AIMC Topic: Australia
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Detection of child depression using machine learning methods.
PloS one
Dec 16, 2021
BACKGROUND: Mental health problems, such as depression in children have far-reaching negative effects on child, family and society as whole. It is necessary to identify the reasons that contribute to this mental illness. Detecting the appropriate sig...
Feasibility study protocol of the PainChek app to assess the efficacy of a social robot intervention for people with dementia.
Journal of advanced nursing
Nov 26, 2021
AIM: This study aims to test the feasibility of the PainChek app to assess pain for people with dementia living in residential aged care facilities (RACFs). It will also identify the optimal dosage and efficacy of a social robot (personal assistant r...
Using a multi-head, convolutional neural network with data augmentation to improve electropherogram classification performance.
Forensic science international. Genetics
Oct 13, 2021
DNA profiles are generated in forensic biology laboratories around the world. It is possible that these profiles are assessed by two independent people in order for the profiles to be 'read'. Recent work has been carried out to develop a neural netwo...
Extreme fire weather is the major driver of severe bushfires in southeast Australia.
Science bulletin
Oct 5, 2021
In Australia, the proportion of forest area that burns in a typical fire season is less than for other vegetation types. However, the 2019-2020 austral spring-summer was an exception, with over four times the previous maximum area burnt in southeast ...
Explainable machine learning models of major crop traits from satellite-monitored continent-wide field trial data.
Nature plants
Oct 4, 2021
Four species of grass generate half of all human-consumed calories. However, abundant biological data on species that produce our food remain largely inaccessible, imposing direct barriers to understanding crop yield and fitness traits. Here, we asse...
Explainable artificial intelligence for pharmacovigilance: What features are important when predicting adverse outcomes?
Computer methods and programs in biomedicine
Sep 26, 2021
BACKGROUND AND OBJECTIVE: Explainable Artificial Intelligence (XAI) has been identified as a viable method for determining the importance of features when making predictions using Machine Learning (ML) models. In this study, we created models that ta...
Australian experience with robot-assisted Roux-en-Y gastric bypass with comparison to a conventional laparoscopic series.
Surgical endoscopy
Sep 15, 2021
BACKGROUND: Robotic surgery is a novel approach to abdominal surgery. In Australia, the uptake of robotic assistance for bariatric surgery has been relatively slow compared to many other countries. The aim of this study is to report the first high vo...
Machine learning risk prediction model for acute coronary syndrome and death from use of non-steroidal anti-inflammatory drugs in administrative data.
Scientific reports
Sep 15, 2021
Our aim was to investigate the usefulness of machine learning approaches on linked administrative health data at the population level in predicting older patients' one-year risk of acute coronary syndrome and death following the use of non-steroidal ...
An Innovative Machine Learning Approach to Predict the Dietary Fiber Content of Packaged Foods.
Nutrients
Sep 14, 2021
Underconsumption of dietary fiber is prevalent worldwide and is associated with multiple adverse health conditions. Despite the importance of fiber, the labeling of fiber content on packaged foods and beverages is voluntary in most countries, making ...
Acceptability, usefulness, and satisfaction with a web-based video-tailored physical activity intervention: The TaylorActive randomized controlled trial.
Journal of sport and health science
Sep 3, 2021
PURPOSE: This study aimed to examine the usage, acceptability, usability, perceived usefulness, and satisfaction of a web-based video-tailored physical activity (PA) intervention (TaylorActive) in adults.
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