Machine Learning Models Predicting Undertriage in Telephone Triage

Telephone triage in Western Australia.

We describe the development and operations of the first large-scale Australian medical telephone triage centre. Studies have commenced to evaluate efficacy and safety of the service, as well as gauge the impact on demand for healthcare services.

Machine Learning for Biomedical Literature Triage

This paper presents a machine learning system for supporting the first task of the biological literature manual curation process, called triage. We compare the performance of various classification models, by experimenting with dataset sampling factors and a set of features, as well as three different machine learning algorithms (Naive Bayes, Support Vector Machine and Logistic Model Trees). Th...

Machine Learning for Better Models for Predicting Bond Prices

Bond prices are a reflection of extremely complex market interactions and policies, making prediction of future prices difficult. This task becomes even more challenging due to the dearth of relevant information, and accuracy is not the only consideration–in trading situations, time is of the essence. Thus, machine learning in the context of bond price predictions should be both fast and accura...

Predicting Market Impact Costs Using Nonparametric Machine Learning Models

Market impact cost is the most significant portion of implicit transaction costs that can reduce the overall transaction cost, although it cannot be measured directly. In this paper, we employed the state-of-the-art nonparametric machine learning models: neural networks, Bayesian neural network, Gaussian process, and support vector regression, to predict market impact cost accurately and to pro...

Telephone triage in-hours: does it work?