Could reasons for admission help to screen unhealthy alcohol use in emergency departments ? A multicenter French study

International audienceBackground Many patients admitted to general emergency departments (EDs) have a pattern of drinking that could lead to future alcohol-related complications. However, it is often difficult to screen these patients in the context of emergency. The aim of this study is to analyze whether reasons for admission could help to screen patients who have an unhealthy alcohol use. Method Patients were recruited among six public hospital ED in France, between 2012 and 2014. During a one-month period in each hospital, anonymous questionnaires including sociodemographic questions, AUDIT-C and RAPS4-QF were administered to each patients visiting the ED. The reason for admission of each patient was noted at the end of their questionnaire by the ED practitioner. Results Ten thousand Four hundred twenty-one patients were included in the analysis. Patients who came to the ED for injuries and mental disorders were more likely to report unhealthy alcohol use than non-harmful use or no use. Among male patients under 65 years old admitted to the ED for a mental disorder, 24.2% drank more than four drinks (40 g ethanol) in typical day at least four time a week in the last 12 months. Among these patients, 79.7% reported daily or almost daily heavy episodic drinking (HED, 60 g ethanol), and all were positive on the RAPS4-QF. Conclusion This study highlights that unhealthy alcohol use is frequent among ED patients and particularly among those who come for injuries or mental disorders. Men under 65 years old with a mental disorder require special attention because of their increased prevalence of daily or almost daily HED

Orchestrating the Development Lifecycle of Machine Learning-based IoT Applications: A Taxonomy and Survey

Machine Learning (ML) and Internet of Things (IoT) are complementary advances: ML techniques unlock the potential of IoT with intelligence, and IoT applications increasingly feed data collected by sensors into ML models, thereby employing results to improve their business processes and services. Hence, orchestrating ML pipelines that encompass model training and implication involved in the holistic development lifecycle of an IoT application often leads to complex system integration. This article provides a comprehensive and systematic survey of the development lifecycle of ML-based IoT applications. We outline the core roadmap and taxonomy and subsequently assess and compare existing standard techniques used at individual stages