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Machine Learning for Biomedical Application

A topical collection in Applied Sciences (ISSN 2076-3417). This collection belongs to the section "Applied Biosciences and Bioengineering".

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Editors


E-Mail Website
Collection Editor
Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, 41-800 Zabrze, Poland
Interests: image and signal processing; artificial intelligence; deep learning
Special Issues, Collections and Topics in MDPI journals

Topical Collection Information

Dear Colleagues,

Biomedicine is a multidisciplinary branch of medical science that consists of many scientific disciplines, e.g., biology, biotechnology, bioinformatics, and genetics; moreover, it covers various medical specialties. In recent years, a huge development of this field of science has been observed. The consequence of this is not only achievements that allow better understanding of the principles of the human body functioning at various levels (cellular, anatomical, and physiological), but also a large amount of data generated, among others as a result of analyses of the human genome or the processing, analysis, and recognition of a wide class of biomedical signals and images obtained through increasingly advanced medical imaging devices. The analysis of these data requires the use of advanced IT methods, which include those related to the use of artificial intelligence, and in particular machine learning.

The Topical Collection will include applications of machine learning in processing, analysis, and recognition of biomedical data. Specific attention will be given to recently developed deep learning techniques and their application in extracting essential information from large biomedical databases. Hence, proposed topics include but are not limited to the following applications of machine learning:

  • Genomic sequence determinations and analysis of gene expression patterns;
  • Processing and analysis of biomedical signals and images;
  • Modifying living organisms according to human purposes;
  • Improving cell and tissue culture technologies;
  • Development of deep learning architectures in analysis of biomedical data.

Prof. Dr. Michał Strzelecki
Dr. Pawel Badura
Collection 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 collection 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. Applied Sciences 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 2400 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

  • machine learning
  • biotechnology
  • signal and image analysis
  • pattern recognition
  • genomics

Published Papers (5 papers)

2023

Jump to: 2022

15 pages, 795 KiB  
Article
Short-Term Risk Estimation and Treatment Planning for Cardiovascular Disease Patients after First Diagnostic Catheterizations with Machine Learning Models
by Guochang Ye, Peshala Thibbotuwawa Gamage, Vignesh Balasubramanian, John K.-J. Li, Ersoy Subasi, Munevver Mine Subasi and Mehmet Kaya
Appl. Sci. 2023, 13(8), 5191; https://doi.org/10.3390/app13085191 - 21 Apr 2023
Cited by 1 | Viewed by 1403
Abstract
Cardiovascular disease (CVD) is the leading cause of death. CVD symptoms may develop within a short-term after diagnostic catheterizations and lead to life-threatening situations. This study is the first to apply machine learning (ML) methods to predict subsequent adverse cardiovascular events/treatments for patients [...] Read more.
Cardiovascular disease (CVD) is the leading cause of death. CVD symptoms may develop within a short-term after diagnostic catheterizations and lead to life-threatening situations. This study is the first to apply machine learning (ML) methods to predict subsequent adverse cardiovascular events/treatments for patients within 90 days after their first diagnostic catheterizations. Patients (6539) without previously diagnosed CVD were selected from the DukeCath dataset. Ten ML methods were used. Three medical outcomes, varied cardiovascular-related scenarios, percutaneous coronary intervention (PCI) treatments, and coronary artery bypass graft (CABG) treatments, were targeted individually. With patient medical history, vital measurements, laboratory results, and the number of diseased vessels, the random forest classifier (RFC) performed best in predicting combined cardiovascular scenarios, including CABG, PCI, valve surgery (VS), stroke, and myocardial infarction (MI), achieving accuracy: 88.17%, sensitivity: 89.72%, specificity: 86.98%, area under receiver operating characteristic (AUROC): 91.68%. The gradient boosting classifier (GBC) performed best in predicting the PCI and CABG treatments (PCI treatments: accuracy: 89.21%, sensitivity: 90.20%, specificity: 88.74%, AUROC: 94.16%; CABG treatments: accuracy: 93.86%, sensitivity: 77.57%, specificity: 96.23%, AUROC: 96.47%). Our results show that the ML applications effectively identify high-risk patients, can provide diagnostic assistance in cardiovascular treatment planning, and improve outcomes in cardiovascular medicine. Full article
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2022

Jump to: 2023

13 pages, 5609 KiB  
Article
Age Prediction from Low Resolution, Dual-Energy X-ray Images Using Convolutional Neural Networks
by Kamil Janczyk, Jacek Rumiński, Tomasz Neumann, Natalia Głowacka and Piotr Wiśniewski
Appl. Sci. 2022, 12(13), 6608; https://doi.org/10.3390/app12136608 - 29 Jun 2022
Cited by 6 | Viewed by 1952
Abstract
Age prediction from X-rays is an interesting research topic important for clinical applications such as biological maturity assessment. It is also useful in many other practical applications, including sports or forensic investigations for age verification purposes. Research on these issues is usually carried [...] Read more.
Age prediction from X-rays is an interesting research topic important for clinical applications such as biological maturity assessment. It is also useful in many other practical applications, including sports or forensic investigations for age verification purposes. Research on these issues is usually carried out using high-resolution X-ray scans of parts of the body, such as images of the hands or images of the chest. In this study, we used low-resolution, dual-energy, full-body X-ray absorptiometry images to train deep learning models to predict age. In particular, we proposed a preprocessing framework and adapted many partially pretrained convolutional neural network (CNN) models to predict the age of children and young adults. We used a new dataset of 910 multispectral images that were weakly annotated by specialists. The experimental results showed that the proposed preprocessing techniques and the adapted approach to the CNN model achieved a discrepancy between chronological age and predicted age of around 15.56 months for low-resolution whole-body X-rays. Furthermore, we found that the main factor that influenced age prediction scores was spatial features, not multispectral features. Full article
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18 pages, 1661 KiB  
Article
Employing a Long-Short-Term Memory Neural Network to Improve Automatic Sleep Stage Classification of Pharmaco-EEG Profiles
by Hadeel Alsolai, Shahnawaz Qureshi, Syed Muhammad Zeeshan Iqbal, Asif Ameer, Dania Cheaha, Lawrence Edward Henesey and Seppo Karrila
Appl. Sci. 2022, 12(10), 5248; https://doi.org/10.3390/app12105248 - 22 May 2022
Cited by 3 | Viewed by 2193
Abstract
An increasing problem in today’s society is the spiraling number of people suffering from various sleep disorders. The research results presented in this paper support the use of a novel method that employs techniques from the classification of sleep disorders for more accurate [...] Read more.
An increasing problem in today’s society is the spiraling number of people suffering from various sleep disorders. The research results presented in this paper support the use of a novel method that employs techniques from the classification of sleep disorders for more accurate scoring. Applying this novel method will assist researchers with better analyzing subject profiles for recommending prescriptions or to alleviate sleep disorders. In biomedical research, the use of animal models is required to experimentally test the safety and efficacy of a drug in the pre-clinical stage. We have developed a novel LSTM Recurrent Neural Network to process Pharmaco-EEG Profiles of rats to automatically score their sleep–wake stages. The results indicate improvements over the current methods; for the case of combined channels, the model accuracy improved by 1% and 3% in binary or multiclass classifications, respectively, to accuracies of 93% and 82%. In the case of using a single channel, binary and multiclass LSTM models for identifying rodent sleep stages using single or multiple electrode positions for binary or multiclass problems have not been evaluated in prior literature. The results reveal that single or combined channels, and binary or multiclass classification tasks, can be applied in the automatic sleep scoring of rodents. Full article
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11 pages, 1225 KiB  
Article
Natural Language Processing and Machine Learning Supporting the Work of a Psychologist and Its Evaluation on the Example of Support for Psychological Diagnosis of Anorexia
by Katarzyna Rojewska, Stella Maćkowska, Michał Maćkowski, Agnieszka Różańska, Klaudia Barańska, Mariusz Dzieciątko and Dominik Spinczyk
Appl. Sci. 2022, 12(9), 4702; https://doi.org/10.3390/app12094702 - 7 May 2022
Cited by 6 | Viewed by 2151
Abstract
Objective: This study sought to address the use of computer-aided diagnosis and therapy for anorexia nervosa. This paper presents the means by which the use of natural language processing methods can augment the work of psychologists. Method: We evaluated this method based on [...] Read more.
Objective: This study sought to address the use of computer-aided diagnosis and therapy for anorexia nervosa. This paper presents the means by which the use of natural language processing methods can augment the work of psychologists. Method: We evaluated this method based on its efficacy when diagnosing anorexia nervosa. Using natural language processing and machine learning, we developed methods for analyzing five basic emotions, analyzing a patient’s body perception, and detecting six potential areas of difficulties for computer support of psychological diagnosis of anorexia. We surveyed 43 psychologists to obtain feedback on these tools. Results: We evaluated efficacy in terms of patient relationship, substantive aspects of the diagnosis, and diagnostic procedures. In terms of patient relationship, we found a noticeable decrease in the patient’s resistance and better support in verifying the substantive scope of the diagnostic thesis. Discussion: The presented methods can be a supporting tool for monitoring the diagnostic process and increasing the degree of self-diagnosis and self-reflection by the patient. This tool can increase the accuracy of the diagnostic process by reducing patient resistance. This will increase access to the patient’s psychopathology. Full article
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26 pages, 6069 KiB  
Article
Fine-Tuning Fuzzy KNN Classifier Based on Uncertainty Membership for the Medical Diagnosis of Diabetes
by Hanaa Salem, Mahmoud Y. Shams, Omar M. Elzeki, Mohamed Abd Elfattah, Jehad F. Al-Amri and Shaima Elnazer
Appl. Sci. 2022, 12(3), 950; https://doi.org/10.3390/app12030950 - 18 Jan 2022
Cited by 35 | Viewed by 3894
Abstract
Diabetes, a metabolic disease in which the blood glucose level rises over time, is one of the most common chronic diseases at present. It is critical to accurately predict and classify diabetes to reduce the severity of the disease and treat it early. [...] Read more.
Diabetes, a metabolic disease in which the blood glucose level rises over time, is one of the most common chronic diseases at present. It is critical to accurately predict and classify diabetes to reduce the severity of the disease and treat it early. One of the difficulties that researchers face is that diabetes datasets are limited and contain outliers and missing data. Additionally, there is a trade-off between classification accuracy and operational law for detecting diabetes. In this paper, an algorithm for diabetes classification is proposed for pregnant women using the Pima Indians Diabetes Dataset (PIDD). First, a preprocessing step in the proposed algorithm includes outlier rejection, imputing missing values, the standardization process, and feature selection of the attributes, which enhance the dataset’s quality. Second, the classifier uses the fuzzy KNN method and modifies the membership function based on the uncertainty theory. Third, a grid search method is applied to achieve the best values for tuning the fuzzy KNN method based on uncertainty membership, as there are hyperparameters that affect the performance of the proposed classifier. In turn, the proposed tuned fuzzy KNN based on uncertainty classifiers (TFKNN) deals with the belief degree, handles membership functions and operation law, and avoids making the wrong categorization. The proposed algorithm performs better than other classifiers that have been trained and evaluated, including KNN, fuzzy KNN, naïve Bayes (NB), and decision tree (DT). The results of different classifiers in an ensemble could significantly improve classification precision. The TFKNN has time complexity O(kn2d), and space complexity O(n2d). The TFKNN model has high performance and outperformed the others in all tests in terms of accuracy, specificity, precision, and average AUC, with values of 90.63, 85.00, 93.18, and 94.13, respectively. Additionally, results of empirical analysis of TFKNN compared to fuzzy KNN, KNN, NB, and DT demonstrate the global superiority of TFKNN in precision, accuracy, and specificity. Full article
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