Revolutionizing Health Management: An Insight into the Impact of AI and Big Data

This article explores the opportunities and challenges of artifcial intelligence (AI) and big data for health management. It argues that AI and big data can revolutionize health management by enabling personalized, preventive, and predictive medicine; enhancing health research and innovation; and transforming health systems and policies. However, it also acknowledges that AI and big data pose ethical, legal, social, and technical challenges and risks that need to be addressed and mitigated. It proposes that ethical and governance frameworks forAI and big data for health should be based on human values and principles. The article provides an overview of the main aspects of health management that can be revolutionized byAI and big data, as well as some recommendations or suggestions for future research or practice in this feld

Assessing the potential utility of commercial ‘big data’ for health research: enhancing small-area deprivation measures with Experian™ Mosaic groups

In contrast to area-based deprivation measures, commercial datasets remain infrequently used in health research and policy. Experian collates numerous commercial and administrative data sources to produce Mosaic groups which stratify households into 15 groups for marketing purposes. We assessed the potential utility of Mosaic groups for health research purposes by investigating their relationships with Indices of Multiple Deprivation (IMD) for the British population. Mosaic groups showed significant associations with IMD quintiles. Correspondence Analysis revealed variations in patterns of association, with Mosaic groups either showing increasing, decreasing, or some mixed trends with deprivation quintiles. These results suggest that Experian's Mosaics additionally measure other aspects of socioeconomic circumstances to those captured by deprivation measures. These commercial data may provide new insights into the social determinants of health at a small area level

Transforming Health through Big Data: Challenges and Considerations

Modern healthcare is increasingly dependent on good data, and effective information systems, for care delivery, and to develop and evaluate health policy. The context of big data differs in significant ways from traditional types of health data, while the use of big data for epidemiology and public health is becoming more common, the use of these tools for health service planning and health policy making lags behind. A large EU funded project (titled MIDAS) that focuses on merging, analysing and visualising data from heterogeneous sources to support health policy makers work in using and accessing health data across EU countries is underway. This paper briefly describes the key challenges that must be met to access, use and make sense of this big data in healthcare, focusing on legal, governance and ethical issues. Unless these issues are dealt with, the promise of Big Data for health, will never be fulfilled

Design and Implementation of a Hybrid Wireless Power and Communication System for Medical Implants

Data collection and analysis from multiple implant nodes in humans can provide targeted medicine and treatment strategies that can prevent many chronic diseases. This data can be collected for a long time and processed using artificial intelligence (AI) techniques in a medical network for early detection and prevention of diseases. Additionally, machine learning (ML) algorithms can be applied for the analysis of big data for health monitoring of the population. Wireless powering, sensing, and communication are essential parts of future wireless implants that aim to achieve the aforementioned goals. In this paper, we present the technical development of a wireless implant that is powered by radio frequency (RF) at 401 MHz, with the sensor data being communicated to an on-body reader. The implant communication is based on two simultaneous wireless links: RF backscatter for implant-to-on-body communication and a galvanic link for intra-body implant-to-implant connectivity. It is demonstrated that RF powering, using the proposed compact antennas, can provide an efficient and integrable system for powering up to an 8 cm depth inside body tissues. Furthermore, the same antennas are utilized for backscatter and galvanic communication

Invited commentary on Stewart and Davis " 'Big data' in mental health research-current status and emerging possibilities"

No abstract available

FogGIS: Fog Computing for Geospatial Big Data Analytics

Cloud Geographic Information Systems (GIS) has emerged as a tool for analysis, processing and transmission of geospatial data. The Fog computing is a paradigm where Fog devices help to increase throughput and reduce latency at the edge of the client. This paper developed a Fog-based framework named Fog GIS for mining analytics from geospatial data. We built a prototype using Intel Edison, an embedded microprocessor. We validated the FogGIS by doing preliminary analysis. including compression, and overlay analysis. Results showed that Fog computing hold a great promise for analysis of geospatial data. We used several open source compression techniques for reducing the transmission to the cloud.Comment: 6 pages, 4 figures, 1 table, 3rd IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics (09-11 December, 2016) Indian Institute of Technology (Banaras Hindu University) Varanasi, Indi

Evaluation of sleep stage classification using feature importance of EEG signal for big data healthcare

Sleep analysis is widely and experimentally considered due to its importance to body health care. Since its sufficiency is essential for a healthy life, people often spend almost a third of their lives sleeping. In this case, a similar sleep pattern is not practiced by every individual, regarding pure healthiness or disorders such as insomnia, apnea, bruxism, epilepsy, and narcolepsy. Therefore, this study aims to determine the classification patterns of sleep stages, using big data for health care. This used a high-dimensional FFT extraction algorithm, as well as a feature importance and tuning classifier, to develop accurate classification. The results showed that the proposed method led to more accurate classification than previous techniques. This was because the previous experiments had been conducted with the feature selection model, with accuracy implemented as a performance evaluation. Meanwhile, the EEG Sleep Stages classification model in this present report was composed of the feature selection and importance of the extraction stage. The previous and present experiments also reached the highest values of accuracy, with the Random Forest and SVM models using 2000 and 3000 features (87.19% and 89.19%, respectively. In this article, we proposed an analysis that the feature importance subsequently influenced the model's accuracy. This was because the proposed method was easily fine-tuned and optimized for each subject to improve sensitivity and reduce false negative occurrences

A Deep Learning Approach to Integrate Medical Big Data for Improving Health Services in Indonesia

Medical Informatics to support health services in Indonesia is proposed in this paper. The focuses of paper to the analysis of Big Data for health care purposes with the aim of improving and developing clinical decision support systems (CDSS) or assessing medical data both for quality assurance and accessibility of health services. Electronic health records (EHR) are very rich in medical data sourced from patient. All the data can be aggregated to produce information, which includes medical history details such as, diagnostic tests, medicines and treatment plans, immunization records, allergies, radiological images, multivariate sensors device, laboratories, and test results. All the information will provide a valuable understanding of disease management system. In Indonesia country, with many rural areas with limited doctor it is an important case to investigate. Data mining about large-scale individuals and populations through EHRs can be combined with mobile networks and social media to inform about health and public policy. To support this research, many researchers have been applied the Deep Learning (DL) approach in data-mining problems related to health informatics. However, in practice, the use of DL is still questionable due to achieve optimal performance, relatively large data and resources are needed, given there are other learning algorithms that are relatively fast but produce close performance with fewer resources and parameterization, and have a better interpretability. In this paper, the advantage of Deep Learning to design medical informatics is described, due to such an approach is needed to make a good CDSS of health services

Radar and RGB-depth sensors for fall detection: a review

This paper reviews recent works in the literature on the use of systems based on radar and RGB-Depth (RGB-D) sensors for fall detection, and discusses outstanding research challenges and trends related to this research field. Systems to detect reliably fall events and promptly alert carers and first responders have gained significant interest in the past few years in order to address the societal issue of an increasing number of elderly people living alone, with the associated risk of them falling and the consequences in terms of health treatments, reduced well-being, and costs. The interest in radar and RGB-D sensors is related to their capability to enable contactless and non-intrusive monitoring, which is an advantage for practical deployment and users’ acceptance and compliance, compared with other sensor technologies, such as video-cameras, or wearables. Furthermore, the possibility of combining and fusing information from The heterogeneous types of sensors is expected to improve the overall performance of practical fall detection systems. Researchers from different fields can benefit from multidisciplinary knowledge and awareness of the latest developments in radar and RGB-D sensors that this paper is discussing