Noninvasive Intracranial Pressure Monitoring Using Advanced Machine Learning Techniques

摘要

This project explored the use of advanced machine learning techniques to noninvasively estimate real-time intracranial pressure (ICP) in traumatic brain injury patients from continuous electronic physiological monitoring data. We hypothesized that advanced machine learning techniques could process and analyze electronic vital signs data collected noninvasively and, when correlated with central nervous system-invasive ICP monitoring data, could provide a valid analytic platform for the noninvasive monitoring of ICP for defined periods and clinical needs. We developed a machine learning algorithm that, using noninvasive vital signs features alone, could estimate the current ICP of a patient with an accuracy of + or - 4.6 mmHg. We further developed algorithms that are capable of predicting patient future ICP with + or - 1.5 mmHg 5 minutes into the future and + or - 5 mmHg standard deviation 2 hours into the future using the continuous recordings of heart rate, systolic blood pressure, mean arterial pressure, and ICP. Realization of this analytic platform will support translation of this work into robust, field-ready clinical instrumentation that permits high-quality ICP monitoring in austere care environments not suitable for central nervous system-invasive ICP monitoring.