RESUMO
The value of machine learning capacity in maternal health, and in particular prediction of preeclampsia will only be realised when there are high quality clinical data provided, representative populations included, different health systems and models of care compared, and a culture of rapid use and application of real-time data and outcomes. This review has been undertaken to provide an overview of the language, and early results of machine learning in a pregnancy and preeclampsia context. Clinicians of all backgrounds are encouraged to learn the language of Machine Learning (ML) and Artificial intelligence (AI) to better understand their potential and utility to improve outcomes for women and their families. This review will outline some definitions and features of ML that will benefit clinician's knowledge in the preeclampsia discipline, and also outline some of the future possibilities for preeclampsia-focussed clinicians via understanding AI. It will further explore the criticality of defining the risk, and outcome being determined.
RESUMO
Atrial fibrillation (AF) and obstructive sleep apnoea (OSA) are both common, often underdiagnosed conditions with serious sequelae. An association between these two conditions has been recognised but the nature of this relationship remains a topic of active debate. Despite that lack of strong, randomised controlled trials, there is a considerable body of data indicating that not only does untreated OSA provide the substrates and triggers for AF but that OSA, itself, is also a therapeutic target for the management of cardiovascular disease in general and of AF in particular.