Statistics in Medicine and Health Sciences
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".
Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 538
Special Issue Editor
Interests: medical statistics; data informatics; statistics in medical journals; statistical computing; statistical modelling; data presentation; bibliometrics; information retrieval
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue aims to bring together information on established statistical reporting practices and data presentation methods in epidemiology, health sciences, dentistry and clinical medicine. Such resources would provide support for researchers at all levels, as well as for students. Randomness and complexity are often crucial characteristics in these fields. The application of entropy, and information theory in general, brings new scientific discoveries and practical solutions to knowledge-based medicine and health sciences.
A high proportion of medical articles are essentially statistical in their presentation. Regardless of the quality of the data and the variables chosen to express the results, the overt evidence of the research is produced as lists of numbers, tables, plots, graphs, and other displays, i.e., descriptive statistics. The communication of descriptive statistics is combined with statistical inference procedures. Medical data can be large, but also have great heterogeneity. Machine learning methods and predictive models have greatly facilitated extensive data analysis and increased the quantity and complexity of using statistical methods. When the purpose is to understand factors that affect developing diseases or estimate the effectiveness of new treatment methods, data presentation methods are crucial. If conclusions are drawn on the basis of computationally complex algorithms not visible in the data (tables or graphs) presented, then the clinicians may not be convinced of the results. Because of the increasing dependence on the medical literature, it is essential to collect new ideas from entropy and information theory for medicine, dentistry and healthcare to support the understanding of new research findings.
This Special Issue is an opportunity for the scientific community to present research on the application of statistical methods in medicine and health sciences. Both original research and review articles will be accepted in this issue.
Dr. Pentti Nieminen
Guest Editor
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- statistical methods
- statistical reporting
- data presentation
- predictive models
- epidemiology
- public health
- clinical research
- health care
- methodology
- information theory
