English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

For COVID-19 patients accurate prediction of disease severityand mortality risk would greatly improve care delivery and resource allocation.There are many patient-related factors, such as pre-existing comorbidities thataffect disease severity. Since rapid automated profiling of peripheral bloodsamples is widely available, we investigated how such data from the peripheralblood of COVID-19 patients might be used to predict clinical outcomes.  Methods: We thus investigated such clinical datasets from COVID-19 patientswith known outcomes by combining statistical comparison and correlation methodswith machine learning algorithms; the latter included decision tree, randomforest, variants of gradient boosting machine, support vector machine,K-nearest neighbour and deep learning methods.  Results: Our work revealed several clinical parameters measurable in bloodsamples, which discriminated between healthy people and COVID-19 positivepatients and showed predictive value for later severity of COVID-19 symptoms.We thus developed a number of analytic methods that showed accuracy andprecision for disease severity and mortality outcome predictions that wereabove 90%.  Conclusions: In sum, we developed methodologies to analyse patient routineclinical data which enables more accurate prediction of COVID-19 patientoutcomes. This type of approaches could, by employing standard hospitallaboratory analyses of patient blood, be utilised to identify, COVID-19patients at high risk of mortality and so enable their treatment to beoptimised.

Predicting Patient COVID-19 Disease Severity by means of Statistical and Machine Learning Analysis of Blood Cell Transcriptome Data