Capturing the ghost in the machine: a process for development and validation of measures of phenomenal consciousness in delirium.

Background: Delirium has conventionally been considered a disorder of consciousness. Alertness and arousal are used as surrogates in clinical practice but are insufficient for the purposes of a more dimensional assessment of consciousness. We present a process of development and validation of candidate measures of phenomenal consciousness that could be applied to the diagnosis of delirium.

Machine learning and clinical neurophysiology.

Clinical neurophysiology constructs a wealth of dynamic information pertaining to the integrity and function of both central and peripheral nervous systems. As with many technological fields, there has been an explosion of data in neurophysiology over recent years, and this requires considerable analysis by experts. Computational algorithms and especially advances in machine learning (ML) have the ability to assist with this task and potentially reveal hidden insights.