Background: Quantitative real-time reverse transcription PCR (qRT-PCR) is a useful tool for assessing gene expression in different tissues, but the choice of adequate controls is critical to normalise the results, thereby avoiding differences and maximizing sensitivity and accuracy. So far, many genes have been used as a single reference gene, without having previously verified their value as controls. This practice can lead to incorrect conclusions and recent evidence indicates a need to use the geometric mean of data from several control genes. Here, we identified an appropriate set of genes to be used as an endogenous reference for quantifying gene expression in human heart tissue.
In this paper we apply a series of Machine Learning models to a recently published unique dataset on the mortality of COVID19 patients. We use a dataset consisting of blood samples of 375 patients admitted to a hospital in the region of Wuhan, China. There are 201 patients who survived hospitalisation and 174 patients who died whilst in hospital. The focus of the paper is not only on seeing which Machine Learning model is able to obtain the absolute highest accuracy but more on the interpretation of what the Machine Learning models provides. We find that
age
,
days in hospital
,
Lymphocyte
and
Neutrophils
are important and robust predictors when predicting a patients mortality. Furthermore, the algorithms we use allows us to observe the marginal impact of each variable on a case-by-case patient level, which might help practicioneers to easily detect anomalous patterns. This paper analyses the
global
and
local
interpretation of the Machine Learning models on patients with COVID19.
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