1
|
Molaei S, Moazen H, Niazkar HR, Sabaei M, Johari MG, Rezaianzadeh A. Application of boosted trees to the prognosis prediction of COVID-19. Health Sci Rep 2024; 7:e2104. [PMID: 38784249 PMCID: PMC11111612 DOI: 10.1002/hsr2.2104] [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: 06/03/2023] [Revised: 02/07/2024] [Accepted: 04/18/2024] [Indexed: 05/25/2024] Open
Abstract
Background and Aims The precise prediction of COVID-19 prognosis remains a clinical challenge. In this regard, early identification of severe cases facilitates the triage and management of COVID-19 cases. The present paper aims to explore the prognosis of COVID-19 patients based on routine laboratory tests taken when patients are admitted. Methods A data set including 1455 COVID-19 patients (727 male, 728 female) and their routine laboratory tests conducted upon hospital admission, age, Intensive Care Unit (ICU) admission, and outcome were gathered. The data set was randomly split into the train (75% of the data) and test data set (25% of the data). The explainable boosting machine (EBM) and extreme gradient boosting (XGBoost) were used for predicting the mortality and ICU admission of COVID-19 cases. Also, feature importance was extracted using EBM and XGBoost. Results The EBM and XGBoost achieved 86.38% and 88.56% accuracy in the test data set, respectively. In addition, EBM and XGBoost predicted the ICU admission with an accuracy of 89.37%, and 79.29% in the test data set for COVID-19 patients, respectively. Also, obtained models indicated that aspartate transaminase (AST), lymphocyte, blood urea nitrogen (BUN), and age are the most significant predictors of COVID-19 mortality. Furthermore, the lymphocyte count, AST, and BUN level were the most significant ICU admission predictors of COVID-19 patients. Conclusions The current study indicated that both EBM and XGBoost could predict the ICU admission and mortality of COVID-19 cases based on routine hematological and clinical chemistry evaluation at the time of admission. Also, based on the results, AST, lymphocyte count, and BUN levels could be used as early predictors of COVID-19 prognosis.
Collapse
Affiliation(s)
- Sajjad Molaei
- Department of Computer EngineeringAmirkabir University of TechnologyTehranIran
| | - Hadi Moazen
- Department of Computer Science and Software EngineeringUniversite LavalQuebecQuebecCanada
| | - Hamid R. Niazkar
- Breast Diseases Research CenterShiraz University of Medical SciencesShirazIran
| | - Masoud Sabaei
- Department of Computer EngineeringAmirkabir University of TechnologyTehranIran
| | - Masoumeh G. Johari
- Breast Diseases Research CenterShiraz University of Medical SciencesShirazIran
| | - Abbas Rezaianzadeh
- Colorectal Research CenterShiraz University of Medical SciencesShirazIran
| |
Collapse
|
2
|
Ghnemat R, Alodibat S, Abu Al-Haija Q. Explainable Artificial Intelligence (XAI) for Deep Learning Based Medical Imaging Classification. J Imaging 2023; 9:177. [PMID: 37754941 PMCID: PMC10532018 DOI: 10.3390/jimaging9090177] [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: 05/06/2023] [Revised: 08/19/2023] [Accepted: 08/23/2023] [Indexed: 09/28/2023] Open
Abstract
Recently, deep learning has gained significant attention as a noteworthy division of artificial intelligence (AI) due to its high accuracy and versatile applications. However, one of the major challenges of AI is the need for more interpretability, commonly referred to as the black-box problem. In this study, we introduce an explainable AI model for medical image classification to enhance the interpretability of the decision-making process. Our approach is based on segmenting the images to provide a better understanding of how the AI model arrives at its results. We evaluated our model on five datasets, including the COVID-19 and Pneumonia Chest X-ray dataset, Chest X-ray (COVID-19 and Pneumonia), COVID-19 Image Dataset (COVID-19, Viral Pneumonia, Normal), and COVID-19 Radiography Database. We achieved testing and validation accuracy of 90.6% on a relatively small dataset of 6432 images. Our proposed model improved accuracy and reduced time complexity, making it more practical for medical diagnosis. Our approach offers a more interpretable and transparent AI model that can enhance the accuracy and efficiency of medical diagnosis.
Collapse
Affiliation(s)
- Rawan Ghnemat
- Department of Computer Science, Princess Sumaya University for Technology, Amman 11941, Jordan
| | - Sawsan Alodibat
- Department of Computer Science, Princess Sumaya University for Technology, Amman 11941, Jordan
| | - Qasem Abu Al-Haija
- Department of Cybersecurity, Princess Sumaya University for Technology, Amman 11941, Jordan
| |
Collapse
|
3
|
Roshani S, Koziel S, Yahya SI, Chaudhary MA, Ghadi YY, Roshani S, Golunski L. Mutual Coupling Reduction in Antenna Arrays Using Artificial Intelligence Approach and Inverse Neural Network Surrogates. SENSORS (BASEL, SWITZERLAND) 2023; 23:7089. [PMID: 37631625 PMCID: PMC10459678 DOI: 10.3390/s23167089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 08/03/2023] [Accepted: 08/07/2023] [Indexed: 08/27/2023]
Abstract
This paper presents a novel approach to reducing undesirable coupling in antenna arrays using custom-designed resonators and inverse surrogate modeling. To illustrate the concept, two standard patch antenna cells with 0.07λ edge-to-edge distance were designed and fabricated to operate at 2.45 GHz. A stepped-impedance resonator was applied between the antennas to suppress their mutual coupling. For the first time, the optimum values of the resonator geometry parameters were obtained using the proposed inverse artificial neural network (ANN) model, constructed from the sampled EM-simulation data of the system, and trained using the particle swarm optimization (PSO) algorithm. The inverse ANN surrogate directly yields the optimum resonator dimensions based on the target values of its S-parameters being the input parameters of the model. The involvement of surrogate modeling also contributes to the acceleration of the design process, as the array does not need to undergo direct EM-driven optimization. The obtained results indicate a remarkable cancellation of the surface currents between two antennas at their operating frequency, which translates into isolation as high as -46.2 dB at 2.45 GHz, corresponding to over 37 dB improvement as compared to the conventional setup.
Collapse
Affiliation(s)
- Saeed Roshani
- Department of Electrical Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah 67771, Iran
| | - Slawomir Koziel
- Department of Engineering, Reykjavik University, 102 Reykjavik, Iceland
- Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, 80-233 Gdansk, Poland
| | - Salah I. Yahya
- Department of Communication and Computer Engineering, Cihan University-Erbil, Erbil 44001, Iraq
- Department of Software Engineering, Faculty of Engineering, Koya University, Koya 46017, Iraq
| | - Muhammad Akmal Chaudhary
- College of Engineering and Information Technology, Ajman University, Ajman 346, United Arab Emirates
| | - Yazeed Yasin Ghadi
- Software Engineering and Computer Science Department, Al Ain University, Al Ain 64141, United Arab Emirates
| | - Sobhan Roshani
- Department of Electrical Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah 67771, Iran
| | - Lukasz Golunski
- Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, 80-233 Gdansk, Poland
| |
Collapse
|
4
|
Zhou L, Zhao C, Liu N, Yao X, Cheng Z. Improved LSTM-based deep learning model for COVID-19 prediction using optimized approach. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023; 122:106157. [PMID: 36968247 PMCID: PMC10017389 DOI: 10.1016/j.engappai.2023.106157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 03/08/2023] [Accepted: 03/13/2023] [Indexed: 05/25/2023]
Abstract
Individuals in any country are badly impacted both economically and physically whenever an epidemic of infectious illnesses breaks out. A novel coronavirus strain was responsible for the outbreak of the coronavirus sickness in 2019. Corona Virus Disease 2019 (COVID-19) is the name that the World Health Organization (WHO) officially gave to the pneumonia that was caused by the novel coronavirus on February 11, 2020. The use of models that are informed by machine learning is currently a major focus of study in the field of improved forecasting. By displaying annual trends, forecasting models can be of use in performing impact assessments of potential outcomes. In this paper, proposed forecast models consisting of time series models such as long short-term memory (LSTM), bidirectional long short-term memory (Bi-LSTM), generalized regression unit (GRU), and dense-LSTM have been evaluated for time series prediction of confirmed cases, deaths, and recoveries in 12 major countries that have been affected by COVID-19. Tensorflow1.0 was used for programming. Indices known as mean absolute error (MAE), root means square error (RMSE), Median Absolute Error (MEDAE) and r2 score are utilized in the process of evaluating the performance of models. We presented various ways to time-series forecasting by making use of LSTM models (LSTM, BiLSTM), and we compared these proposed methods to other machine learning models to evaluate the performance of the models. Our study suggests that LSTM based models are among the most advanced models to forecast time series data.
Collapse
Affiliation(s)
- Luyu Zhou
- Department of Pharmacy, College of Biology, Hunan University, Changsha, Hunan 410082, China
- Institute for Translational Medicine, The Affiliated Hospital of Qingdao University, College of Medicine, Qingdao University, Qingdao 266021, China
| | - Chun Zhao
- Department of Pharmacy, College of Biology, Hunan University, Changsha, Hunan 410082, China
| | - Ning Liu
- Institute for Translational Medicine, The Affiliated Hospital of Qingdao University, College of Medicine, Qingdao University, Qingdao 266021, China
| | - Xingduo Yao
- Institute for Translational Medicine, The Affiliated Hospital of Qingdao University, College of Medicine, Qingdao University, Qingdao 266021, China
| | - Zewei Cheng
- Institute for Translational Medicine, The Affiliated Hospital of Qingdao University, College of Medicine, Qingdao University, Qingdao 266021, China
| |
Collapse
|
5
|
Berdahl CT, Baker L, Mann S, Osoba O, Girosi F. Strategies to Improve the Impact of Artificial Intelligence on Health Equity: Scoping Review. JMIR AI 2023; 2:e42936. [PMID: 38875587 PMCID: PMC11041459 DOI: 10.2196/42936] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 12/14/2022] [Accepted: 12/29/2022] [Indexed: 06/16/2024]
Abstract
BACKGROUND Emerging artificial intelligence (AI) applications have the potential to improve health, but they may also perpetuate or exacerbate inequities. OBJECTIVE This review aims to provide a comprehensive overview of the health equity issues related to the use of AI applications and identify strategies proposed to address them. METHODS We searched PubMed, Web of Science, the IEEE (Institute of Electrical and Electronics Engineers) Xplore Digital Library, ProQuest U.S. Newsstream, Academic Search Complete, the Food and Drug Administration (FDA) website, and ClinicalTrials.gov to identify academic and gray literature related to AI and health equity that were published between 2014 and 2021 and additional literature related to AI and health equity during the COVID-19 pandemic from 2020 and 2021. Literature was eligible for inclusion in our review if it identified at least one equity issue and a corresponding strategy to address it. To organize and synthesize equity issues, we adopted a 4-step AI application framework: Background Context, Data Characteristics, Model Design, and Deployment. We then created a many-to-many mapping of the links between issues and strategies. RESULTS In 660 documents, we identified 18 equity issues and 15 strategies to address them. Equity issues related to Data Characteristics and Model Design were the most common. The most common strategies recommended to improve equity were improving the quantity and quality of data, evaluating the disparities introduced by an application, increasing model reporting and transparency, involving the broader community in AI application development, and improving governance. CONCLUSIONS Stakeholders should review our many-to-many mapping of equity issues and strategies when planning, developing, and implementing AI applications in health care so that they can make appropriate plans to ensure equity for populations affected by their products. AI application developers should consider adopting equity-focused checklists, and regulators such as the FDA should consider requiring them. Given that our review was limited to documents published online, developers may have unpublished knowledge of additional issues and strategies that we were unable to identify.
Collapse
Affiliation(s)
- Carl Thomas Berdahl
- RAND Corporation, Santa Monica, CA, United States
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States
- Department of Emergency Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | | | - Sean Mann
- RAND Corporation, Santa Monica, CA, United States
| | - Osonde Osoba
- RAND Corporation, Santa Monica, CA, United States
| | | |
Collapse
|
6
|
Gaidai O, Xing Y. COVID-19 Epidemic Forecast in Brazil. Bioinform Biol Insights 2023; 17:11779322231161939. [PMID: 37065993 PMCID: PMC10090958 DOI: 10.1177/11779322231161939] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 02/16/2023] [Indexed: 04/18/2023] Open
Abstract
This study advocates a novel spatio-temporal method for accurate prediction of COVID-19 epidemic occurrence probability at any time in any Brazil state of interest, and raw clinical observational data have been used. This article describes a novel bio-system reliability approach, particularly suitable for multi-regional environmental and health systems, observed over a sufficient time period, resulting in robust long-term forecast of the virus outbreak probability. COVID-19 daily numbers of recorded patients in all affected Brazil states were taken into account. This work aimed to benchmark novel state-of-the-art methods, making it possible to analyse dynamically observed patient numbers while taking into account relevant regional mapping. Advocated approach may help to monitor and predict possible future epidemic outbreaks within a large variety of multi-regional biological systems. Suggested methodology may be used in various modern public health applications, efficiently using their clinical survey data.
Collapse
Affiliation(s)
- Oleg Gaidai
- College of Engineering Science and
Technology, Shanghai Ocean University, Shanghai, China
| | - Yihan Xing
- Department of Mechanical and Structural
Engineering and Materials Science, University of Stavanger, Stavanger, Norway
- Yihan Xing, University of Stavanger, 4036
Stavanger, P.O. Box 8600, Norway.
| |
Collapse
|
7
|
Alshagathrh FM, Househ MS. Artificial Intelligence for Detecting and Quantifying Fatty Liver in Ultrasound Images: A Systematic Review. BIOENGINEERING (BASEL, SWITZERLAND) 2022; 9:bioengineering9120748. [PMID: 36550954 PMCID: PMC9774180 DOI: 10.3390/bioengineering9120748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 10/20/2022] [Accepted: 10/30/2022] [Indexed: 12/05/2022]
Abstract
BACKGROUND Non-alcoholic Fatty Liver Disease (NAFLD) is growing more prevalent worldwide. Although non-invasive diagnostic approaches such as conventional ultrasonography and clinical scoring systems have been proposed as alternatives to liver biopsy, their efficacy has been called into doubt. Artificial Intelligence (AI) is now combined with traditional diagnostic processes to improve the performance of non-invasive approaches. OBJECTIVE This study explores how well various AI methods function and perform on ultrasound (US) images to diagnose and quantify non-alcoholic fatty liver disease. METHODOLOGY A systematic review was conducted to achieve this objective. Five science bibliographic databases were searched, including PubMed, Association for Computing Machinery ACM Digital Library, Institute of Electrical and Electronics Engineers IEEE Xplore, Scopus, and Google Scholar. Only peer-reviewed English articles, conferences, theses, and book chapters were included. Data from studies were synthesized using narrative methodologies per Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria. RESULTS Forty-nine studies were included in the systematic review. According to the qualitative analysis, AI significantly enhanced the diagnosis of NAFLD, Non-Alcoholic Steatohepatitis (NASH), and liver fibrosis. In addition, modalities, image acquisition, feature extraction and selection, data management, and classifiers were assessed and compared in terms of performance measures (i.e., accuracy, sensitivity, and specificity). CONCLUSION AI-supported systems show potential performance increases in detecting and quantifying steatosis, NASH, and liver fibrosis in NAFLD patients. Before real-world implementation, prospective studies with direct comparisons of AI-assisted modalities and conventional techniques are necessary.
Collapse
|
8
|
An Empirical Analysis of an Optimized Pretrained Deep Learning Model for COVID-19 Diagnosis. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:9771212. [PMID: 35928972 PMCID: PMC9344483 DOI: 10.1155/2022/9771212] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Revised: 06/23/2022] [Accepted: 06/30/2022] [Indexed: 11/17/2022]
Abstract
As a result of the COVID-19 outbreak, which has put the world in an unprecedented predicament, thousands of people have died. Data from structured and unstructured sources are combined to create user-friendly platforms for clinicians and researchers in an integrated bioinformatics approach. The diagnosis and treatment of COVID-19 disease can be accelerated using AI-based platforms. In the battle against the virus, however, researchers and decision-makers must contend with an ever-increasing volume of data, referred to as “big data.” VGG19 and ResNet152V2 pretrained deep learning architectures were used in this study. With these datasets, we could train and fine-tune our model on lung ultrasound frames from healthy people as well as from patients with COVID-19 and pneumonia. In two separate experiments, we evaluated two different classes of predictive models: one against pneumonia and the other against non-COVID-19. COVID-19 can be detected and diagnosed accurately and efficiently using these models, according to the findings. Therefore, the use of these inexpensive and affordable deep learning methods should be considered as a reliable method for the diagnosis of COVID-19.
Collapse
|