A causal learning framework for the analysis and interpretation of COVID-19 clinical data. (arXiv:2105.06998v1 [cs.LG])

We present a workflow for clinical data analysis that relies on Bayesian
Structure Learning (BSL), an unsupervised learning approach, robust to noise
and biases, that allows to incorporate prior medical knowledge into the
learning process and that provides explainable results in the form of a graph
showing the causal connections among the analyzed features. The workflow
consists in a multi-step approach that goes from identifying the main causes of
patient’s outcome through BSL, to the realization of a tool suitable for
clinical practice, based on a Binary Decision Tree (BDT), to recognize patients
at high-risk with information available already at hospital admission time. We
evaluate our approach on a feature-rich COVID-19 dataset, showing that the
proposed framework provides a schematic overview of the multi-factorial
processes that jointly contribute to the outcome. We discuss how these
computational findings are confirmed by current understanding of the COVID-19
pathogenesis. Further, our approach yields to a highly interpretable tool
correctly predicting the outcome of 85% of subjects based exclusively on 3
features: age, a previous history of chronic obstructive pulmonary disease and
the PaO2/FiO2 ratio at the time of arrival to the hospital. The inclusion of
additional information from 4 routine blood tests (Creatinine, Glucose, pO2 and
Sodium) increases predictive accuracy to 94.5%.

Source: https://arxiv.org/abs/2105.06998


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