Stout Santos (dahliatext39)

The FAIR Principles are a set of recommendations that aim to underpin knowledge discovery and integration by making the research outcomes Findable, Accessible, Interoperable and Reusable. These guidelines encourage the accurate recording and exchange of data, coupled with contextual information about their creation, expressed in domain-specific standards and machine-readable formats. This paper analyses the potential support to FAIRness of the openEHR specifications and reference implementation, by theoretically assessing their compliance with each of the 15 FAIR principles. Our study highlights how the openEHR approach, thanks to its computable semantics-oriented design, is inherently FAIR-enabling and is a promising implementation strategy for creating FAIR-compliant Clinical Data Repositories (CDRs).International Organizations are seriously concerned about the fake news phenomenon. UNESCO has defined the term of misinformation/disinformation, which are the two faces of fake news. European Commission has conducted a survey about "Fake News" through EU citizens to estimate the awareness and people behaviour related to the appearance of fake news and disinformation on electronic. The findings are quite worrying, since about 40% come across fake news daily and 85% evaluate fake news as a problem. The aim of this work is to introduce an Artificial Intelligence approach, the Decision Trees algorithm to identify fake news on the COVID-19.Acute kidney injury (AKI) is a common and potentially life-threatening condition, which often occurs in the intensive care unit. We propose a machine learning model based on recurrent neural networks to continuously predict AKI. We internally validated its predictive performance, both in terms of discrimination and calibration, and assessed its interpretability. Our model achieved good discrimination (AUC 0.80-0.94). Such a continuous model can support clinicians to promptly recognize and treat AKI patients and may improve their outcomes.In this paper efforts have been made to record the actual, real cost of health care services in a Neonatal Intensive Care Unit (N.I.C.U.) of a public hospital. It is well known that, in recent years, the hospitals have been reimbursed with the system of Diagnosis-Related Groups (D.R.G.'s). The purpose of this study is to determine whether the costs according with D.R.G.'s correspond to the actual-real cost, as this is recorded in the N.I.C.U. This cost is called direct cost. Here is a case study of a premature neonate in the intensive care unit (N.I.C.U.). From the outset, the age of pregnancy, the birth weight, the duration of hospitalization in N.I.C.U. and the needs of the newborn in oxygen, medication, as well as nutrition are defined which are very important in shaping the cost. Then, the cost is calculated according to the D.R.G.'s system. By setting three basic diagnoses (I.C.D.-10), we find the D.R.G. which better describes the case, as well as the associated costs. Then, we calculate the direct cost and list all the consumables, exams, staff costs, overheads. Comparing the two results we find that the cost of D.R.G. does not meet the direct cost of hospitalization. There is a significant deviation from the actual real cost, which proves the under-costing of the health services. The D.R.G.'s system leads hospitals to increase their financial deficits and provide degraded quality health services. It is necessary to readjust the D.R.G.'s according to the reality and the redefinition of the hospital's reimbursement system to meet the direct - real cost of the health services offered.One of the important questions in the research on neural coding is how the preceding axonal activity affects the signal propagation speed of the following one. We present an approach to solving this problem by introducing a multi-level spike count for activity quantification and fitting a family of linear regression models to the data. The best-achieved score is R2=0.89 and the