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Mathematics
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Computational Modelling and Analytical Framework for Medical Applications
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Computational Modelling and Analytical Framework for Medical Applications

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E3: Mathematical Biology".

Deadline for manuscript submissions: closed (30 September 2024) | Viewed by 3642

Special Issue Editors

Dr. Dumitru Trucu

E-Mail Website
Guest Editor
Division of Mathematics, University of Dundee, Dundee DD1 4HN, UK
Interests: mathematical modelling; numerical analysis for PDEs; computational modelling and analytical framework for cancer cell invasion
Prof. Dr. Raluca Eftimie

E-Mail Website
Guest Editor
Department of Mathematics, University of Dundee, Dundee DD1 4HN, UK
Interests: mathematical biology; applied mathematics

Special Issue Information

Dear Colleagues,

The Special Issue "Computational Modelling and Analytical Framework for Medical Applications" aims to summarize recent developments and trends in computational modeling and analytical frameworks for medical applications. The scope of this Special Issue covers the application of mathematical, computational, and statistical methods in order to understand and predict medical processes, develop new medical technologies, diagnose and treat diseases, and analyze medical data. It will provide a platform for interdisciplinary exchange between mathematicians, computer scientists, medical researchers, and engineers working in this field.

The Special Issue will cover a wide range of topics including, but not limited to, the following:

  • Medical image analysis and processing;
  • Computational modeling of physiological systems;
  • Single-scale and multi-scale mathematical models (describing single-scale and multi-scale physiological systems);
  • Statistical learning for medical data analysis;
  • Parameter identification for deterministic and stochastic systems;
  • Medical device design and optimization;
  • Predictive and personalized medicine;
  • Medical decision-making and diagnosis;
  • Simulation of medical procedures and treatments;
  • Applications of machine learning and artificial intelligence in medicine;
  • Big data analysis in medical research.

Dr. Dumitru Trucu
Prof. Dr. Raluca Eftimie
Guest Editors

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

  • medical applications
  • computational modeling
  • analytical framework
  • mathematical methods
  • medical data analysis
  • medical image analysis
  • medical device design
  • machine learning
  • artificial intelligence
  • big data analysis

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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

18 pages, 2132 KiB  
Article
Prediction of Cognitive Impairment Using Sleep Lifelog Data and LSTM Model
by Junhee Hong, Youngjin Seol, Seunghyun Lee, Janghyeok Yoon, Jiho Lee, Ki-Su Park and Ji-Wan Ha
Mathematics 2024, 12(20), 3208; https://doi.org/10.3390/math12203208 - 13 Oct 2024
Viewed by 1460
Abstract
Rapid elderly population growth has increased the number of patients with cognitive impairment (CI). Early detection and ongoing medical treatment can slow CI progression and significantly reduce the cost of managing patients. However, distinguishing CI from natural cognitive decline associated with aging is [...] Read more.
Rapid elderly population growth has increased the number of patients with cognitive impairment (CI). Early detection and ongoing medical treatment can slow CI progression and significantly reduce the cost of managing patients. However, distinguishing CI from natural cognitive decline associated with aging is challenging. Previous studies conducted to identify patients with CI using lifelog data did not consider changes in lifelog data over time because each data point was learned individually. This study introduces a model that predicts patients with CI based on sleep lifelog data and analyzes significant sleep factors that influence cognitive decline. This study followed three steps: (1) collecting sleep lifelog data from elderly Korean people and reconstructing sleep lifelog data as time-series data; (2) building a model to classify CI using a time series of sleep lifelog data and a long short-term memory model; and (3) identifying sleep factors that influence the onset of CI using an explainable AI algorithm. The proposed CI classification model achieved a sensitivity of 0.89, a specificity of 0.80, and an area under the receiver operating characteristic curve of 0.92. This study will facilitate the noninvasive screening, diagnosis, and continuous monitoring of CI in the elderly. Full article
(This article belongs to the Special Issue Computational Modelling and Analytical Framework for Medical Applications)
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17 pages, 859 KiB  
Article
Individualized Prediction of Blood Glucose Outcomes Using Compositional Data Analysis
by Alvis Cabrera, Ernesto Estremera, Aleix Beneyto, Lyvia Biagi, Iván Contreras, Josep Antoni Martín-Fernández and Josep Vehí
Mathematics 2023, 11(21), 4517; https://doi.org/10.3390/math11214517 - 2 Nov 2023
Viewed by 1584
Abstract
This paper presents an individualized multiple linear regression model based on compositional data where we predict the mean and coefficient of variation of blood glucose in individuals with type 1 diabetes for the long-term (2 and 4 h). From these predictions, we estimate [...] Read more.
This paper presents an individualized multiple linear regression model based on compositional data where we predict the mean and coefficient of variation of blood glucose in individuals with type 1 diabetes for the long-term (2 and 4 h). From these predictions, we estimate the minimum and maximum glucose values to provide future glycemic status. The proposed methodology has been validated using a dataset of 226 real adult patients with type 1 diabetes (Replace BG (NCT02258373)). The obtained results show a median balanced accuracy and sensitivity of over 90% and 80%, respectively. A information system has been implemented and validated to update patients on their glycemic status and associated risks for the next few hours. Full article
(This article belongs to the Special Issue Computational Modelling and Analytical Framework for Medical Applications)
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