Identifying epilepsy surgery referral candidates with natural language processing in an Australian context.

Objective: Epilepsy surgery is known to be underutilized. Machine learning-natural language processing (ML-NLP) may be able to assist with identifying patients suitable for referral for epilepsy surgery evaluation.

Methods: Data were collected from two tertiary hospitals for patients seen in neurology outpatients for whom the diagnosis of "epilepsy" was mentioned.

Delays in the diagnosis of ischaemic stroke presenting with persistent reduced level of consciousness: A systematic review.

Introduction: Stroke presenting with a reduced level of consciousness (RLOC) may result in diagnostic error and/or delay. Missed or delayed diagnosis of acute ischaemic stroke may preclude otherwise applicable hyperacute stroke interventions. The frequency, reasons for, and consequences of diagnostic error and delay due to RLOC are uncertain.

Identifying epilepsy surgery candidates with natural language processing: A systematic review.

Introduction: Epilepsy surgery is an underutilised, efficacious management strategy for selected individuals with drug-resistant epilepsy. Natural language processing (NLP) may aid in the identification of patients who are suitable to undergo evaluation for epilepsy surgery. The feasibility of this approach is yet to be determined.