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Viderman D, Kotov A, Popov M, Abdildin Y. Machine and deep learning methods for clinical outcome prediction based on physiological data of COVID-19 patients: a scoping review. Int J Med Inform 2024; 182:105308. [PMID: 38091862 DOI: 10.1016/j.ijmedinf.2023.105308] [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: 09/15/2023] [Revised: 11/20/2023] [Accepted: 12/03/2023] [Indexed: 01/07/2024]
Abstract
INTRODUCTION Since the beginning of the COVID-19 pandemic, numerous machine and deep learning (MDL) methods have been proposed in the literature to analyze patient physiological data. The objective of this review is to summarize various aspects of these methods and assess their practical utility for predicting various clinical outcomes. METHODS We searched PubMed, Scopus, and Cochrane Library, screened and selected the studies matching the inclusion criteria. The clinical analysis focused on the characteristics of the patient cohorts in the studies included in this review, the specific tasks in the context of the COVID-19 pandemic that machine and deep learning methods were used for, and their practical limitations. The technical analysis focused on the details of specific MDL methods and their performance. RESULTS Analysis of the 48 selected studies revealed that the majority (∼54 %) of them examined the application of MDL methods for the prediction of survival/mortality-related patient outcomes, while a smaller fraction (∼13 %) of studies also examined applications to the prediction of patients' physiological outcomes and hospital resource utilization. 21 % of the studies examined the application of MDL methods to multiple clinical tasks. Machine and deep learning methods have been shown to be effective at predicting several outcomes of COVID-19 patients, such as disease severity, complications, intensive care unit (ICU) transfer, and mortality. MDL methods also achieved high accuracy in predicting the required number of ICU beds and ventilators. CONCLUSION Machine and deep learning methods have been shown to be valuable tools for predicting disease severity, organ dysfunction and failure, patient outcomes, and hospital resource utilization during the COVID-19 pandemic. The discovered knowledge and our conclusions and recommendations can also be useful to healthcare professionals and artificial intelligence researchers in managing future pandemics.
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Affiliation(s)
- Dmitriy Viderman
- Department of Surgery, School of Medicine, Nazarbayev University, Astana, Kazakhstan; Department of Anesthesiology, Intensive Care, and Pain Medicine, National Research Oncology Center, Astana, Kazakhstan.
| | - Alexander Kotov
- Department of Computer Science, College of Engineering, Wayne State University, Detroit, USA.
| | - Maxim Popov
- Department of Computer Science, School of Engineering and Digital Sciences, Nazarbayev University, Astana, Kazakhstan.
| | - Yerkin Abdildin
- Department of Mechanical and Aerospace Engineering, School of Engineering and Digital Sciences, Nazarbayev University, Astana, Kazakhstan.
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Abdulnazar A, Kugic A, Schulz S, Stadlbauer V, Kreuzthaler M. O2 supplementation disambiguation in clinical narratives to support retrospective COVID-19 studies. BMC Med Inform Decis Mak 2024; 24:29. [PMID: 38297364 PMCID: PMC10829265 DOI: 10.1186/s12911-024-02425-2] [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: 06/01/2023] [Accepted: 01/15/2024] [Indexed: 02/02/2024] Open
Abstract
BACKGROUND Oxygen saturation, a key indicator of COVID-19 severity, poses challenges, especially in cases of silent hypoxemia. Electronic health records (EHRs) often contain supplemental oxygen information within clinical narratives. Streamlining patient identification based on oxygen levels is crucial for COVID-19 research, underscoring the need for automated classifiers in discharge summaries to ease the manual review burden on physicians. METHOD We analysed text lines extracted from anonymised COVID-19 patient discharge summaries in German to perform a binary classification task, differentiating patients who received oxygen supplementation and those who did not. Various machine learning (ML) algorithms, including classical ML to deep learning (DL) models, were compared. Classifier decisions were explained using Local Interpretable Model-agnostic Explanations (LIME), which visualize the model decisions. RESULT Classical ML to DL models achieved comparable performance in classification, with an F-measure varying between 0.942 and 0.955, whereas the classical ML approaches were faster. Visualisation of embedding representation of input data reveals notable variations in the encoding patterns between classic and DL encoders. Furthermore, LIME explanations provide insights into the most relevant features at token level that contribute to these observed differences. CONCLUSION Despite a general tendency towards deep learning, these use cases show that classical approaches yield comparable results at lower computational cost. Model prediction explanations using LIME in textual and visual layouts provided a qualitative explanation for the model performance.
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Affiliation(s)
- Akhila Abdulnazar
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria
- CBmed GmbH - Center for Biomarker Research in Medicine, Graz, Austria
| | - Amila Kugic
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria
| | - Stefan Schulz
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria
| | - Vanessa Stadlbauer
- CBmed GmbH - Center for Biomarker Research in Medicine, Graz, Austria
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Medical University of Graz, Graz, Austria
| | - Markus Kreuzthaler
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria.
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Veronese-Araújo A, de Lucena DD, Aguiar-Brito I, Modelli de Andrade LG, Cristelli MP, Tedesco-Silva H, Medina-Pestana JO, Rangel ÉB. Oxygen Requirement in Overweight/Obese Kidney Transplant Recipients with COVID-19: An Observational Cohort Study. Diagnostics (Basel) 2023; 13:2168. [PMID: 37443562 DOI: 10.3390/diagnostics13132168] [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: 05/09/2023] [Revised: 06/04/2023] [Accepted: 06/08/2023] [Indexed: 07/15/2023] Open
Abstract
INTRODUCTION Obesity is one of the components of the cardiometabolic syndrome that contributes to COVID-19 progression and mortality. Immunosuppressed individuals are at greater risk of the COVID-19 burden. Therefore, we sought to investigate the impact of the combination of overweight/obesity and kidney transplant on oxygen (O2) requirements in the COVID-19 setting. METHODS Retrospective analysis of 284 kidney transplant recipients (KTRs) from March/2020 to August/2020 in a single center. We investigated the risk factors associated with O2 requirements in overweight/obese KTRs. RESULTS Overall, 65.1% had a BMI (body mass index) ≥ 25 kg/m2, 52.4% were male, the mean age was 53.3 ± 11 years old, 78.4% had hypertension, and 41.1% had diabetes mellitus. BMI was an independent risk factor for O2 requirements (OR = 1.07, p = 0.02) alongside age, lymphopenia, and hyponatremia. When overweight/obese KTRs were older, smokers, they presented higher levels of lactate dehydrogenase (LDH), and lower levels of estimated glomerular filtration rate (eGFR), lymphocytes, and sodium at admission, and they needed O2 more often. CONCLUSION Being overweight/obese is associated with greater O2 requirements in KTRs, in particular in older people and smokers, with worse kidney allograft functions, more inflammation, and lower sodium levels. Therefore, the early identification of factors that predict a worse outcome in overweight/obese KTRs affected by COVID-19 contributes to risk stratification and therapeutic decisions.
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Affiliation(s)
- Alexandre Veronese-Araújo
- Department of Medicine, Nephrology Division, Federal University of São Paulo, São Paulo 04038-031, SP, Brazil
| | - Débora D de Lucena
- Department of Medicine, Nephrology Division, Federal University of São Paulo, São Paulo 04038-031, SP, Brazil
- Hospital do Rim, São Paulo 04038-002, SP, Brazil
| | - Isabella Aguiar-Brito
- Department of Medicine, Nephrology Division, Federal University of São Paulo, São Paulo 04038-031, SP, Brazil
| | | | | | - Hélio Tedesco-Silva
- Department of Medicine, Nephrology Division, Federal University of São Paulo, São Paulo 04038-031, SP, Brazil
- Hospital do Rim, São Paulo 04038-002, SP, Brazil
| | - José O Medina-Pestana
- Department of Medicine, Nephrology Division, Federal University of São Paulo, São Paulo 04038-031, SP, Brazil
- Hospital do Rim, São Paulo 04038-002, SP, Brazil
| | - Érika B Rangel
- Department of Medicine, Nephrology Division, Federal University of São Paulo, São Paulo 04038-031, SP, Brazil
- Hospital do Rim, São Paulo 04038-002, SP, Brazil
- Hospital Israelita Albert Einstein, São Paulo 05652-900, SP, Brazil
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Maslova O, Vladimirova T, Videnin A, Gochhait S, Pyatin V. Comparative study of quality of life 9 months post-COVID-19 infection with SARS-CoV-2 of varying degrees of severity: impact of hospitalization vs. outpatient treatment. FRONTIERS IN SOCIOLOGY 2023; 8:1143561. [PMID: 37260721 PMCID: PMC10229053 DOI: 10.3389/fsoc.2023.1143561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 04/17/2023] [Indexed: 06/02/2023]
Abstract
Purpose This experimental study was conducted during the post-COVID-19 period to investigate the relationship between the quality of life 9 months after and the severity of the SARS-CoV-2 infection in two scenarios: hospitalization (with/without medical oxygen) and outpatient treatment. Methods We employed the EQ-5D-5L Quality of Life tests and the PSQI as a survey to evaluate respondents' quality of life 9 months after a previous SARS-CoV-2 infection of varying severity. Results We identified a clear difference in the quality of life of respondents, as measured on the 100-point scale of the EQ-5D-5L test, which was significantly lower 9 months after a previous SARS-CoV-2 infection for Group 1 (n = 14), respondents who had received medical attention for SARS-CoV-2 infection in a hospital with oxygen treatment, compared to those with the SARS-CoV-2 infection who were treated without oxygen treatment (Group 2) (n = 12) and those who were treated on an outpatient basis (Group 3) (n = 13) (H = 7.08 p = 0.029). There were no intergroup differences in quality of life indicators between hospitalized patients (Group 2) and groups 1 and 3. PSQI survey results showed that "mobility," "self-care," "daily activities," "pain/discomfort," and "anxiety/ depression" did not differ significantly between the groups, indicating that these factors were not associated with the severity of the SARS-CoV-2 infection. On the contrary, the respondents demonstrated significant inter-group differences (H = 7.51 p = 0.023) and the interdependence of respiratory difficulties with the severity of clinically diagnosed SARS-CoV-2 infection. This study also demonstrated significant differences in the values of sleep duration, sleep disorders, and daytime sleepiness indicators between the three groups of respondents, which indicate the influence of the severity of the infection. The PSQI test results revealed significant differences in "bedtime" (H = 6.00 p = 0.050) and "wake-up time" (H = 11.17 p = 0.004) between Groups 1 and 3 of respondents. At 9 months after COVID-19, respondents in Group 1 went to bed at a later time (pp = 0.02727) and woke up later (p = 0.003) than the respondents in Group 3. Conclusion This study is the first of its kind in the current literature to report on the quality of life of respondents 9 months after being diagnosed with COVID-19 and to draw comparisons between cohorts of hospitalized patients who were treated with medical oxygen vs. the cohorts of outpatient patients. The study's findings regarding post-COVID-19 quality of life indicators and their correlation with the severity of the SARS-CoV-2 infection can be used to categorize patients for targeted post-COVID-19 rehabilitation programs.
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Affiliation(s)
- Olga Maslova
- Neurosociology Laboratory, Neurosciences Research Institute, Samara State Medical University, Samara, Russia
| | - Tatiana Vladimirova
- Department of Otorhinolaryngology, Samara State Medical University, Samara, Russia
| | - Arseny Videnin
- Institute of Clinical Medicine, Samara State Medical University, Samara, Russia
| | - Saikat Gochhait
- Neurosociology Laboratory, Neurosciences Research Institute, Samara State Medical University, Samara, Russia
- Symbiosis International (Deemed University), Pune, India
| | - Vasily Pyatin
- Neurosociology Laboratory, Neurosciences Research Institute, Samara State Medical University, Samara, Russia
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Paul SG, Saha A, Biswas AA, Zulfiker MS, Arefin MS, Rahman MM, Reza AW. Combating Covid-19 using machine learning and deep learning: Applications, challenges, and future perspectives. ARRAY 2023; 17:100271. [PMID: 36530931 PMCID: PMC9737520 DOI: 10.1016/j.array.2022.100271] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 12/05/2022] [Accepted: 12/07/2022] [Indexed: 12/14/2022] Open
Abstract
COVID-19, a worldwide pandemic that has affected many people and thousands of individuals have died due to COVID-19, during the last two years. Due to the benefits of Artificial Intelligence (AI) in X-ray image interpretation, sound analysis, diagnosis, patient monitoring, and CT image identification, it has been further researched in the area of medical science during the period of COVID-19. This study has assessed the performance and investigated different machine learning (ML), deep learning (DL), and combinations of various ML, DL, and AI approaches that have been employed in recent studies with diverse data formats to combat the problems that have arisen due to the COVID-19 pandemic. Finally, this study shows the comparison among the stand-alone ML and DL-based research works regarding the COVID-19 issues with the combinations of ML, DL, and AI-based research works. After in-depth analysis and comparison, this study responds to the proposed research questions and presents the future research directions in this context. This review work will guide different research groups to develop viable applications based on ML, DL, and AI models, and will also guide healthcare institutes, researchers, and governments by showing them how these techniques can ease the process of tackling the COVID-19.
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Affiliation(s)
- Showmick Guha Paul
- Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh
| | - Arpa Saha
- Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh
| | - Al Amin Biswas
- Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh,Corresponding author
| | - Md. Sabab Zulfiker
- Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh
| | - Mohammad Shamsul Arefin
- Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh,Department of Computer Science and Engineering, Chittagong University of Engineering and Technology, Chittagong, Bangladesh
| | - Md. Mahfujur Rahman
- Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh
| | - Ahmed Wasif Reza
- Department of Computer Science and Engineering, East West University, Dhaka, Bangladesh
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Muto R, Fukuta S, Watanabe T, Shindo Y, Kanemitsu Y, Kajikawa S, Yonezawa T, Inoue T, Ichihashi T, Shiratori Y, Maruyama S. Predicting oxygen requirements in patients with coronavirus disease 2019 using an artificial intelligence-clinician model based on local non-image data. Front Med (Lausanne) 2022; 9:1042067. [PMID: 36530899 PMCID: PMC9748157 DOI: 10.3389/fmed.2022.1042067] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 11/14/2022] [Indexed: 06/12/2024] Open
Abstract
BACKGROUND When facing unprecedented emergencies such as the coronavirus disease 2019 (COVID-19) pandemic, a predictive artificial intelligence (AI) model with real-time customized designs can be helpful for clinical decision-making support in constantly changing environments. We created models and compared the performance of AI in collaboration with a clinician and that of AI alone to predict the need for supplemental oxygen based on local, non-image data of patients with COVID-19. MATERIALS AND METHODS We enrolled 30 patients with COVID-19 who were aged >60 years on admission and not treated with oxygen therapy between December 1, 2020 and January 4, 2021 in this 50-bed, single-center retrospective cohort study. The outcome was requirement for oxygen after admission. RESULTS The model performance to predict the need for oxygen by AI in collaboration with a clinician was better than that by AI alone. Sodium chloride difference >33.5 emerged as a novel indicator to predict the need for oxygen in patients with COVID-19. To prevent severe COVID-19 in older patients, dehydration compensation may be considered in pre-hospitalization care. CONCLUSION In clinical practice, our approach enables the building of a better predictive model with prompt clinician feedback even in new scenarios. These can be applied not only to current and future pandemic situations but also to other diseases within the healthcare system.
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Affiliation(s)
- Reiko Muto
- Department of Nephrology, Nagoya University Graduate School of Medicine, Nagoya, Japan
- Department of Internal Medicine, Aichi Prefectural Aichi Hospital, Okazaki, Japan
- Department of Molecular Medicine and Metabolism, Research Institute of Environmental Medicine, Nagoya University, Nagoya, Japan
| | - Shigeki Fukuta
- Artificial Intelligence Laboratory, Fujitsu Limited, Kawasaki, Japan
| | | | - Yuichiro Shindo
- Department of Internal Medicine, Aichi Prefectural Aichi Hospital, Okazaki, Japan
- Department of Respiratory Medicine, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Yoshihiro Kanemitsu
- Department of Internal Medicine, Aichi Prefectural Aichi Hospital, Okazaki, Japan
- Department of Respiratory Medicine, Allergy and Clinical Immunology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan
| | - Shigehisa Kajikawa
- Department of Internal Medicine, Aichi Prefectural Aichi Hospital, Okazaki, Japan
- Department of Respiratory Medicine and Allergology, Aichi Medical University Hospital, Nagakute, Japan
| | - Toshiyuki Yonezawa
- Department of Internal Medicine, Aichi Prefectural Aichi Hospital, Okazaki, Japan
- Department of Respiratory Medicine and Allergology, Aichi Medical University Hospital, Nagakute, Japan
| | - Takahiro Inoue
- Department of Internal Medicine, Aichi Prefectural Aichi Hospital, Okazaki, Japan
- Department of Respiratory Medicine, Fujita Health University School of Medicine, Toyoake, Japan
| | - Takuji Ichihashi
- Department of Internal Medicine, Aichi Prefectural Aichi Hospital, Okazaki, Japan
| | - Yoshimune Shiratori
- Center for Healthcare Information Technology (C-HiT), Nagoya University, Nagoya, Japan
- Medical IT Center, Nagoya University Hospital, Nagoya, Japan
| | - Shoichi Maruyama
- Department of Nephrology, Nagoya University Graduate School of Medicine, Nagoya, Japan
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Becerra-Sánchez A, Rodarte-Rodríguez A, Escalante-García NI, Olvera-González JE, De la Rosa-Vargas JI, Zepeda-Valles G, Velásquez-Martínez EDJ. Mortality Analysis of Patients with COVID-19 in Mexico Based on Risk Factors Applying Machine Learning Techniques. Diagnostics (Basel) 2022; 12:1396. [PMID: 35741207 PMCID: PMC9222115 DOI: 10.3390/diagnostics12061396] [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: 05/07/2022] [Revised: 05/28/2022] [Accepted: 05/30/2022] [Indexed: 11/17/2022] Open
Abstract
The new pandemic caused by the COVID-19 virus has generated an overload in the quality of medical care in clinical centers around the world. Causes that originate this fact include lack of medical personnel, infrastructure, medicines, among others. The rapid and exponential increase in the number of patients infected by COVID-19 has required an efficient and speedy prediction of possible infections and their consequences with the purpose of reducing the health care quality overload. Therefore, intelligent models are developed and employed to support medical personnel, allowing them to give a more effective diagnosis about the health status of patients infected by COVID-19. This paper aims to propose an alternative algorithmic analysis for predicting the health status of patients infected with COVID-19 in Mexico. Different prediction models such as KNN, logistic regression, random forests, ANN and majority vote were evaluated and compared. The models use risk factors as variables to predict the mortality of patients from COVID-19. The most successful scheme is the proposed ANN-based model, which obtained an accuracy of 90% and an F1 score of 89.64%. Data analysis reveals that pneumonia, advanced age and intubation requirement are the risk factors with the greatest influence on death caused by virus in Mexico.
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Affiliation(s)
- Aldonso Becerra-Sánchez
- Unidad Académica de Ingenieía Eléctrica, Universidad Autónoma de Zacatecas, Zacatecas 98000, Mexico; (A.R.-R.); (J.I.D.l.R.-V.); (G.Z.-V.); (E.d.J.V.-M.)
| | - Armando Rodarte-Rodríguez
- Unidad Académica de Ingenieía Eléctrica, Universidad Autónoma de Zacatecas, Zacatecas 98000, Mexico; (A.R.-R.); (J.I.D.l.R.-V.); (G.Z.-V.); (E.d.J.V.-M.)
| | - Nivia I. Escalante-García
- Laboratorio de Iluminación Artificial, Tecnológico Nacional de México Campus Pabellón de Arteaga, Aguascalientes 20670, Mexico; (N.I.E.-G.); (J.E.O.-G.)
| | - José E. Olvera-González
- Laboratorio de Iluminación Artificial, Tecnológico Nacional de México Campus Pabellón de Arteaga, Aguascalientes 20670, Mexico; (N.I.E.-G.); (J.E.O.-G.)
| | - José I. De la Rosa-Vargas
- Unidad Académica de Ingenieía Eléctrica, Universidad Autónoma de Zacatecas, Zacatecas 98000, Mexico; (A.R.-R.); (J.I.D.l.R.-V.); (G.Z.-V.); (E.d.J.V.-M.)
| | - Gustavo Zepeda-Valles
- Unidad Académica de Ingenieía Eléctrica, Universidad Autónoma de Zacatecas, Zacatecas 98000, Mexico; (A.R.-R.); (J.I.D.l.R.-V.); (G.Z.-V.); (E.d.J.V.-M.)
| | - Emmanuel de J. Velásquez-Martínez
- Unidad Académica de Ingenieía Eléctrica, Universidad Autónoma de Zacatecas, Zacatecas 98000, Mexico; (A.R.-R.); (J.I.D.l.R.-V.); (G.Z.-V.); (E.d.J.V.-M.)
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Biotechnological Perspectives to Combat the COVID-19 Pandemic: Precise Diagnostics and Inevitable Vaccine Paradigms. Cells 2022; 11:cells11071182. [PMID: 35406746 PMCID: PMC8997755 DOI: 10.3390/cells11071182] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 03/26/2022] [Accepted: 03/28/2022] [Indexed: 01/27/2023] Open
Abstract
The outbreak of the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause for the ongoing global public health emergency. It is more commonly known as coronavirus disease 2019 (COVID-19); the pandemic threat continues to spread aroundthe world with the fluctuating emergence of its new variants. The severity of COVID-19 ranges from asymptomatic to serious acute respiratory distress syndrome (ARDS), which has led to a high human mortality rate and disruption of socioeconomic well-being. For the restoration of pre-pandemic normalcy, the international scientific community has been conducting research on a war footing to limit extremely pathogenic COVID-19 through diagnosis, treatment, and immunization. Since the first report of COVID-19 viral infection, an array of laboratory-based and point-of-care (POC) approaches have emerged for diagnosing and understanding its status of outbreak. The RT-PCR-based viral nucleic acid test (NAT) is one of the rapidly developed and most used COVID-19 detection approaches. Notably, the current forbidding status of COVID-19 requires the development of safe, targeted vaccines/vaccine injections (shots) that can reduce its associated morbidity and mortality. Massive and accelerated vaccination campaigns would be the most effective and ultimate hope to end the COVID-19 pandemic. Since the SARS-CoV-2 virus outbreak, emerging biotechnologies and their multidisciplinary approaches have accelerated the understanding of molecular details as well as the development of a wide range of diagnostics and potential vaccine candidates, which are indispensable to combating the highly contagious COVID-19. Several vaccine candidates have completed phase III clinical studies and are reported to be effective in immunizing against COVID-19 after their rollout via emergency use authorization (EUA). However, optimizing the type of vaccine candidates and its route of delivery that works best to control viral spread is crucial to face the threatening variants expected to emerge over time. In conclusion, the insights of this review would facilitate the development of more likely diagnostics and ideal vaccines for the global control of COVID-19.
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A Machine Learning Approach to Predict the Rehabilitation Outcome in Convalescent COVID-19 Patients. J Pers Med 2022; 12:jpm12030328. [PMID: 35330328 PMCID: PMC8953386 DOI: 10.3390/jpm12030328] [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: 02/03/2022] [Revised: 02/18/2022] [Accepted: 02/18/2022] [Indexed: 11/18/2022] Open
Abstract
Background: After the acute disease, convalescent coronavirus disease 2019 (COVID-19) patients may experience several persistent manifestations that require multidisciplinary pulmonary rehabilitation (PR). By using a machine learning (ML) approach, we aimed to evaluate the clinical characteristics predicting the effectiveness of PR, expressed by an improved performance at the 6-min walking test (6MWT). Methods: Convalescent COVID-19 patients referring to a Pulmonary Rehabilitation Unit were consecutively screened. The 6MWT performance was partitioned into three classes, corresponding to different degrees of improvement (low, medium, and high) following PR. A multiclass supervised classification learning was performed with random forest (RF), adaptive boosting (ADA-B), and gradient boosting (GB), as well as tree-based and k-nearest neighbors (KNN) as instance-based algorithms. Results: To train and validate our model, we included 189 convalescent COVID-19 patients (74.1% males, mean age 59.7 years). RF obtained the best results in terms of accuracy (83.7%), sensitivity (84.0%), and area under the ROC curve (94.5%), while ADA-B reached the highest specificity (92.7%). Conclusions: Our model enables a good performance in predicting the rehabilitation outcome in convalescent COVID-19 patients.
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