A causal learning framework for the analysis and interpretation of COVID-19 clinical data

TL;DR

This paper introduces a Bayesian structure learning workflow for analyzing COVID-19 clinical data, providing causal insights and an interpretable predictive tool with high accuracy based on minimal features.

Contribution

It presents a novel, noise-robust causal learning framework that integrates prior medical knowledge and yields an explainable, high-accuracy clinical prediction tool for COVID-19 outcomes.

Findings

The framework accurately predicts COVID-19 outcomes with 85% accuracy using 3 features.

Adding 4 routine blood tests increases accuracy to 94.5%.

The causal graph aligns with current COVID-19 pathogenesis understanding.

Abstract

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%.