Ativa is honored to be working with Associate Professor James Fackler on developing and validating machine learning applications for critical care using digital blood samples from Ativa’s Diagnostic Workstation. Dr Fackler is a valued member of the Clinical Advisory Board and will advise Ativa on applying point of care hematology and metabolic measurement cards to provide a rapid and definitive diagnosis of sepsis in the critical care setting. A white paper will be authored to demonstrate machine learning algorithms in the early detection of sepsis and monitoring of therapeutic response.
Dr Fackler explains, “In today’s world, when a CBC is ordered, clinicians generally only look at three numbers. The white count, the percentage of polys, and maybe the platelet count. In the new exciting world of Ativa, we analyze N parameters and feed each one into our predictive algorithm. Therefore, we will “see” and analyze patterns that are quite literally invisible to even experienced clinicians.”
“There is a completely untapped intellectual and commercial market(s) in the measurement of responses to sepsis therapies (rather than “just” the early detection of sepsis). We have focused on the “dysregulated” host response in the etiology of sepsis. Yet, timely POC data to monitor therapeutic responses (or “re-regulation”) will prove crucial to precisely guide both escalations and de-escalations of therapies.”