An Approach for SARS-CoV-2 Infected Cases Report Analysis

The information about Coronavirus disease 2019 (COVID-19), especially about infected cases in every country is very urgent. In this paper, an algorithm to analyze the COVID19 infected case reports is introduced. Fifty-two (52) reported cases from LuatVietnam - a reputable Vietnamese online newspaper - were taken as input. The retrieved data were analyzed and classified. The analysis output was saved into a CSV file showing the essential extracted information about infected cases. Each output row contains Patient ID, Gender, Age, Address and Status. Based on the tested results, the algorithm achieved the accuracy of 86.67% with the average processing time per patient of 0.103 milliseconds.

[1]  N. Bashir,et al.  COVID-19 infection: Origin, transmission, and characteristics of human coronaviruses , 2020, Journal of Advanced Research.

[2]  Wenyu Liu,et al.  A Weakly-Supervised Framework for COVID-19 Classification and Lesion Localization From Chest CT , 2020, IEEE Transactions on Medical Imaging.

[3]  G. Remuzzi,et al.  COVID-19 and Italy: what next? , 2020, The Lancet.

[4]  D. Tran An open toolbox for generating map of actively confirmed SARS-CoV-2 or COVID-19 cases in Vietnam , 2020 .

[5]  Long Jiang Zhang,et al.  Coronavirus Disease 2019 (COVID-19): A Perspective from China , 2020, Radiology.

[6]  Mirella Lapata,et al.  Text Summarization with Pretrained Encoders , 2019, EMNLP.

[7]  Dou Shen Text Summarization , 2009, Encyclopedia of Database Systems.

[8]  Incheon Paik,et al.  Classification of Taxonomical Relationship by Word Embedding , 2018, 2018 IEEE International Conference on Cognitive Computing (ICCC).

[9]  Wes McKinney,et al.  Data Structures for Statistical Computing in Python , 2010, SciPy.

[10]  Eliseo Reategui,et al.  Using a Text Mining Tool to Support Text Summarization , 2012, 2012 IEEE 12th International Conference on Advanced Learning Technologies.

[11]  Anders Søgaard,et al.  A Survey of Cross-lingual Word Embedding Models , 2017, J. Artif. Intell. Res..

[12]  Xiang Tao,et al.  A Pragmatic Approach to Increase Accuracy of Chinese Word-Segmentation , 2010, 2010 International Forum on Information Technology and Applications.

[13]  J.N. Madhuri,et al.  Extractive Text Summarization Using Sentence Ranking , 2019, 2019 International Conference on Data Science and Communication (IconDSC).

[14]  Yi-Chi Wu,et al.  The outbreak of COVID-19: An overview , 2020, Journal of the Chinese Medical Association : JCMA.

[15]  D. Dong,et al.  The Role of Imaging in the Detection and Management of COVID-19: A Review , 2020, IEEE Reviews in Biomedical Engineering.

[16]  Muhammad M. Mehdi,et al.  Optimized Word Segmentation for the Word Based Cursive Handwriting Recognition , 2013, 2013 European Modelling Symposium.

[17]  Poonam,et al.  Coronavirus Disease COVID-19: A New Threat to Public Health. , 2020, Current topics in medicinal chemistry.

[18]  Jun Zhao,et al.  How to Generate a Good Word Embedding , 2015, IEEE Intelligent Systems.

[19]  Marcelo Götz,et al.  Architectural solutions for enhancing the real-time behavior of distributed embedded systems , 2003, Proceedings of the Eighth International Workshop on Object-Oriented Real-Time Dependable Systems, 2003. (WORDS 2003)..