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Mathematics
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Applied Medical Statistics: Theory, Computation, Applicability, 2nd Edition
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Applied Medical Statistics: Theory, Computation, Applicability, 2nd Edition

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D1: Probability and Statistics".

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 6219

Special Issue Editor

Prof. Dr. Sorana D. Bolboacă

E-Mail Website
Guest Editor
Department of Medical Informatics and Biostatistics, “Iuliu Haţieganu” University of Medicine and Pharmacy, Louis Pasteur Str., No. 6, 400349 Cluj-Napoca, Romania
Interests: applied and computational statistics; molecular modeling; genetic analysis; statistical modeling in medicine; integrated health informatics system; medical diagnostic research; statistical inference; medical imaging analysis; assisted decision systems; research ethics; social media and health information; evidence-based medicine
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Scientific knowledge in medicine must be reproducible, with accurate and reliable statistical analysis. Both components are a ‘must’ since scientific research in medicine aims to improve medical outcomes. Statistical methods need proper understanding by researchers and readers to produce the expected outcomes, namely successful medical diagnosis and treatment. Considerable progress in medical statistics has been achieved during the last decade, and many methods exist, but sometimes they are too complex to be easily understood by physicians.

This Special Issue on “Applied Medical Statistics: Theory, Computation, Applicability II” aims to gather under the same umbrella statistical guidelines as structured and straightforward tools to be easily understood by physicians and replicated by researchers. The manuscripts must clearly and easily explain information about the methods, how they work, and their clinical applicability, as instruments for critical appraisal of the medical scientific literature. Statistical methods and applications in medicine, dental medicine, pharmacy, and veterinary medicine are welcome.

Prof. Dr. Sorana D. Bolboacă
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. Mathematics is an international peer-reviewed open access semimonthly 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 data analysis
  • statistical guideline
  • statistical modeling
  • computational statistics
  • applied statistics

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Published Papers (3 papers)

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Research

10 pages, 585 KiB  
Article
Efficiency Index for Binary Classifiers: Concept, Extension, and Application
by Andrew J. Larner
Mathematics 2023, 11(11), 2435; https://doi.org/10.3390/math11112435 - 24 May 2023
Viewed by 1103
Abstract
Many metrics exist for the evaluation of binary classifiers, all with their particular advantages and shortcomings. Recently, an “Efficiency Index” (EI) for the evaluation of classifiers has been proposed, based on the consistency (or matching) and contradiction (or mismatching) of outcomes. This metric [...] Read more.
Many metrics exist for the evaluation of binary classifiers, all with their particular advantages and shortcomings. Recently, an “Efficiency Index” (EI) for the evaluation of classifiers has been proposed, based on the consistency (or matching) and contradiction (or mismatching) of outcomes. This metric and its confidence intervals are easy to calculate from the base data in a 2 × 2 contingency table, and their values can be qualitatively and semi-quantitatively categorised. For medical tests, in which context the Efficiency Index was originally proposed, it facilitates the communication of risk (of the correct diagnosis versus misdiagnosis) to both clinicians and patients. Variants of the Efficiency Index (balanced, unbiased) which take into account disease prevalence and test cut-offs have also been described. The objectives of the current paper were firstly to extend the EI construct to other formulations (balanced level, quality), and secondly to explore the utility of the EI and all four of its variants when applied to the dataset of a large prospective test accuracy study of a cognitive screening instrument. This showed that the balanced level, quality, and unbiased formulations of the EI are more stringent measures. Full article
(This article belongs to the Special Issue Applied Medical Statistics: Theory, Computation, Applicability, 2nd Edition)
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25 pages, 5796 KiB  
Article
Performance Analysis and Assessment of Type 2 Diabetes Screening Scores in Patients with Non-Alcoholic Fatty Liver Disease
by Norma Latif Fitriyani, Muhammad Syafrudin, Siti Maghfirotul Ulyah, Ganjar Alfian, Syifa Latif Qolbiyani, Chuan-Kai Yang, Jongtae Rhee and Muhammad Anshari
Mathematics 2023, 11(10), 2266; https://doi.org/10.3390/math11102266 - 12 May 2023
Cited by 1 | Viewed by 1908
Abstract
Type 2 diabetes (T2D) and non-alcoholic fatty liver disease (NAFLD) are worldwide chronic diseases that have strong relationships with one another and commonly exist together. Type 2 diabetes is considered one of the risk factors for NAFLD, so its occurrence in people with [...] Read more.
Type 2 diabetes (T2D) and non-alcoholic fatty liver disease (NAFLD) are worldwide chronic diseases that have strong relationships with one another and commonly exist together. Type 2 diabetes is considered one of the risk factors for NAFLD, so its occurrence in people with NAFLD is highly likely. As the high and increasing number of T2D and NAFLD, which potentially followed by existing together number, an analysis and assessment of T2D screening scores in people with NAFLD is necessary to be done. To prevent this potential case, an effective early prediction model is also required to be developed, which could help the patients avoid the dangers of both existing diseases. Therefore, in this study, analysis and assessment of T2D screening scores in people with NAFLD and the early prediction model utilizing a forward logistic regression-based feature selection method and multi-layer perceptrons are proposed. Our analysis and assessment results showed that the prevalence of T2D among patients with NAFLD was 8.13% (for prediabetes) and 37.19% (for diabetes) in two population-based NAFLD datasets. The variables related to clinical tests, such as alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), gamma-glutamyl transferase (GGT), and systolic blood pressure (SBP), were found to be statistically significant predictors (p-values < 0.001) that indicate a strong association with T2D among patients with NAFLD in both the prediabetes and diabetes NAFLD datasets. Finally, our proposed model showed the best performance in terms of all performance evaluation metrics compared to existing various machine learning models and also the models using variables recommended by WHO/CDC/ADA, with achieved accuracy as much as 92.11% and 83.05% and its improvement scores after feature selection of 1.35% and 5.35%, for the first and second dataset, respectively. Full article
(This article belongs to the Special Issue Applied Medical Statistics: Theory, Computation, Applicability, 2nd Edition)
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17 pages, 2340 KiB  
Article
Prediction of Out-of-Hospital Cardiac Arrest Survival Outcomes Using a Hybrid Agnostic Explanation TabNet Model
by Hung Viet Nguyen and Haewon Byeon
Mathematics 2023, 11(9), 2030; https://doi.org/10.3390/math11092030 - 25 Apr 2023
Cited by 5 | Viewed by 2259
Abstract
Survival after out-of-hospital cardiac arrest (OHCA) is contingent on time-sensitive interventions taken by onlookers, emergency call operators, first responders, emergency medical services (EMS) personnel, and hospital healthcare staff. By building integrated cardiac resuscitation systems of care, measurement systems, and techniques for assuring the [...] Read more.
Survival after out-of-hospital cardiac arrest (OHCA) is contingent on time-sensitive interventions taken by onlookers, emergency call operators, first responders, emergency medical services (EMS) personnel, and hospital healthcare staff. By building integrated cardiac resuscitation systems of care, measurement systems, and techniques for assuring the correct execution of evidence-based treatments by bystanders, EMS professionals, and hospital employees, survival results can be improved. To aid in OHCA prognosis and treatment, we develop a hybrid agnostic explanation TabNet (HAE-TabNet) model to predict OHCA patient survival. According to the results, the HAE-TabNet model has an “Area under the receiver operating characteristic curve value” (ROC AUC) score of 0.9934 (95% confidence interval 0.9933–0.9935), which outperformed other machine learning models in the previous study, such as XGBoost, k-nearest neighbors, random forest, decision trees, and logistic regression. In order to achieve model prediction explainability for a non-expert in the artificial intelligence field, we combined the HAE-TabNet model with a LIME-based explainable model. This HAE-TabNet model may assist medical professionals in the prognosis and treatment of OHCA patients effectively. Full article
(This article belongs to the Special Issue Applied Medical Statistics: Theory, Computation, Applicability, 2nd Edition)
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