Literature Mining of Antibiotic Resistance

Kweon, Seungchan

Literature Mining of Antibiotic Resistance

dc.contributor.author	Kweon, Seungchan	en
dc.date.accessioned	2022-12-15T21:41:44Z	en
dc.date.available	2022-12-15T21:41:44Z	en
dc.date.issued	2022-12-14	en
dc.description.abstract	Antibiotic resistance is a very important part of human health that needs to be constantly kept track of in order to give proper medication to patients. However, the large number of papers that come out makes it very difficult for a human to keep up. Thus, this project aims to create an automated way to extract meaningful information out of antibiotic resistance papers. It aims to create an automated way to collect and parse the papers. Then, it tries to analyze the papers and extract meaningful information out of it. In order to do so the project was divided into 4 main parts. In the first part antibiotic resistance genes were gathered to be used as search queries. In the second part PubMed papers were gathered using the genes. In the third part DeepEventMine was used to extract information from the papers. In the last part the extracted information was analyzed. The results from the data gathering process were good and lots of relevant papers were gathered. DeepEventMine was run successfully but most of the output was not very useful. The analysis part was mostly focused on statistical analysis and gave some useful results. As for the final deliverable the code used for data collection works well and can be used on other projects that work with medical papers. Detailed information on how to work with DeepEventMine can be found in this report. Finally, more work can be done in the analysis section to produce more information.	en
dc.description.notes	AntibioticResistanceReport.zip - Final report (Zip file from Overleaf) AntibioticResistanceReport.pdf - Final report (PDF version) AntibioticResistancePresentation.pptx - Final presentation (PowerPoint version) AntibioticResistancePresentation.pdf - Final presentation (PDF version) AntibioticResistanceResult.zip - 7 files giving results, including "readme.txt" AntibioticResistanceCode.zip - "readme.txt" and 4 code directories, for the 4 project parts	en
dc.identifier.uri	http://hdl.handle.net/10919/112911	en
dc.language.iso	en_US	en
dc.publisher	Virginia Tech	en
dc.subject	antibiotic resistance	en
dc.subject	data mining	en
dc.subject	event extraction	en
dc.title	Literature Mining of Antibiotic Resistance	en
dc.type	Presentation	en
dc.type	Report	en