Publications by authors named "A J Lawlor"
Background: The increasing prevalence of sedentary lifestyles has prompted the development of innovative public health interventions, such as smartphone apps that deliver personalized exercise programs. The widespread availability of mobile technologies (eg, smartphone apps and wearable activity trackers) provides a cost-effective, scalable way to remotely deliver personalized exercise programs to users. Using machine learning (ML), specifically reinforcement learning (RL), may enhance user engagement and effectiveness of these programs by tailoring them to individual preferences and needs.
View Article and Find Full Text PDFObjective: This study reviewed the current state of machine learning (ML) research for the prediction of sports-related injuries. It aimed to chart the various approaches used and assess their efficacy, considering factors such as data heterogeneity, model specificity and contextual factors when developing predictive models.
Design: Scoping review.
Article Synopsis
- - PIONEER is a European initiative that combines the efforts of 34 organizations across 9 countries to enhance prostate cancer screening, diagnosis, and treatment using big data analytics.
- - The project has created a comprehensive platform that integrates various data sources, helping to respond to critical research questions and improve understanding of prostate cancer management at different stages.
- - Despite successes, challenges with real-world data persist, necessitating ongoing collaboration and knowledge sharing, which PIONEER aims to support as part of the UroEvidenceHub initiative by the European Association of Urology.
Over the preceding decades, the widespread dependence on anthelmintic drugs for managing nematodes in grazing equids has given rise to resistance against commonly used anthelmintics in various countries. This study explores the prevalence of anthelmintic resistance across 44 horse farms in Ireland. Anthelmintic efficacy was evaluated through fecal egg count reduction (FECR) tests employing the mini-FLOTAC technique.
View Article and Find Full Text PDFObjectives: To train the machine and deep learning models to automate the justification analysis of radiology referrals in accordance with iGuide categorisation, and to determine if prediction models can generalise across multiple clinical sites and outperform human experts.
Methods: Adult brain computed tomography (CT) referrals from scans performed in three CT centres in Ireland in 2020 and 2021 were retrospectively collected. Two radiographers analysed the justification of 3000 randomly selected referrals using iGuide, with two consultant radiologists analysing the referrals with disagreement.