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BioMedInformatics
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Feature Papers on Methods in Biomedical Informatics
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Feature Papers on Methods in Biomedical Informatics

A special issue of BioMedInformatics (ISSN 2673-7426). This special issue belongs to the section "Methods in Biomedical Informatics".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 2786

Special Issue Editor

Prof. Dr. Rosalba Giugno

E-Mail Website
Guest Editor
LS2_10—Bioinformatics, Università degli Studi di Verona, 37129 Verona, Italy
Interests: bioinformatics; computational biology; medical imaging analysis; artificial intelligence; machine learning; data analysis; personalized medicine; predictive modeling; healthcare innovation; methodological advancements
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Special Issue on Methods in Biomedical Informatics provides an insightful exploration into cutting-edge techniques shaping the intersection of healthcare and technology. This collection of Feature Papers delves deep into innovative methodologies driving advancements in the field of biomedical informatics. Authors present rigorous research and novel approaches, elucidating diverse aspects of data analysis, artificial intelligence, and computational modeling in healthcare applications. From precision medicine to healthcare analytics, the papers offer a comprehensive overview of the current landscape, highlighting the pivotal role of informatics in revolutionizing healthcare delivery. This Special Issue serves as a valuable resource for researchers, practitioners, and policymakers striving to harness the power of informatics for enhancing healthcare outcomes and decision-making processes.

Dr. Rosalba Giugno
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

  • biomedical informatics
  • data analysis
  • artificial intelligence
  • computational modeling
  • precision medicine
  • healthcare analytics
  • healthcare technology
  • clinical informatics
  • health data management
  • bioinformatics

Published Papers (3 papers)

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Research

23 pages, 6506 KiB  
Article
Selection of the Discriming Feature Using the BEMD’s BIMF for Classification of Breast Cancer Mammography Image
by Fatima Ghazi, Aziza Benkuider, Fouad Ayoub and Khalil Ibrahimi
BioMedInformatics 2024, 4(2), 1202-1224; https://doi.org/10.3390/biomedinformatics4020066 - 9 May 2024
Viewed by 327
Abstract
Mammogram exam images are useful in identifying diseases, such as breast cancer, which is one of the deadliest cancers, affecting adult women around the world. Computational image analysis and machine learning techniques can help experts identify abnormalities in these images. In this work [...] Read more.
Mammogram exam images are useful in identifying diseases, such as breast cancer, which is one of the deadliest cancers, affecting adult women around the world. Computational image analysis and machine learning techniques can help experts identify abnormalities in these images. In this work we present a new system to help diagnose and analyze breast mammogram images. To do this, the system a method the Selection of the Most Discriminant Attributes of the images preprocessed by BEMD “SMDA-BEMD”, this entails picking the most pertinent traits from the collection of variables that characterize the state under study. A reduction of attribute based on a transformation of the data also called an extraction of characteristics by extracting the Haralick attributes from the Co-occurrence Matrices Methods “GLCM” this reduction which consists of replacing the initial set of data by a new reduced set, constructed at from the initial set of features extracted by images decomposed using Bidimensional Empirical Multimodal Decomposition “BEMD”, for discrimination of breast mammogram images (healthy and pathology) using BEMD. This decomposition makes it possible to decompose an image into several Bidimensional Intrinsic Mode Functions “BIMFs” modes and a residue. The results obtained show that mammographic images can be represented in a relatively short space by selecting the most discriminating features based on a supervised method where they can be differentiated with high reliability between healthy mammographic images and pathologies, However, certain aspects and findings demonstrate how successful the suggested strategy is to detect the tumor. A BEMD technique is used as preprocessing on mammographic images. This suggested methodology makes it possible to obtain consistent results and establishes the discrimination threshold for mammography images (healthy and pathological), the classification rate is improved (98.6%) compared to existing cutting-edge techniques in the field. This approach is tested and validated on mammographic medical images from the Kenitra-Morocco reproductive health reference center (CRSRKM) which contains breast mammographic images of normal and pathological cases. Full article
(This article belongs to the Special Issue Feature Papers on Methods in Biomedical Informatics)
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13 pages, 15764 KiB  
Article
Lip-Reading Advancements: A 3D Convolutional Neural Network/Long Short-Term Memory Fusion for Precise Word Recognition
by Themis Exarchos, Georgios N. Dimitrakopoulos, Aristidis G. Vrahatis, Georgios Chrysovitsiotis, Zoi Zachou and Efthymios Kyrodimos
BioMedInformatics 2024, 4(1), 410-422; https://doi.org/10.3390/biomedinformatics4010023 - 4 Feb 2024
Viewed by 998
Abstract
Lip reading, the art of deciphering spoken words from the visual cues of lip movements, has garnered significant interest for its potential applications in diverse fields, including assistive technologies, human–computer interaction, and security systems. With the rapid advancements in technology and the increasing [...] Read more.
Lip reading, the art of deciphering spoken words from the visual cues of lip movements, has garnered significant interest for its potential applications in diverse fields, including assistive technologies, human–computer interaction, and security systems. With the rapid advancements in technology and the increasing emphasis on non-verbal communication methods, the significance of lip reading has expanded beyond its traditional boundaries. These technological advancements have led to the generation of large-scale and complex datasets, necessitating the use of cutting-edge deep learning tools that are adept at handling such intricacies. In this study, we propose an innovative approach combining 3D Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to tackle the challenging task of word recognition from lip movements. Our research leverages a meticulously curated dataset, named MobLip, encompassing various speech patterns, speakers, and environmental conditions. The synergy between the spatial information extracted by 3D CNNs and the temporal dynamics captured by LSTMs yields impressive results, achieving an accuracy rate of up to 87.5%, showcasing robustness to lighting variations and speaker diversity. Comparative experiments demonstrate our model’s superiority over existing lip-reading approaches, underlining its potential for real-world deployment. Furthermore, we discuss ethical considerations and propose avenues for future research, such as multimodal integration with audio data and expanded language support. In conclusion, our 3D CNN-LSTM architecture presents a promising solution to the complex problem of word recognition from lip movements, contributing to the advancement of communication technology and opening doors to innovative applications in an increasingly visual world. Full article
(This article belongs to the Special Issue Feature Papers on Methods in Biomedical Informatics)
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22 pages, 4583 KiB  
Article
Machine Learning Models for Predicting Personalized Tacrolimus Stable Dosages in Pediatric Renal Transplant Patients
by Sergio Sánchez-Herrero, Laura Calvet and Angel A. Juan
BioMedInformatics 2023, 3(4), 926-947; https://doi.org/10.3390/biomedinformatics3040057 - 14 Oct 2023
Viewed by 1104
Abstract
Tacrolimus, characterized by a narrow therapeutic index, significant toxicity, adverse effects, and interindividual variability, necessitates frequent therapeutic drug monitoring and dose adjustments in renal transplant recipients. This study aimed to compare machine learning (ML) models utilizing pharmacokinetic data to predict tacrolimus blood concentration. [...] Read more.
Tacrolimus, characterized by a narrow therapeutic index, significant toxicity, adverse effects, and interindividual variability, necessitates frequent therapeutic drug monitoring and dose adjustments in renal transplant recipients. This study aimed to compare machine learning (ML) models utilizing pharmacokinetic data to predict tacrolimus blood concentration. This prediction underpins crucial dose adjustments, emphasizing patient safety. The investigation focuses on a pediatric cohort. A subset served as the derivation cohort, creating the dose-prediction algorithm, while the remaining data formed the validation cohort. The study employed various ML models, including artificial neural network, RandomForestRegressor, LGBMRegressor, XGBRegressor, AdaBoostRegressor, BaggingRegressor, ExtraTreesRegressor, KNeighborsRegressor, and support vector regression, and their performances were compared. Although all models yielded favorable fit outcomes, the ExtraTreesRegressor (ETR) exhibited superior performance. It achieved measures of −0.161 for MPE, 0.995 for AFE, 1.063 for AAFE, and 0.8 for R2, indicating accurate predictions and meeting regulatory standards. The findings underscore ML’s predictive potential, despite the limited number of samples available. To address this issue, resampling was utilized, offering a viable solution within medical datasets for developing this pioneering study to predict tacrolimus trough concentration in pediatric transplant recipients. Full article
(This article belongs to the Special Issue Feature Papers on Methods in Biomedical Informatics)
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