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Using machine learning techniques to develop risk prediction models to predict graft failure following kidney transplantation: protocol for a retrospective cohort study.

F1000Research
A mechanism to predict graft failure before the actual kidney transplantation occurs is crucial to clinical management of chronic kidney disease patients.  Several kidney graft outcome prediction models, developed using machine learning methods, are...

Characterization of phenolic compounds and aroma active compounds in feijoa juice from four New Zealand grown cultivars by LC-MS and HS-SPME-GC-O-MS.

Food research international (Ottawa, Ont.)
The phenolic compounds and aroma active compounds in feijoa (Acca sellowiana (O.Berg) Burret) juice from four New Zealand grown cultivars (Apollo, Unique, Opal Star, and Wiki Tu) were investigated. A high total phenolic content (maximum 1.89 mg GAE/m...

A comparison of machine learning and logistic regression in modelling the association of body condition score and submission rate.

Preventive veterinary medicine
The effect of body condition score (BCS) on reproductive outcomes is complex, dynamic and non-linear with interaction and confounding. The flexibility inherent in machine learning algorithms makes them attractive for analysing complex data. This stud...

Towards implementation of AI in New Zealand national diabetic screening program: Cloud-based, robust, and bespoke.

PloS one
Convolutional Neural Networks (CNNs) have become a prominent method of AI implementation in medical classification tasks. Grading Diabetic Retinopathy (DR) has been at the forefront of the development of AI for ophthalmology. However, major obstacles...

A predictive model of recreational water quality based on adaptive synthetic sampling algorithms and machine learning.

Water research
Predicting recreational water quality is one of the most difficult tasks in water management with major implications for humans and society. Many data-driven models have been used to predict water quality indicators to allow a real time assessment of...

A machine learning model for predicting risk of hospital readmission within 30 days of discharge: validated with LACE index and patient at risk of hospital readmission (PARR) model.

Medical & biological engineering & computing
The objective of this study was to design and develop a predictive model for 30-day risk of hospital readmission using machine learning techniques. The proposed predictive model was then validated with the two most commonly used risk of readmission m...

Comparison of Support Vector Machine, Naïve Bayes and Logistic Regression for Assessing the Necessity for Coronary Angiography.

International journal of environmental research and public health
(1) Background: Coronary angiography is considered to be the most reliable method for the diagnosis of cardiovascular disease. However, angiography is an invasive procedure that carries a risk of complications; hence, it would be preferable for an ap...

THEIA™ development, and testing of artificial intelligence-based primary triage of diabetic retinopathy screening images in New Zealand.

Diabetic medicine : a journal of the British Diabetic Association
AIM: To develop and evaluate an artificial intelligence triage system with high sensitivity for detecting referable diabetic retinopathy and maculopathy, while maintaining high specificity for non-referable disease, for clinical implementation within...

Ethics and standards in the use of artificial intelligence in medicine on behalf of the Royal Australian and New Zealand College of Radiologists.

Journal of medical imaging and radiation oncology
INTRODUCTION: The Royal Australian and New Zealand College of Radiologists (RANZCR) led the medical community in Australia and New Zealand in considering the impact of machine learning and artificial intelligence (AI) in health care. RANZCR identifie...