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Entropy
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Applications of Statistical Methods in Medicine and Biology
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Applications of Statistical Methods in Medicine and Biology

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".

Deadline for manuscript submissions: closed (29 November 2022) | Viewed by 4292

Special Issue Editors

Dr. Cristina Costa-Santos

E-Mail Website
Guest Editor
1. Centre for Health Technology and Services Research (CINTESIS), Faculty of Medicine, University of Porto, 4200-450 Porto, Portugal
2. Department of Community Medicine, Information and Health Decision Sciences-MEDCIDS, Faculty of Medicine, University of Porto, 4200-450 Porto, Portugal
Interests: biostatistics; agreement and reliability; complexity measures; health data science; obstetrics and gynecology
Dr. Teresa S. Henriques

E-Mail Website1 Website2
Guest Editor
1. Centre for Health Technology and Services Research (CINTESIS), Faculty of Medicine, University of Porto, 4200-450 Porto, Portugal
2. Department of Community Medicine, Information and Health Decision Sciences-MEDCIDS, Faculty of Medicine, University of Porto, 4200-450 Porto, Portugal
Interests: complexity measures; time series analysis; biostatistics; health data science; machine learning
Dr. Andreia Teixeira

E-Mail Website
Guest Editor
1. Centre for Health Technology and Services Research (CINTESIS), Faculty of Medicine, University of Porto, 4200-450 Porto, Portugal
2. Department of Community Medicine, Information and Health Decision Sciences-MEDCIDS, Faculty of Medicine, University of Porto, 4200-450 Porto, Portugal
3. ADiT-LAB, Instituto Politécnico de Viana do Castelo, Rua Escola Industrial e Comercial Nun’Álvares, 4900-347 Viana do Castelo, Portugal
Interests: biostatistics; data analysis; information theory; data mining and Kolmogorov complexity
Special Issues, Collections and Topics in MDPI journals
Dr. Luísa Castro

E-Mail Website1 Website2
Guest Editor
1. Centre for Health Technology and Services Research (CINTESIS), Faculty of Medicine University of Porto, 4200-450 Porto, Portugal
2. Department of Community Medicine, Information and Health Decision Sciences-MEDCIDS, Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal
3. School of Health of Polytechnic of Porto, 4200-072 Porto, Portugal
Interests: biostatistics; data analysis; time series analysis; complexity measures; scales validation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Health care decision-making should be based on high-quality data-driven scientific evidence. Both in medicine and biology, data should be obtained, stored, analyzed, and interpreted according to the most appropriate practices and, in particular, the most powerful statistical techniques.

In this Special Issue, we invite contributions that focus on new methods used to analyze medical or biological research data. Applications of sophisticated techniques and entropy-based approaches for data analysis are also welcomed. This Special Issue aims to be a forum for the presentation of applications of statistical Methods in medicine and biology.

Dr. Cristina Costa-Santos
Dr. Teresa Henriques
Prof. Dr. Andreia Teixeira
Dr. Luísa Castro
Guest Editors

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

  • intelligent data analyses
  • linear and nonlinear analyses
  • complexity measures
  • applications
  • biostatistics
  • medical statistics
  • statistics
  • data analysis
  • health data science

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Published Papers (2 papers)

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Research

9 pages, 4642 KiB  
Article
Comparison of Non-Uniform Image Quality Caused by Anode Heel Effect between Two Digital Radiographic Systems Using a Circular Step-Wedge Phantom and Mutual Information
by Ching-Ting Chang and Ming-Chung Chou
Entropy 2022, 24(12), 1781; https://doi.org/10.3390/e24121781 - 6 Dec 2022
Viewed by 1800
Abstract
The purpose of this study was to compare non-uniform image quality caused by the anode heel effect between two radiographic systems using a circular step-wedge (CSW) phantom and the normalized mutual information (nMI) metric. Ten repeated radiographic images of the CSW and contrast-detail [...] Read more.
The purpose of this study was to compare non-uniform image quality caused by the anode heel effect between two radiographic systems using a circular step-wedge (CSW) phantom and the normalized mutual information (nMI) metric. Ten repeated radiographic images of the CSW and contrast-detail resolution (CDR) phantoms were acquired from two digital radiographic systems with 16- and 12-degree anode angles, respectively, using various kVp and mAs. To compare non-uniform image quality, the CDR phantom was physically rotated at different orientations, and the directional nMI metrics were calculated from the CSW images. The directional visible ratio (VR) metrics were calculated from the CDR images. Analysis of variance (ANOVA) was performed to understand whether the nMI metric significantly changed with kVp, mAs, and orientations with Bonferroni correction. Mann–Whitney’s U test was performed to compare the metrics between the two systems. Contrary to the VR metrics, the nMI metrics significantly changed with orientations in both radiographic systems. In addition, the system with the 12-degree anode angle exhibited less uniform image quality compared to the system with the 16-degree anode angle. A CSW phantom using the directional nMI metric can be significantly helpful to compare non-uniform image quality between two digital radiographic systems. Full article
(This article belongs to the Special Issue Applications of Statistical Methods in Medicine and Biology)
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13 pages, 6948 KiB  
Article
An Entropy-Based Measure of Complexity: An Application in Lung-Damage
by Pilar Ortiz-Vilchis and Aldo Ramirez-Arellano
Entropy 2022, 24(8), 1119; https://doi.org/10.3390/e24081119 - 14 Aug 2022
Cited by 5 | Viewed by 1697
Abstract
The computed tomography (CT) chest is a tool for diagnostic tests and the early evaluation of lung infections, pulmonary interstitial damage, and complications caused by common pneumonia and COVID-19. Additionally, computer-aided diagnostic systems and methods based on entropy, fractality, and deep learning have [...] Read more.
The computed tomography (CT) chest is a tool for diagnostic tests and the early evaluation of lung infections, pulmonary interstitial damage, and complications caused by common pneumonia and COVID-19. Additionally, computer-aided diagnostic systems and methods based on entropy, fractality, and deep learning have been implemented to analyse lung CT images. This article aims to introduce an Entropy-based Measure of Complexity (EMC). In addition, derived from EMC, a Lung Damage Measure (LDM) is introduced to show a medical application. CT scans of 486 healthy subjects, 263 diagnosed with COVID-19, and 329 with pneumonia were analysed using the LDM. The statistical analysis shows a significant difference in LDM between healthy subjects and those suffering from COVID-19 and common pneumonia. The LDM of common pneumonia was the highest, followed by COVID-19 and healthy subjects. Furthermore, LDM increased as much as clinical classification and CO-RADS scores. Thus, LDM is a measure that could be used to determine or confirm the scored severity. On the other hand, the d-summable information model best fits the information obtained by the covering of the CT; thus, it can be the cornerstone for formulating a fractional LDM. Full article
(This article belongs to the Special Issue Applications of Statistical Methods in Medicine and Biology)
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