The most recent version of Sentrian’s Remote Patient Intelligence platform takes the current agile disease deterioration modeling system which includes rich input from clinicians in the form of natural language and combines it with historical data and machine learning. This hybrid machine learning approach, which we call Clinician-Directed Machine Learning (CDML), allows us to overcome the key barrier to wide adoption and deployment of remote monitoring in healthcare: complexity, cost, and time involved in delivering the predictive models that allow machines to make sense of the clinical and subjective data we can now gather at scale. CDML combines clinicians’ knowledge of the biological mechanisms that drive the human body and the diseases that attack it with machines’ ability to find personalized patterns predictive of hospitalization. The Sentrian RPI platform provides a rich user interface with which clinicians can express their insights. CDML uses these insights to prime a machine learning system that looks at objective, historical data to transform clinical knowledge and intuition into objective predictive models that can be applied at scale across large patient populations to detect health problems early so that patients and clinicians can react in the critical first stages where interventions are less invasive, cheaper, and more likely to succeed. The Sentrian RPI architecture delivers a scalable platform which allows models to be developed through CDML and makes it possible to deploy those models across very large patient populations and with a wide range of remote biosensors and devices.
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