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Bioengineering
Special Issues
Mathematical Models for Medical Diagnosis and Testing
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Mathematical Models for Medical Diagnosis and Testing

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 30 August 2025 | Viewed by 635

Special Issue Editors

Dr. Peiyuan Zhou

E-Mail Website
Guest Editor
System Design Engineering Department, University of Waterloo, Waterloo, ON N2L 3G1, Canada
Interests: machine learning; pattern recognition; healthcare data analysis; clinical data analysis; time series analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Andrew K. C. Wong

E-Mail
Guest Editor
System Design Engineering Department, University of Waterloo, Waterloo, ON N2L 3G1, Canada
Interests: machine intelligence; pattern recognition; knowledge representation; bioinformatics; protein binding; protein sequencing analysis; statistical modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on advancing the development and application of mathematical models for medical diagnosis and testing. Submissions presenting innovative frameworks that combine mathematical models with computational methods to address critical challenges in healthcare are highly encouraged. Examples include improving diagnostic accuracy, managing complex and imbalanced datasets, and developing robust models to support clinical decision-making. Additionally, this Special Issue welcomes contributions emphasizing practical utility, such as interpretability and transparency, which are essential for high-stakes medical applications. The main goal is to bridge theoretical advancements with real-world healthcare challenges, delivering actionable insights and impactful solutions.

Submissions are invited on topics including, but not limited to, the following:

  1. Development of mathematical and statistical models for disease diagnosis.
  2. Applications of machine learning and artificial intelligence in medical testing.
  3. Algorithms for handling noisy, imbalanced, or high-dimensional clinical data.
  4. Transparent and interpretable modeling frameworks to support medical decision-making.
  5. Techniques for rare disease detection and anomaly identification.
  6. Integration of domain-specific medical knowledge with computational techniques.
  7. Real-world case studies demonstrating the application of advanced models in clinical settings.

All research areas are considered relevant, provided that experimentation and/or predictive simulations serve as the primary focus of the study.

Dr. Peiyuan Zhou
Prof. Dr. Andrew K. C. Wong
Guest Editors

Jiecao Wang
Guest Editor Assistant
Affiliation: System Design Engineering Department, University of Waterloo, Waterloo, ON N2L 3G1, Canada
Website: https://www.linkedin.com/in/jiecaowang/
E-mail:
Interests: machine intelligence; pattern recognition; knowledge representation; bioinformatics; computer vision; multimodal learning; image analysis

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. Bioengineering 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 2700 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

  • mathematical modeling
  • machine learning
  • artificial intelligence
  • pattern discovery
  • medical diagnosis
  • clinical data analysis
  • predictive analytics
  • explainable AI
  • statistical modeling
  • anomaly detection
  • rare disease detection
  • knowledge discovery
  • health informatics

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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

17 pages, 4169 KiB  
Article
Benchmarking Interpretability in Healthcare Using Pattern Discovery and Disentanglement
by Pei-Yuan Zhou, Amane Takeuchi, Fernando Martinez-Lopez, Malikeh Ehghaghi, Andrew K. C. Wong and En-Shiun Annie Lee
Bioengineering 2025, 12(3), 308; https://doi.org/10.3390/bioengineering12030308 - 18 Mar 2025
Viewed by 403
Abstract
The healthcare industry seeks to integrate AI into clinical applications, yet understanding AI decision making remains a challenge for healthcare practitioners as these systems often function as black boxes. Our work benchmarks the Pattern Discovery and Disentanglement (PDD) system’s unsupervised learning algorithm, which [...] Read more.
The healthcare industry seeks to integrate AI into clinical applications, yet understanding AI decision making remains a challenge for healthcare practitioners as these systems often function as black boxes. Our work benchmarks the Pattern Discovery and Disentanglement (PDD) system’s unsupervised learning algorithm, which provides interpretable outputs and clustering results from clinical notes to aid decision making. Using the MIMIC-IV dataset, we process free-text clinical notes and ICD-9 codes with Term Frequency-Inverse Document Frequency and Topic Modeling. The PDD algorithm discretizes numerical features into event-based features, discovers association patterns from a disentangled statistical feature value association space, and clusters clinical records. The output is an interpretable knowledge base linking knowledge, patterns, and data to support decision making. Despite being unsupervised, PDD demonstrated performance comparable to supervised deep learning models, validating its clustering ability and knowledge representation. We benchmark interpretability techniques—Feature Permutation, Gradient SHAP, and Integrated Gradients—on the best-performing models (in terms of F1, ROC AUC, balanced accuracy, etc.), evaluating these based on sufficiency, comprehensiveness, and sensitivity metrics. Our findings highlight the limitations of feature importance ranking and post hoc analysis for clinical diagnosis. Meanwhile, PDD’s global interpretability effectively compensates for these issues, helping healthcare practitioners understand the decision-making process and providing suggestive clusters of diseases to assist their diagnosis. Full article
(This article belongs to the Special Issue Mathematical Models for Medical Diagnosis and Testing)
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