Shannon M. Standridge

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This research analyzed the clinical notes of epilepsy patients using techniques from corpus linguistics and machine learning and predicted which patients are candidates for neurosurgery, i.e. have intractable epilepsy, and which are not. Information-theoretic and machine learning techniques are used to determine whether and how sets of clinic notes from(More)
OBJECTIVE The constant progress in computational linguistic methods provides amazing opportunities for discovering information in clinical text and enables the clinical scientist to explore novel approaches to care. However, these new approaches need evaluation. We describe an automated system to compare descriptions of epilepsy patients at three different(More)
AIM To describe the clinical course and pathological diagnosis of a 12-year-old female who presented with an acute syndrome of right hemispheric epilepsy and cortical dysfunction and brain MRI demonstrating atrophy of the left cerebral and right cerebellar hemispheres. RESULTS The patient presented with occasional partial seizures consisting of a left(More)
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