Background and purpose With the increasing adoption of electronic records in the health
system, machine learning‐enabled techniques offer the opportunity for greater computer …

Clinical coding is an important task, which is required for accurate activity-based funding.
Natural language processing may be able to assist with improving the efficiency and …

Objective Long-term follow-up of population-based prospective studies is often achieved
through linkages to coded regional or national health care data. Our knowledge of the …

Background Stroke severity is an important predictor of patient outcomes and is commonly
measured with the National Institutes of Health Stroke Scale (NIHSS) scores. Because these …

Determining acute ischemic stroke (AIS) etiology is fundamental to secondary stroke
prevention efforts but can be diagnostically challenging. We trained and validated an …

Objective In UK Biobank (UKB), a large population-based prospective study, cases of many
diseases are ascertained through linkage to routinely collected, coded national health …

Background Better phenotyping of routinely collected coded data would be useful for
research and health improvement. For example, the precision of coded data for hemorrhagic …

Aim: To use available electronic administrative records to identify data reliability, predict
discharge destination, and identify risk factors associated with specific outcomes following …

Introduction Administrative hospital diagnostic coding data are increasingly being used in
identifying incident and prevalent stroke cases, for outcome audit and for 'big data'research …

Background: Stroke misdiagnosis, associated with poor outcomes, is estimated to occur in
9% of all stroke patients. Objectives: We hypothesized that machine learning (ML) could …