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Mathematics
Special Issues
Advanced Methods and Applications in Medical Informatics
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Advanced Methods and Applications in Medical Informatics

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 4439

Special Issue Editors

Dr. Chengsheng Mao

E-Mail Website
Guest Editor
Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
Interests: machine learning; data mining; artificial intelligence; medical informatics; bioinformatics
Dr. Jie Xu

E-Mail Website
Guest Editor
College of Medicine, University of Florida, Gainesville, FL, USA
Interests: data mining; artificial intelligence; machine learning; health informatics; electronic health records

Special Issue Information

Dear Colleagues,

Rapid advancements in information technology and data analytics have revolutionized the field of healthcare. Medical informatics, also known as health informatics, focuses on the application of information technology, data science, and machine learning methods to enhance the acquisition, storage, retrieval, and use of healthcare information. To further explore the latest developments in this domain, we are pleased to announce a Special Issue of Mathematics on "Advanced Methods and Applications in Medical Informatics".

This Special Issue aims to provide a platform for researchers, academicians, and industry professionals to share novel findings, methodologies, and models that can contribute to further advancing the field. We invite submissions that address various aspects of medical informatics, including but not limited to the following:

  • Artificial intelligence and machine learning in healthcare;
  • Big data analytics for medical decision making;
  • Electronic health records and clinical information systems;
  • Natural language processing for medical text mining;
  • Predictive modeling and data-driven approaches in healthcare;
  • Health information exchange and interoperability;
  • Medical imaging and signal processing;
  • Telemedicine and mobile health applications;
  • Patient data privacy, security, and ethics;
  • Data visualization and human–computer interactions in healthcare.

We encourage original research papers, reviews, or case studies that demonstrate innovative approaches, theoretical frameworks, experimental evaluations, and practical applications in medical informatics. Submissions should highlight the significance of the research, its contribution to the field, and its potential impact on healthcare delivery, patient outcomes, or health policy.

Dr. Chengsheng Mao
Dr. Jie Xu
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 informatics
  • electronic health records
  • medical data analysis
  • medical decision making

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

Published Papers (3 papers)

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Research

32 pages, 8231 KiB  
Article
LiSENCE: A Hybrid Ligand and Sequence Encoder Network for Predicting CYP450 Inhibitors in Safe Multidrug Administration
by Abena Achiaa Atwereboannah, Wei-Ping Wu, Sophyani B. Yussif, Muhammed Amin Abdullah, Edwin K. Tenagyei, Chiagoziem C. Ukuoma, Yeong Hyeon Gu and Mugahed A. Al-antari
Mathematics 2025, 13(9), 1376; https://doi.org/10.3390/math13091376 - 23 Apr 2025
Viewed by 169
Abstract
Adverse drug–drug interactions (DDIs) often arise from cytochrome P450 (CYP450) enzyme inhibition, which is vital for metabolism. The accurate identification of CYP450 inhibitors is crucial, but current machine learning models struggle to assess the importance of key inputs like ligand SMILES and protein [...] Read more.
Adverse drug–drug interactions (DDIs) often arise from cytochrome P450 (CYP450) enzyme inhibition, which is vital for metabolism. The accurate identification of CYP450 inhibitors is crucial, but current machine learning models struggle to assess the importance of key inputs like ligand SMILES and protein sequences, limiting their biological insights. The proposed study developed LiSENCE, an artificial intelligence (AI) framework to identify CYP450 inhibitors. It aimed to enhance prediction accuracy and provide biological insights, improving drug development and patient safety regarding drug–drug interactions: The innovative LiSENCE AI framework comprised four modules: the Ligand Encoder Network (LEN), Sequence Encoder Network (SEN), classification module, and explainability (XAI) module. The LEN and SEN, as deep learning pipelines, extract high-level features from drug ligand strings and CYP protein target sequences, respectively. These features are combined to improve prediction performance, with the XAI module providing biological interpretations. Data were outsourced from three databases: ligand/compound SMILES strings from the PubChem and ChEMBL databases and protein target sequences from the Protein Data Bank (PDB) for five CYP isoforms: 1A2, 2C9, 2C19, 2D6, and 3A4. The model attains an average accuracy of 89.2%, with the LEN and SEN contributing 70.1% and 63.3%, respectively. The evaluation performance records 97.0% AUC, 97.3% specificity, 92.2% sensitivity, 93.8% precision, 83.3% F1-score, and 87.8% MCC. LiSENCE outperforms baseline models in identifying inhibitors, offering valuable interpretability through heatmap analysis, which aids in advancing drug development research. Full article
(This article belongs to the Special Issue Advanced Methods and Applications in Medical Informatics)
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16 pages, 16362 KiB  
Article
Hypergraph-Based Multitask Feature Selection with Temporally Constrained Group Sparsity Learning on fMRI
by Youzhi Qu, Kai Fu, Linjing Wang, Yu Zhang, Haiyan Wu and Quanying Liu
Mathematics 2024, 12(11), 1733; https://doi.org/10.3390/math12111733 - 2 Jun 2024
Cited by 2 | Viewed by 1213
Abstract
Localizing the brain regions affected by tasks is crucial to understanding the mechanisms of brain function. However, traditional statistical analysis does not accurately identify the brain regions of interest due to factors such as sample size, task design, and statistical effects. Here, we [...] Read more.
Localizing the brain regions affected by tasks is crucial to understanding the mechanisms of brain function. However, traditional statistical analysis does not accurately identify the brain regions of interest due to factors such as sample size, task design, and statistical effects. Here, we propose a hypergraph-based multitask feature selection framework, referred to as HMTFS, which we apply to a functional magnetic resonance imaging (fMRI) dataset to extract task-related brain regions. HMTFS is characterized by its ability to construct a hypergraph through correlations between subjects, treating each subject as a node to preserve high-order information of time-varying signals. Additionally, it manages feature selection across different time windows in fMRI data as multiple tasks, facilitating time-constrained group sparse learning with a smoothness constraint. We utilize a large fMRI dataset from the Human Connectome Project (HCP) to validate the performance of HMTFS in feature selection. Experimental results demonstrate that brain regions selected by HMTFS can provide higher accuracy for downstream classification tasks compared to other competing feature selection methods and align with findings from previous neuroscience studies. Full article
(This article belongs to the Special Issue Advanced Methods and Applications in Medical Informatics)
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16 pages, 8070 KiB  
Article
Ultrasound Computed Tomography Reflection Imaging with Coherence-Factor Beamforming for Breast Tumor Early Detection
by Zuoxun Hou, Ruichen Yuan, Zihao Wang, Xiaorui Wei, Chujian Ren, Jiale Zhou and Xiaolei Qu
Mathematics 2024, 12(7), 1106; https://doi.org/10.3390/math12071106 - 7 Apr 2024
Cited by 2 | Viewed by 1778
Abstract
Breast cancer is a global health concern, emphasizing the need for early detection. However, current mammography struggles to effectively image dense breasts. Breast ultrasound can be an adjunctive method, but it is highly dependent on operator skill. Ultrasound computed tomography (USCT) reflection imaging [...] Read more.
Breast cancer is a global health concern, emphasizing the need for early detection. However, current mammography struggles to effectively image dense breasts. Breast ultrasound can be an adjunctive method, but it is highly dependent on operator skill. Ultrasound computed tomography (USCT) reflection imaging provides high-quality 3D images, but often uses delay-and-sum (DAS) beamforming, which limits its image quality. This article proposes the integration of coherence-factor (CF) beamforming into ultrasound computed tomography (USCT) reflection imaging to enhance image quality. CF assesses the focus quality of beamforming by analyzing the signal coherence across different channels, assigning higher weights to high-quality focus points and thereby improving overall image quality. Numerical simulations and phantom experiments using our built USCT prototype were conducted to optimize the imaging parameters and assess and compare the image quality of CF and DAS beamforming. Numerical simulations demonstrated that CF beamforming can significantly enhance image quality. Phantom experiments with our prototype revealed that CF beamforming significantly improves image resolution (from 0.35 mm to 0.14 mm) and increases contrast ratio (from 24.54 dB to 63.28 dB). The integration of CF beamforming into USCT reflection imaging represents a substantial improvement in image quality, offering promise for enhanced breast cancer detection and imaging capabilities. Full article
(This article belongs to the Special Issue Advanced Methods and Applications in Medical Informatics)
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