Biomedical information is buried in millions of published articles, and so it is necessary to use text mining techniques to skim published articles for relevant information. In this study, we used biomedical text mining techniques to introduce a liver bacterial infection knowledge-acquisition information system. Materials and methods: Bacteria names were selected from Medline MeSH data and it was searched to identify the most frequent bacteria associated with the liver using a text mining system and time based analyses were used to show the evolution of treatments. Results: Liver infections constitute a major threat to public health, and our study shows that there is a need for better drugs. Conclusion: Both pharmaceutical industry and healthcare providers are encouraged to investigate challenges related with major liver infections and create strategies to develop new drugs.

Biomedical information is buried in millions of published articles, and so it is necessary to use text mining techniques to skim published articles for relevant information. In this study, we used biomedical text mining techniques to introduce a liver bacterial infection knowledge-acquisition information system. Materials and methods: Bacteria names were selected from Medline MeSH data and it was searched to identify the most frequent bacteria associated with the liver using a text mining system and time based analyses were used to show the evolution of treatments. Results: Liver infections constitute a major threat to public health, and our study shows that there is a need for better drugs. Conclusion: Both pharmaceutical industry and healthcare providers are encouraged to investigate challenges related with major liver infections and create strategies to develop new drugs.