Augmented Intelligence in Life Sciences and Healthcare: What Your Data Can Show You (While You Decide What To Do)

Gunnar Carlsson

Artificial intelligence is rapidly developing in many domains, including life sciences and health care. The broader notion of augmented intelligence, where machines interact with humans, is of a great deal of importance. Data sets in life sciences and healthcare are large and complex to the point of being beyond human comprehension.

What if you could “see” the shape of this data in its purest topological form, and interrogate it from this vantage point not achievable with conventional analysis methods? What might you find? What new discoveries might you make? And what might you do next?

Tonight’s talk from Gunnar Carlsson, mathematics Professor Emeritus at Stanford University and co-founder of Ayasdi, will feature examples of a methodology called Topological Data Analysis (TDA) applied to life sciences and healthcare data of a variety of types and sources. This approach has been developing rapidly in the last ten years, supports augmented intelligence, and does not require annotated data.

Case studies of data and derived insights for next step decision-making include:

– stratifying asthma and diabetes based on genomic and phenotypic/EMR data
– identifying optimal clinical care paths for knee replacement and bowel surgery based on variations detected in physician protocol and patient outcomes
– the progression of infectious disease
– the relationship of fMRI data with temporal information about tasks and task transitions

Attention will be given to the process used to examine the data, and how to decide what to do next.

Didn’t see your use case covered? Bring your questions. Whether you are an investigator, drug or device developer, clinician, regulatory professional, basic scientist, or other with a life sciences interest, all questions are welcome at our extended Q&A.