BackgroundCompetitions in text mining have been used to measure the performance of automatic text processing solutions against a manually annotated gold standard corpus (GSC). The preparation of the GSC is time-consuming and costly and the final corpus consists at the most of a few thousand documents annotated with a limited set of semantic groups. To overcome these shortcomings, the CALBC project partners (PPs) have produced a large-scale annotated biomedical corpus with four different semantic groups through the harmonisation of annotations from automatic text mining solutions, the first version of the Silver Standard Corpus (SSC-I). The four semantic groups are chemical entities and drugs (CHED), genes and proteins (PRGE), diseases and disorders (DISO) and species (SPE). This corpus has been used for the First CALBC Challenge asking the participants to annotate the corpus with their text processing solutions.ResultsAll four PPs from the CALBC project and in addition, 12 challenge participants (CPs) contributed annotated data sets for an evaluation against the SSC-I. CPs could ignore the training data and deliver the annotations from their genuine annotation system, or could train a machine-learning approach on the provided pre-annotated data. In general, the performances of the annotation solutions were lower for entities from the categories CHED and PRGE in comparison to the identification of entities categorized as DISO and SPE. The best performance over all semantic groups were achieved from two annotation solutions that have been trained on the SSC-I.The data sets from participants were used to generate the harmonised Silver Standard Corpus II (SSC-II), if the participant did not make use of the annotated data set from the SSC-I for training purposes. The performances of the participants’ solutions were again measured against the SSC-II. The performances of the annotation solutions showed again better results for DISO and SPE in comparison to CHED and PRGE.ConclusionsThe SSC-I delivers a large set of annotations (1,121,705) for a large number of documents (100,000 Medline abstracts). The annotations cover four different semantic groups and are sufficiently homogeneous to be reproduced with a trained classifier leading to an average F-measure of 85%. Benchmarking the annotation solutions against the SSC-II leads to better performance for the CPs’ annotation solutions in comparison to the SSC-I.

Background: This systematic review and meta-analysis aimed to assess the association of hypernatremia with the outcomes of COVID-19 patients. Methods: We performed a systematic literature search on PubMed, Google Scholar, and Science Direct until October 2021 and found a total of 131 papers. With meticulous screening finally, 17 papers met the inclusion criteria. COVID-19 patients with sodium levels greater than the reference level were the study population and the outcome of interest was the poor outcome; such as mortality, mechanical ventilation, intensive care unit (ICU) admission, and prolonged hospital stay. The pooled estimate was calculated as the odds ratio (OR). Results: There were 19,032 patients with hypernatremia in the 17 studies included. An overall random effect meta-analysis showed that hypernatremia was associated with mortality (OR: 3.18 [1.61, 6.28], P < .0001, I 2 = 91.99%), prolong hospitalization (OR: 1.97 [1.37, 2.83], P < .001, I 2 = 0.00%) and Ventilation (OR: 5.40 [1.89, 15.42], P < .001, I 2 = 77.35%), ICU admission (OR: 3.99 [0.89, 17.78], P = .07, I 2 = 86.79%). Meta-regression analysis showed the association of age with the ICU outcome of hypernatremia patients. Whereas, other parameters like male, hypertension, chronic kidney disease, and diabetes mellitus did not significantly influence the odds ratio. Conclusion: Hypernatremia was markedly associated with poor outcomes in patients with COVID-19. Hence, a blood ionogram is warranted and special attention must be given to hypernatremia COVID-19 patients.

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This paper introduces Strict Partial Order Networks (SPON), a novel neural network architecture designed to enforce asymmetry and transitive properties as soft constraints. We apply it to induce hypernymy relations by training with is-a pairs. We also present an augmented variant of SPON that can generalize type information learned for in-vocabulary terms to previously unseen ones. An extensive evaluation over eleven benchmarks across different tasks shows that SPON consistently either outperforms or attains the state of the art on all but one of these benchmarks.