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Applied Sciences
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Medical Big Data and Artificial Intelligence for Healthcare
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Medical Big Data and Artificial Intelligence for Healthcare

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

Deadline for manuscript submissions: closed (20 December 2023) | Viewed by 8978

Special Issue Editors

Prof. Dr. Yudong Zhang

E-Mail Website
Guest Editor
School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK
Interests: artificial intelligence; deep learning; medical image processing; pattern recognition; transfer learning; medical image analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Jin Hong

E-Mail Website
Guest Editor
Zhongshan Institute of Changchun University of Science and Technology, Zhongshan, China
Interests: deep learning; medical image analysis; computer vision
Dr. Shuwen Chen

E-Mail Website
Guest Editor
School of Physics and Information Engineering, Jiangsu Second Normal University, Nanjing 211200, China
Interests: AI and DL; medical imaging; graph neural network; computer-aided diagnosis; AIoT

Special Issue Information

Dear Colleagues,

The growth of big medical data, together with the expansion of computational models in healthcare, has aided researchers and practitioners in analyzing big healthcare data.

Furthermore, big data analytics have been used for a number of applications in healthcare, such as  personalized medicine and prescriptive analytics, waste and care variability reduction, clinical risk intervention and predictive analytics, the automated external and internal reporting of patient data, and the standardization of medical terms and patient registries.

Big data in healthcare research encourages exploratory medical research, as AI-based data-driven analysis allows us to move forward faster than hypothesis-driven research can.

Prof. Dr. Yu-Dong Zhang
Dr. Jin Hong
Dr. Shuwen Chen
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

  • big data
  • healthcare
  • artificial intelligence

Published Papers (5 papers)

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Editorial

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5 pages, 797 KiB  
Editorial
Medical Big Data and Artificial Intelligence for Healthcare
by Yudong Zhang, Jin Hong and Shuwen Chen
Appl. Sci. 2023, 13(6), 3745; https://doi.org/10.3390/app13063745 - 15 Mar 2023
Cited by 3 | Viewed by 2244
Abstract
Big data have altered the way we manage, explore, evaluate, analyze, and leverage data across many different industries [...] Full article
(This article belongs to the Special Issue Medical Big Data and Artificial Intelligence for Healthcare)
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Research

Jump to: Editorial

19 pages, 977 KiB  
Article
MVMSGAT: Integrating Multiview, Multi-Scale Graph Convolutional Networks with Biological Prior Knowledge for Predicting Bladder Cancer Response to Neoadjuvant Therapy
by Xu Luo, Xiaoqing Chen and Yu Yao
Appl. Sci. 2024, 14(2), 669; https://doi.org/10.3390/app14020669 - 12 Jan 2024
Viewed by 667
Abstract
The incidence of bladder cancer is on the rise, and its molecular heterogeneity presents significant challenges for personalized cancer therapy. Transcriptome data can characterize the variability among patients. Traditional machine-learning methods often struggle with high-dimensional genomic data, falling into the ’curse of dimensionality’. [...] Read more.
The incidence of bladder cancer is on the rise, and its molecular heterogeneity presents significant challenges for personalized cancer therapy. Transcriptome data can characterize the variability among patients. Traditional machine-learning methods often struggle with high-dimensional genomic data, falling into the ’curse of dimensionality’. To address this challenge, we have developed MVMSGAT, an innovative predictive model tailored for forecasting responses to neoadjuvant therapy in bladder cancer patients. MVMSGAT significantly enhances model performance by incorporating multi-perspective biological prior knowledge. It initially utilizes the Boruta algorithm to select key genes from transcriptome data, subsequently constructing a comprehensive graph of gene co-expression and protein–protein interactions. MVMSGAT further employs a graph convolutional neural network to integrate this information within a multiview knowledge graph, amalgamating biological knowledge maps from various scales using an attention mechanism. For validation, MVMSGAT was tested using a five-fold cross-validation approach on two specific GEO datasets, GSE169455 and GSE69795, involving a total of 210 bladder cancer samples. MVMSGAT demonstrated superior performance, with the following metrics (mean ± standard deviation): AUC-ROC of 0.8724±0.0511, accuracy of 0.7789±0.068, F1 score of 0.8529±0.0338, and recall of 0.9231±0.0719. These results underscore the potential of MVMSGAT in advancing personalized treatment and precision medicine in bladder cancer. Full article
(This article belongs to the Special Issue Medical Big Data and Artificial Intelligence for Healthcare)
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11 pages, 764 KiB  
Article
Novel Study for the Early Identification of Injury Risks in Athletes Using Machine Learning Techniques
by Rocío Elizabeth Duarte Ayala, David Pérez Granados, Carlos Alberto González Gutiérrez, Mauricio Alberto Ortega Ruíz, Natalia Rojas Espinosa and Emanuel Canto Heredia
Appl. Sci. 2024, 14(2), 570; https://doi.org/10.3390/app14020570 - 09 Jan 2024
Viewed by 833
Abstract
This innovative study addresses the prevalent issue of sports injuries, particularly focusing on ankle injuries, utilizing advanced analytical tools such as artificial intelligence (AI) and machine learning (ML). Employing a logistic regression model, the research achieves a remarkable accuracy of 90.0%, providing a [...] Read more.
This innovative study addresses the prevalent issue of sports injuries, particularly focusing on ankle injuries, utilizing advanced analytical tools such as artificial intelligence (AI) and machine learning (ML). Employing a logistic regression model, the research achieves a remarkable accuracy of 90.0%, providing a robust predictive tool for identifying and classifying athletes with injuries. The comprehensive evaluation of performance metrics, including recall, precision, and F1-Score, emphasizes the model’s reliability. Key determinants like practicing sports with injury risk and kinesiophobia reveal significant associations, offering vital insights for early risk detection and personalized preventive strategies. The study’s contribution extends beyond predictive modeling, incorporating a predictive factors analysis that sheds light on the nuanced relationships between various predictors and the occurrence of injuries. In essence, this research not only advances our understanding of sports injuries but also presents a potent tool with practical implications for injury prevention in athletes, bridging the gap between data-driven insights and actionable strategies. Full article
(This article belongs to the Special Issue Medical Big Data and Artificial Intelligence for Healthcare)
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25 pages, 3683 KiB  
Article
Prediction of Urinary Tract Infection in IoT-Fog Environment for Smart Toilets Using Modified Attention-Based ANN and Machine Learning Algorithms
by Abdullah Alqahtani, Shtwai Alsubai, Adel Binbusayyis, Mohemmed Sha, Abdu Gumaei and Yu-Dong Zhang
Appl. Sci. 2023, 13(10), 5860; https://doi.org/10.3390/app13105860 - 09 May 2023
Cited by 2 | Viewed by 1791
Abstract
UTI (Urinary Tract Infection) has become common with maximum error rates in diagnosis. With the current progress on DM (Data Mining) based algorithms, several research projects have tried such algorithms due to their ability in making optimal decisions and efficacy in resolving complex [...] Read more.
UTI (Urinary Tract Infection) has become common with maximum error rates in diagnosis. With the current progress on DM (Data Mining) based algorithms, several research projects have tried such algorithms due to their ability in making optimal decisions and efficacy in resolving complex issues. However, conventional research has failed to attain accurate predictions due to improper feature selection. To resolve such existing pitfalls, this research intends to employ suitable ML (Machine Learning)-based algorithms for predicting UTI in IoT-Fog environments, which will be applicable to a smart toilet. Additionally, bio-inspired algorithms have gained significant attention in recent eras due to their capability in resolving complex optimization issues. Considering this, the current study proposes MFB-FA (Modified Flashing Behaviour-based Firefly Algorithm) for feature selection. This research initializes the FF (Firefly) population and interchanges the constant absorption coefficient value with the chaotic maps as the chaos possesses an innate ability to evade getting trapped in local optima with the improvement in determining global optimum. Further, GM (Gaussian Map) is taken into account for moving all the FFs to a global optimum in an individual iteration. Due to such nature, this algorithm possesses a better optimization ability than other swarm intelligence approaches. Finally, classification is undertaken by the proposed MANN-AM (Modified Artificial Neural Network with Attention Mechanism). The main intention for proposing this network involves its ability to focus on small and significant data. Moreover, ANNs possess the ability for learning and modelling complex and non-linear relationships, in which the present study considers it. The proposed method is compared internally by using Random Forest, Naive Bayes and K-Nearest Neighbour to show the efficacy of the proposed model. The overall performance of this study is assessed with regard to standard performance metrics for confirming its optimal performance in UTI prediction. The proposed model has attained optimal values such as accuracy as 0.99, recall as 0.99, sensitivity as 1, precision as 1, specificity as 0.99 and f1-score as 0.99. Full article
(This article belongs to the Special Issue Medical Big Data and Artificial Intelligence for Healthcare)
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15 pages, 1368 KiB  
Article
Voting-Based Contour-Aware Framework for Medical Image Segmentation
by Qiao Deng, Rongli Zhang, Siyue Li, Jin Hong, Yu-Dong Zhang, Winnie Chiu Wing Chu and Lin Shi
Appl. Sci. 2023, 13(1), 84; https://doi.org/10.3390/app13010084 - 21 Dec 2022
Cited by 1 | Viewed by 1530
Abstract
Accurate and automatic segmentation of medical images is in increasing demand for assisting disease diagnosis and surgical planning. Although Convolutional Neural Networks (CNNs) have shown great promise in medical image segmentation, they prefer to learn texture features over shape information. Moreover, recent studies [...] Read more.
Accurate and automatic segmentation of medical images is in increasing demand for assisting disease diagnosis and surgical planning. Although Convolutional Neural Networks (CNNs) have shown great promise in medical image segmentation, they prefer to learn texture features over shape information. Moreover, recent studies have shown the promise that learning the data in a meaningful order can make the network perform better. Inspired by these points, we aimed to propose a two-stage medical image segmentation framework based on contour-aware CNN and voting strategy, which could consider the contour information and a meaningful learning order. In the first stage, we introduced a plug-and-play contour enhancement module that could be integrated into the encoder–decoder architecture to assist the model in learning boundary representations. In the second stage, we employed a voting strategy to update the model using easy samples in order to further increase the performance of our model. We conducted studies of the two publicly available CHAOS (MR) and hippocampus MRI datasets. The experimental results show that, when compared to the recent and popular existing models, the proposed framework can boost overall segmentation accuracy and achieve compelling performance, with dice coefficients of 91.2 ± 2.6% for the CHAOS dataset and 88.2 ± 0.4% for the hippocampus dataset. Full article
(This article belongs to the Special Issue Medical Big Data and Artificial Intelligence for Healthcare)
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