Many key health biomarkers are captured in timeseries data:

• blood pressure, heart rate, EKG, EEG, CRP, …

We need artificial intelligence to handle this at scale:

• Can we train AI to detect important diagnostic or predictive events in time, across multi-modal and multi-channel timeseries data?
• Can we automatically cluster related phenomena in real-time?
• Can we build databases and digital biobanks of timeseries
  symptoms, and instantly find similar patterns?

Our RITA algorithm uses transformers to address these tasks over multi-channel timeseries – and is currently being investigated for epileptic seizure prediction.

In progress: building a data platform that supports integration of embeddings and database data.