preprints.org > computer science and mathematics > artificial intelligence and machine learning > doi: 10.20944/preprints202005.0171.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 9 May 2020 / Approved: 10 May 2020 / Online: 10 May 2020 (15:29:43 CEST)
Version 2 : Received: 15 June 2020 / Approved: 15 June 2020 / Online: 15 June 2020 (11:16:23 CEST)

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 1.6million cases while more than 105,000 have died due to it, with these figures rising on a daily basis 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 an effective paradigm that allows for the utilization of cases’ 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 interpret and solve new cases even at their early stage to the advanced stage. The approach adopted in this study employs a natural language processing (NLP) technique to parse records of cases and thereafter formalize each case which is represented as a mini-ontology file. The formalized case is therefore parsed into a CBR model to allow for classification of the case into positive or negative to COVID-19. Meanwhile, feature extraction for each case is done by classifying tokens extracted by the NLP approach into special, temporal and thematic classes before encoding them using an ontology modeling method. The CBR model therefore leverages on the formalized features to compute the similarity of the new case with extracted similar cases from the archive of the CBR model. The proposed framework was populated with 68 cases obtained from the Italian Society of Medical and Interventional Radiology (SIRM) repository. Results obtained revealed that the proposed approach leverages on locations (spatial) and time (temporal) of contagion to successfully detect cases even in their early stages of two days onward before the incubation period of fourteen days. The proposed framework achieved an accuracy of 97.10%, sensitivity of 0.98 and specificity of .066. The study found that the proposed model can assist physicians to easily diagnose and isolate cases, thereby minimizing the rate of contagion and reducing false diagnosis as observed in some parts of the globe.

Coronavirus; case-based reasoning; ontology; natural language processing

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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