1
|
Kim SH, Seo HC, Choi S, Joo S. Tele-monitoring system for intensive care ventilators in isolation rooms. Sci Rep 2023; 13:15207. [PMID: 37709819 PMCID: PMC10502084 DOI: 10.1038/s41598-023-42229-4] [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: 05/19/2023] [Accepted: 09/07/2023] [Indexed: 09/16/2023] Open
Abstract
The COVID-19 pandemic and discovery of new mutant strains have a devastating impact worldwide. Patients with severe COVID-19 require various equipment, such as ventilators, infusion pumps, and patient monitors, and a dedicated medical team to operate and monitor the equipment in isolated intensive care units (ICUs). Medical staff must wear personal protective equipment to reduce the risk of infection. This study proposes a tele-monitoring system for isolation ICUs to assist in the monitoring of COVID-19 patients. The tele-monitoring system consists of three parts: medical-device panel image processing, transmission, and tele-monitoring. This system can monitor the ventilator screen with obstacles, receive and store data, and provide real-time monitoring and data analysis. The proposed tele-monitoring system is compared with previous studies, and the image combination algorithm for reconstruction is evaluated using structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR). The system achieves an SSIM score of 0.948 in the left side and a PSNR of 23.414 dB in the right side with no obstacles. It also reduces blind spots, with an SSIM score of 0.901 and a PSNR score of 18.13 dB. The proposed tele-monitoring system is compatible with both wired and wireless communication, making it accessible in various situations. It uses camera and performs live data monitoring, and the two monitoring systems complement each other. The system also includes a comprehensive database and an analysis tool, allowing medical staff to collect and analyze data on ventilator use, providing them a quick, at-a-glance view of the patient's condition. With the implementation of this system, patient outcomes may be improved and the burden on medical professionals may be reduced during the COVID-19 pandemic-like situations.
Collapse
Affiliation(s)
- Su Hyeon Kim
- Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Hyo-Chang Seo
- Digital Therapeutics Research Center, Smart Healthcare Research Institute, Samsung Medical Center, Seoul, South Korea
| | - Sanghoon Choi
- Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Segyeong Joo
- Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
| |
Collapse
|
2
|
Irshad RR, Hussain S, Sohail SS, Zamani AS, Madsen DØ, Alattab AA, Ahmed AAA, Norain KAA, Alsaiari OAS. A Novel IoT-Enabled Healthcare Monitoring Framework and Improved Grey Wolf Optimization Algorithm-Based Deep Convolution Neural Network Model for Early Diagnosis of Lung Cancer. SENSORS (BASEL, SWITZERLAND) 2023; 23:2932. [PMID: 36991642 PMCID: PMC10052730 DOI: 10.3390/s23062932] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 03/02/2023] [Accepted: 03/03/2023] [Indexed: 06/19/2023]
Abstract
Lung cancer is a high-risk disease that causes mortality worldwide; nevertheless, lung nodules are the main manifestation that can help to diagnose lung cancer at an early stage, lowering the workload of radiologists and boosting the rate of diagnosis. Artificial intelligence-based neural networks are promising technologies for automatically detecting lung nodules employing patient monitoring data acquired from sensor technology through an Internet-of-Things (IoT)-based patient monitoring system. However, the standard neural networks rely on manually acquired features, which reduces the effectiveness of detection. In this paper, we provide a novel IoT-enabled healthcare monitoring platform and an improved grey-wolf optimization (IGWO)-based deep convulution neural network (DCNN) model for lung cancer detection. The Tasmanian Devil Optimization (TDO) algorithm is utilized to select the most pertinent features for diagnosing lung nodules, and the convergence rate of the standard grey wolf optimization (GWO) algorithm is modified, resulting in an improved GWO algorithm. Consequently, an IGWO-based DCNN is trained on the optimal features obtained from the IoT platform, and the findings are saved in the cloud for the doctor's judgment. The model is built on an Android platform with DCNN-enabled Python libraries, and the findings are evaluated against cutting-edge lung cancer detection models.
Collapse
Affiliation(s)
- Reyazur Rashid Irshad
- Department of Computer Science, College of Science and Arts, Najran University, Sharurah 68341, Saudi Arabia
| | - Shahid Hussain
- Department of Computer Science and Engineering, Sejong University, Seoul 30019, Republic of Korea
| | - Shahab Saquib Sohail
- Department of Computer Science and Engineering, School of Engineering Sciences and Technology, Jamia Hamdard, New Delhi 110062, India
| | - Abu Sarwar Zamani
- Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
| | - Dag Øivind Madsen
- USN School of Business, University of South-Eastern Norway, 3511 Hønefoss, Norway
| | - Ahmed Abdu Alattab
- Department of Computer Science, College of Science and Arts, Najran University, Sharurah 68341, Saudi Arabia
- Department of Computer Science, Faculty of Computer Science and Information Systems, Thamar University, Thamar 87246, Yemen
| | | | | | - Omar Ali Saleh Alsaiari
- Department of Computer Science, College of Science and Arts, Najran University, Sharurah 68341, Saudi Arabia
| |
Collapse
|
3
|
Bakkes T, van Diepen A, De Bie A, Montenij L, Mojoli F, Bouwman A, Mischi M, Woerlee P, Turco S. Automated detection and classification of patient-ventilator asynchrony by means of machine learning and simulated data. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 230:107333. [PMID: 36640603 DOI: 10.1016/j.cmpb.2022.107333] [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: 11/14/2022] [Revised: 12/20/2022] [Accepted: 12/31/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND AND OBJECTIVE Mechanical ventilation is a lifesaving treatment for critically ill patients in an Intensive Care Unit (ICU) or during surgery. However, one potential harm of mechanical ventilation is related to patient-ventilator asynchrony (PVA). PVA can cause discomfort to the patient, damage to the lungs, and an increase in the length of stay in the ICU and on the ventilator. Therefore, automated detection algorithms are being developed to detect and classify PVAs, with the goal of optimizing mechanical ventilation. However, the development of these algorithms often requires large labeled datasets; these are generally difficult to obtain, as their collection and labeling is a time-consuming and labor-intensive task, which needs to be performed by clinical experts. METHODS In this work, we aimed to develop a computer algorithm for the automatic detection and classification of PVA. The algorithm employs a neural network for the detection of the breath of the patient. The development of the algorithm was aided by simulations from a recently published model of the patient-ventilator interaction. RESULTS The proposed method was effective, providing an algorithm with reliable detection and classification results of over 90% accuracy. Besides presenting a detection and classification algorithm for a variety of PVAs, here we show that using simulated data in combination with clinical data increases the variability in the training dataset, leading to a gain in performance and generalizability. CONCLUSIONS In the future, these algorithms can be utilized to gain a better understanding of the clinical impact of PVAs and help clinicians to better monitor their ventilation strategies.
Collapse
Affiliation(s)
- Tom Bakkes
- Eindhoven University of Technology, Groene Loper 19, 5612 AP Eindhoven, the Netherlands.
| | - Anouk van Diepen
- Eindhoven University of Technology, Groene Loper 19, 5612 AP Eindhoven, the Netherlands
| | - Ashley De Bie
- Catharina Ziekenhuis Eindhoven, Michelangelolaan 2, 5623 EJ Eindhoven, the Netherlands
| | - Leon Montenij
- Catharina Ziekenhuis Eindhoven, Michelangelolaan 2, 5623 EJ Eindhoven, the Netherlands
| | - Francesco Mojoli
- Department of Diagnostic, University of Pavia, S.da Nuova, 65, 27100 Pavia, Italy
| | - Arthur Bouwman
- Catharina Ziekenhuis Eindhoven, Michelangelolaan 2, 5623 EJ Eindhoven, the Netherlands
| | - Massimo Mischi
- Eindhoven University of Technology, Groene Loper 19, 5612 AP Eindhoven, the Netherlands
| | - Pierre Woerlee
- Eindhoven University of Technology, Groene Loper 19, 5612 AP Eindhoven, the Netherlands
| | - Simona Turco
- Eindhoven University of Technology, Groene Loper 19, 5612 AP Eindhoven, the Netherlands
| |
Collapse
|
4
|
A model-based approach to generating annotated pressure support waveforms. J Clin Monit Comput 2022; 36:1739-1752. [PMID: 35142976 PMCID: PMC9637593 DOI: 10.1007/s10877-022-00822-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 01/29/2022] [Indexed: 10/19/2022]
Abstract
Large numbers of asynchronies during pressure support ventilation cause discomfort and higher work of breathing in the patient, and are associated with an increased mortality. There is a need for real-time decision support to detect asynchronies and assist the clinician towards lung-protective ventilation. Machine learning techniques have been proposed to detect asynchronies, but they require large datasets with sufficient data diversity, sample size, and quality for training purposes. In this work, we propose a method for generating a large, realistic and labeled, synthetic dataset for training and validating machine learning algorithms to detect a wide variety of asynchrony types. We take a model-based approach in which we adapt a non-linear lung-airway model for use in a diverse patient group and add a first-order ventilator model to generate labeled pressure, flow, and volume waveforms of pressure support ventilation. The model was able to reproduce basic measured lung mechanics parameters. Experienced clinicians were not able to differentiate between the simulated waveforms and clinical data (P = 0.44 by Fisher's exact test). The detection performance of the machine learning trained on clinical data gave an overall comparable true positive rate on clinical data and on simulated data (an overall true positive rate of 94.3% and positive predictive value of 93.5% on simulated data and a true positive rate of 98% and positive predictive value of 98% on clinical data). Our findings demonstrate that it is possible to generate labeled pressure and flow waveforms with different types of asynchronies.
Collapse
|
5
|
Shanbehzadeh M, Nopour R, Kazemi-Arpanahi H. Internet of Things (IoT) Adoption Model for Early Identification and Monitoring of COVID-19 Cases: A Systematic Review. Int J Prev Med 2022; 13:112. [PMID: 36247189 PMCID: PMC9564228 DOI: 10.4103/ijpvm.ijpvm_667_20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Accepted: 09/21/2021] [Indexed: 01/08/2023] Open
Abstract
Background The 2019 coronavirus disease (COVID-19) is a mysterious and highly infectious disease that was declared a pandemic by the World Health Organization. The virus poses a great threat to global health and the economy. Currently, in the absence of effective treatment or vaccine, leveraging advanced digital technologies is of great importance. In this respect, the Internet of Things (IoT) is useful for smart monitoring and tracing of COVID-19. Therefore, in this study, we have reviewed the literature available on the IoT-enabled solutions to tackle the current COVID-19 outbreak. Methods This systematic literature review was conducted using an electronic search of articles in the PubMed, Google Scholar, ProQuest, Scopus, Science Direct, and Web of Science databases to formulate a complete view of the IoT-enabled solutions to monitoring and tracing of COVID-19 according to the FITT (Fit between Individual, Task, and Technology) model. Results In the literature review, 28 articles were identified as eligible for analysis. This review provides an overview of technological adoption of IoT in COVID-19 to identify significant users, either primary or secondary, required technologies including technical platform, exchange, processing, storage and added-value technologies, and system tasks or applications at "on-body," "in-clinic/hospital," and even "in-community" levels. Conclusions The use of IoT along with advanced intelligence and computing technologies for ubiquitous monitoring and tracking of patients in quarantine has made it a critical aspect in fighting the spread of the current COVID-19 and even future pandemics.
Collapse
Affiliation(s)
- Mostafa Shanbehzadeh
- Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran
| | - Raoof Nopour
- Department of Health Information Management, Student Research Committee, School of Health Management and Information Sciences Branch, Iran University of Medical Sciences, Tehran, Iran
| | - Hadi Kazemi-Arpanahi
- Department of Health Information Technology, Abadan University of Medical Sciences, Abadan, Iran,Department of Student Research Committee, Abadan University of Medical Sciences, Abadan, Iran,Address for correspondence: Dr. Hadi Kazemi-Arpanahi, Assistant professor of Health Information Management, Department of Health Information Technology, Abadan University of Medical Sciences, Abadan, Iran. E-mail:
| |
Collapse
|
6
|
Sheu RK, Chen LC, Wu CL, Pardeshi MS, Pai KC, Huang CC, Chen CY, Chen WC. Multi-Modal Data Analysis for Pneumonia Status Prediction Using Deep Learning (MDA-PSP). Diagnostics (Basel) 2022; 12:diagnostics12071706. [PMID: 35885612 PMCID: PMC9317409 DOI: 10.3390/diagnostics12071706] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 06/27/2022] [Accepted: 07/09/2022] [Indexed: 11/30/2022] Open
Abstract
Evaluating several vital signs and chest X-ray (CXR) reports regularly to determine the recovery of the pneumonia patients at general wards is a challenge for doctors. A recent study shows the identification of pneumonia by the history of symptoms and signs including vital signs, CXR, and other clinical parameters, but they lack predicting the recovery status after starting treatment. The goal of this paper is to provide a pneumonia status prediction system for the early affected patient’s discharge from the hospital within 7 days or late discharge more than 7 days. This paper aims to design a multimodal data analysis for pneumonia status prediction using deep learning classification (MDA-PSP). We have developed a system that takes an input of vital signs and CXR images of the affected patient with pneumonia from admission day 1 to day 3. The deep learning then classifies the health status improvement or deterioration for predicting the possible discharge state. Therefore, the scope is to provide a highly accurate prediction of the pneumonia recovery on the 7th day after 3-day treatment by the SHAP (SHapley Additive exPlanation), imputation, adaptive imputation-based preprocessing of the vital signs, and CXR image feature extraction using deep learning based on dense layers-batch normalization (BN) with class weights for the first 7 days’ general ward patient in MDA-PSP. A total of 3972 patients with pneumonia were enrolled by de-identification with an adult age of 71 mean ± 17 sd and 64% of them were male. After analyzing the data behavior, appropriate improvement measures are taken by data preprocessing and feature vectorization algorithm. The deep learning method of Dense-BN with SHAP features has an accuracy of 0.77 for vital signs, 0.92 for CXR, and 0.75 for the combined model with class weights. The MDA-PSP hybrid method-based experiments are proven to demonstrate higher prediction accuracy of 0.75 for pneumonia patient status. Henceforth, the hybrid methods of machine and deep learning for pneumonia patient discharge are concluded to be a better approach.
Collapse
Affiliation(s)
- Ruey-Kai Sheu
- Department of Computer Science, Tunghai University, Taichung 407224, Taiwan; (R.-K.S.); (K.-C.P.); (C.-C.H.); (C.-Y.C.); (W.-C.C.)
| | - Lun-Chi Chen
- Department of Computer Science, Tunghai University, Taichung 407224, Taiwan; (R.-K.S.); (K.-C.P.); (C.-C.H.); (C.-Y.C.); (W.-C.C.)
- Correspondence: ; Tel.: +886-04-2359-0415
| | - Chieh-Liang Wu
- Department of Critical Care Medicine, Taichung Veterans General Hospital, Taichung 40705, Taiwan;
- Department of Industrial Engineering and Enterprise Information, Tunghai University, Taichung 407224, Taiwan
- Department of Automatic Control Engineering, Feng Chia University, Taichung 407102, Taiwan
| | | | - Kai-Chih Pai
- Department of Computer Science, Tunghai University, Taichung 407224, Taiwan; (R.-K.S.); (K.-C.P.); (C.-C.H.); (C.-Y.C.); (W.-C.C.)
| | - Chien-Chung Huang
- Department of Computer Science, Tunghai University, Taichung 407224, Taiwan; (R.-K.S.); (K.-C.P.); (C.-C.H.); (C.-Y.C.); (W.-C.C.)
- Department of Critical Care Medicine, Taichung Veterans General Hospital, Taichung 40705, Taiwan;
| | - Chia-Yu Chen
- Department of Computer Science, Tunghai University, Taichung 407224, Taiwan; (R.-K.S.); (K.-C.P.); (C.-C.H.); (C.-Y.C.); (W.-C.C.)
| | - Wei-Cheng Chen
- Department of Computer Science, Tunghai University, Taichung 407224, Taiwan; (R.-K.S.); (K.-C.P.); (C.-C.H.); (C.-Y.C.); (W.-C.C.)
| |
Collapse
|
7
|
Design and Implementation of IoT Data-Driven Intelligent Law Classroom Teaching System. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:8003909. [PMID: 35371242 PMCID: PMC8975672 DOI: 10.1155/2022/8003909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 12/06/2021] [Accepted: 12/09/2021] [Indexed: 11/23/2022]
Abstract
In this paper, we conduct in-depth research and analysis by building an IoT data-driven intelligent law classroom teaching system and implementing it in the actual teaching process. Firstly, the application requirements and main functions of the classroom interactive system are analysed and studied in depth; the overall design of the classroom interactive system based on wireless communication is carried out; and the classroom interactive system consisting of teacher's receiver, wireless receiver, student's handheld terminal, teacher's receiver upper computer software, student's handheld terminal upper computer software, and data management website is developed. Platform and communication part: middleware comprises multiple modules such as real-time memory event database, task management system, and event management system. The system uses USB communication, serial time-sharing communication, Zigbee wireless communication, WebSocket, and other technologies to realize data communication between several modules, and some key technologies are studied and improved in detail. Finally, the system is deployed to the actual experimental application scenario, and the software and hardware construction of the IoT innovation lab is completed. The lab equipment is officially put into use, the information platform is also in the trial run stage, and the system is tested in time during the operation stage to provide the basis for the improvement of the system functions. Use data to conduct an in-depth analysis of learning situations and learning process analysis, optimize the reasonable construction of a personalized learning environment, serve personalized learning, and achieve the most optimal learning effect. The secondary development of the Turing robot provides a personalized auxiliary Q&A system for teaching, allowing artificial intelligence-assisted teaching to be integrated into the curriculum. With the help of teaching law courses, the empirical study demonstrates that the personalized learning environment has significant advantages in improving teaching effectiveness.
Collapse
|
8
|
Artificial Intelligence for Physiotherapy and Rehabilitation. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
9
|
Tai Y, Gao B, Li Q, Yu Z, Zhu C, Chang V. Trustworthy and Intelligent COVID-19 Diagnostic IoMT Through XR and Deep-Learning-Based Clinic Data Access. IEEE INTERNET OF THINGS JOURNAL 2021; 8:15965-15976. [PMID: 35782175 PMCID: PMC8769002 DOI: 10.1109/jiot.2021.3055804] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 01/10/2021] [Accepted: 01/27/2021] [Indexed: 05/21/2023]
Abstract
This article presents a novel extended reality (XR) and deep-learning-based Internet-of-Medical-Things (IoMT) solution for the COVID-19 telemedicine diagnostic, which systematically combines virtual reality/augmented reality (AR) remote surgical plan/rehearse hardware, customized 5G cloud computing and deep learning algorithms to provide real-time COVID-19 treatment scheme clues. Compared to existing perception therapy techniques, our new technique can significantly improve performance and security. The system collected 25 clinic data from the 347 positive and 2270 negative COVID-19 patients in the Red Zone by 5G transmission. After that, a novel auxiliary classifier generative adversarial network-based intelligent prediction algorithm is conducted to train the new COVID-19 prediction model. Furthermore, The Copycat network is employed for the model stealing and attack for the IoMT to improve the security performance. To simplify the user interface and achieve an excellent user experience, we combined the Red Zone's guiding images with the Green Zone's view through the AR navigate clue by using 5G. The XR surgical plan/rehearse framework is designed, including all COVID-19 surgical requisite details that were developed with a real-time response guaranteed. The accuracy, recall, F1-score, and area under the ROC curve (AUC) area of our new IoMT were 0.92, 0.98, 0.95, and 0.98, respectively, which outperforms the existing perception techniques with significantly higher accuracy performance. The model stealing also has excellent performance, with the AUC area of 0.90 in Copycat slightly lower than the original model. This study suggests a new framework in the COVID-19 diagnostic integration and opens the new research about the integration of XR and deep learning for IoMT implementation.
Collapse
Affiliation(s)
- Yonghang Tai
- Yunnan Key Laboratory of Opto-Electronic Information TechnologyYunnan Normal UniversityKunming650500China
| | - Bixuan Gao
- Yunnan Key Laboratory of Opto-Electronic Information TechnologyYunnan Normal UniversityKunming650500China
| | - Qiong Li
- Yunnan Key Laboratory of Opto-Electronic Information TechnologyYunnan Normal UniversityKunming650500China
| | - Zhengtao Yu
- Faculty of Information Engineering and AutomationKunming University of Science and TechnologyKunming650093China
| | - Chunsheng Zhu
- Southern University of Science and TechnologyShenzhen518055China
| | | |
Collapse
|
10
|
Shanbehzadeh M, Kazemi-Arpanahi H, Orooji A, Mobarak S, Jelvay S. Performance evaluation of selected machine learning algorithms for COVID-19 prediction using routine clinical data: With versus Without CT scan features. JOURNAL OF EDUCATION AND HEALTH PROMOTION 2021; 10:285. [PMID: 34667785 PMCID: PMC8459865 DOI: 10.4103/jehp.jehp_1424_20] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2020] [Accepted: 11/19/2020] [Indexed: 06/13/2023]
Abstract
BACKGROUND Given coronavirus disease (COVID-19's) unknown nature, diagnosis, and treatment is very complex up to the present time. Thus, it is essential to have a framework for an early prediction of the disease. In this regard, machines learning (ML) could be crucial to extract concealed patterns from mining of huge raw datasets then it establishes high-quality predictive models. At this juncture, we aimed to apply different ML techniques to develop clinical predictive models and select the best performance of them. MATERIALS AND METHODS The dataset of Ayatollah Talleghani hospital, COVID-19 focal center affiliated to Abadan University of Medical Sciences have been taken into consideration. The dataset used in this study consists of 501 case records with two classes (COVID-19 and non COVID-19) and 32 columns for the diagnostic features. ML algorithms such as Naïve Bayesian, Bayesian Net, random forest (RF), multilayer perceptron, K-star, C4.5, and support vector machine were developed. Then, the recital of selected ML models was assessed by the comparison of some performance indices such as accuracy, sensitivity, specificity, precision, F-score, and receiver operating characteristic (ROC). RESULTS The experimental results indicate that RF algorithm with the accuracy of 92.42%, specificity of 75.70%, precision of 92.30%, sensitivity of 92.40%, F-measure of 92.00%, and ROC of 97.15% has the best capability for COVID-19 diagnosis and screening. CONCLUSION The empirical results reveal that RF model yielded higher performance as compared to other six classification models. It is promising to the implementation of RF model in the health-care settings to increase the accuracy and speed of disease diagnosis for primary prevention, screening, surveillance, and early treatment.
Collapse
Affiliation(s)
- Mostafa Shanbehzadeh
- Assistant Professor of Health Information Management, Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran
| | - Hadi Kazemi-Arpanahi
- Assistant Professor of Health Information Management, Department of Health Information Technology, Abadan University of Medical Sciences, Abadan, Iran
- Assistant Professor of Health Information Management, Student Research Committee, Abadan University of Medical Sciences, Abadan, Iran
| | - Azam Orooji
- Assistant Professor of Medical Informatics, School of Medicine, North Khorasan University of Medical Science, North Khorasan, Iran
| | - Sara Mobarak
- Assistant Professor of Infectious Diseases, School of Medicine, Abadan University of Medical Sciences, Abadan, Iran
| | - Saeed Jelvay
- MSc of Health Information Technology, Department of Student Research Committee, Abadan University of Medical Sciences, Abadan, Iran
| |
Collapse
|
11
|
Thomas MJ, Lal V, Baby AK, Rabeeh Vp M, James A, Raj AK. Can technological advancements help to alleviate COVID-19 pandemic? a review. J Biomed Inform 2021; 117:103787. [PMID: 33862231 PMCID: PMC8056973 DOI: 10.1016/j.jbi.2021.103787] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 03/22/2021] [Accepted: 04/10/2021] [Indexed: 12/18/2022]
Abstract
The COVID-19 pandemic is continuing, and the innovative and efficient contributions of the emerging modern technologies to the pandemic responses are too early and cannot be completely quantified at this moment. Digital technologies are not a final solution but are the tools that facilitate a quick and effective pandemic response. In accordance, mobile applications, robots and drones, social media platforms (such as search engines, Twitter, and Facebook), television, and associated technologies deployed in tackling the COVID-19 (SARS-CoV-2) outbreak are discussed adequately, emphasizing the current-state-of-art. A collective discussion on reported literature, press releases, and organizational claims are reviewed. This review addresses and highlights how these effective modern technological solutions can aid in healthcare (involving contact tracing, real-time isolation monitoring/screening, disinfection, quarantine enforcement, syndromic surveillance, and mental health), communication (involving remote assistance, information sharing, and communication support), logistics, tourism, and hospitality. The study discusses the benefits of these digital technologies in curtailing the pandemic and 'how' the different sectors adapted to these in a shorter period. Social media and television's role in ensuring global connectivity and serving as a common platform to share authentic information among the general public were summarized. The World Health Organization and Governments' role globally in-line with the prevention of propagation of false news, spreading awareness, and diminishing the severity of the COVID-19 was discussed. Furthermore, this collective review is helpful to investigators, health departments, Government organizations, and policymakers alike to facilitate a quick and effective pandemic response.
Collapse
Affiliation(s)
- Mervin Joe Thomas
- Dept. of Mechanical Engg., National Institute of Technology Calicut, Kerala 673601, India
| | - Vishnu Lal
- Dept. of Mechanical Engg., National Institute of Technology Calicut, Kerala 673601, India
| | - Ajith Kurian Baby
- Dept. of Mechanical Engg., National Institute of Technology Calicut, Kerala 673601, India
| | - Muhammad Rabeeh Vp
- School of Materials Science and Engg., National Institute of Technology Calicut, Kerala 673601, India
| | - Alosh James
- Solar Energy Center, Dept. of Mechanical Engg., National Institute of Technology Calicut, Kerala 673601, India
| | - Arun K Raj
- Dept. of Mechanical Engg., Indian Institute of Technology Bombay, Maharashtra 400076, India.
| |
Collapse
|
12
|
Piccialli F, di Cola VS, Giampaolo F, Cuomo S. The Role of Artificial Intelligence in Fighting the COVID-19 Pandemic. INFORMATION SYSTEMS FRONTIERS : A JOURNAL OF RESEARCH AND INNOVATION 2021; 23:1467-1497. [PMID: 33935585 PMCID: PMC8072097 DOI: 10.1007/s10796-021-10131-x] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 03/28/2021] [Indexed: 05/25/2023]
Abstract
The first few months of 2020 have profoundly changed the way we live our lives and carry out our daily activities. Although the widespread use of futuristic robotaxis and self-driving commercial vehicles has not yet become a reality, the COVID-19 pandemic has dramatically accelerated the adoption of Artificial Intelligence (AI) in different fields. We have witnessed the equivalent of two years of digital transformation compressed into just a few months. Whether it is in tracing epidemiological peaks or in transacting contactless payments, the impact of these developments has been almost immediate, and a window has opened up on what is to come. Here we analyze and discuss how AI can support us in facing the ongoing pandemic. Despite the numerous and undeniable contributions of AI, clinical trials and human skills are still required. Even if different strategies have been developed in different states worldwide, the fight against the pandemic seems to have found everywhere a valuable ally in AI, a global and open-source tool capable of providing assistance in this health emergency. A careful AI application would enable us to operate within this complex scenario involving healthcare, society and research.
Collapse
Affiliation(s)
- Francesco Piccialli
- Department of Mathematics and Applications “R. Caccioppoli”, University of Naples Federico II, Naples, 80126 Italy
| | - Vincenzo Schiano di Cola
- Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, 80125 Italy
| | - Fabio Giampaolo
- Department of Mathematics and Applications “R. Caccioppoli”, University of Naples Federico II, Naples, 80126 Italy
| | - Salvatore Cuomo
- Department of Mathematics and Applications “R. Caccioppoli”, University of Naples Federico II, Naples, 80126 Italy
| |
Collapse
|
13
|
Shanbehzadeh M, Kazemi-Arpanahi H, Nopour R. Performance evaluation of selected decision tree algorithms for COVID-19 diagnosis using routine clinical data. Med J Islam Repub Iran 2021; 35:29. [PMID: 34169041 PMCID: PMC8214035 DOI: 10.47176/mjiri.35.29] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Indexed: 11/09/2022] Open
Abstract
Background: The novel 2019 Coronavirus disease (COVID-19) poses a great threat to global public health and the economy. The earlier detection of COVID-19 is the key to its treatment and mitigating the transmission of the virus. Given that Machine Learning (ML) could be potentially useful in COVID-19 identification, we compared 7 decision tree (DT) algorithms to select the best clinical diagnostic model. Methods: A hospital-based retrospective dataset was used to train the selected DT algorithms. The performance of DT models was measured using performance criteria, such as accuracy, sensitivity, specificity, receiver operating characteristic (ROC), and precision-recall curves (PRC). Finally, the best decision model was obtained based on comparing the mentioned performance criteria. Results: Based on the Gini Index (GI) scoring model, 13 diagnostic criteria, including the lung lesion existence (GI= 0217), fever (GI= 0.205), history of contact with suspected people (GI= 0.188), O2 saturation rate in the blood (GI= 0.181), rhinorrhea (GI= 0.177), dyspnea (GI = 0.177), cough (GI = 0.159), history of taking the immunosuppressive drug (GI= 0.145), history of respiratory failure (ARDS) (GI= 0.141), lung lesion situation (GI= 0.133) and appearance (GI= 0.126), diarrhea (GI= 0.112), and nausea and vomiting (GI = 0.092) have been obtained as the most important criteria in diagnosing COVID-19. The results indicated that the J-48, with the accuracy= 0.85, F-Score= 0.85, ROC= 0.926, and PRC= 0.93, had the best performance for diagnosing COVID-19. Conclusion: According to the empirical results, it is promising to implement J-48 in health care settings to increase the accuracy and speed of COVID-19 diagnosis.
Collapse
Affiliation(s)
- Mostafa Shanbehzadeh
- Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran
| | - Hadi Kazemi-Arpanahi
- Department of Health Information Technology, Abadan Faculty of Medical Sciences, Abadan, Iran
- Student Research Committee, Abadan Faculty of Medical Sciences, Abadan, Iran
| | - Raoof Nopour
- Department of Health Information Technology and Management, School of Paramedical, Tehran University of Medical Sciences, Tehran, Iran
| |
Collapse
|
14
|
Bhattarai S, Gupta A, Ali E, Ali M, Riad M, Adhikari P, Mostafa JA. Can Big Data and Machine Learning Improve Our Understanding of Acute Respiratory Distress Syndrome? Cureus 2021; 13:e13529. [PMID: 33786236 PMCID: PMC7996475 DOI: 10.7759/cureus.13529] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 02/24/2021] [Indexed: 11/05/2022] Open
Abstract
Acute respiratory distress syndrome (ARDS) accounts for 10% of all diagnoses in the Intensive Care Unit, and about 40% of the patients succumb to the disease. Clinical methods alone can result in the under-recognition of this heterogeneous syndrome. The purpose of this study is to evaluate the role that big data and machine learning (ML) have played in understanding the heterogeneity of the disease and the development of various prediction algorithms. Most of the work in the field of ML in ARDS has been in the development of prediction models that have comparable efficacies to that of traditional models. Prediction algorithms have been useful in identifying new variables that may be important to consider in the future, supplementing the unknown information with the help of available noninvasive parameters, as well as predicting mortality. Phenotype identification using an unsupervised ML algorithm has been pivotal in classifying the heterogeneous population into more homogenous classes. Big data generated from ventilators in the form of ventilator waveform analysis and images in the form of radiomics have also been leveraged for the identification of the syndrome and can be incorporated into a clinical decision support system. Although the results are promising, lack of generalizability, "black box" nature of algorithms and concerns about "alarm fatigue" should be addressed for more mainstream adoption of these models.
Collapse
Affiliation(s)
- Sanket Bhattarai
- Internal Medicine, California Institute of Behavioral Neurosciences & Psychology, Fairfield, USA
| | - Ashish Gupta
- Research, California Institute of Behavioral Neurosciences & Psychology, Fairfield, USA
| | - Eiman Ali
- Research, California Institute of Behavioral Neurosciences & Psychology, Fairfield, USA
| | - Moeez Ali
- Internal Medicine, California Institute of Behavioral Neurosciences & Psychology, Fairfield, USA
| | - Mohamed Riad
- Internal Medicine, California Institute of Behavioral Neurosciences & Psychology, Fairfield, USA
| | - Prakash Adhikari
- Internal Medicine, California Institute of Behavioral Neurosciences & Psychology, Fairfield, USA
- Internal Medicine, Piedmont Athens Regional Medical Center, Athens, USA
| | - Jihan A Mostafa
- Psychiatry, California Institute of Behavioral Neurosciences & Psychology, Fairfield, USA
| |
Collapse
|
15
|
Symptom Tracking and Experimentation Platform for Covid-19 or Similar Infections. COMPUTERS 2021. [DOI: 10.3390/computers10020022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Remote symptom tracking is critical for the prevention of Covid-19 spread. The qualified medical staff working in the call centers of primary health care units have to take critical decisions often based on vague information about the patient condition. The congestion and the medical protocols that are constantly changing often lead to incorrect decisions. The proposed platform allows the remote assessment of symptoms and can be useful for patients, health institutes and researchers. It consists of mobile desktop applications and medical sensors connected to cloud infrastructure. The unique features offered by the proposed solution are: (a) dynamic adaptation of Medical Protocols (MP) is supported (for the definition of alert rules, sensor sampling strategy and questionnaire structure) covering different medical cases (pre- or post-hospitalization, vulnerable population, etc.), (b) anonymous medical data can be statistically processed in the context of the research about an infection such as Covid-19, (c) reliable diagnosis is supported since several factors are taken into consideration, (d) the platform can be used to drastically reduce the congestion in various healthcare units. For the demonstration of (b), new classification methods based on similarity metrics have been tested for cough sound classification with an accuracy in the order of 90%.
Collapse
|
16
|
Artificial Intelligence for Physiotherapy and Rehabilitation. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_339-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
|