AutoSS: A Deep Learning-Based Soft Sensor for Handling Time-Series Input Data

Soft Sensors are data-driven technologies that allow to have estimations of quantities that are impossible or costly to be measured. Unfortunately, the design of effective soft sensors is heavily impacted by time-consuming feature engineering steps that may lead to sub-optimal information, especially when dealing with time-series input data. While domain knowledge may come into help when handling feature extraction in soft sensing applications, the feature extraction typically limits the adoption of such technologies: In this work, we propose AutoSS, a Deep-Learning based approach that allows to overcome such issue. By exploiting autoencoders, dilated convolutions and an ad-hoc defined architecture, AutoSS allows to develop effective soft sensing modules even with time-series input data. The effectiveness of AutoSS is demonstrated on a real-world case study related to Internet of Things equipment

Prescribing practice and off-label use of psychotropic medications in post-acute brain injury rehabilitation centres: A cross-sectional survey

Objective: Guidance on pharmacotherapy of neurobehavioural sequelae post-acquired brain injury (ABI) is limited. Clinicians face the choice of prescribing off-label. This survey assesses prescribing practice and off-label use of psychotropic medications in Italian brain injury rehabilitation centres and factors associated with atypical antipsychotics use. Materials and methods: Centres were identified through the roster of the Italian Society for Rehabilitation Medicine. Information was collected through a structured questionnaire. This study calculated the prevalence of centres reporting to use off-label individual medications and unconditional logistic regression Odds Ratio (OR), with 95% confidence interval (95% CI) of atypical antipsychotics use. Results: Psychotropic medications were commonly used. More than 50% of the 35 centres (participation ratio 87.5%) reported to use off-label selected antipsychotics, mostly for agitation (90.5%) and behavioural disturbances (19.0%), and antidepressants, mostly for insomnia (37.5%) and pain (25.0%). Atypical antipsychotic use was directly associated with age <40 years (OR=2.68; 95% CI=1.25-5.76), recent ABI (1.74; 0.74-4.09), not with reported off-label use (0.98; 0.44-2.18). Conclusion: In clinical practice, the effectiveness and safety of medications, in particular off-label, should be systematically monitored. Studies are needed to improve the quality of evidence guiding pharmacotherapy and to evaluate effectiveness and safety of off-label prescribin