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Asteris PG, Gandomi AH, Armaghani DJ, Kokoris S, Papandreadi AT, Roumelioti A, Papanikolaou S, Tsoukalas MZ, Triantafyllidis L, Koutras EI, Bardhan A, Mohammed AS, Naderpour H, Paudel S, Samui P, Ntanasis-Stathopoulos I, Dimopoulos MA, Terpos E. Prognosis of COVID-19 severity using DERGA, a novel machine learning algorithm. Eur J Intern Med 2024; 125:67-73. [PMID: 38458880 DOI: 10.1016/j.ejim.2024.02.037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 02/23/2024] [Accepted: 02/29/2024] [Indexed: 03/10/2024]
Abstract
It is important to determine the risk for admission to the intensive care unit (ICU) in patients with COVID-19 presenting at the emergency department. Using artificial neural networks, we propose a new Data Ensemble Refinement Greedy Algorithm (DERGA) based on 15 easily accessible hematological indices. A database of 1596 patients with COVID-19 was used; it was divided into 1257 training datasets (80 % of the database) for training the algorithms and 339 testing datasets (20 % of the database) to check the reliability of the algorithms. The optimal combination of hematological indicators that gives the best prediction consists of only four hematological indicators as follows: neutrophil-to-lymphocyte ratio (NLR), lactate dehydrogenase, ferritin, and albumin. The best prediction corresponds to a particularly high accuracy of 97.12 %. In conclusion, our novel approach provides a robust model based only on basic hematological parameters for predicting the risk for ICU admission and optimize COVID-19 patient management in the clinical practice.
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Affiliation(s)
- Panagiotis G Asteris
- Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Athens, Greece
| | - Amir H Gandomi
- Faculty of Engineering & IT, University of Technology Sydney, Sydney, NSW 2007, Australia; University Research and Innovation Center (EKIK), Óbuda University, 1034 Budapest, Hungary
| | - Danial J Armaghani
- School of Civil and Environmental Engineering, University of Technology Sydney, NSW 2007, Australia
| | - Styliani Kokoris
- Laboratory of Hematology and Hospital Blood Transfusion Department, University General Hospital "Attikon", National and Kapodistrian University of Athens, Medical School, Greece
| | - Anastasia T Papandreadi
- Software and Applications Department, University General Hospital "Attikon", National and Kapodistrian University of Athens, Medical School, Greece
| | - Anna Roumelioti
- Department of Hematology and Lymphoma BMTU, Evangelismos General Hospital, Athens, Greece
| | - Stefanos Papanikolaou
- NOMATEN Centre of Excellence, National Center for Nuclear Research, ulica A. Sołtana 7, 05-400 Swierk/Otwock, Poland
| | - Markos Z Tsoukalas
- Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Athens, Greece
| | - Leonidas Triantafyllidis
- Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Athens, Greece
| | - Evangelos I Koutras
- Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Athens, Greece
| | - Abidhan Bardhan
- Civil Engineering Department, National Institute of Technology Patna, Bihar, India
| | - Ahmed Salih Mohammed
- Engineering Department, American University of Iraq, Sulaimani, Kurdistan-Region, Iraq
| | - Hosein Naderpour
- Institute of Industrial Science, University of Tokyo, Tokyo, Japan
| | - Satish Paudel
- Department of Civil and Environmental Engineering, University of Nevada, Reno, US
| | - Pijush Samui
- Civil Engineering Department, National Institute of Technology Patna, Bihar, India
| | - Ioannis Ntanasis-Stathopoulos
- Department of Clinical Therapeutics, Medical School, Faculty of Medicine, National Kapodistrian University of Athens, Athens, Greece
| | - Meletios A Dimopoulos
- Department of Clinical Therapeutics, Medical School, Faculty of Medicine, National Kapodistrian University of Athens, Athens, Greece
| | - Evangelos Terpos
- Department of Clinical Therapeutics, Medical School, Faculty of Medicine, National Kapodistrian University of Athens, Athens, Greece.
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Cavalcanti-Dantas VDM, Fernandes B, Dantas PHLF, Uchoa GR, Mendes AF, Araújo Júnior WOD, Castellano LRC, Fernandes AIV, Goulart LR, Oliveira RADS, Assis PACD, Souza JRD, Morais CNLD. Differential epitope prediction across diverse circulating variants of SARS-COV-2 in Brazil. Comput Biol Chem 2024; 112:108139. [PMID: 38972100 DOI: 10.1016/j.compbiolchem.2024.108139] [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: 10/18/2023] [Revised: 06/09/2024] [Accepted: 06/22/2024] [Indexed: 07/09/2024]
Abstract
COVID-19, caused by the SARS-COV-2 virus, induces numerous immunological reactions linked to the severity of the clinical condition of those infected. The surface Spike protein (S protein) present in Sars-CoV-2 is responsible for the infection of host cells. This protein presents a high rate of mutations, which can increase virus transmissibility, infectivity, and immune evasion. Therefore, we propose to evaluate, using immunoinformatic techniques, the predicted epitopes for the S protein of seven variants of Sars-CoV-2. MHC class I and II epitopes were predicted and further assessed for their immunogenicity, interferon-gamma (IFN-γ) inducing capacity, and antigenicity. For B cells, linear and structural epitopes were predicted. For class I MHC epitopes, 40 epitopes were found for the clades of Wuhan, Clade 2, Clade 3, and 20AEU.1, Gamma, and Delta, in addition to 38 epitopes for Alpha and 44 for Omicron. For MHC II, there were differentially predicted epitopes for all variants and eight equally predicted epitopes. These were evaluated for differences in the MHC II alleles to which they would bind. Regarding B cell epitopes, 16 were found in the Wuhan variant, 14 in 22AEU.1 and in Clade 3, 15 in Clade 2, 11 in Alpha and Delta, 13 in Gamma, and 9 in Omicron. When compared, there was a reduction in the number of predicted epitopes concerning the Spike protein, mainly in the Delta and Omicron variants. These findings corroborate the need for updates seen today in bivalent mRNA vaccines against COVID-19 to promote a targeted immune response to the main circulating variant, Omicron, leading to more robust protection against this virus and avoiding cases of reinfection. When analyzing the specific epitopes for the RBD region of the spike protein, the Omicron variant did not present a B lymphocyte epitope from position 390, whereas the epitope at position 493 for MHC was predicted only for the Alpha, Gamma, and Omicron variants.
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Affiliation(s)
| | | | | | | | | | | | | | - Ana Isabel Vieira Fernandes
- Health Promotion Department of the Medical Sciences Center and Division for Infectious and Parasitic Diseases, Lauro Wanderley University Hospital, Federal University of Paraiba, Brazil
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Krishna C, Kumar D, Kushwaha DS. A Comprehensive Survey on Pandemic Patient Monitoring System: Enabling Technologies, Opportunities, and Research Challenges. WIRELESS PERSONAL COMMUNICATIONS 2023; 131:1-48. [PMID: 37360140 PMCID: PMC10235850 DOI: 10.1007/s11277-023-10535-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 05/21/2023] [Indexed: 06/28/2023]
Abstract
Sporadic occurrences of transmissible diseases have severe and long-lasting effects on humankind throughout history. These outbreaks have molded the political, economic, and social aspects of human life. Pandemics have redefined some of the basic beliefs of modern healthcare, pushing researchers and scientists to develop innovative solutions to be better equipped for future emergencies. Numerous attempts have been made to fight Covid-19-like pandemics using technologies such as the Internet of Things, wireless body area network, blockchain, and machine learning. Since the disease is highly contagious, novel research in patients' health monitoring system is essential for the constant monitoring of pandemic patients with minimal or no human intervention. With the ongoing pandemic of SARS-CoV-2, popularly known as Covid-19, innovations for monitoring of patients' vitals and storing them securely have risen more than ever. Analyzing the stored patients' data can further assist healthcare workers in their decision-making process. In this paper, we surveyed the research works on remote monitoring of pandemic patients admitted in hospitals or quarantined at home. First, an overview of pandemic patient monitoring is given followed by a brief introduction of enabling technologies i.e. Internet of Things, blockchain, and machine learning to implement the system. The reviewed works have been classified into three categories; remote monitoring of pandemic patients using IoT, blockchain-based storage or sharing platforms for patients' data, and processing/analyzing the stored patients' data using machine learning for prognosis and diagnosis. We also identified several open research issues to set directions for future research.
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Affiliation(s)
- Charu Krishna
- Department of Computer Science & Engineering, Motilal Nehru National Institute of Technology Allahabad, Prayagraj, UP 211004 India
| | - Dinesh Kumar
- Department of Computer Science & Engineering, National Institute of Technology Jamshedpur, Jamshedpur, Jharkhand 831014 India
| | - Dharmender Singh Kushwaha
- Department of Computer Science & Engineering, Motilal Nehru National Institute of Technology Allahabad, Prayagraj, UP 211004 India
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Susič D, Syed-Abdul S, Dovgan E, Jonnagaddala J, Gradišek A. Artificial intelligence based personalized predictive survival among colorectal cancer patients. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 231:107435. [PMID: 36842345 DOI: 10.1016/j.cmpb.2023.107435] [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: 03/24/2022] [Revised: 12/14/2022] [Accepted: 02/18/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVE Colorectal cancer is a major health concern. It is now the third most common cancer and the fourth leading cause of cancer mortality worldwide. The aim of this study was to evaluate the performance of machine learning algorithms for predicting survival of colorectal cancer patients 1 to 5 years after diagnosis, and identify the most important variables. METHODS A sample of 1236 patients diagnosed with colorectal cancer and 118 predictor variables has been used. The outcome of interest was a binary variable indicating whether the patient survived the number of years in question or not. 20 predictor variables were selected using mutual information score with the outcome. We implemented 11 machine learning algorithms and evaluated their performance with a 5 by 2-fold cross-validation with stratified folds and with paired Student's t-tests. We compared the results with the Kaplan-Meier estimator and Cox's proportional hazard regression. RESULTS Using the 20 most important predictor variables for each of the survival years, the logistic regression algorithm achieved an area under the receiver operating characteristic curve of 0.850 (0.014 SD, 0.840-0.860 95 % CI) for the 1-year, and 0.872 (0.014 SD, 0.861-0.882 95% CI) for the 5-year survival prediction. Using only the 5 most important predictor variables, the corresponding values are 0.793 (0.020 SD, 0.778-0.807 95% CI) and 0.794 (0.011 SD, 0.785-0.802 95% CI). The most important variables for 1-year prediction were number of R residual, M distant metastasis, overall stage, probable recurrence within 5 years, and tumour length, whereas for 5-year prediction the most important were probable recurrence within 5 years, R residual, M distant metastasis, number of positive lymph nodes, and palliative chemotherapy. Biomarkers do not appear among the top 20 most important ones. For all survival intervals, the probability of the top model agrees with the Kaplan-Meier estimate, both in the interval of one standard deviation and in the 95% confidence interval. CONCLUSIONS The findings suggest that machine learning algorithms can predict the survival probability of colorectal cancer patients and can be used to inform the patients and assist decision-making in clinical care management. In addition, this study unveils the most essential variables for estimating survival short- and long-term among patients with Colorectal cancer.
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Affiliation(s)
- David Susič
- Jožef Stefan Institute, Jamova cesta 39, SI-1000 Ljubljana, Slovenia; Jožef Stefan International Postgraduate School, Jamova cesta 39, SI-1000 Ljubljana, Slovenia
| | - Shabbir Syed-Abdul
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan.
| | - Erik Dovgan
- Jožef Stefan Institute, Jamova cesta 39, SI-1000 Ljubljana, Slovenia
| | | | - Anton Gradišek
- Jožef Stefan Institute, Jamova cesta 39, SI-1000 Ljubljana, Slovenia.
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Wen R, Zhang M, Xu R, Gao Y, Liu L, Chen H, Wang X, Zhu W, Lin H, Liu C, Zeng X. COVID-19 imaging, where do we go from here? Bibliometric analysis of medical imaging in COVID-19. Eur Radiol 2023; 33:3133-3143. [PMID: 36892649 PMCID: PMC9996554 DOI: 10.1007/s00330-023-09498-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 12/08/2022] [Accepted: 01/29/2023] [Indexed: 03/10/2023]
Abstract
OBJECTIVES We conducted a systematic and comprehensive bibliometric analysis of COVID-19-related medical imaging to determine the current status and indicate possible future directions. METHODS This research provides an analysis of Web of Science Core Collection (WoSCC) indexed articles on COVID-19 and medical imaging published between 1 January 2020 and 30 June 2022, using the search terms "COVID-19" and medical imaging terms (such as "X-ray" or "CT"). Publications based solely on COVID-19 themes or medical image themes were excluded. CiteSpace was used to identify the predominant topics and generate a visual map of countries, institutions, authors, and keyword networks. RESULTS The search included 4444 publications. The journal with the most publications was European Radiology, and the most co-cited journal was Radiology. China was the most frequently cited country in terms of co-authorship, with the Huazhong University of Science and Technology being the institution contributing with the highest number of relevant co-authorships. Research trends and leading topics included: assessment of initial COVID-19-related clinical imaging features, differential diagnosis using artificial intelligence (AI) technology and model interpretability, diagnosis systems construction, COVID-19 vaccination, complications, and predicting prognosis. CONCLUSIONS This bibliometric analysis of COVID-19-related medical imaging helps clarify the current research situation and developmental trends. Subsequent trends in COVID-19 imaging are likely to shift from lung structure to function, from lung tissue to other related organs, and from COVID-19 to the impact of COVID-19 on the diagnosis and treatment of other diseases. Key Points • We conducted a systematic and comprehensive bibliometric analysis of COVID-19-related medical imaging from 1 January 2020 to 30 June 2022. • Research trends and leading topics included assessment of initial COVID-19-related clinical imaging features, differential diagnosis using AI technology and model interpretability, diagnosis systems construction, COVID-19 vaccination, complications, and predicting prognosis. • Future trends in COVID-19-related imaging are likely to involve a shift from lung structure to function, from lung tissue to other related organs, and from COVID-19 to the impact of COVID-19 on the diagnosis and treatment of other diseases.
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Affiliation(s)
- Ru Wen
- Medical College, Guizhou University, Guizhou, 550000, People's Republic of China.,Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), 30 Gao Tan Yan St, 400038, Chongqing, People's Republic of China.,Department of Medical Imaging, Guizhou Provincial People Hospital, No.83, East Zhongshan Road, Nanming District, Guizhou Province, 550000, Guiyang City, People's Republic of China
| | - Mudan Zhang
- Guizhou Medical University, Guiyang, Guizhou Province, 550000, People's Republic of China
| | - Rui Xu
- Department of Medical Imaging, Guizhou Provincial People Hospital, No.83, East Zhongshan Road, Nanming District, Guizhou Province, 550000, Guiyang City, People's Republic of China
| | - Yingming Gao
- College of Life Science, Guizhou University, Guiyang, Guizhou Province, 550000, People's Republic of China
| | - Lin Liu
- Department of Respiratory Medicine, Guizhou Provincial People Hospital, Guiyang City, Guizhou Province, 550000, People's Republic of China
| | - Hui Chen
- Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), 30 Gao Tan Yan St, 400038, Chongqing, People's Republic of China
| | - Xingang Wang
- Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), 30 Gao Tan Yan St, 400038, Chongqing, People's Republic of China
| | - Wenyan Zhu
- Medical Department, Yidu Cloud (Beijing) Technology Co., Ltd., Beijing, 100191, People's Republic of China
| | - Huafang Lin
- Medical Department, Yidu Cloud (Beijing) Technology Co., Ltd., Beijing, 100191, People's Republic of China
| | - Chen Liu
- Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), 30 Gao Tan Yan St, 400038, Chongqing, People's Republic of China.
| | - Xianchun Zeng
- Department of Medical Imaging, Guizhou Provincial People Hospital, No.83, East Zhongshan Road, Nanming District, Guizhou Province, 550000, Guiyang City, People's Republic of China.
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Kim DY, Choi KH, Kim JH, Hong J, Choi SM, Park MS, Cho KH. Deep learning-based personalised outcome prediction after acute ischaemic stroke. J Neurol Neurosurg Psychiatry 2023; 94:369-378. [PMID: 36650037 DOI: 10.1136/jnnp-2022-330230] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 12/06/2022] [Indexed: 01/19/2023]
Abstract
BACKGROUND Whether deep learning models using clinical data and brain imaging can predict the long-term risk of major adverse cerebro/cardiovascular events (MACE) after acute ischaemic stroke (AIS) at the individual level has not yet been studied. METHODS A total of 8590 patients with AIS admitted within 5 days of symptom onset were enrolled. The primary outcome was the occurrence of MACEs (a composite of stroke, acute myocardial infarction or death) over 12 months. The performance of deep learning models (DeepSurv and Deep-Survival-Machines (DeepSM)) and traditional survival models (Cox proportional hazards (CoxPH) and random survival forest (RSF)) were compared using the time-dependent concordance index ([Formula: see text] index). RESULTS Given the top 1 to all 60 clinical factors according to feature importance, CoxPH and RSF yielded [Formula: see text] index of 0.7236-0.8222 and 0.7279-0.8335, respectively. Adding image features improved the performance of deep learning models and traditional models assisted by deep learning models. DeepSurv and DeepSM yielded the best [Formula: see text] index of 0.8496 and 0.8531 when images were added to all 39 relevant clinical factors, respectively. In feature importance, brain image was consistently ranked highly. Deep learning models automatically extracted the image features directly from personalised brain images and predicted the risk and date of future MACEs at the individual level. CONCLUSIONS Deep learning models using clinical data and brain images could improve the prediction of MACEs and provide personalised outcome prediction for patients with AIS. Deep learning models will allow us to develop more accurate and tailored prognostic prediction systems that outperform traditional models.
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Affiliation(s)
- Doo-Young Kim
- Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju, Korea (the Republic of)
| | - Kang-Ho Choi
- Department of Neurology, Chonnam National University Medical School and Hospital, Gwangju, Korea (the Republic of) .,Department of Biomedical Sciences, Chonnam National University, Gwangju, Korea (the Republic of)
| | - Ja-Hae Kim
- Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju, Korea (the Republic of) .,Department of Nuclear Medicine, Chonnam National University Medical School and Hospital, Gwangju, Korea (the Republic of)
| | - Jina Hong
- Department of Biomedical Sciences, Chonnam National University, Gwangju, Korea (the Republic of)
| | - Seong-Min Choi
- Department of Neurology, Chonnam National University Medical School and Hospital, Gwangju, Korea (the Republic of)
| | - Man-Seok Park
- Department of Neurology, Chonnam National University Medical School and Hospital, Gwangju, Korea (the Republic of)
| | - Ki-Hyun Cho
- Department of Neurology, Chonnam National University Medical School and Hospital, Gwangju, Korea (the Republic of)
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A comprehensive review of COVID-19 detection techniques: From laboratory systems to wearable devices. Comput Biol Med 2022; 149:106070. [PMID: 36099862 PMCID: PMC9433350 DOI: 10.1016/j.compbiomed.2022.106070] [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: 02/04/2022] [Revised: 08/03/2022] [Accepted: 08/27/2022] [Indexed: 11/30/2022]
Abstract
Screening of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) among symptomatic and asymptomatic patients offers unique opportunities for curtailing the transmission of novel coronavirus disease 2019, commonly known as COVID-19. Molecular diagnostic techniques, namely reverse transcription loop-mediated isothermal amplification (RT-LAMP), reverse transcription-polymerase chain reaction (RT-PCR), and immunoassays, have been frequently used to identify COVID-19 infection. Although these techniques are robust and accurate, mass testing of potentially infected individuals has shown difficulty due to the resources, manpower, and costs it entails. Moreover, as these techniques are typically used to test symptomatic patients, healthcare systems have failed to screen asymptomatic patients, whereas the spread of COVID-19 by these asymptomatic individuals has turned into a crucial problem. Besides, respiratory infections or cardiovascular conditions generally demonstrate changes in physiological parameters, namely body temperature, blood pressure, and breathing rate, which signifies the onset of diseases. Such vitals monitoring systems have shown promising results employing artificial intelligence (AI). Therefore, the potential use of wearable devices for monitoring asymptomatic COVID-19 individuals has recently been explored. This work summarizes the efforts that have been made in the domains from laboratory-based testing to asymptomatic patient monitoring via wearable systems.
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Machine Learning-Based COVID-19 Patients Triage Algorithm Using Patient-Generated Health Data from Nationwide Multicenter Database. Infect Dis Ther 2022; 11:787-805. [PMID: 35174469 PMCID: PMC8853007 DOI: 10.1007/s40121-022-00600-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 01/28/2022] [Indexed: 12/24/2022] Open
Abstract
Introduction A prompt severity
assessment model of patients with confirmed infectious diseases could enable efficient diagnosis while alleviating burden on the medical system. This study aims to develop a SARS-CoV-2 severity assessment model and establish a medical system that allows patients to check the severity of their cases and informs them to visit the appropriate clinic center on the basis of past treatment data of other patients with similar severity levels. Methods This paper provides the development processes of a severity assessment model using machine learning techniques and its application on SARS-CoV-2-infected patients. The proposed model is trained on a nationwide data set provided by a Korean government agency and only requires patients’ basic personal data, allowing them to judge the severity of their own cases. After modeling, the boosting-based decision tree model was selected as the classifier while mortality rate was interpreted as the probability score. The data set was collected from all Korean citizens with confirmed COVID-19 between February 2020 and July 2021 (N = 149,471). Results The experiments achieved high model performance with an approximate precision of 0.923 and area under the curve of receiver operating characteristic (AUROC) score of 0.950 [95% tolerance interval (TI) 0.940–0.958, 95% confidence interval (CI) 0.949–0.950]. Moreover, our experiments identified the most important variables affecting the severity in the model via sensitivity analysis. Conclusion A prompt severity assessment model for managing infectious people has been attained through using a nationwide data set. It has demonstrated its superior performance by surpassing that of conventional risk assessments. With the model’s high performance and easily accessible features, the triage algorithm is expected to be particularly useful when patients monitor their health status by themselves through smartphone applications.
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