Healthcare Fireside Chats Episode 2 – Operationalizing Your Data with Deep Learning

The industry estimates that nearly 80% of organizational data is unstructured.

While rich in insight, unstructured data is incredibly challenging to search and analyze due to the complexity of content such as scientific research, scanned documents, clinician notes, pathology reports and more.

Fortunately, advancements in deep learning are creating a paradigm shift in how we can approach this data through transformer-based models.

Unlike a traditional deep learning model that processes each term separately and outside the context of the sequence, transformers use self-attention to build rich representations of each constituent in the data span, allowing these models to understand the relevance of the location of a term, the relation of one term to the next (even if far away from each other) and more. When trained on larger datasets, these models reach remarkable accuracy and recall for understanding unstructured data like large documents of natural language text.

In the next installation of our Healthcare Fireside Chats, we’re joined by Dell Technologies‘ Global & Federal Healthcare CTO, Michael Giannopoulos. We’ll discuss the rise of unstructured content in healthcare and how Vyasa is applying transformers to improve how the industry can access and gain insight from this valuable data.

Watch more below: