miniproject: viral epidemics and disease

What diseases co-occur with viral epidemics?

owner:

Priya

collaborators:

miniproject summary

proposed activities

• Use the communal corpus epidemic50noCov articles.
• Scrutinizing the 50 articles to know the true positives and false positives, that is, whether the articles are about viral epidemic or not.
• Using ami search to find whether the articles mentioned any comorbidity in a viral epidemic or not.
• Sectioning the articles using ami:section to extract the relevant information on comorbidity. Annotating with dictionaries to create ami DataTables.
• Refining and rerunning the query to get a corpus of 950 articles.
• Using relevant ML technique for the classification of data whether the articles are based on viral epidemic and the diseases/disorders that co-occur.

outcomes

• A spreadsheet as well as a graph will be developed based on the comorbidity during a viral epidemic and their count.
• Development of the ML model for data classification on accuracy.

corpora

• Initially the communal corpus epidemic50noCov will be used.
• Later a corpus of 950 articles will be created.

dictionaries

• Disease (https://github.com/petermr/openVirus/tree/master/dictionaries/diseases)

software

• AMI for creating and using dictionaries, sectioning.
• SPARQL for creating dictionaries.
• KNIME for workflow and analytics.
• Python and relevant libraries (keras) for ML and data visualization.

constraints

Progress done

1. The 50 articles in communal corpus epidemic50noCov were binary classified manually and a spreadsheet was developed.
2. The corpus was sectioned using ami section using reference from https://github.com/petermr/openVirus/wiki/ami:section.
3. getpapers was used to create a corpus of 950 articles regarding human viral epidemics(expect COVID-19) by the syntax getpapers -q "viral epidemics AND human NOT COVID NOT corona virus NOT SARS-Cov-2" -o disease_mp -f ve/log.txt -k 955 -x -p. XML -954 files & PDF -913 files were created.