Options
Barrett, Michael
Preferred name
Barrett, Michael
Official Name
Barrett, Michael
Research Output
Now showing 1 - 2 of 2
- PublicationPredicting Admissions From a Paediatric Emergency Department – Protocol for Developing and Validating a Low-Dimensional Machine Learning Prediction ModelIntroduction: Patients boarding in the Emergency Department can contribute to overcrowding, leading to longer waiting times and patients leaving without being seen or completing their treatment. The early identification of potential admissions could act as an additional decision support tool to alert clinicians that a patient needs to be reviewed for admission and would also be of benefit to bed managers in advance bed planning for the patient. We aim to create a low-dimensional model predicting admissions early from the paediatric Emergency Department. Methods and Analysis: The methodology Cross Industry Standard Process for Data Mining (CRISP-DM) will be followed. The dataset will comprise of 2 years of data, ~76,000 records. Potential predictors were identified from previous research, comprising of demographics, registration details, triage assessment, hospital usage and past medical history. Fifteen models will be developed comprised of 3 machine learning algorithms (Logistic regression, naïve Bayes and gradient boosting machine) and 5 sampling methods, 4 of which are aimed at addressing class imbalance (undersampling, oversampling, and synthetic oversampling techniques). The variables of importance will then be identified from the optimal model (selected based on the highest Area under the curve) and used to develop an additional low-dimensional model for deployment. Discussion: A low-dimensional model comprised of routinely collected data, captured up to post triage assessment would benefit many hospitals without data rich platforms for the development of models with a high number of predictors. Novel to the planned study is the use of data from the Republic of Ireland and the application of sampling techniques aimed at improving model performance impacted by an imbalance between admissions and discharges in the outcome variable.
280Scopus© Citations 2 - PublicationFactors that influence family and parental preferences and decision making for unscheduled paediatric healthcare: a systematic review protocol(Health Research Board, 2019-09-30)
; ; ; ; ; ; ; ; ; There is a plethora of factors that dictate where parents and families choose to seek unscheduled healthcare for their child; and the complexity of these decisions can present a challenge for policy makers and healthcare planners as these behaviours can have a significant impact on resources in the health system. The systematic review will seek to identify the factors that influence parents' and families' preferences and decision making when seeking unscheduled paediatric healthcare. Five databases will be searched for published studies (CINAHL, PubMed, SCOPUS, PsycInfo, EconLit) and grey literature will also be searched. Inclusion and exclusion criteria will be applied and articles assessed for quality. A narrative approach will be used to synthesise the evidence that emerges from the review. By collating the factors that influence decision-making and attendance at these services, the review can inform future health policies and strategies seeking to expand primary care to support the provision of accessible and responsive care. The systematic review will also inform the design of a discrete choice experiment (DCE) which will seek to determine parental and family preferences for unscheduled paediatric healthcare. Policies such as Sláintecare that seek to expand primary care and reduce hospital admissions from emergency departments need to be cognisant of the nuanced and complex factors that govern patients' behaviour.183Scopus© Citations 4