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