Dynamically Personalized Detection of Hemorrhage
Proceedings of the 4th Machine Learning for Healthcare Conference, PMLR 106:109-123, 2019.
Rapid detection of hemorrhage is of major interest to the critical care community, enabling clinicians to take swift actions to mitigate adverse outcomes. In this paper, we describe a model that allows rapid detection of the onset of hemorrhage by monitoring the Central Venous Pressure (CVP). As opposed to prior work in the domain, our model does not rely on prior availability of a stable physiology of a patient as a baseline of reference, and it makes generative assumptions on the monitored vital sign. This allows for rapid on-the-fly personalization to a previously unseen patient’s physiology. This property makes the proposed approach particularly relevant to e.g. trauma care and other scenarios where reference hemodynamic data may not be readily available for any new patient. We compare our model against strong discriminative alternatives and demonstrate its potential utility through empirical evaluation.