Until August 2007, Australia was one of only three countries internationally recognised to be free of equine influenza (EI). This report documents the diagnosis of the first cases of EI in Australian horses and summarises the investigations that took place over the next 5 days. During that time, a multifocal outbreak was identified across eastern New South Wales and south-eastern Queensland. The use of an influenza type A pan-reactive real-time reverse transcription polymerase chain reaction allowed rapid confirmation of suspect cases of EI.
AimThe role of machine learning on clinical documentation for predictive outcomes remains undefined. We aimed to compare three neural networks on inpatient providers’ notes to predict mortality in neonatal hypoxic‐ischaemic encephalopathy (HIE).MethodsUsing Children's Hospitals Neonatal Database, non‐anomalous neonates with HIE treated with therapeutic hypothermia were identified at a single‐centre. Data were linked with the initial seven days of documentation. Exposures were derived using the databases and applying convolutional and two recurrent neural networks. The primary outcome was mortality. The predictive accuracy and performance measures for models were determined.ResultsThe cohort included 52 eligible infants. Most infants survived (n = 36, 69%) and 23 had severe HIE (44%). Neural networks performed above baseline and differed in their median accuracy for predicting mortality (P = .0001): recurrent models with long short‐term memory 69% (25th, 75th percentile 65, 73%) and gated‐recurrent model units 65% (62, 69%) and convolutional 72% (64, 96%). Convolutional networks’ median specificity was 81% (72, 97%).ConclusionThe neural network models demonstrated fundamental validity in predicting mortality using inpatient provider documentation. Convolutional models had high specificity for (excluding) mortality in neonatal HIE. These findings provide a platform for future model training and ultimately tool development to assist clinicians in patient assessments and risk stratifications.
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