Target informed client recruitment for efficient federated learning in healthcare.

Background: Modern machine learning and deep learning methods have been widely incorporated in decision making processes in healthcare in the form of decision support mechanisms. In healthcare, data are abundant but typically not centrally available and, therefore, require some form of aggregation to facilitate training procedures. Aggregating sensitive data poses a significant privacy risk, which is why, both in Europe and the United States, legal frameworks regulate the treatment of such data.

Probabilistic Forecasting With Modified N-BEATS Networks.

In this article, we present a modification to the state-of-the-art N-BEATS deep learning architecture for the univariate time series point forecasting problem for generating parametric probabilistic forecasts. Next, we propose an extension to this probabilistic N-BEATS architecture to allow optimizing probabilistic forecasts from both a traditional forecast accuracy perspective as well as a forecast stability perspective, where the latter is defined in terms of a change in the forecast distribution for a specific time period caused by updating the probabilistic forecast for this time period when new observations become available (i.e.

A Literature Survey and Experimental Evaluation of the State-of-the-Art in Uplift Modeling: A Stepping Stone Toward the Development of Prescriptive Analytics.

Prescriptive analytics extends on predictive analytics by allowing to estimate an outcome in function of control variables, allowing as such to establish the required level of control variables for realizing a desired outcome. Uplift modeling is at the heart of prescriptive analytics and aims at estimating the net difference in an outcome resulting from a specific action or treatment that is applied. In this article, a structured and detailed literature survey on uplift modeling is provided by identifying and contrasting various groups of approaches.

Conventional, Hybrid, or Electric Vehicles: Which Technology for an Urban Distribution Centre?

Freight transport has an important impact on urban welfare. It is estimated to be responsible for 25% of CO2 emissions and up to 50% of particles matters generated by the transport sector in cities. Facing that problem, the European Commission set the objective of reaching free CO2 city logistics by 2030 in major urban areas.