Application of Biostatistics in Medical Sciences and Global Health

Topic Information

Dear Colleagues,

We are pleased to invite you to contribute to our Topic, "Application of Biostatistics in Medical Sciences and Global Health", which aims to showcase recent advancements in statistical modeling, machine learning, and artificial intelligence (AI) in medical research. This Topic focuses on innovative biostatistical methodologies that enhance disease burden estimation, improve diagnostic accuracy, and optimize treatment strategies.

The integration of biostatistics and machine learning has transformed how we study and combat diseases. Advanced statistical models are crucial for accurately measuring the burden of diseases across populations, particularly for cardiovascular diseases, diabetes, and cancer. Machine learning and AI have proven valuable for predictive modeling, risk stratification, and personalized treatment strategies.

A special emphasis is placed on Raman spectroscopy in cancer research. This technique has shown promise in distinguishing cancer subtypes based on spectral data, offering a highly accurate method for early cancer detection. Studies integrating Raman spectroscopy with AI and statistical modeling are highly encouraged.

In addition, antimicrobial resistance (AMR) poses a growing global health challenge. We welcome research applying statistical modeling and machine learning to study AMR trends, predict resistance patterns, and develop targeted interventions.

We encourage contributions on the following topics:

• Statistical modeling of disease burden and epidemiological trend;
• Machine learning and AI applications in cardiology, diabetes, and cancer;
• Predictive analytics for treatment response and patient outcomes;
• Raman spectroscopy in cancer classification;
• Computational and statistical approaches to antimicrobial resistance.

We look forward to receiving your submissions and advancing the role of biostatistics in global health.

Prof. Dr. Bogdan Oancea
Dr. Adrian Pană
Prof. Dr. Cǎtǎlina Liliana Andrei
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Journal of Personalized Medicine jpm	-	6.0	2011	21.5 Days	CHF 2600	Submit
Mathematics mathematics	2.2	4.6	2013	18.4 Days	CHF 2600	Submit
Applied Sciences applsci	2.5	5.5	2011	19.8 Days	CHF 2400	Submit
Stats stats	1.0	1.8	2018	18.2 Days	CHF 1600	Submit
Healthcare healthcare	2.7	4.7	2013	21.5 Days	CHF 2700	Submit

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Published Papers (3 papers)

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All JPM Mathematics Applied Sciences Stats Healthcare

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14 pages, 879 KB  

Open AccessArticle

Predicting Factors Associated with Extended Hospital Stay After Postoperative ICU Admission in Hip Fracture Patients Using Statistical and Machine Learning Methods: A Retrospective Single-Center Study

by Volkan Alparslan, Sibel Balcı, Ayetullah Gök, Can Aksu, Burak İnner, Sevim Cesur, Hadi Ufuk Yörükoğlu, Berkay Balcı, Pınar Kartal Köse, Veysel Emre Çelik, Serdar Demiröz and Alparslan Kuş

Healthcare 2025, 13(19), 2507; https://doi.org/10.3390/healthcare13192507 - 2 Oct 2025

Viewed by 332

Abstract

Background: Hip fractures are common in the elderly and often require ICU admission post-surgery due to high ASA scores and comorbidities. Length of hospital stay after ICU is a crucial indicator affecting patient recovery, complication rates, and healthcare costs. This study aimed to [...] Read more.

Background: Hip fractures are common in the elderly and often require ICU admission post-surgery due to high ASA scores and comorbidities. Length of hospital stay after ICU is a crucial indicator affecting patient recovery, complication rates, and healthcare costs. This study aimed to develop and validate a machine learning-based model to predict the factors associated with extended hospital stay (>7 days from surgery to discharge) in hip fracture patients requiring postoperative ICU care. The findings could help clinicians optimize ICU bed utilization and improve patient management strategies. Methods: In this retrospective single-centre cohort study conducted in a tertiary ICU in Turkey (2017–2024), 366 ICU-admitted hip fracture patients were analysed. Conventional statistical analyses were performed using SPSS 29, including Mann–Whitney U and chi-squared tests. To identify independent predictors associated with extended hospital stay, Least Absolute Shrinkage and Selection Operator (LASSO) regression was applied for variable selection, followed by multivariate binary logistic regression analysis. In addition, machine learning models (binary logistic regression, random forest (RF), extreme gradient boosting (XGBoost) and decision tree (DT)) were trained to predict the likelihood of extended hospital stay, defined as the total number of days from the date of surgery until hospital discharge, including both ICU and subsequent ward stay. Model performance was evaluated using AUROC, F1 score, accuracy, precision, recall, and Brier score. SHAP (SHapley Additive exPlanations) values were used to interpret feature contributions in the XGBoost model. Results: The XGBoost model showed the best performance, except for precision. The XGBoost model gave an AUROC of 0.80, precision of 0.67, recall of 0.92, F1 score of 0.78, accuracy of 0.71 and Brier score of 0.18. According to SHAP analysis, time from fracture to surgery, hypoalbuminaemia and ASA score were the variables that most affected the length of stay of hospitalisation. Conclusions: The developed machine learning model successfully classified hip fracture patients into short and extended hospital stay groups following postoperative intensive care. This classification model has the potential to aid in patient flow management, resource allocation, and clinical decision support. External validation will further strengthen its applicability across different settings. Full article

(This article belongs to the Topic Application of Biostatistics in Medical Sciences and Global Health)

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12 pages, 683 KB  

Open AccessReview

The Use of Double Poisson Regression for Count Data in Health and Life Science—A Narrative Review

by Sebastian Appelbaum, Julia Stronski, Uwe Konerding and Thomas Ostermann

Stats 2025, 8(4), 90; https://doi.org/10.3390/stats8040090 - 1 Oct 2025

Viewed by 443

Abstract

Count data are present in many areas of everyday life. Unfortunately, such data are often characterized by over- and under-dispersion. In 1986, Efron introduced the Double Poisson distribution to account for this problem. The aim of this work is to examine the application [...] Read more.

Count data are present in many areas of everyday life. Unfortunately, such data are often characterized by over- and under-dispersion. In 1986, Efron introduced the Double Poisson distribution to account for this problem. The aim of this work is to examine the application of this distribution in regression analyses performed in health-related literature by means of a narrative review. The databases Science Direct, PBSC, Pubmed PsycInfo, PsycArticles, CINAHL and Google Scholar were searched for applications. Two independent reviewers extracted data on Double Poisson Regression Models and their applications in the health and life sciences. From a total of 1644 hits, 84 articles were pre-selected and after full-text screening, 13 articles remained. All these articles were published after 2011 and most of them targeted epidemiological research. Both over- and under-dispersion was present and most of the papers used the generalized additive models for location, scale, and shape (GAMLSS) framework. In summary, this narrative review shows that the first steps in applying Efron’s idea of double exponential families for empirical count data have already been successfully taken in a variety of fields in the health and life sciences. Approaches to ease their application in clinical research should be encouraged. Full article

(This article belongs to the Topic Application of Biostatistics in Medical Sciences and Global Health)

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25 pages, 374 KB  

Open AccessFeature PaperArticle

Goodness-of-Fit Tests for Combined Unilateral and Bilateral Data

by Jia Zhou and Chang-Xing Ma

Mathematics 2025, 13(15), 2501; https://doi.org/10.3390/math13152501 - 3 Aug 2025

Viewed by 392

Abstract

Clinical trials involving paired organs often yield a mixture of unilateral and bilateral data, where each subject may contribute either one or two responses. While unilateral responses from different individuals can be treated as independent, bilateral responses from the same individual are likely [...] Read more.

Clinical trials involving paired organs often yield a mixture of unilateral and bilateral data, where each subject may contribute either one or two responses. While unilateral responses from different individuals can be treated as independent, bilateral responses from the same individual are likely correlated. Various statistical methods have been developed to account for this intra-subject correlation in the bilateral data, and in practice, it is crucial to select a model that properly accounts for this correlation to ensure accurate inference. Previous research has investigated goodness-of-fit test statistics for correlated bilateral data under different group settings, assuming fully observed paired outcomes. In this work, we extend these methods to the more general and practically common setting where unilateral and bilateral data are combined. We examine the performance of various goodness-of-fit statistics under different statistical models, including the Clayton copula model. Simulation results indicate that the performance of the goodness-of-fit tests is model-dependent, especially when the sample size is small and/or the intra-subject correlation is high. However, the three bootstrap methods generally offer more robust performance. In real world applications from otolaryngologic and ophthalmologic studies, model choice significantly impacts conclusions, emphasizing the need for appropriate model assessment in practice. Full article

(This article belongs to the Topic Application of Biostatistics in Medical Sciences and Global Health)

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