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BioMedInformatics
Special Issues
Editor's Choice Series for Medical Statistics and Data Science Section
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Editor's Choice Series for Medical Statistics and Data Science Section

A special issue of BioMedInformatics (ISSN 2673-7426). This special issue belongs to the section "Medical Statistics and Data Science".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 2032

Special Issue Editor

Dr. Pentti Nieminen

E-Mail Website
Guest Editor
Medical Informatics and Data Analysis Research Group, University of Oulu, P.O. Box 5000, FI-90014 Oulu, Finland
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,

The Editor's Choice Series for Medical Statistics and Data Science Section presents a curated compilation focusing on advanced statistical methodologies and data science applications within the medical domain. This series highlights innovative approaches, analytical tools, and methodologies shaping the landscape of medical research, clinical trials, and healthcare decision making.

Encompassing diverse areas such as biostatistics, epidemiology, clinical trial design, predictive modeling, and data-driven healthcare, this series delves into the techniques enhancing evidence-based medicine and driving advancements in healthcare practices. Moving from robust statistical frameworks for clinical studies to predictive analytics aiding personalized healthcare, these articles elucidate the pivotal role of statistics and data science in medical advancements.

Please note that this series does not consider brief reports for publication, emphasizing comprehensive studies, methodological analyses, and practical applications within medical statistics and data science.

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. BioMedInformatics is an international peer-reviewed open access quarterly 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 1000 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

  • biostatistics
  • epidemiology
  • clinical trials
  • predictive modeling
  • data-driven healthcare
  • evidence-based medicine
  • statistical frameworks
  • medical research
  • healthcare decision-making
  • statistical reporting
  • health sciences
  • dentistry

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

Published Papers (2 papers)

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Research

22 pages, 1980 KiB  
Article
Causal Discovery for Patient Classification Using Health-Related Quality of Life Questionnaires
by Maria Ganopoulou, Konstantinos Fokianos, Christos Bakirtzis, Lefteris Angelis and Theodoros Moysiadis
BioMedInformatics 2025, 5(2), 28; https://doi.org/10.3390/biomedinformatics5020028 - 23 May 2025
Viewed by 285
Abstract
Background: Health-related quality of life (HRQoL) questionnaires are essential for understanding the physical, psychological, lifestyle, and social factors that impact patients’ well-being. Causal discovery demonstrates significant potential in this direction; however, it has not yet been thoroughly assessed. This study aimed to explore [...] Read more.
Background: Health-related quality of life (HRQoL) questionnaires are essential for understanding the physical, psychological, lifestyle, and social factors that impact patients’ well-being. Causal discovery demonstrates significant potential in this direction; however, it has not yet been thoroughly assessed. This study aimed to explore the perspective of utilizing causal discovery as a methodological tool for binary classification of patients based on HRQoL questionnaire data. Methods: The focus was on questionnaire structures similar to the EQ-5D-5L, which includes both ordinal and quantitative items. A customized classification algorithm is proposed, which utilizes the differences between the causal structures derived from the HRQoL questionnaire answers of patients who belong to two distinct groups. This algorithm was evaluated using the correct classification rate (CCR) and the misclassification rate (MR) based on simulated data under conditions of varying sample size and causal structures’ complexity, and within a real-world data application. Results: In both the simulation and application, the CCR exhibited larger values compared to the MR; however, the percentages that the algorithm could not result in a decision were, in general, not negligible. The adjusted CCR (algorithm yields a decision) exhibited substantially improved values compared to the CCR in both analyses. Within the application, the algorithm showed mixed performance compared to a standard stepwise binary logistic regression approach. Conclusions: The proposed algorithm has the potential to correctly classify patients, but further investigation is needed to evaluate its performance under different scenarios in a large-scale real-world setting. Determining the necessary conditions for successful classification would result in effectively exploiting causal discovery to further advance the role of HRQoL questionnaires in patient care and management. Full article
(This article belongs to the Special Issue Editor's Choice Series for Medical Statistics and Data Science Section)
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10 pages, 926 KiB  
Article
Cross-National Analysis of Opioid Prescribing Patterns: Enhancements and Insights from the OralOpioids R Package in Canada and the United States
by Ankona Banerjee, Kenneth Nobleza, Duc T. Nguyen and Erik Stricker
BioMedInformatics 2024, 4(3), 2107-2116; https://doi.org/10.3390/biomedinformatics4030112 - 16 Sep 2024
Cited by 1 | Viewed by 1238
Abstract
Background: The opioid crisis remains a significant public health challenge in North America, highlighted by the substantial need for tools to analyze and understand opioid potency and prescription patterns. Methods: The OralOpioids package automates the retrieval, processing, and analysis of opioid data from [...] Read more.
Background: The opioid crisis remains a significant public health challenge in North America, highlighted by the substantial need for tools to analyze and understand opioid potency and prescription patterns. Methods: The OralOpioids package automates the retrieval, processing, and analysis of opioid data from Health Canada’s Drug Product Database (DPD) and the U.S. Food and Drug Administration’s (FDA) National Drug Code (NDC) database. It includes functions such as load_Opioid_Table, which integrates country-specific data processing and Morphine Equivalent Dose (MED) calculations, providing a comprehensive dataset for analysis. The package facilitates a comprehensive examination of opioid prescriptions, allowing researchers to identify high-risk opioids and patterns that could inform policy and healthcare practices. Results: The integration of MED calculations with Canadian and U.S. data provides a robust tool for assessing opioid potency and prescribing practices. The OralOpioids R package is an essential tool for public health researchers, enabling a detailed analysis of North American opioid prescriptions. Conclusions: By providing easy access to opioid potency data and supporting cross-national studies, the package plays a critical role in addressing the opioid crisis. It suggests a model for similar tools that could be adapted for global use, enhancing our capacity to manage and mitigate opioid misuse effectively. Full article
(This article belongs to the Special Issue Editor's Choice Series for Medical Statistics and Data Science Section)
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