Machine Learning Applications for COVID-19 and Its Complications: Screening, Diagnosis, Treatment, and Prognosis
A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".
Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 49193
Interests: imaging methods; machine learning and image/data analysis; neuroscience; neurology; physiology; animal models and other disease domain expertise
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Dear COVID-19 researchers,
Since the first report of severe respiratory illness caused by coronavirus disease 2019 (COVID-19) in mid-December 2019, over 128 million individuals have been infected, resulting in over 2.8 million deaths worldwide as of 31 March 2021. Many COVID-19 patients have mild or asymptomatic infections, while others deteriorate rapidly with multi-organ failure. There are already multiple resurgences. A large array of clinical and demographic variables associated with COVID-19 have been identified. Remarkable progress has been made in our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pathogenicity and disease manifestation. Public health measures are effective in curbing the spread of SARS-CoV-2, and new and effective treatments and vaccines are becoming available.
Machine learning methods are increasingly being used in medicine, including in the study of COVID-19. Machine learning employs computer algorithms to learn relationships amongst different data elements and inform outcomes. In contrast to conventional analysis methods (such as linear or logistic regression), the exact relationship amongst different data elements with respect to outcome variables does not need to be explicitly specified. In addition to approximating physician skills, machine learning algorithms can also find novel relationships not readily apparent to humans. Many studies have shown that machine learning outperforms logistic regression and classification tree models, as well as humans, in many tasks in medicine. Machine learning is particularly useful in dealing with large and complex datasets. With increasing computing power and the growing relevance of big data in medicine, machine learning is expected to play an important role in clinical practice.
I would like to invite you to participate in this very exciting Special Issue on “Machine Learning Applications in COVID-19 and Its Complications: Screening, Diagnosis, Treatment, and Prognosis”, with a focus on the use of machine learning algorithms to investigate how symptoms, clinical, demographic, laboratory, radiological imaging variables contribute to the diagnosis and prognosis of COVID-19. Examples of possible machine learning applications include, but are not limited to: i) diagnosis and prognosis of COVID-19 clinical outcomes (such as mortality and escalated care), ii) in-hospital acquired diagnosis (such as AKI and ARDS), iii) treatments (such as mechanical ventilation, steroids, and anticoagulants), and vi) diagnosis and prognosis post-acute COVID-19 sequelae. Studies on other related topics are also welcomed.
Dr. Tim Duong
Manuscript Submission Information
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- Machine learning
- COVID-19 and its Complications