Scientific advances in electrophysiology and clinical neuroscience have highlighted electroen-cephalography (EEG) biomarkers that allow to discriminate between different disorders of consciousness such as vegetative and minimally conscious state. In the current work, we present an automated approach to measuring consciousness disorders and guiding clinical diagnostics. This approach allows to extract scientifically validated biomarkers from incoming clinical EEG-recordings. Probabilistic predictions of an undi-agnosed patient's state of consciousness are then obtained by interrogating statistical models informed by database records of these biomark-ers. Predictions are subsequently summarized and deployed to the clinician in form of a self-contained HTML-report which supports interactive visualization and navigation. We additionally conducted replication and robustness analyses , which indicate that the EEG-biomarkers can be successfully employed in a wide range of practical contexts.