In recent decades, there have been increasing utility of computational bio-statistical methods to clinical and health examination. This new generation of research is also known as biometrics or biometry in the Internet-of-Medical-Things (IoMT), and extends to applications such as medical research, epidemiology, clinical and public health science. Biostatistics involves the utility of quantification indicators to analyze medical data and signals from biological systems effectively. This involves determining correlation among different groups of data that seemed unrelated. In medical research, biostatistics has become an indispensable tool in enhancement of clinical diagnosis and treatment. For instance, it may be utilized in the study of cardiology, or respiratory disease, development and testing of new drugs, assessment of population health, analyzing the severity of cancer condition, evaluating mental health and psychiatric symptoms, monitoring disease infection, understanding biological system response or geographical patterns of disease, and studying anatomical dysfunction and disability. Biometry may also involve statistical work in areas of social and natural science, such as physics, chemistry, biology, agriculture, archaeology, veterinary, etc.
More specifically, future generation biostatistics techniques involve essential roles in designing studies and analyzing data from huge datasets biometry in the IoMT domain. It can formulate the scientific questions, determine the appropriate sampling techniques, coordinate data collection procedures, and perform the relevant statistical analyses to resolve the formulated scientific questions. In particular, active statistical methodologies include Bayesian methods, high-speed computing and simulation, data analysis, and key performance indicators for the analysis of IoMT data from epidemiologic studies or clinical trials.
This special issue focuses on the applications for problems in the medical domain, covering the solution for special problems or finding the potential correlation between diseases and some factors that seem unrelated using computational techniques. The main goal of this special issue is to provide the overview of the current state-of-the-art advances in health informatics and algorithms used for searching solutions as well as analyzing difficult treatment problems in IoMT.
Potential topics include, but are not limited to:
Medical statistical analysis, operation research, and information management in surgical, clinical, or psychiatric studies.
Biostatistical applications based on IoMT to provide online medical consultants including diagnosis, therapy planning, and treatment follow-ups, etc.
Deep learning based biometrics techniques in medical diagnostics.
Big data analysis techniques towards medical domain such as collection, analysis, learning, processing of widely used medical data.
Health informatics and statistical analysis of biological systems in the IoMT computational domain.
Management of biomedical data to assist with clinical decision-making and therapy guidance using statistics of IoMT data.
Submission Guideline: Authors must mention in their cover letter for each SI manuscript that the particular manuscript is for the theme and name of Guest Editors of SI consideration so that the Guest Editors can be notified separately. Guidelines for preparation of the manuscripts are available at the journal website.
READ THIS NOTE BEFORE SUBMISSION: Prospective authors should submit an electronic copy of their complete manuscript through the journal Manuscript Tracking System according to the following timetable:
Manuscript Due: 31 August 2019
First Round of Reviews: 30 September 2019
Second Round of Reviews: 31 October 2019
Revised manuscript due: 30 November 2019
Camera-ready version: 01 January 2020
Publication Date: 01 February 2020
Prof Kelvin KL Wong
School of Electrical and Electronic Engineering, The University of Adelaide, Australia.
Prof Giancarlo Fortino
University of Calabria (Unical), Rende (CS), Italy.
Prof Jimmy Zhihua Liu
Department of Biostatistics, Harvard School of Public Health, USA.
Assoc Prof Simon Fong
Department of Computer and Information Science, University of Macau, Macau.