Can machine-learning improve cardiovascular risk prediction using routine clinical data

BackgroundCurrent approaches to predict cardiovascular risk fail to identify many people who would benefit from preventive treatment, while others receive unnecessary intervention. Machine-learning offers opportunity to improve accuracy by exploiting complex interactions between risk factors. We assessed whether machine-learning can improve cardiovascular risk prediction.MethodsProspective cohort study using routine clinical data of 378,256 patients from UK family practices, free from cardiovascular disease at outset. Four machine-learning algorithms (random forest, logistic regression, gradient boosting machines, neural networks) were compared to an established algorithm (American College of Cardiology guidelines) to predict first cardiovascular event over 10-years. Predictive accuracy was assessed by area under the ‘receiver operating curve’ (AUC); and sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) to predict 7.5% cardiovascular risk (threshold for initiating statins).Findings24,970 incident cardiovascular events (6.6%) occurred. Compared to the established risk prediction algorithm (AUC 0.728, 95% CI 0.723–0.735), machine-learning algorithms improved prediction: random forest +1.7% (AUC 0.745, 95% CI 0.739–0.750), logistic regression +3.2% (AUC 0.760, 95% CI 0.755–0.766), gradient boosting +3.3% (AUC 0.761, 95% CI 0.755–0.766), neural networks +3.6% (AUC 0.764, 95% CI 0.759–0.769). The 78 highest achieving (neural networks) algorithm predicted 4,998/7,404 cases (sensitivity79 67.5%, PPV 18.4%) and 53,458/75,585 non-cases (specificity 70.7%, NPV 95.7%), correctly predicting 355 (+7.6%) more patients who developed cardiovascular disease compared to the established algorithm.ConclusionsMachine-learning significantly improves accuracy of cardiovascular risk prediction, increasing the number of patients identified who could benefit from preventive treatment, while avoiding unnecessary treatment of others

Beneficial soil microbiome for sustainable agricultural production

The projected increase in world population and the need to reduce the reliance on non-renewable inputs, such as synthetic agrochemicals, are challenging the current vision of agriculture. In particular, to achieve a fair and sustainable global food security, disruptive changes in crop production are unavoidable. A promising strategy proposes to exploit the metabolic capabilities of soil microbial communities, i.e., the microbiome, to conjugate stable yield with reduced impact on the agroecosystem. In this chapter, we introduce the microbiome populating the root-soil interface from an evolutionary perspective. Next, we discuss the molecular bases of plant-microbe interactions in soil and how these interactions impact plant growth, development and health. We illustrate how plant-probiotic members of the microbiome can be isolated from soil and further characterized for their biological activities, a key pre-requisite for translational applications. In addition, we focus on paradigmatic examples of soil microbes turned into inoculants for agriculture, their fate on soil, their impact on the native microbiome and the beneficial effects exerted on crop productio