thomas Silverston ◽  
Taku Yamazaki ◽  
Takumi Miyoshi
Keyword(s):  
Smart Home ◽  
Large Scale ◽  
Traffic Modeling ◽  
The Novel ◽  
Network Behavior ◽  
Performance Accuracy ◽  
Traffic Generator ◽  
Multiple Devices ◽  
Iot Devices ◽  
The Internet Of Things

We now use the Internet of things (IoT) in our everyday lives. The novel IoT devices collect cyber–physical data and provide information on the environment. Hence, IoT traffic will count for a major part of Internet traffic; however, its impact on the network is still widely unknown. IoT devices are prone to cyberattacks because of constrained resources or misconfigurations. It is essential to characterize IoT traffic and identify each device to monitor the IoT network and discriminate among legitimate and anomalous IoT traffic. In this study, we deployed a smart-home testbed comprising several IoT devices to study IoT traffic. We performed extensive measurement experiments using a novel IoT traffic generator tool called IoTTGen. This tool can generate traffic from multiple devices, emulating large-scale scenarios with different devices under different network conditions. We analyzed the IoT traffic properties by computing the entropy value of traffic parameters and visually observing the traffic on behavior shape graphs. We propose a new method for identifying traffic entropy-based devices, computing the entropy values of traffic features. The method relies on machine learning to classify the traffic. The proposed method succeeded in identifying devices with a performance accuracy up to 94% and is robust with unpredictable network behavior with traffic anomalies spreading in the network.

scholarly journals Assessment of Dietary Habits, Physical Activity and Lifestyle in Medical University Students

2021 ◽  
Vol 13 (6) ◽  
pp. 3572
Author(s):  
Lavinia-Maria Pop ◽  
Magdalena Iorga ◽  
Iulia-Diana Muraru ◽  
Florin-Dumitru Petrariu
Keyword(s):  
Physical Activity ◽  
Medical Students ◽  
Fast Food ◽  
Dietary Habits ◽  
Healthy Life ◽  
Related Data ◽  
The Third ◽  
Health Related ◽  
Medical University ◽  
Busy Schedule

A busy schedule and demanding tasks challenge medical students to adjust their lifestyle and dietary habits. The aim of this study was to identify dietary habits and health-related behaviours among students. A number of 403 students (80.40% female, aged M = 21.21 ± 4.56) enrolled in a medical university provided answers to a questionnaire constructed especially for this research, which was divided into three parts: the first part collected socio-demographic, anthropometric, and medical data; the second part inquired about dietary habits, lifestyle, sleep, physical activity, water intake, and use of alcohol and cigarettes; and the third part collected information about nutrition-related data and the consumption of fruit, vegetables, meat, eggs, fish, and sweets. Data were analysed using SPSS v24. Students usually slept M = 6.71 ± 1.52 h/day, and one-third had self-imposed diet restrictions to control their weight. For both genders, the most important meal was lunch, and one-third of students had breakfast each morning. On average, the students consumed 1.64 ± 0.88 l of water per day and had 220 min of physical activity per week. Data about the consumption of fruit, vegetables, meat, eggs, fish, sweets, fast food, coffee, tea, alcohol, or carbohydrate drinks were presented. The results of our study proved that medical students have knowledge about how to maintain a healthy life and they practice it, which is important for their subsequent professional life.

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