ArticleInformatics in medicine unlocked2020
A case-based reasoning framework for early detection and diagnosis of novel coronavirus.
Article in Informatics in medicine unlocked, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed, 60 citations in OpenAlex.
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- Towards Improved XAI-Based Epidemiological Research into the Next Potential Pandemic.Life (Basel, Switzerland) · 2024Review
- Multimodal deep learning for COVID-19 prognosis prediction in the emergency department: a bi-centric study.Scientific reports · 2023Article
- Using Decision Trees as an Expert System for Clinical Decision Support for COVID-19.Interactive journal of medical research · 2023Article
- Research Progress and Improvement Ideas of Anti-Epidemic Resilience in China's Urban Communities.International journal of environmental research and public health · 2022Review
- Immunity-based Ebola optimization search algorithm for minimization of feature extraction with reduction in digital mammography using CNN models.Scientific reports · 2022Article
- Enhancing reasoning through reduction of vagueness using fuzzy OWL-2 for representation of breast cancer ontologies.Neural computing & applications · 2022Article
- Fuzzy case-based reasoning approach for finding COVID-19 patients priority in hospitals at source shortage period.Expert systems with applications · 2021Article
- Review
- CovFrameNet: An Enhanced Deep Learning Framework for COVID-19 Detection.IEEE access : practical innovations, open solutions · 2021Article
- A Novel Smart City-Based Framework on Perspectives for Application of Machine Learning in Combating COVID-19.BioMed research international · 2021Review
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Authors and funding
2 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Coronavirus, also known as COVID-19, has been declared a pandemic by the World Health Organization (WHO). At the time of conducting this study, it had recorded over 11,301,850 confirmed cases while more than 531,806 have died due to it, with these figures rising daily across the globe. The burden of this highly contagious respiratory disease is that it presents itself in both symptomatic and asymptomatic patterns in those already infected, thereby leading to an exponential rise in the number of contractions of the disease and fatalities. It is, therefore, crucial to expedite the process of early detection and diagnosis of the disease across the world. The case-based reasoning (CBR) model is a compelling paradigm that allows for the utilization of case-specific knowledge previously experienced, concrete problem situations or specific patient cases for solving new cases. This study, therefore, aims to leverage the very rich database of cases of COVID-19 to solve new cases. The approach adopted in this study employs the use of an improved CBR model for state-of-the-art reasoning task in the classification of suspected cases of COVID-19. The CBR model leverages on a novel feature selection and the semantic-based mathematical model proposed in this study for case similarity computation. An initial population of the archive was achieved from 71 (67 adults and 4 pediatrics) cases obtained from the Italian Society of Medical and Interventional Radiology (SIRM) repository. Results obtained revealed that the proposed approach in this study successfully classified suspected cases into their categories with an accuracy of 94.54%. The study found that the proposed model can support physicians to easily diagnose suspected cases of COVID-19 based on their medical records without subjecting the specimen to laboratory tests. As a result, there will be a global minimization of contagion rate occasioned by slow testing and in addition, reduced false-positive rates of diagnosed cases as observed in some parts of the globe.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.