AIMC Topic: New Zealand
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Urgency vs triage prioritisation: appropriateness of referrer-rated urgency of referrals to a public dermatology service.
The New Zealand medical journal
Jun 27, 2025
AIM: To characterise the appropriateness of community referrer-rated urgency among dermatology referrals.
Development of Machine Learning-Based Risk Prediction Models to Predict Rapid Weight Gain in Infants: Analysis of Seven Cohorts.
JMIR public health and surveillance
Jun 18, 2025
BACKGROUND: Rapid weight gain (RWG) during infancy, defined as an upward crossing of one centile line on a weight growth chart, is highly predictive of subsequent obesity risk. Identification of infant RWG could facilitate obesity risk assessment fro...
Learning to care, caring to learn: the evolving nature of medical education.
The New Zealand medical journal
May 23, 2025
As Otago Medical School marks its 150th anniversary, this paper reflects on what it means to train doctors for both today and the decades ahead. It traces the school's evolution from its nineteenth-century foundations through key innovations in curri...
Modelling and mapping of subsurface nitrate-attenuation index in agricultural landscapes.
Journal of environmental management
May 5, 2025
Environmental management of nutrient losses from agricultural lands is required to reduce their potential impacts on the quality of groundwater and eutrophication of surface waters in agricultural landscapes. However, accurate accounting and manageme...
Exploring the Views of Dermatologists, General Practitioners, and Melanographers on the Use of AI Tools in the Context of Good Decision-Making When Detecting Melanoma: Qualitative Interview Study.
JMIR dermatology
Mar 24, 2025
BACKGROUND: Evidence that artificial intelligence (AI) may improve melanoma detection has led to calls for increased human-AI collaboration in clinical workflows. However, AI-based support may entail a wide range of specific functions for AI. To appr...
Using machine learning to explore the efficacy of administrative variables in prediction of subjective-wellbeing outcomes in New Zealand.
Scientific reports
Feb 25, 2025
The growing acknowledgment of population wellbeing as a key indicator of societal prosperity has propelled governments worldwide to devise policies aimed at improving their citizens' overall wellbeing. In New Zealand, the General Social Survey provid...
Machine learning techniques to predict diabetic ketoacidosis and HbA1c above 7% among individuals with type 1 diabetes - A large multi-centre study in Australia and New Zealand.
Nutrition, metabolism, and cardiovascular diseases : NMCD
Jan 9, 2025
BACKGROUND AND AIMS: Type 1 diabetes and diabetic ketoacidosis (DKA) have a significant impact on individuals and society across a wide spectrum. Our objective was to utilize machine learning techniques to predict DKA and HbA1c>7 %.
Male
Middle Aged
Humans
Risk Assessment
Machine Learning
Deep Learning
Decision Support Techniques
Female
Prospective Studies
Adolescent
Australia
Risk Factors
Biomarkers
Young Adult
Diabetes Mellitus, Type 1
Predictive Value of Tests
Prognosis
Adult
Diabetic Ketoacidosis
Glycated Hemoglobin
Support Vector Machine
New Zealand
Predicting the risk of diabetes complications using machine learning and social administrative data in a country with ethnic inequities in health: Aotearoa New Zealand.
BMC medical informatics and decision making
Sep 27, 2024
BACKGROUND: In the age of big data, linked social and administrative health data in combination with machine learning (ML) is being increasingly used to improve prediction in chronic disease, e.g., cardiovascular diseases (CVD). In this study we aime...
The future is bright: artificial intelligence for trainee medical officers in Australia and New Zealand.
Internal medicine journal
Sep 21, 2024
Given their frontline role in Australia and Aotearoa New Zealand (ANZ) healthcare, trainee medical officers (TMOs) will play a crucial role in the development and use of artificial intelligence (AI) for clinical care, ongoing medical education and re...
DIGIPREDICT: physiological, behavioural and environmental predictors of asthma attacks-a prospective observational study using digital markers and artificial intelligence-study protocol.
BMJ open respiratory research
May 22, 2024
INTRODUCTION: Asthma attacks are a leading cause of morbidity and mortality but are preventable in most if detected and treated promptly. However, the changes that occur physiologically and behaviourally in the days and weeks preceding an attack are ...
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