Neurologic disability level at hospital discharge is an important outcome in many clinical
research studies. Outside of clinical trials, neurologic outcomes must typically be extracted …

Background: Applications of natural language processing to mental health notes are not
common given the sensitive nature of the associated narratives. The CEGS N-GRID 2016 …

This research analyzed the clinical notes of epilepsy patients using techniques from corpus
linguistics and machine learning and predicted which patients are candidates for …

Importance Clinical text reports from head computed tomography (CT) represent rich,
incompletely utilized information regarding acute brain injuries and neurologic outcomes …

BACKGROUND CONTEXT The increasing volume of free-text notes available in electronic
health records has created an opportunity for natural language processing (NLP) algorithms …

Objective To establish whether or not a natural language processing technique could
identify two common inpatient neurosurgical comorbidities using only text reports of inpatient …

Objective: Natural language processing (NLP) methods hold promise for improving clinical
prediction by utilising information otherwise hidden in the clinical notes of electronic health …

Background Manual coding of phenotypes in brain radiology reports is time consuming. We
developed a natural language processing (NLP) algorithm to enable automatic identification …

Early admission to the neurosciences intensive care unit (NSICU) is associated with
improved patient outcomes. Natural language processing offers new possibilities for mining …

Objectives To determine the scope of the application of natural language processing to free-
text clinical notes in post-acute care and provide a foundation for future natural language …