Publication: A supervised, externally validated machine learning model for artifact and drainage detection in high-resolution intracranial pressure monitoring data
A supervised, externally validated machine learning model for artifact and drainage detection in high-resolution intracranial pressure monitoring data
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Huo, S., Nelde, A., Meisel, C., Scheibe, F., Meisel, A., Endres, M., Vajkoczy, P., Wolf, S., Willms, J. F., Boss, J. M., & Keller, E. (2024). A supervised, externally validated machine learning model for artifact and drainage detection in high-resolution intracranial pressure monitoring data. Journal of Neurosurgery, 141(2), 509–517. https://doi.org/10.3171/2023.12.jns231670
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OBJECTIVE: In neurocritical care, data from multiple biosensors are continuously measured, but only sporadically acknowledged by the attending physicians. In contrast, machine learning (ML) tools can analyze large amounts of data continuously, taking advantage of underlying information. However, the performance of such ML-based solutions is limited by different factors, for example, by patient motion, manipulation, or, as in the case of external ventricular drains (EVDs), the drainage of CSF to control intracranial pressure (ICP). The
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Huo, S., Nelde, A., Meisel, C., Scheibe, F., Meisel, A., Endres, M., Vajkoczy, P., Wolf, S., Willms, J. F., Boss, J. M., & Keller, E. (2024). A supervised, externally validated machine learning model for artifact and drainage detection in high-resolution intracranial pressure monitoring data. Journal of Neurosurgery, 141(2), 509–517. https://doi.org/10.3171/2023.12.jns231670
