Stephen Weng

TL;DR: In this article, the authors assessed whether machine-learning can improve cardiovascular risk prediction and found that machine learning offers an opportunity to improve accuracy by exploiting complex interactions between risk factors, which can increase the number of patients who could benefit from preventive treatment, while avoiding unnecessary treatment of others.

TL;DR: Several risk factors for both overweight and obesity in childhood are identifiable during infancy and future research needs to focus on whether it is clinically feasible for healthcare professionals to identify infants at greatest risk.

TL;DR: Interventions that aim to improve diet and parental responsiveness to infant cues showed most promise in terms of self‐reported behavioural change despite the known risk factors, there were very few intervention studies for pregnant women that continue during infancy.

TL;DR: In this paper, the authors used the Clinical Practice Research Datalink (CPRD) linked with the Index of Multiple Deprivation (IMD) data to identify patients diagnosed with Type 2 diabetes between 2007 and 2017.

TL;DR: Using a prediction algorithm to identify at-risk infants could reduce levels of child overweight and obesity by enabling health professionals to target prevention more effectively and evaluate the clinical validity, feasibility, and acceptability of communicating this risk.