In healthcare, researchers and clinicians need to base decisions on the best possible view of data. As 80% of data resides in unstructured sources, rapid effective access to the knowledge buried in text documents is essential. Critical information around clinical outcomes is often buried in unstructured text, in scientific papers, conference articles, or medical notes. Natural language processing-based text mining provides a powerful solution to these challenges.
The ability to text mine MEDLINE abstracts and full-text journal articles on patient rounds has allowed GUMC’s clinical informationists to quickly search large amounts of medical literature that would otherwise be unsearchable, thus allowing them to provide additional answers to physicians’ clinical questions.
In this webinar, Jonathan Hartmann and Jane Reed explore the application and impact of text mining at Georgetown, for both patient care and clinical research. They discuss the value and application of text analytics for:
- the retrieval of clinical research at the point of care
- the identification of genes associated with inflammatory bowel disease to improve understanding of their molecular processes and for drug targeting and repurposing