Methods of performance analysis in women's Australian football: a scoping review.

Background: The first women's Australian football (AF) professional competition was established in 2017, resulting in advancement in performance analysis capabilities within the sport. Given the specific constraints of women's AF, it is currently unclear what match-play performance analysis methods and techniques are implemented. Therefore, the aim of this scoping review was to describe and critically appraise physical, technical, and tactical performance analysis methods that have been employed in women's and girls' AF match-play.

Empirical validation of ELM trained neural networks for financial modelling.

The purpose of this work is to compare predictive performance of neural networks trained using the relatively novel technique of training single hidden layer feedforward neural networks (SFNN), called Extreme Learning Machine (ELM), with commonly used backpropagation-trained recurrent neural networks (RNN) as applied to the task of financial market prediction. Evaluated on a set of large capitalisation stocks on the Australian market, specifically the components of the ASX20, ELM-trained SFNNs showed superior performance over RNNs for individual stock price prediction. While this conclusion of efficacy holds generally, long short-term memory (LSTM) RNNs were found to outperform for a small subset of stocks.

Improving mortality models in the ICU with high-frequency data.

Background: Assessment of the performance of Intensive Care Units (ICU) is of vital importance for an effective healthcare system. Such assessment ensures that the limited resources of the healthcare system are allocated where they are most needed. Severity scoring systems are employed for this purpose and improving these systems is a continuing area of research which has focused on the use of more complex techniques and new variables.

Investigating healthcare contacts of Dialysis patients by age and gender.

Background: The objective of this paper is to utilise a clinical costing system to investigate differences in the patient journey, defined as the sequence and timing of contacts with the Gold Coast Hospital and Health Services (GCHHS), for four dialysis patient groups defined based on age and gender. It is hypothesised that frequency of contact and form of contact will differ based on both gender and age.

Methods: Data were provided for 393 patients discharged from the GCHHS facility with dialysis treatment between the 1st of January 2015 and the 31st of December 2016.

Text Mining and Automation for Processing of Patient Referrals.

Background: Various tasks within health care processes are repetitive and time-consuming, requiring personnel who could be better utilized elsewhere. The task of assigning clinical urgency categories to internal patient referrals is one such case of a time-consuming process, which may be amenable to automation through the application of text mining and natural language processing (NLP) techniques.

Objective: This article aims to trial and evaluate a pilot study for the first component of the task-determining reasons for referrals.