Using the Field Artificial Intelligence Triage (FAIT) tool to predict hospital critical care resource utilization in patients with truncal gunshot wounds.

Background: Tiered trauma triage systems have resulted in a significant mortality reduction, but models have remained unchanged. The aim of this study was to develop and test an artificial intelligence algorithm to predict critical care resource utilization.

Methods: We queried the ACS-TQIP 2017-18 database for truncal gunshot wounds(GSW).

Development of a field artificial intelligence triage tool: Confidence in the prediction of shock, transfusion, and definitive surgical therapy in patients with truncal gunshot wounds.

Background: In-field triage tools for trauma patients are limited by availability of information, linear risk classification, and a lack of confidence reporting. We therefore set out to develop and test a machine learning algorithm that can overcome these limitations by accurately and confidently making predictions to support in-field triage in the first hours after traumatic injury.

Methods: Using an American College of Surgeons Trauma Quality Improvement Program-derived database of truncal and junctional gunshot wound (GSW) patients (aged 16-60 years), we trained an information-aware Dirichlet deep neural network (field artificial intelligence triage).

Information Aware max-norm Dirichlet networks for predictive uncertainty estimation.

Precise estimation of uncertainty in predictions for AI systems is a critical factor in ensuring trust and safety. Deep neural networks trained with a conventional method are prone to over-confident predictions. In contrast to Bayesian neural networks that learn approximate distributions on weights to infer prediction confidence, we propose a novel method, Information Aware Dirichlet networks, that learn an explicit Dirichlet prior distribution on predictive distributions by minimizing a bound on the expected max norm of the prediction error and penalizing information associated with incorrect outcomes.

Low-dose chronic prenatal alcohol exposure abolishes the pro-cognitive effects of angiotensin IV.

Prenatal ethanol exposure (PAE) in humans results in a spectrum of disorders including deficits in learning and memory. Animal models to date have typically used high ethanol doses but have not identified the biochemical changes underlying the cognitive deficit. This study used treatment of mouse breeding harems with 5% ethanol via drinking water throughout pregnancy and lactation and explored the behavioural consequences in the progeny at 3-6 months of age using the open field test, novel object recognition test and elevated plus maze to measure anxiety and memory consolidation.