1
|
Tay J, Yen YH, Rivera K, Chou EH, Wang CH, Chou FY, Sun JT, Han ST, Tsai TP, Chen YC, Bhakta T, Tsai CL, Lu TC, Huei-Ming Ma M. Development and External Validation of Clinical Features-based Machine Learning Models for Predicting COVID-19 in the Emergency Department. West J Emerg Med 2024; 25:67-78. [PMID: 38205987 PMCID: PMC10777189 DOI: 10.5811/westjem.60243] [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: 02/20/2023] [Revised: 09/29/2023] [Accepted: 10/02/2023] [Indexed: 01/12/2024] Open
Abstract
Introduction Timely diagnosis of patients affected by an emerging infectious disease plays a crucial role in treating patients and avoiding disease spread. In prior research, we developed an approach by using machine learning (ML) algorithms to predict serious acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection based on clinical features of patients visiting an emergency department (ED) during the early coronavirus 2019 (COVID-19) pandemic. In this study, we aimed to externally validate this approach within a distinct ED population. Methods To create our training/validation cohort (model development) we collected data retrospectively from suspected COVID-19 patients at a US ED from February 23-May 12, 2020. Another dataset was collected as an external validation (testing) cohort from an ED in another country from May 12-June 15, 2021. Clinical features including patient demographics and triage information were used to train and test the models. The primary outcome was the confirmed diagnosis of COVID-19, defined as a positive reverse transcription polymerase chain reaction test result for SARS-CoV-2. We employed three different ML algorithms, including gradient boosting, random forest, and extra trees classifiers, to construct the predictive model. The predictive performances were evaluated with the area under the receiver operating characteristic curve (AUC) in the testing cohort. Results In total, 580 and 946 ED patients were included in the training and testing cohorts, respectively. Of them, 98 (16.9%) and 180 (19.0%) were diagnosed with COVID-19. All the constructed ML models showed acceptable discrimination, as indicated by the AUC. Among them, random forest (0.785, 95% confidence interval [CI] 0.747-0.822) performed better than gradient boosting (0.774, 95% CI 0.739-0.811) and extra trees classifier (0.72, 95% CI 0.677-0.762). There was no significant difference between the constructed models. Conclusion Our study validates the use of ML for predicting COVID-19 in the ED and demonstrates its potential for predicting emerging infectious diseases based on models built by clinical features with temporal and spatial heterogeneity. This approach holds promise for scenarios where effective diagnostic tools for an emerging infectious disease may be lacking in the future.
Collapse
Affiliation(s)
- Joyce Tay
- National Taiwan University Hospital, Department of Emergency Medicine, Taipei, Taiwan
| | - Yi-Hsuan Yen
- Baylor Scott and White All Saints Medical Center, Department of Emergency Medicine, Fort Worth, Texas
| | - Kevin Rivera
- Texas Christian University, School of Medicine, Fort Worth, Texas
| | - Eric H Chou
- Baylor Scott and White All Saints Medical Center, Department of Emergency Medicine, Fort Worth, Texas
- Baylor University Medical Center, Department of Emergency Medicine, Dallas, Texas
| | - Chih-Hung Wang
- National Taiwan University Hospital, Department of Emergency Medicine, Taipei, Taiwan
- National Taiwan University, College of Medicine, Department of Emergency Medicine, Taipei, Taiwan
| | - Fan-Ya Chou
- National Taiwan University Hospital, Department of Emergency Medicine, Taipei, Taiwan
- National Taiwan University, College of Medicine, Department of Emergency Medicine, Taipei, Taiwan
| | - Jen-Tang Sun
- Far Eastern Memorial Hospital, Department of Emergency Medicine, New Taipei City, Taiwan
| | - Shih-Tsung Han
- Chang Gung Memorial Hospital at Linkou, Department of Emergency Medicine, Taoyuan, Taiwan
| | - Tzu-Ping Tsai
- Taipei Veterans General Hospital, Department of Emergency Medicine, Taipei, Taiwan
| | - Yen-Chia Chen
- Taipei Veterans General Hospital, Department of Emergency Medicine, Taipei, Taiwan
| | - Toral Bhakta
- Baylor Scott and White All Saints Medical Center, Department of Emergency Medicine, Fort Worth, Texas
| | - Chu-Lin Tsai
- National Taiwan University Hospital, Department of Emergency Medicine, Taipei, Taiwan
- National Taiwan University, College of Medicine, Department of Emergency Medicine, Taipei, Taiwan
| | - Tsung-Chien Lu
- National Taiwan University Hospital, Department of Emergency Medicine, Taipei, Taiwan
- National Taiwan University, College of Medicine, Department of Emergency Medicine, Taipei, Taiwan
| | - Matthew Huei-Ming Ma
- National Taiwan University Hospital, Department of Emergency Medicine, Taipei, Taiwan
- National Taiwan University, College of Medicine, Department of Emergency Medicine, Taipei, Taiwan
- National Taiwan University Hospital Yunlin Branch, Department of Emergency Medicine, Yunlin County, Taiwan
| |
Collapse
|
2
|
Cho G, Park JR, Choi Y, Ahn H, Lee H. Detection of COVID-19 epidemic outbreak using machine learning. Front Public Health 2023; 11:1252357. [PMID: 38174072 PMCID: PMC10764024 DOI: 10.3389/fpubh.2023.1252357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 12/01/2023] [Indexed: 01/05/2024] Open
Abstract
Background The coronavirus disease (COVID-19) pandemic has spread rapidly across the world, creating an urgent need for predictive models that can help healthcare providers prepare and respond to outbreaks more quickly and effectively, and ultimately improve patient care. Early detection and warning systems are crucial for preventing and controlling epidemic spread. Objective In this study, we aimed to propose a machine learning-based method to predict the transmission trend of COVID-19 and a new approach to detect the start time of new outbreaks by analyzing epidemiological data. Methods We developed a risk index to measure the change in the transmission trend. We applied machine learning (ML) techniques to predict COVID-19 transmission trends, categorized into three labels: decrease (L0), maintain (L1), and increase (L2). We used Support Vector Machine (SVM), Random Forest (RF), and XGBoost (XGB) as ML models. We employed grid search methods to determine the optimal hyperparameters for these three models. We proposed a new method to detect the start time of new outbreaks based on label 2, which was sustained for at least 14 days (i.e., the duration of maintenance). We compared the performance of different ML models to identify the most accurate approach for outbreak detection. We conducted sensitivity analysis for the duration of maintenance between 7 days and 28 days. Results ML methods demonstrated high accuracy (over 94%) in estimating the classification of the transmission trends. Our proposed method successfully predicted the start time of new outbreaks, enabling us to detect a total of seven estimated outbreaks, while there were five reported outbreaks between March 2020 and October 2022 in Korea. It means that our method could detect minor outbreaks. Among the ML models, the RF and XGB classifiers exhibited the highest accuracy in outbreak detection. Conclusion The study highlights the strength of our method in accurately predicting the timing of an outbreak using an interpretable and explainable approach. It could provide a standard for predicting the start time of new outbreaks and detecting future transmission trends. This method can contribute to the development of targeted prevention and control measures and enhance resource management during the pandemic.
Collapse
Affiliation(s)
- Giphil Cho
- Department of Artificial Intelligence and Software, Kangwon National University, Samcheok-si, Republic of Korea
| | - Jeong Rye Park
- Department of Mathematics, Kyungpook National University, Daegu, Republic of Korea
| | - Yongin Choi
- Busan Center for Medical Mathematics, National Institute for Mathematical Sciences, Daejeon, Republic of Korea
| | - Hyeonjeong Ahn
- Department of Statistics, Kyungpook National University, Daegu, Republic of Korea
| | - Hyojung Lee
- Department of Statistics, Kyungpook National University, Daegu, Republic of Korea
| |
Collapse
|
3
|
Verma P, Gupta A, Kumar M, Gill SS. FCMCPS-COVID: AI propelled fog-cloud inspired scalable medical cyber-physical system, specific to coronavirus disease. INTERNET OF THINGS (AMSTERDAM, NETHERLANDS) 2023; 23:100828. [PMID: 37274449 PMCID: PMC10214767 DOI: 10.1016/j.iot.2023.100828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 05/11/2023] [Accepted: 05/20/2023] [Indexed: 06/06/2023]
Abstract
Medical cyber-physical systems (MCPS) firmly integrate a network of medical objects. These systems are highly efficacious and have been progressively used in the Healthcare 4.0 to achieve continuous high-quality services. Healthcare 4.0 encompasses numerous emerging technologies and their applications have been realized in the monitoring of a variety of virus outbreaks. As a growing healthcare trend, coronavirus disease (COVID-19) can be cured and its spread can be prevented using MCPS. This virus spreads from human to human and can have devastating consequences. Moreover, with the alarmingly rising death rate and new cases across the world, there is an urgent need for continuous identification and screening of infected patients to mitigate their spread. Motivated by the facts, we propose a framework for early detection, prevention, and control of the COVID-19 outbreak by using novel Industry 5.0 technologies. The proposed framework uses a dimensionality reduction technique in the fog layer, allowing high-quality data to be used for classification purposes. The fog layer also uses the ensemble learning-based data classification technique for the detection of COVID-19 patients based on the symptomatic dataset. In addition, in the cloud layer, social network analysis (SNA) has been performed to control the spread of COVID-19. The experimental results reveal that compared with state-of-the-art methods, the proposed framework achieves better results in terms of accuracy (82.28 %), specificity (91.42 %), sensitivity (90 %) and stability with effective response time. Furthermore, the utilization of CVI-based alert generation at the fog layer improves the novelty aspects of the proposed system.
Collapse
Affiliation(s)
- Prabal Verma
- Department of Information Technology, National Institute of Technology, Srinagar, India
| | - Aditya Gupta
- Department of Computer Science and Engineering, Manipal University Jaipur, Jaipur, India
| | - Mohit Kumar
- Department of Information Technology, National Institute of Technology, Jalandhar, India
| | - Sukhpal Singh Gill
- School of Electronic Engineering and Computer Science, Queen Mary University Of London, UK
| |
Collapse
|
4
|
Gomes JC, de Freitas Barbosa VA, de Santana MA, de Lima CL, Calado RB, Júnior CRB, de Almeida Albuquerque JE, de Souza RG, de Araújo RJE, Moreno GMM, Soares LAL, Júnior LARM, de Souza RE, dos Santos WP. Rapid protocols to support COVID-19 clinical diagnosis based on hematological parameters. RESEARCH ON BIOMEDICAL ENGINEERING 2023; 39:509-539. [PMCID: PMC10239225 DOI: 10.1007/s42600-023-00286-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 05/22/2023] [Indexed: 08/27/2024]
Abstract
Purpose In December 2019, the Covid-19 pandemic began in the world. To reduce mortality, in addiction to mass vaccination, it is necessary to massify and accelerate clinical diagnosis, as well as creating new ways of monitoring patients that can help in the construction of specific treatments for the disease. Objective In this work, we propose rapid protocols for clinical diagnosis of COVID-19 through the automatic analysis of hematological parameters using evolutionary computing and machine learning. These hematological parameters are obtained from blood tests common in clinical practice. Method We investigated the best classifier architectures. Then, we applied the particle swarm optimization algorithm (PSO) to select the most relevant attributes: serum glucose, troponin, partial thromboplastin time, ferritin, D-dimer, lactic dehydrogenase, and indirect bilirubin. Then, we assessed again the best classifier architectures, but now using the reduced set of features. Finally, we used decision trees to build four rapid protocols for Covid-19 clinical diagnosis by assessing the impact of each selected feature. The proposed system was used to support clinical diagnosis and assessment of disease severity in patients admitted to intensive and semi-intensive care units as a case study in the city of Paudalho, Brazil. Results We developed a web system for Covid-19 diagnosis support. Using a 100-tree random forest, we obtained results for accuracy, sensitivity, and specificity superior to 99%. After feature selection, results were similar. The four empirical clinical protocols returned accuracies, sensitivities and specificities superior to 98%. Conclusion By using a reduced set of hematological parameters common in clinical practice, it was possible to achieve results of accuracy, sensitivity, and specificity comparable to those obtained with RT-PCR. It was also possible to automatically generate clinical decision protocols, allowing relatively accurate clinical diagnosis even without the aid of the web decision support system.
Collapse
Affiliation(s)
| | - Valter Augusto de Freitas Barbosa
- Academic Unit of Serra Talhada, Rural Federal University of Pernambuco, Serra Talhada, Brazil
- Federal University of Pernambuco, Recife, Brazil
| | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
5
|
Costanzo S, Flores A. COVID-19 Contagion Risk Estimation Model for Indoor Environments. SENSORS (BASEL, SWITZERLAND) 2022; 22:7668. [PMID: 36236766 PMCID: PMC9571772 DOI: 10.3390/s22197668] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 10/05/2022] [Accepted: 10/06/2022] [Indexed: 06/16/2023]
Abstract
COVID-19 is an infectious disease mainly transmitted through aerosol particles. Physical distancing can significantly reduce airborne transmission at a short range, but it is not a sufficient measure to avoid contagion. In recent months, health authorities have identified indoor spaces as possible sources of infection, mainly due to poor ventilation, making it necessary to take measures to improve indoor air quality. In this work, an accurate model for COVID-19 contagion risk estimation based on the Wells-Riley probabilistic approach for indoor environments is proposed and implemented as an Android mobile App. The implemented algorithm takes into account all relevant parameters, such as environmental conditions, age, kind of activities, and ventilation conditions, influencing the risk of contagion to provide the real-time probability of contagion with respect to the permanence time, the maximum allowed number of people for the specified area, the expected number of COVID-19 cases, and the required number of Air Changes per Hour. Alerts are provided to the user in the case of a high probability of contagion and CO2 concentration. Additionally, the app exploits a Bluetooth signal to estimate the distance to other devices, allowing the regulation of social distance between people. The results from the application of the model are provided and discussed for different scenarios, such as offices, restaurants, classrooms, and libraries, thus proving the effectiveness of the proposed tool, helping to reduce the spread of the virus still affecting the world population.
Collapse
Affiliation(s)
- Sandra Costanzo
- DIMES, Università della Calabria, 87036 Rende, Italy
- CNR-IREA Consiglio Nazionale delle Ricerche, 80124 Naples, Italy
- ICEmB, Inter-University National Research Center on Interactions between Electromagnetic Fields and Biosystems, 16145 Genoa, Italy
- CNIT, Consorzio Nazionale Interuniversitario per le Telecomunicazioni, 43124 Parma, Italy
| | | |
Collapse
|
6
|
Saeed A, Zaffar M, Abbas MA, Quraishi KS, Shahrose A, Irfan M, Huneif MA, Abdulwahab A, Alduraibi SK, Alshehri F, Alduraibi AK, Almushayti Z. A Turf-Based Feature Selection Technique for Predicting Factors Affecting Human Health during Pandemic. Life (Basel) 2022; 12:life12091367. [PMID: 36143404 PMCID: PMC9502730 DOI: 10.3390/life12091367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 08/22/2022] [Accepted: 08/24/2022] [Indexed: 11/30/2022] Open
Abstract
Worldwide, COVID-19 is a highly contagious epidemic that has affected various fields. Using Artificial Intelligence (AI) and particular feature selection approaches, this study evaluates the aspects affecting the health of students throughout the COVID-19 lockdown time. The research presented in this paper plays a vital role in indicating the factor affecting the health of students during the lockdown in the COVID-19 pandemic. The research presented in this article investigates COVID-19’s impact on student health using feature selections. The Filter feature selection technique is used in the presented work to statistically analyze all the features in the dataset, and for better accuracy. ReliefF (TuRF) filter feature selection is tuned and utilized in such a way that it helps to identify the factors affecting students’ health from a benchmark dataset of students studying during COVID-19. Random Forest (RF), Gradient Boosted Decision Trees (GBDT), Support Vector Machine (SVM), and 2- layer Neural Network (NN), helps in identifying the most critical indicators for rapid intervention. Results of the approach presented in the paper identified that the students who maintained their weight and kept themselves busy in health activities in the pandemic, such student’s remained healthy through this pandemic and study from home in a positive manner. The results suggest that the 2- layer NN machine-learning algorithm showed better accuracy (90%) to predict the factors affecting on health issues of students during COVID-19 lockdown time.
Collapse
Affiliation(s)
- Alqahtani Saeed
- Department of Surgery, Faculty of Medicine, Najran University, Najran 61441, Saudi Arabia
| | - Maryam Zaffar
- Faculty of Computer Sciences, IBADAT International University, Islamabad 44000, Pakistan
- Correspondence:
| | - Mohammed Ali Abbas
- Faculty of Computer Sciences, IBADAT International University, Islamabad 44000, Pakistan
| | - Khurrum Shehzad Quraishi
- Department of Chemical Engineering, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Islamabad 44000, Pakistan
| | - Abdullah Shahrose
- Department of Computer Science, HITEC University, Taxila 47080, Pakistan
| | - Muhammad Irfan
- Electrical Engineering Department, College of Engineering, Najran University Saudi Arabia, Najran 61441, Saudi Arabia
| | - Mohammed Ayed Huneif
- Department of Pediatrics, College of Medicine, Najran University, Najran 61441, Saudi Arabia
| | - Alqahtani Abdulwahab
- Department of Pediatrics, College of Medicine, Najran University, Najran 61441, Saudi Arabia
| | | | - Fahad Alshehri
- Department of Radiology, College of Medicine, Qassim University, Buraidah 52571, Saudi Arabia
| | - Alaa Khalid Alduraibi
- Department of Radiology, College of Medicine, Qassim University, Buraidah 52571, Saudi Arabia
| | - Ziyad Almushayti
- Department of Radiology, College of Medicine, Qassim University, Buraidah 52571, Saudi Arabia
| |
Collapse
|
7
|
Supervised Learning Models for the Preliminary Detection of COVID-19 in Patients Using Demographic and Epidemiological Parameters. INFORMATION 2022. [DOI: 10.3390/info13070330] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
The World Health Organization labelled the new COVID-19 breakout a public health crisis of worldwide concern on 30 January 2020, and it was named the new global pandemic in March 2020. It has had catastrophic consequences on the world economy and well-being of people and has put a tremendous strain on already-scarce healthcare systems globally, particularly in underdeveloped countries. Over 11 billion vaccine doses have already been administered worldwide, and the benefits of these vaccinations will take some time to appear. Today, the only practical approach to diagnosing COVID-19 is through the RT-PCR and RAT tests, which have sometimes been known to give unreliable results. Timely diagnosis and implementation of precautionary measures will likely improve the survival outcome and decrease the fatality rates. In this study, we propose an innovative way to predict COVID-19 with the help of alternative non-clinical methods such as supervised machine learning models to identify the patients at risk based on their characteristic parameters and underlying comorbidities. Medical records of patients from Mexico admitted between 23 January 2020 and 26 March 2022, were chosen for this purpose. Among several supervised machine learning approaches tested, the XGBoost model achieved the best results with an accuracy of 92%. It is an easy, non-invasive, inexpensive, instant and accurate way of forecasting those at risk of contracting the virus. However, it is pretty early to deduce that this method can be used as an alternative in the clinical diagnosis of coronavirus cases.
Collapse
|
8
|
Abstract
To date, the protracted pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has had widespread ramifications for the economy, politics, public health, etc. Based on the current situation, definitively stopping the spread of the virus is infeasible in many countries. This does not mean that populations should ignore the pandemic; instead, normal life needs to be balanced with disease prevention and control. This paper highlights the use of Internet of Things (IoT) for the prevention and control of coronavirus disease (COVID-19) in enclosed spaces. The proposed booking algorithm is able to control the gathering of crowds in specific regions. K-nearest neighbors (KNN) is utilized for the implementation of a navigation system with a congestion control strategy and global path planning capabilities. Furthermore, a risk assessment model is designed based on a “Sliding Window-Timer” algorithm, providing an infection risk assessment for individuals in potential contact with patients.
Collapse
|