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Internet of Health Things: Opportunities and Challenges
Published in Utpal Chakraborty, Amit Banerjee, Jayanta Kumar Saha, Niloy Sarkar, Chinmay Chakraborty, Artificial Intelligence and the Fourth Industrial Revolution, 2022
Emeka Chukwu, Lauit Garg, Ryan Zahra
This chapter highlights the information revolution and the related opportunities and challenges. It specifically looks at the inherent problems with healthcare systems that stem from the presence of multiple actors and how digital systems have attempted to solve them. The critical problem of the shortage of skilled personnel at the primary healthcare level has led to undercollection or zero collection of vital statistics and biostatistics of patients. The chapter further discusses the emergence of autonomous systems with control systems and the full spectrum of digitization, with specific focus on the Internet of Health Things (IoHT). The key applications of IoHT are discussed, along with the key opportunities and pitfalls and what to look out for when architecting one. The architecture, network, power, and other design considerations of a maternal health use case model are discussed. The full prototype components of a maternal health self-service kiosk are described to illustrate this.
Current Status of Biotechnology Manpower Development in Nigeria
Published in Sylvia Uzochukwu, Nwadiuto (Diuto) Esiobu, Arinze Stanley Okoli, Emeka Godfrey Nwoba, Christpeace Nwagbo Ezebuiro, Charles Oluwaseun Adetunji, Abdulrazak B. Ibrahim, Benjamin Ewa Ubi, Biosafety and Bioethics in Biotechnology, 2022
James C. Ogbonna, Benjamin Ewa Ubi, Abdulrazak Ibrahim, Ebiamadon Andi Brisibe, Abubakar Gidado, Mwajim Bukar, Aliyu Daja
In the development of their curricula at the postgraduate levels, most of the universities have taken into cognisance the need to ensure that the appropriate courses are taught. Some of the basic courses include biochemistry, microbiology, cell biology, molecular biology, plant/animal and microbial genetics, applied physics and instrumentation, laboratory techniques, infection and immunity, bioinformatics and biostatistics, genetic engineering, plant biotechnology and crop improvement, biodiversity and technologies, nanotechnology, natural products, virology, fermentation and biochemical engineering, protein engineering, laboratory project, genetic technology, immunodiagnostics, intellectual property rights, etc. Aside from these basic courses, there are many courses in the various applied areas of biotechnology, namely, medical biotechnology, industrial biotechnology, food and agricultural biotechnology and environmental biotechnology. The overriding principle behind teaching these courses to postgraduate students, essentially, appears to be the need to endow graduates with an articulate knowledge and sound understanding of the concepts and techniques of biotechnology in order to meet critical needs of society with particular reference to all aspects of human existence, being altered by new biotechnologies.
Using Statistics in Clinical Practice: A Gap Between Training and Application
Published in Marilyn Sue Bogner, Human Error in Medicine, 2018
Roberta L. Klatzky, James Geiwitz, Susan C. Fischer
Part of the MD-STAT project was a survey of medical schools to determine the nature of their statistical offerings and requirements. Of the 40 medical schools that were sampled, two thirds required some type of training in statistics, and almost all offered statistics courses as électives. Ofthose that did require course work in statistics, the average number of courses required was slightly greater than one. Most of the schools offered only those courses that they required plus one or two additional courses. Several of the schools offered multiple statistics courses as électives through their epidemiology, biostatistics, or medical informatics departments. These courses covered a wide range of topics (e.g., probability, multivariate statistics, epidemiological methods, computers and decision making, and research methods).
Reproducible research: a minority opinion
Published in Journal of Experimental & Theoretical Artificial Intelligence, 2018
It seems clear to me that Reproducibility as proposed by the round table, and supporters within the machine learning community, has never been a central tenet of science. Nevertheless, like Peng, some might still argue that it is useful as ‘a minimum standard’ when true statistical Replication is not practical to do. If that is so, should one consider Replicability the true ‘gold standard’ or ‘cornerstone of science’, with Reproducibility acting as an occasional stand-in? It is true that there are an increasing number of research fields where statistics takes on a fundamental role; Biostatistics and Geostatistics are two such examples. Yet, even in these areas, we primarily seek evidence for, or against, a scientific hypothesis not a statistical one. Replicability, at least in a formal sense, is tied strongly to the idea of statistical hypothesis testing. This idea was introduced by Fisher (1925), refined by Neyman and Pearson (1933) and became an integral part of some, but by no means all, sciences much later. This time-line would not include most of the major events in science, particularly in Physics. Therefore, I would claim even Replicability fails to make the grade. Surely then, only Retestability has any real claim to be a gold standard. Reproducibility is far too weak to be considered even a minimum one.