Rare diseases are those that affect less than 200,000 people and, as can be expected, studying these elusive diseases can be difficult. There are millions of public and private documents that contain insights to help diagnose and understand. However, leveraging this document data has many challenges. Often there are numerous names for the same disease, or disease subtypes, and semantically similar symptoms and descriptions can be difficult to drill down on without considerable manual effort and time.
The emergence of deep learning as a powerful tool in text analytics, and its ability to identify semantically similar content, is one of the driving forces behind the creation of Synapse. Synapse is connected to master document resources such as PubMed and Patents, and is also designed to securely ingest and analyze user sources. Once a Synapse Smart Table is connected to these sources, deep learning agents can help explore the data and populate the sheet with their findings. For example you can start with a few symptoms and expand to explore others used in the same context within your data, find semantically similar symptoms and add them to your search parameters, ask natural language questions about your content and populate a smart column with the answers. Synapse allows efficient and deep exploration of massive amounts of text data that would otherwise remain hidden and take numerous manual hours to uncover.
Read About Other Use Cases
Automated identification of emergent technologies, patents, and companies from unstructured text can be challenging. Synapse can uncover even the most obscured similarities between technologies hidden deep in documents, making discovery efficient and effective.
Business intelligence focuses on the acquisition of huge quantities of data and leveraging that data into actionable insights. Synapse Radar and Trendlines enable deep, efficient research to uncover information hidden in text.
Manual research is time consuming and modern search tools are limited in scope. Synapse utilizes its understanding of semantics to drive efficient discovery and allows the focus to return to the current case.