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Machine Learning in Biomedical Applications
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Machine Learning in Biomedical Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: 10 May 2025 | Viewed by 4948

Special Issue Editors

Dr. Zunyi Tang

E-Mail Website
Guest Editor
Center for Medical Education and Career Development, Fukushima Medical University, Fukushima 960-1295, Japan
Interests: biomedical signal processing; biomedical instrumentation; health informatics; artificial intelligence
Dr. Yoshinobu Murayama

E-Mail Website
Guest Editor
Bio-physiological Engineering Laboratory (BPELAB), Department of Electrical and Electronics Engineering, College of Engineering, Nihon University, Koriyama, Japan
Interests: basal body temperature; healthcare data analysis; assisted reproductive technology; embryo engineering
Prof. Dr. Mitsuhiro Ogawa

E-Mail Website
Guest Editor
Department of Information and Electronic Engineering, Teikyo University, Tokyo, Japan
Interests: biomedical engineering; welfare engineering; game science
Prof. Dr. Shuxue Ding

E-Mail Website
Guest Editor
School of Artificial Intelligence, Guilin University of Electronic Technology, Guilin, China
Interests: signal and image processing; artificial intelligence; big data

Special Issue Information

Dear Colleagues,

Machine learning (ML) has emerged as a transformative force in the field of biomedical research, presenting unprecedented opportunities to revolutionize preventive measures, diagnostics, treatment strategies, and healthcare outcomes. This Special Issue focuses on the diverse applications of machine learning in the biomedical domain, highlighting its potential to extract meaningful insights from complex biomedical data and biological information.

Contributions to this Special Issue explore a broad spectrum of topics, including, but not limited to, intelligent physiological monitoring and sensing using wearable sensors and smart devices, machine learning and artificial intelligence methodologies for biomedical signal/data measurement analysis and interpretation, intelligent decision support systems for enhancing health outcomes, intelligent informatics for extended digital health reality, and data science and data engineering for biomedicine and health, as well as personalized and pervasive health technologies.

Researchers and practitioners are invited to submit their original work, addressing the challenges and breakthroughs in applying machine learning techniques to biomedical problems. Through this Special Issue, we aim to encourage collaboration and knowledge exchange, driving advancements that contribute to the improvement of healthcare outcomes and the overall well-being of individuals.

In this Special Issue, we welcome original research articles and reviews and eagerly anticipate receiving your valuable contributions.

Dr. Zunyi Tang
Dr. Yoshinobu Murayama
Prof. Dr. Mitsuhiro Ogawa
Prof. Dr. Shuxue Ding
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. 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

  • artificial intelligence
  • machine learning
  • physiological measurement and instrumentation
  • biomedical signal and image processing
  • biomedical modeling and computing
  • disease diagnosis and clinical applications
  • wearable sensors and smart devices
  • assisted reproductive technology
  • health informatics
  • home healthcare and wellness management

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Published Papers (5 papers)

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Research

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16 pages, 11669 KiB  
Article
Machine Learning-Based Predictions of Mortality and Readmission in Type 2 Diabetes Patients in the ICU
by Tung-Lai Hu, Chuang-Min Chao, Chien-Chih Wu, Te-Nien Chien and Chengcheng Li
Appl. Sci. 2024, 14(18), 8443; https://doi.org/10.3390/app14188443 - 19 Sep 2024
Viewed by 900
Abstract
Prognostic outcomes for patients with type 2 diabetes in the intensive care unit (ICU), including mortality and readmission rates, are critical for informed clinical decision-making. Although existing research has established a link between type 2 diabetes and adverse outcomes in the ICU, the [...] Read more.
Prognostic outcomes for patients with type 2 diabetes in the intensive care unit (ICU), including mortality and readmission rates, are critical for informed clinical decision-making. Although existing research has established a link between type 2 diabetes and adverse outcomes in the ICU, the potential of machine learning techniques for enhancing predictive accuracy has not been fully realized. This study seeks to develop and validate predictive models employing machine learning algorithms to forecast mortality and 30-day post-discharge readmission rates among ICU type 2 diabetes patients, thereby enhancing predictive accuracy and supporting clinical decision-making. Data were extracted and preprocessed from the MIMIC-III database, focusing on 14,222 patients with type 2 diabetes and their corresponding ICU admission records. Comprehensive information, including vital signs, laboratory results, and demographic characteristics, was utilized. Six machine learning algorithms—bagging, AdaBoost, GaussianNB, logistic regression, MLP, and SVC—were developed and evaluated using 10-fold cross-validation to predict mortality at 3 days, 30 days, and 365 days, as well as 30-day post-discharge readmission rates. The machine learning models demonstrated strong predictive performance for both mortality and readmission rates. Notably, the bagging and AdaBoost models showed superior performance in predicting mortality across various time intervals, achieving AUC values up to 0.8112 and an accuracy of 0.8832. In predicting 30-day readmission rates, the MLP and AdaBoost models yielded the highest performance, with AUC values reaching 0.8487 and accuracy rates of 0.9249. The integration of electronic health record data with advanced machine learning techniques significantly enhances the accuracy of mortality and readmission predictions in ICU type 2 diabetes patients. These models facilitate the identification of high-risk patients, enabling timely interventions, improving patient outcomes, and demonstrating the significant potential of machine learning in clinical prediction and decision support. Full article
(This article belongs to the Special Issue Machine Learning in Biomedical Applications)
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13 pages, 4373 KiB  
Article
Supervised Machine Learning to Predict Drilling Temperature of Bone
by Md Ashequl Islam, Nur Saifullah Bin Kamarrudin, Muhammad Farzik Ijaz, Ruslizam Daud, Khairul Salleh Basaruddin, Abdulnasser Nabil Abdullah and Hiroshi Takemura
Appl. Sci. 2024, 14(17), 8001; https://doi.org/10.3390/app14178001 - 7 Sep 2024
Viewed by 487
Abstract
Surgeons face a significant challenge due to the heat generated during drilling, as excessive temperatures at the bone–tool interface can lead to irreversible damage to the regenerative soft tissue and result in thermal osteonecrosis. While previous studies have explored the use of machine [...] Read more.
Surgeons face a significant challenge due to the heat generated during drilling, as excessive temperatures at the bone–tool interface can lead to irreversible damage to the regenerative soft tissue and result in thermal osteonecrosis. While previous studies have explored the use of machine learning to predict the temperature rise during bone drilling, this in vitro study introduces a comprehensive approach by combining the Response Surface Methodology (RSM) with advanced machine learning techniques. The main objective lies in the comprehensive evaluation and comparison of support vector machine (SVM) and random forest (RF) models specifically for the optimization of the bone drilling parameters to prevent thermal bone necrosis. A total of 27 experiments were conducted using a multi-level factorial method, with analysis performed via the Minitab software version 19.1. Performance metrics such as the mean squared error (MSE), mean absolute percentage error (MAPE), and coefficient of determination (R2) were used to assess model accuracy. The RF model emerged as the most effective, with R2 values of 94.2% for testing and 97.3% for training data, significantly outperforming other models in predicting temperature fluctuations. This study demonstrates the superior predictive capabilities of the RF model and offers a robust framework for the optimization of surgical procedures to mitigate the risk of thermal damage. Full article
(This article belongs to the Special Issue Machine Learning in Biomedical Applications)
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13 pages, 3063 KiB  
Article
Temperature Dynamics in Early Pregnancy: Implications for Improving In Vitro Fertilization Outcomes
by Yoshinobu Murayama, Tomoki Abe and Zunyi Tang
Appl. Sci. 2024, 14(16), 7392; https://doi.org/10.3390/app14167392 - 21 Aug 2024
Viewed by 468
Abstract
In assisted reproductive technology, in vitro fertilization involves cultivating embryos in an artificial environment, often yielding lower-quality embryos compared to in vivo conditions. This study investigated core body temperature (CBT) fluctuations in mice during early pregnancy. Their CBT was measured with a high [...] Read more.
In assisted reproductive technology, in vitro fertilization involves cultivating embryos in an artificial environment, often yielding lower-quality embryos compared to in vivo conditions. This study investigated core body temperature (CBT) fluctuations in mice during early pregnancy. Their CBT was measured with a high temporal resolution to identify the optimal thermal conditions during the first five days post-fertilization, aiming to improve in vitro culture conditions. Data were collected from 12 female mice, with 8 becoming pregnant, using temperature loggers every minute for 11 days. Data analysis focused on trends, circadian rhythms, frequency components, and complexity using multiscale entropy (MSE). The results for the pregnant mice showed a mean CBT increase from 37.23 °C to 37.56 °C post-mating, primarily during the light phase, with a significant average rise of 0.58 °C. A Fourier analysis identified dominant 24, 12, 8, and 6 h components, with the 24 h component decreasing by 57%. Irregular fluctuations decreased, and MSE indicated increased complexity in the CBT time series post-mating. These results suggest that reducing diurnal temperature variations and maintaining a slightly elevated mean CBT of approximately 37.5 °C, with controlled minor fluctuations, may enhance embryo quality in pregnant mice. This study provides a reference for temperature regulation in embryo culture, improving embryo quality by aligning in vitro conditions with the natural thermal environment of the fallopian tubes. Full article
(This article belongs to the Special Issue Machine Learning in Biomedical Applications)
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19 pages, 6606 KiB  
Article
Efficient Sleep–Wake Cycle Staging via Phase–Amplitude Coupling Pattern Classification
by Vinícius Rosa Cota, Simone Del Corso, Gianluca Federici, Gabriele Arnulfo and Michela Chiappalone
Appl. Sci. 2024, 14(13), 5816; https://doi.org/10.3390/app14135816 - 3 Jul 2024
Viewed by 1007
Abstract
The objective and automatic detection of the sleep–wake cycle (SWC) stages is essential for the investigation of its physiology and dysfunction. Here, we propose a machine learning model for the classification of SWC stages based on the measurement of synchronization between neural oscillations [...] Read more.
The objective and automatic detection of the sleep–wake cycle (SWC) stages is essential for the investigation of its physiology and dysfunction. Here, we propose a machine learning model for the classification of SWC stages based on the measurement of synchronization between neural oscillations of different frequencies. Publicly available electrophysiological recordings of mice were analyzed for the computation of phase–amplitude couplings, which were then supplied to a multilayer perceptron (MLP). Firstly, we assessed the performance of several architectures, varying among different input choices and numbers of neurons in the hidden layer. The top performing architecture was then tested using distinct extrapolation strategies that would simulate applications in a real lab setting. Although all the different choices of input data displayed high AUC values (>0.85) for all the stages, the ones using larger input datasets performed significantly better. The top performing architecture displayed high AUC values (>0.95) for all the extrapolation strategies, even in the worst-case scenario in which the training with a single day and single animal was used to classify the rest of the data. Overall, the results using multiple performance metrics indicate that the usage of a basic MLP fed with highly descriptive features such as neural synchronization is enough to efficiently classify SWC stages. Full article
(This article belongs to the Special Issue Machine Learning in Biomedical Applications)
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Review

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27 pages, 6061 KiB  
Review
Artificial Intelligence in Biomaterials: A Comprehensive Review
by Yasemin Gokcekuyu, Fatih Ekinci, Mehmet Serdar Guzel, Koray Acici, Sahin Aydin and Tunc Asuroglu
Appl. Sci. 2024, 14(15), 6590; https://doi.org/10.3390/app14156590 - 28 Jul 2024
Viewed by 1534
Abstract
The importance of biomaterials lies in their fundamental roles in medical applications such as tissue engineering, drug delivery, implantable devices, and radiological phantoms, with their interactions with biological systems being critically important. In recent years, advancements in deep learning (DL), artificial intelligence (AI), [...] Read more.
The importance of biomaterials lies in their fundamental roles in medical applications such as tissue engineering, drug delivery, implantable devices, and radiological phantoms, with their interactions with biological systems being critically important. In recent years, advancements in deep learning (DL), artificial intelligence (AI), machine learning (ML), supervised learning (SL), unsupervised learning (UL), and reinforcement learning (RL) have significantly transformed the field of biomaterials. These technologies have introduced new possibilities for the design, optimization, and predictive modeling of biomaterials. This review explores the applications of DL and AI in biomaterial development, emphasizing their roles in optimizing material properties, advancing innovative design processes, and accurately predicting material behaviors. We examine the integration of DL in enhancing the performance and functional attributes of biomaterials, explore AI-driven methodologies for the creation of novel biomaterials, and assess the capabilities of ML in predicting biomaterial responses to various environmental stimuli. Our aim is to elucidate the pivotal contributions of DL, AI, and ML to biomaterials science and their potential to drive the innovation and development of superior biomaterials. It is suggested that future research should further deepen these technologies’ contributions to biomaterials science and explore new application areas. Full article
(This article belongs to the Special Issue Machine Learning in Biomedical Applications)
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