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. selleck kinase inhibitor 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 comparison of different models indicates the importance of long and very short nerve fiber memory. Further studies are required to understand the complex axonal mechanisms responsible for the discovered phenomena.Studies investigating the suitability of SNOMED CT in COVID-19 datasets are still scarce. The purpose of this study was to evaluate the suitability of SNOMED CT for structured searches of COVID-19 studies, using the German Corona Consensus Dataset (GECCO) as example. Suitability of the international standard SNOMED CT was measured with the scoring system ISO/TS 21564, and intercoder reliability of two independent mapping specialists was evaluated. The resulting analysis showed that the majority of data items had either a complete or partial equivalent in SNOMED CT (complete equivalent 141 items; partial equivalent 63 items; no equivalent 1 item). Intercoder reliability was moderate, possibly due to non-establishment of mapping rules and high percentage (74%) of different but similar concepts among the 86 non-equal chosen concepts. The study shows that SNOMED CT can be utilized for COVID-19 cohort browsing. However, further studies investigating mapping rules and further international terminologies are necessary.Automated text classification is a natural language processing (NLP) technology that could significantly facilitate scientific literature selection. A specific topical dataset of 630 article abstracts was obtained from the PubMed database. We proposed 27 parametrized options of PubMedBERT model and 4 ensemble models to solve a binary classification task on that dataset. Three hundred tests with resamples were performed in each classification approach. The best PubMedBERT model demonstrated F1-score = 0.857 while the best ensemble model reached F1-score = 0.853. We concluded that the short scientific texts classification quality might be improved using the latest state-of-art approaches.During the current COVID-19 pandemic, the rapid availability of profound information is crucial in order to derive information about diagnosis, disease trajectory, treatment or to adapt the rules of conduct in public. The increased importance of preprints for COVID-19 research initiated the design of the preprint search engine preVIEW. Conceptually, it is a lightweight semantic search engine focusing on easy inclusion of specialized COVID-19 textual collections and provides a user friendly web interface for semantic information retrieval. In order to support semantic search functionality, we integrated a text mining workflow for indexing with relevant terminologies. Currently, diseases, human genes and SARS-CoV-2 proteins are annotated, and more will be added in future. The system integrates collections from several different preprint servers that are used in the biomedical domain to publish non-peer-reviewed work, thereby enabling one central access point for the users. In addition, our service offers facet searching, export functionality and an API access.