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Mathematics
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Advances in Applied Statistics, Biostatistics, and Medical Informatics
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Advances in Applied Statistics, Biostatistics, and Medical Informatics

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

Deadline for manuscript submissions: 31 January 2027 | Viewed by 603

Editors

Dr. Lin Ge

E-Mail Website
Guest Editor
Department of Epidemiology and Biostatistics at the School of Public Health, Indiana University Bloomington (IUB), Bloomington, IN, USA
Interests: biostatistics; public health; surveillance research; capture-recapture method; misclassification; neuroimaging statistics
Dr. Shengxin Tu

E-Mail Website
Guest Editor Assistant
Vanderbilt University Medical Center, Nashville, TN, USA
Interests: biostatistics; statistics in medicine

Special Issue Information

Dear Colleagues,

Advances in applied statistics, biostatistics, and medical informatics play a crucial role in modern biomedical research and health data science. The rapid growth of complex data from biomedical experiments, genomic studies, electronic health records, and other sources offers both valuable opportunities and methodological challenges. Obtaining robust and meaningful insights from such data requires solid statistical foundations and efficient computational methods. Traditional approaches often face limitations, for example, when handling high-dimensional data, complex dependence structures, or missing values. Strengthening rigorous statistical methods is essential to ensure validity, interpretability, and reproducibility, ultimately supporting robust evidence evaluation and informed decision-making in biomedical and health-related research.

We are pleased to invite you to contribute to this Special Issue, which aims to highlight novel mathematical theories, statistical models, and computational approaches that advance applied statistics, biostatistics, and medical informatics. Contributions that integrate rigorous statistics with biomedical and health applications through methodological innovation, algorithm development, or real-world implementation are especially encouraged.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following: clinical trials, causal inference, survival analysis, high-dimensional inference, machine learning, bioinformatics, medical imaging analytics, and health informatics.

We look forward to receiving your contributions.

Dr. Lin Ge
Guest Editor

Dr. Shengxin Tu
Guest Editor Assistant

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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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

  • applied statistics
  • biostatistics
  • medical informatics
  • statistical modeling
  • clinical trials
  • causal inference
  • machine learning
  • high-dimensional data
  • bioinformatics
  • survival analysis

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Research

19 pages, 1286 KB  
Article
A Multi-Criteria Sample Selection Framework Using Uncertainty, Reliability, Representativeness, and Non-Redundancy for Emergency Department Prediction
by Daun Jeong, SangJun Moon and Jae Yong Yu
Mathematics 2026, 14(14), 2457; https://doi.org/10.3390/math14142457 - 8 Jul 2026
Viewed by 346
Abstract
Background: Selecting informative training samples is a fundamental yet challenging problem in predictive modeling, particularly in heterogeneous clinical data. Although supervised learning is typically formulated as an optimization problem over model parameters, the composition of the training set substantially influences generalization performance. In [...] Read more.
Background: Selecting informative training samples is a fundamental yet challenging problem in predictive modeling, particularly in heterogeneous clinical data. Although supervised learning is typically formulated as an optimization problem over model parameters, the composition of the training set substantially influences generalization performance. In this study, we propose a multi-criteria score-based sample selection framework for a machine learning setting. Method: Four sample-level scores were defined to quantify predictive uncertainty, representativeness, non-redundancy, and reliability. These scores were normalized and combined using three integration schemes: additive weighting, reliability-gated weighting, and rank-based aggregation. For each chief complaint category, a baseline model was trained either on the full training set, on score-selected subsets and on random size-matched subsets. Performance was assessed using the area under the receiver operating characteristic curve (AUROC), the area under the precision–recall curve, sensitivity, and specificity, with classification thresholds determined by the Youden index. Results: Across experiments, integrated score-based subset selection outperformed both full-data training and random subsampling in terms of mean AUROC, while often showing lower variability across chief complaints. Conclusions: The results suggest that sample utility in clinical tabular data is intrinsically multi-dimensional and that explicitly modeling this structure can improve predictive discrimination. Full article
(This article belongs to the Special Issue Advances in Applied Statistics, Biostatistics, and Medical Informatics)
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