Predicting Patient COVID-19 Disease Severity by means of Statistical and Machine Learning Analysis of Clinical Blood Testing Data.

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Abstract

An accurate prediction of COVID-19 patient disease severity would greatly improve care delivery and resource allocation, and thereby reduce mortality risks, especially in less developed countries. There are many patient-related factors, such as pre-existing comorbidities that affect disease severity that could be used to aid prediction.
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.
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.
Our work revealed several clinical parameters measurable in blood samples as factors that can discriminate between healthy people and COVID-19 positive patients, and showed their value in predicting later severity of COVID-19 symptoms. We thus developed a number of analytical methods that showed accuracy and precision scores for disease severity predictions as above 90%.
We developed methodologies to analyse patient routine clinical data which enables more accurate prediction of COVID-19 patient outcomes. This type of approach 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 optimised hospital facility for COVID-19 treatment.