NRTS: A Client-Server Architecture for Supporting Education in a Neonatal Resuscitation Simulation Scenario.

In this paper, we describe Neonatal Resuscitation Training Simulator (NRTS), an Android mobile app designed to support medical experts to input, transmit and record data during a High-Fidelity Simulation course for neonatal resuscitation. This mobile app allows one to automatically send all the recorded data from the Neonatal Intensive Care Unit (NICU) of Casale Monferrato Children's Hospital, (Italy) to a server in the cloud managed by the University of Piemonte Orientale (Italy). The medical instructor can then view statistics on simulation exercises, that may be used during the debriefing phase for the evaluation of multidisciplinary teams involved in the simulation scenarios.

Explainable process trace classification: An application to stroke.

Medical process trace classification exploits the activity sequences logged by an healthcare organization to classify traces themselves on the basis of some performance properties; this information can be used for quality assessment. State-of-the-art process trace classification resorts to deep learning, a very powerful technique which however suffers from the lack of explainability. In this paper we aim at addressing this issue, motivated by a relevant application, i.

High-Fidelity simulation-based program improves flow state scale in the perinatal team.

Background: We aimed to evaluate the degree of realism and involvement, stress management and awareness of performance improvement in practitioners taking part in high fidelity simulation (HFS) training program for delivery room (DR) management, by means of a self-report test such as flow state scale (FSS).

Methods: This is an observational pretest-test study. Between March 2016 and May 2019, fourty-three practitioners (physicians, midwives, nurses) grouped in multidisciplinary teams were admitted to our training High Fidelity Simulation center.

Revealing structural evolution occurring from photo-initiated polymer network formation.

Photopolymerization is a key enabling technology offering spatial and temporal control to allow for future functional materials to be made to meet societal needs. However, gaining access to robust experimental techniques to describe the evolution of nanoscale morphology in photo-initiated polymeric systems has proven so far to be a challenging task. Here, we show that these physical transformations can be monitored and quantified at the nanoscale in situ and in real-time.

Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey.

Objectives: This survey analyses the latest literature contributions to clinical decision support systems (DSSs) on a two-year period (2017-2018), focusing on the approaches that adopt Artificial Intelligence (AI) techniques in a broad sense. The goal is to analyse the distribution of data-driven AI approaches with respect to "classical" knowledge-based ones, and to consider the issues raised and their possible solutions.

Methods: We included PubMed and Web of Science publications, focusing on contributions describing clinical DSSs that adopted one or more AI methodologies.