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Rehman SU, Sadek I, Huang B, Manickam S, Mahmoud LN. IoT-based emergency cardiac death risk rescue alert system. MethodsX 2024; 13:102834. [PMID: 39071997 PMCID: PMC11278581 DOI: 10.1016/j.mex.2024.102834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Accepted: 06/28/2024] [Indexed: 07/30/2024] Open
Abstract
The use of technology in healthcare is one of the most critical application areas today. With the development of medical applications, people's quality of life has improved. However, it is impractical and unnecessary for medium-risk people to receive specialized daily hospital monitoring. Due to their health status, they will be exposed to a high risk of severe health damage or even life-threatening conditions without monitoring. Therefore, remote, real-time, low-cost, wearable, and effective monitoring is ideal for this problem. Many researchers mentioned that their studies could use electrocardiogram (ECG) detection to discover emergencies. However, how to respond to discovered emergencies in household life is still a research gap in this field.•This paper proposes a real-time monitoring of ECG signals and sending them to the cloud for Sudden Cardiac Death (SCD) prediction.•Unlike previous studies, the proposed system has an additional emergency response mechanism to alert nearby community healthcare workers when SCD is predicted to occur.
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Affiliation(s)
| | - Ibrahim Sadek
- Biomedical Engineering Dept, Faculty of Engineering, Helwan University, Helwan, Cairo, Egypt
| | - Binhua Huang
- National Advanced IPv6 Centre, Universiti Sains Malaysia, Malaysia
| | | | - Lamees N Mahmoud
- Biomedical Engineering Dept, Faculty of Engineering, Helwan University, Helwan, Cairo, Egypt
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2
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Castro C, Leiva V, Garrido D, Huerta M, Minatogawa V. Blockchain in clinical trials: Bibliometric and network studies of applications, challenges, and future prospects based on data analytics. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 255:108321. [PMID: 39053350 DOI: 10.1016/j.cmpb.2024.108321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2024] [Revised: 06/14/2024] [Accepted: 07/07/2024] [Indexed: 07/27/2024]
Abstract
This study conducts a comprehensive analysis on the usage of the blockchain technology in clinical trials, based on a curated corpus of 107 scientific articles from the year 2016 through the first quarter of 2024. Utilizing a methodological framework that integrates bibliometric analysis, network analysis, thematic mapping, and latent Dirichlet allocation, the study explores the terrain and prospective developments within this usage based on data analytics. Through a meticulous examination of the analyzed articles, the present study identifies seven key thematic areas, highlighting the diverse applications and interdisciplinary nature of blockchain in clinical trials. Our findings reveal blockchain capability to enhance data management, participant consent processes, as well as overall trial transparency, efficiency, and security. Additionally, the investigation discloses the emerging synergy between blockchain and advanced technologies, such as artificial intelligence and federated learning, proposing innovative directions for improving clinical research methodologies. Our study underscores the collaborative efforts in dealing with the complexities of integrating blockchain into the areas of clinical trials and healthcare, delineating the transformative potential of blockchain technology in revolutionizing these areas by addressing challenges and promoting practices of efficient, secure, and transparent research. The delineated themes and networks of collaboration provide a blueprint for future inquiry, showing the importance of empirical research to narrow the gap between theoretical promise and practical implementation.
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Affiliation(s)
- Cecilia Castro
- Centre of Mathematics, Universidade do Minho, Braga, Portugal
| | - Víctor Leiva
- Escuela de Ingeniería Industrial, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile.
| | - Diego Garrido
- Escuela de Ingeniería Industrial, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile
| | - Mauricio Huerta
- Escuela de Ingeniería Industrial, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile
| | - Vinicius Minatogawa
- Escuela de Ingeniería en Construcción y Transporte, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile
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3
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Santosh Kumar Patra P, Tripathy B. Hybrid optimal feature selection-based iterative deep convolution learning for COVID-19 classification system. Comput Biol Med 2024; 181:109031. [PMID: 39173484 DOI: 10.1016/j.compbiomed.2024.109031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 08/11/2024] [Accepted: 08/13/2024] [Indexed: 08/24/2024]
Abstract
The COVID-19 pandemic has necessitated the development of innovative and efficient methods for early detection and diagnosis. Integrating Internet of Things (IoT) devices and applications in healthcare has facilitated various functions. This work aims to employ practical artificial intelligence (AI) approaches to extract meaningful information from the vast amount of IoT data to perform disease prediction tasks. However, traditional AI methods need help in feature analysis due to the complexity and scale of IoT data. So, this work implements the optimal iterative COVID-19 classification network (OICC-Net) using machine learning optimization and deep learning approaches. Initially, the preprocessing operation normalizes the dataset with uniform values. Here, random forest infused particle swarm-based black widow optimization (RFI-PS-BWO) algorithm was used to get the disease-specific patterns from SARS-CoV-2 (SC2), and other disease classes, where patterns of the SC2 virus are very similar to those of other virus classes. In addition, an iterative deep convolution learning (IDCL) feature selection method is used to distinguish features from the RFI-PS-BWO data. This iterative process enhances the performance of feature selection by providing improved representation and reducing the dimensionality of the input data. Then, a one-dimensional convolutional neural network (1D-CNN) was employed to classify and identify the extracted features from SC2 with no virus classes. The 1D-CNN model is trained using a large dataset of COVID-19 samples, enabling it to learn intricate patterns and make accurate predictions. It was tested and found that the proposed OICC-Net system is more accurate than current methods, with a score of 99.97 % for F1-score, 100 % for sensitivity, 100 % for specificity, 99.98 % for precision, and 99.99 % for recall.
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Affiliation(s)
- P Santosh Kumar Patra
- Research Scholar, Department of Computer Science and Engineering, Biju Patnaik University of Technology, Rourkela, Odisha, 769015, India.
| | - Biswajit Tripathy
- Professor, Master of Computer Applications, Einstein College of Computer Application and Management, Khurda, Odisha, 752060, India.
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Yin Y, Ahmadianfar I, Karim FK, Elmannai H. Advanced forecasting of COVID-19 epidemic: Leveraging ensemble models, advanced optimization, and decomposition techniques. Comput Biol Med 2024; 175:108442. [PMID: 38678939 DOI: 10.1016/j.compbiomed.2024.108442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2024] [Revised: 03/25/2024] [Accepted: 04/07/2024] [Indexed: 05/01/2024]
Abstract
In the global effort to address the outbreak of the new coronavirus pneumonia (COVID-19) pandemic, accurate forecasting of epidemic patterns has become crucial for implementing successful interventions aimed at preventing and controlling the spread of the disease. The correct prediction of the course of COVID-19 outbreaks is a complex and challenging task, mainly because of the significant volatility in the data series related to COVID-19. Previous studies have been limited by the exclusive use of individual forecasting techniques in epidemic modeling, disregarding the integration of diverse prediction procedures. The lack of attention to detail in this situation can yield worse-than-ideal results. Consequently, this study introduces a novel ensemble framework that integrates three machine learning methods (kernel ridge regression (KRidge), Deep random vector functional link (dRVFL), and ridge regression) within a linear relationship (L-KRidge-dRVFL-Ridge). The optimization of this framework is accomplished through a distinctive approach, specifically adaptive differential evolution and particle swarm optimization (A-DEPSO). Moreover, an effective decomposition method, known as time-varying filter empirical mode decomposition (TVF-EMD), is employed to decompose the input variables. A feature selection technique, specifically using the light gradient boosting machine (LGBM), is also implemented to extract the most influential input variables. The daily datasets of COVID-19 collected from two countries, namely Italy and Poland, were used as the experimental examples. Additionally, all models are implemented to forecast COVID-19 at two-time horizons: 10- and 14-day ahead (t+10 and t+14). According to the results, the proposed model can yield higher correlation coefficient (R) for both case studies: Italy (t+10 = 0.965, t+14 = 0.961) and Poland (t+10 = 0.952, t+14 = 0.940) than the other models. The experimental results demonstrate that the model suggested in this paper has outstanding results in various kinds of complex epidemic prediction situations. The proposed ensemble model demonstrates exceptional accuracy and resilience, outperforming all similar models in terms of efficacy.
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Affiliation(s)
- Yingyu Yin
- School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, 510665, China.
| | - Iman Ahmadianfar
- Information and Communication Technology Research Group, Scientific Research Center, Al-Ayen University, Thi-Qar, Nasiriyah, 64001, Iraq.
| | - Faten Khalid Karim
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O.BOX 84428, Riyadh 11671, Saudi Arabia.
| | - Hela Elmannai
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O.BOX 84428, Riyadh 11671, Saudi Arabia.
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Uzundurukan A, Poncet S, Boffito DC, Micheau P. CT-FEM of the human thorax: Frequency response function and 3D harmonic analysis at resonance. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 246:108062. [PMID: 38359553 DOI: 10.1016/j.cmpb.2024.108062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 01/30/2024] [Accepted: 02/05/2024] [Indexed: 02/17/2024]
Abstract
BACKGROUND AND OBJECTIVE High-frequency chest wall compression (HFCC) therapy by airway clearance devices (ACDs) acts on the rheological properties of bronchial mucus to assist in clearing pulmonary secretions. Investigating low-frequency vibrations on the human thorax through numerical simulations is critical to ensure consistency and repeatability of studies by reducing extreme variability in body measurements across individuals. This study aims to present the numerical investigation of the harmonic acoustic excitation of ACDs on the human chest as a gentle and effective HFCC therapy. METHODS Four software programs were sequentially used to visualize medical images, decrease the number of surfaces, generate and repair meshes, and conduct numerical analysis, respectively. The developed methodology supplied the validation of the effect of HFCC through computed tomography-based finite element analysis (CT-FEM) of a human thorax. To illustrate the vibroacoustic characteristics of the HFCC therapy device, a 146-decibel sound pressure level (dBSPL) was applied on the back-chest surface of the model. Frequency response function (FRF) across 5-100 Hz was analyzed to characterize the behaviour of the human thorax with the state-space model. RESULTS We discovered that FRF pertaining to accelerance equals 0.138 m/s2N at the peak frequency of 28 Hz, which is consistent with two independent experimental airway clearance studies reported in the literature. The state-space model assessed two apparent resonance frequencies at 28 Hz and 41 Hz for the human thorax. The total displacement, kinetic energy density, and elastic strain energy density were furthermore quantified at 1 µm, 5.2 µJ/m3, and 140.7 µJ/m3, respectively, at the resonance frequency. In order to deepen our understanding of the impact on internal organs, the model underwent simulations in both the time domain and frequency domain for a comprehensive analysis. CONCLUSION Overall, the present study enabled determining and validating FRF of the human thorax to roll out the inconsistencies, contributing to the health of individuals with investigating gentle but effective HFCC therapy conditions with ACDs. This innovative finding furthermore provides greater clarity and a tangible understanding of the subject by simulating the responses of CT-FEM of the human thorax and internal organs at resonance.
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Affiliation(s)
- Arife Uzundurukan
- Centre de Recherche Acoustique-Signal-Humain, Université de Sherbrooke, 2500 Bd de l'Université, Sherbrooke, QC J1K 2R1, Canada.
| | - Sébastien Poncet
- Centre de Recherche Acoustique-Signal-Humain, Université de Sherbrooke, 2500 Bd de l'Université, Sherbrooke, QC J1K 2R1, Canada
| | - Daria Camilla Boffito
- Department of Chemical Engineering, École Polytechnique de Montréal, 2500 Chem. de Polytechnique, Montréal, QC H3T 1J4, Canada
| | - Philippe Micheau
- Centre de Recherche Acoustique-Signal-Humain, Université de Sherbrooke, 2500 Bd de l'Université, Sherbrooke, QC J1K 2R1, Canada
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6
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Ju H, Cui Y, Su Q, Juan L, Manavalan B. CODENET: A deep learning model for COVID-19 detection. Comput Biol Med 2024; 171:108229. [PMID: 38447500 DOI: 10.1016/j.compbiomed.2024.108229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 02/20/2024] [Accepted: 02/25/2024] [Indexed: 03/08/2024]
Abstract
Conventional COVID-19 testing methods have some flaws: they are expensive and time-consuming. Chest X-ray (CXR) diagnostic approaches can alleviate these flaws to some extent. However, there is no accurate and practical automatic diagnostic framework with good interpretability. The application of artificial intelligence (AI) technology to medical radiography can help to accurately detect the disease, reduce the burden on healthcare organizations, and provide good interpretability. Therefore, this study proposes a new deep neural network (CNN) based on CXR for COVID-19 diagnosis - CodeNet. This method uses contrastive learning to make full use of latent image data to enhance the model's ability to extract features and generalize across different data domains. On the evaluation dataset, the proposed method achieves an accuracy as high as 94.20%, outperforming several other existing methods used for comparison. Ablation studies validate the efficacy of the proposed method, while interpretability analysis shows that the method can effectively guide clinical professionals. This work demonstrates the superior detection performance of a CNN using contrastive learning techniques on CXR images, paving the way for computer vision and artificial intelligence technologies to leverage massive medical data for disease diagnosis.
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Affiliation(s)
- Hong Ju
- Heilongjiang Agricultural Engineering Vocational College, China
| | - Yanyan Cui
- Beidahuang Industry Group General Hospital, Harbin, China
| | - Qiaosen Su
- Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, 16419, Gyeonggi-do, Republic of Korea
| | - Liran Juan
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150001, China.
| | - Balachandran Manavalan
- Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, 16419, Gyeonggi-do, Republic of Korea.
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Rahman MZU, Akbar MA, Leiva V, Martin-Barreiro C, Imran M, Riaz MT, Castro C. An IoT-fuzzy intelligent approach for holistic management of COVID-19 patients. Heliyon 2024; 10:e22454. [PMID: 38163138 PMCID: PMC10756970 DOI: 10.1016/j.heliyon.2023.e22454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 11/13/2023] [Accepted: 11/13/2023] [Indexed: 01/03/2024] Open
Abstract
In this study, an internet of things (IoT)-enabled fuzzy intelligent system is introduced for the remote monitoring, diagnosis, and prescription of treatment for patients with COVID-19. The main objective of the present study is to develop an integrated tool that combines IoT and fuzzy logic to provide timely healthcare and diagnosis within a smart framework. This system tracks patients' health by utilizing an Arduino microcontroller, a small and affordable computer that reads data from various sensors, to gather data. Once collected, the data are processed, analyzed, and transmitted to a web page for remote access via an IoT-compatible Wi-Fi module. In cases of emergencies, such as abnormal blood pressure, cardiac issues, glucose levels, or temperature, immediate action can be taken to monitor the health of critical COVID-19 patients in isolation. The system employs fuzzy logic to recommend medical treatments for patients. Sudden changes in these medical conditions are remotely reported through a web page to healthcare providers, relatives, or friends. This intelligent system assists healthcare professionals in making informed decisions based on the patient's condition.
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Affiliation(s)
- Muhammad Zia Ur Rahman
- Department of Mechanical, Mechatronics and Manufacturing Engineering, University of Engineering and Technology Lahore, Faisalabad, Pakistan
| | | | - Víctor Leiva
- Escuela de Ingeniería Industrial, Universidad Católica de Valparaíso, Valparaíso, Chile
| | - Carlos Martin-Barreiro
- Facultad de Ciencias Naturales y Matemáticas, ESPOL, Guayaquil, Ecuador
- Facultad de Ingeniería, Universidad Espíritu Santo, Samborondón, Ecuador
| | - Muhammad Imran
- Department of Mechanical, Mechatronics and Manufacturing Engineering, University of Engineering and Technology Lahore, Faisalabad, Pakistan
- Department of Mechanical Engineering, Tsinghua University, Beijing, China
| | - Muhammad Tanveer Riaz
- Department of Mechanical, Mechatronics and Manufacturing Engineering, University of Engineering and Technology Lahore, Faisalabad, Pakistan
| | - Cecilia Castro
- Centre of Mathematics, Universidade do Minho, Braga, Portugal
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8
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Liu X, Li B, Liu Q, Zhang L, Zhao R, Wu D, Wang L, Wang Z, Xie G, Feng W. Multifunctional dumbbell probes-based logic circuits: microRNAs logic detection and tumor cells identification. Anal Chim Acta 2023; 1280:341856. [PMID: 37858550 DOI: 10.1016/j.aca.2023.341856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 09/22/2023] [Accepted: 09/27/2023] [Indexed: 10/21/2023]
Abstract
BACKGROUND The powerful logic processing capability of DNA logic circuits over multiple input signals perfectly meets the demands of multi-biomarker-based clinical diagnostics. As important biomarkers for cancer diagnosis and treatment, the orthogonal differential expression of microRNAs (miRNAs) in different diseases and different cancer cells makes the precise logical detection of multiple miRNAs particularly critical. RESULTS Therefore, we constructed two fundamental "AND" and "OR" logic gates and one "AND-OR" logic gate on the basis of our proposed multifunctional dumbbell probes. These logic gates allowed for the logical profiling of multiple cancer-associated miRNAs. In addition, by making simple adjustments to the functional modules of multifunctional dumbbell probes, the three logic gates we proposed could be easily transformed without the use of sophisticated probe design. Remarkably, these logic gates, in particular the "AND-OR" logic gate, were able to compute several miRNAs simultaneously, demonstrating excellent cell identification capabilities. SIGNIFICANCE Overall, this work provided a new idea for accurately distinguishing multiple cell types and showed great application prospects.
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Affiliation(s)
- Xin Liu
- Key Laboratory of Medical Diagnostics of Ministry of Education, Department of Laboratory Medicine, Chongqing Medical Laboratory Microfluidics and SPRi Engineering Research Center, Chongqing Medical University, No. 1 Yi Xue Yuan Road, Chongqing, 400016, PR China
| | - Baiying Li
- Key Laboratory of Medical Diagnostics of Ministry of Education, Department of Laboratory Medicine, Chongqing Medical Laboratory Microfluidics and SPRi Engineering Research Center, Chongqing Medical University, No. 1 Yi Xue Yuan Road, Chongqing, 400016, PR China
| | - Qian Liu
- Department of Nuclear Medicine, The Second Hospital of Chongqing Medical University, Chongqing 400010, PR China
| | - Li Zhang
- Key Laboratory of Medical Diagnostics of Ministry of Education, Department of Laboratory Medicine, Chongqing Medical Laboratory Microfluidics and SPRi Engineering Research Center, Chongqing Medical University, No. 1 Yi Xue Yuan Road, Chongqing, 400016, PR China
| | - Rong Zhao
- Key Laboratory of Medical Diagnostics of Ministry of Education, Department of Laboratory Medicine, Chongqing Medical Laboratory Microfluidics and SPRi Engineering Research Center, Chongqing Medical University, No. 1 Yi Xue Yuan Road, Chongqing, 400016, PR China
| | - Di Wu
- Key Laboratory of Medical Diagnostics of Ministry of Education, Department of Laboratory Medicine, Chongqing Medical Laboratory Microfluidics and SPRi Engineering Research Center, Chongqing Medical University, No. 1 Yi Xue Yuan Road, Chongqing, 400016, PR China
| | - Luojia Wang
- Key Laboratory of Medical Diagnostics of Ministry of Education, Department of Laboratory Medicine, Chongqing Medical Laboratory Microfluidics and SPRi Engineering Research Center, Chongqing Medical University, No. 1 Yi Xue Yuan Road, Chongqing, 400016, PR China
| | - Zhongzhong Wang
- Key Laboratory of Medical Diagnostics of Ministry of Education, Department of Laboratory Medicine, Chongqing Medical Laboratory Microfluidics and SPRi Engineering Research Center, Chongqing Medical University, No. 1 Yi Xue Yuan Road, Chongqing, 400016, PR China
| | - Guoming Xie
- Key Laboratory of Medical Diagnostics of Ministry of Education, Department of Laboratory Medicine, Chongqing Medical Laboratory Microfluidics and SPRi Engineering Research Center, Chongqing Medical University, No. 1 Yi Xue Yuan Road, Chongqing, 400016, PR China.
| | - Wenli Feng
- Key Laboratory of Medical Diagnostics of Ministry of Education, Department of Laboratory Medicine, Chongqing Medical Laboratory Microfluidics and SPRi Engineering Research Center, Chongqing Medical University, No. 1 Yi Xue Yuan Road, Chongqing, 400016, PR China.
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Ospina R, Ferreira AGO, de Oliveira HM, Leiva V, Castro C. On the Use of Machine Learning Techniques and Non-Invasive Indicators for Classifying and Predicting Cardiac Disorders. Biomedicines 2023; 11:2604. [PMID: 37892978 PMCID: PMC10604302 DOI: 10.3390/biomedicines11102604] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 09/14/2023] [Accepted: 09/19/2023] [Indexed: 10/29/2023] Open
Abstract
This research aims to enhance the classification and prediction of ischemic heart diseases using machine learning techniques, with a focus on resource efficiency and clinical applicability. Specifically, we introduce novel non-invasive indicators known as Campello de Souza features, which require only a tensiometer and a clock for data collection. These features were evaluated using a comprehensive dataset of heart disease cases from a machine learning data repository. Our findings highlight the ability of machine learning algorithms to not only streamline diagnostic procedures but also reduce diagnostic errors and the dependency on extensive clinical testing. Three key features-mean arterial pressure, pulsatile blood pressure index, and resistance-compliance indicator-were found to significantly improve the accuracy of machine learning algorithms in binary heart disease classification. Logistic regression achieved the highest average accuracy among the examined classifiers when utilizing these features. While such novel indicators contribute substantially to the classification process, they should be integrated into a broader diagnostic framework that includes comprehensive patient evaluations and medical expertise. Therefore, the present study offers valuable insights for leveraging data science techniques in the diagnosis and management of cardiovascular diseases.
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Affiliation(s)
- Raydonal Ospina
- Department of Statistics, Universidade Federal da Bahia, Salvador 40110-909, Brazil
- Department of Statistics, CASTLab, Universidade Federal de Pernambuco, Recife 50670-901, Brazil
| | - Adenice G. O. Ferreira
- Department of Statistics, CASTLab, Universidade Federal de Pernambuco, Recife 50670-901, Brazil
| | - Hélio M. de Oliveira
- Department of Statistics, CASTLab, Universidade Federal de Pernambuco, Recife 50670-901, Brazil
| | - Víctor Leiva
- School of Industrial Engineering, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362807, Chile
| | - Cecilia Castro
- Centre of Mathematics, Universidade do Minho, 4710-057 Braga, Portugal
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10
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Li ML, Zhang F, Chen YY, Luo HY, Quan ZW, Wang YF, Huang LT, Wang JH. A state-of-the-art review of functional magnetic resonance imaging technique integrated with advanced statistical modeling and machine learning for primary headache diagnosis. Front Hum Neurosci 2023; 17:1256415. [PMID: 37746052 PMCID: PMC10513061 DOI: 10.3389/fnhum.2023.1256415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 08/14/2023] [Indexed: 09/26/2023] Open
Abstract
Primary headache is a very common and burdensome functional headache worldwide, which can be classified as migraine, tension-type headache (TTH), trigeminal autonomic cephalalgia (TAC), and other primary headaches. Managing and treating these different categories require distinct approaches, and accurate diagnosis is crucial. Functional magnetic resonance imaging (fMRI) has become a research hotspot to explore primary headache. By examining the interrelationships between activated brain regions and improving temporal and spatial resolution, fMRI can distinguish between primary headaches and their subtypes. Currently the most commonly used is the cortical brain mapping technique, which is based on blood oxygen level-dependent functional magnetic resonance imaging (BOLD-fMRI). This review sheds light on the state-of-the-art advancements in data analysis based on fMRI technology for primary headaches along with their subtypes. It encompasses not only the conventional analysis methodologies employed to unravel pathophysiological mechanisms, but also deep-learning approaches that integrate these techniques with advanced statistical modeling and machine learning. The aim is to highlight cutting-edge fMRI technologies and provide new insights into the diagnosis of primary headaches.
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Affiliation(s)
- Ming-Lin Li
- Department of Family Medicine, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Fei Zhang
- Department of Family Medicine, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Yi-Yang Chen
- Department of Family Medicine, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
- Department of Family Medicine, Liaoning Health Industry Group Fukuang General Hospital, Fushun, Liaoning, China
| | - Han-Yong Luo
- Department of Family Medicine, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Zi-Wei Quan
- Department of Family Medicine, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Yi-Fei Wang
- Department of Family Medicine, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Le-Tian Huang
- Department of Oncology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Jia-He Wang
- Department of Family Medicine, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
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11
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Rezazadeh B, Asghari P, Rahmani AM. Computer-aided methods for combating Covid-19 in prevention, detection, and service provision approaches. Neural Comput Appl 2023; 35:14739-14778. [PMID: 37274420 PMCID: PMC10162652 DOI: 10.1007/s00521-023-08612-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 04/11/2023] [Indexed: 06/06/2023]
Abstract
The infectious disease Covid-19 has been causing severe social, economic, and human suffering across the globe since 2019. The countries have utilized different strategies in the last few years to combat Covid-19 based on their capabilities, technological infrastructure, and investments. A massive epidemic like this cannot be controlled without an intelligent and automatic health care system. The first reaction to the disease outbreak was lockdown, and researchers focused more on developing methods to diagnose the disease and recognize its behavior. However, as the new lifestyle becomes more normalized, research has shifted to utilizing computer-aided methods to monitor, track, detect, and treat individuals and provide services to citizens. Thus, the Internet of things, based on fog-cloud computing, using artificial intelligence approaches such as machine learning, and deep learning are practical concepts. This article aims to survey computer-based approaches to combat Covid-19 based on prevention, detection, and service provision. Technically and statistically, this article analyzes current methods, categorizes them, presents a technical taxonomy, and explores future and open issues.
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Affiliation(s)
- Bahareh Rezazadeh
- Computer Engineering Department, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Parvaneh Asghari
- Department of Computer Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran
| | - Amir Masoud Rahmani
- Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin, 64002 Taiwan
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12
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Weibull Regression and Machine Learning Survival Models: Methodology, Comparison, and Application to Biomedical Data Related to Cardiac Surgery. BIOLOGY 2023; 12:biology12030442. [PMID: 36979135 PMCID: PMC10045304 DOI: 10.3390/biology12030442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 02/26/2023] [Accepted: 03/08/2023] [Indexed: 03/18/2023]
Abstract
In this article, we propose a comparative study between two models that can be used by researchers for the analysis of survival data: (i) the Weibull regression model and (ii) the random survival forest (RSF) model. The models are compared considering the error rate, the performance of the model through the Harrell C-index, and the identification of the relevant variables for survival prediction. A statistical analysis of a data set from the Heart Institute of the University of São Paulo, Brazil, has been carried out. In the study, the length of stay of patients undergoing cardiac surgery, within the operating room, was used as the response variable. The obtained results show that the RSF model has less error rate for the training and testing data sets, at 23.55% and 20.31%, respectively, than the Weibull model, which has an error rate of 23.82%. Regarding the Harrell C-index, we obtain the values 0.76, 0.79, and 0.76, for the RSF and Weibull models, respectively. After the selection procedure, the Weibull model contains variables associated with the type of protocol and type of patient being statistically significant at 5%. The RSF model chooses age, type of patient, and type of protocol as relevant variables for prediction. We employ the randomForestSRC package of the R software to perform our data analysis and computational experiments. The proposal that we present has many applications in biology and medicine, which are discussed in the conclusions of this work.
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