miniproject: viral epidemics and disease

What diseases co-occur with viral epidemics?

owner:

Priya

collaborators:

Dheeraj

miniproject summary

proposed activities

• Use the communal corpus epidemic50noCov consisting of 50 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/wiki/Dictionary:-Diseases)

software

• getpapers to create the corpus of 950 articles from EuPMC.
• AMI for creating and using dictionaries, sectioning.
• SPARQL for creating dictionaries.
• KNIME for workflow and analytics.

constraints

Initial Summary

(by collaborator Dheeraj)

Progress done

1. The 50 articles in communal corpus epidemic50noCov were binary classified as true and false positives manually and a spreadsheet was developed.
2. ami search was used in the corpus of 50 articles and the html DataTables on disease dictionary were created.
3. The corpus was sectioned using ami section as per the reference from https://github.com/petermr/openVirus/wiki/ami:section.
4. 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 mpc -f mpc/log.txt -k 950 -x -p. JATS - 950 files, a log text document, XML -949 files & PDF -903 files were created.
5. ami search was used successfully in the 950 article corpus, which was segmented into 4 folders each containing 200-250 articles.
6. The 950 article corpus was sectioned successfully using ami section.

Things need to be done

(in the 950 article corpus)

1. To upload the 950 article corpus in GitHub(Issue rectifying).
2. To binary classify true and false positives manually.
3. To use KNIME software for binary classification.
4. To test the data classification on accuracy.

Blocking

1. Learning KNIME to use in binary classification.