Informatics Seminar Recordings
Public Health Information Credibility in the Era of “Fake News”
Helping the Public Evaluate Health Information Credibility in the Era of “Fake Health News”
With the emergence of new Web media platforms and the ubiquity of social media, critical evaluation of online health information has taken on a new dimension and urgency. At the same time, many established information quality evaluation guidelines address information characteristics other than the content (e.g., authority, currency) and do not address information presented via novel Web technologies. This talk will describe a research program that develops a methodological approach for analyzing diverse online health information sources. It will also present a window into the universe of non-evidence-based online health information, particularly as it pertains to the possibility of curing type 2 diabetes. The presentation will use the above evaluation criteria to describe how these sites portray complexity of type 2 diabetes, characterize healthcare establishment, use language and emotional cues, discuss medical research, and convey certainty. It will also address the potential role of technology in supporting users in the changing digital health ecosystem.
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Deep Learning Architecture for Extracting Protein-Protein Interactions
An End-to-End Deep Learning Architecture for Extracting Protein-Protein Interactions Affected by Genetic Mutations
As part of the BioCreative VI Track IV we built a supervised relation extraction model capable of taking a test article and returning a list of interacting protein pairs identified by their Entrez Gene IDs. Such pairs represent proteins participating in a binary protein-protein interaction (PPI) relation where the interaction is additionally affected by a genetic mutation (PPIm). In this study, we explored a PPIm relation extraction by deploying a three-component pipeline involving deep learning-based named entity recognition and relation classification models along with a knowledge-based approach for gene normalization. We propose several recall-focused improvements to our original challenge entry which placed 2nd in the competition. On exact matching, the new system achieved test results of 37.78% micro-F1 with a precision of 38.22% and recall of 37.34% which corresponds to an improvement by approximately 3 micro-F1 points. When matching on HomoloGene IDs, we report similarly competitive test results at 46.17% micro-F1 with a precision and recall of 46.67% and 45.59%, corresponding to an improvement of more than 8 micro-F1 points over the prior best result.
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