Does COVID-19 Clinical Status Associate with Outcome Severity? An Unsupervised Machine Learning Approach for Knowledge Extraction.

Eleni Karlafti,Chrisovalantis Kakanis,Georgia Dimitriadou,Katerina Eufraimidou,Michail Anthopoulos,Kristine Kasarjyan,Ariadni Eufraimidou,Christos Savopoulos,Michaela Chrysanthi Vittoraki,Georgia Kaiafa,Athanasios Anagnostis,Evangelia Kotzakioulafi,Triantafyllos Didangelos

doi:10.3390/jpm11121380

Abstract

Since the beginning of the COVID-19 pandemic, 195 million people have been infected and 4.2 million have died from the disease or its side effects. Physicians, healthcare scientists and medical staff continuously try to deal with overloaded hospital admissions, while in parallel, they try to identify meaningful correlations between the severity of infected patients with their symptoms, comorbidities and biomarkers. Artificial intelligence (AI) and machine learning (ML) have been used recently in many areas related to COVID-19 healthcare. The main goal is to manage effectively the wide variety of issues related to COVID-19 and its consequences. The existing applications of ML to COVID-19 healthcare are based on supervised classifications which require a labeled training dataset, serving as reference point for learning, as well as predefined classes. However, the existing knowledge about COVID-19 and its consequences is still not solid and the points of common agreement among different scientific communities are still unclear. Therefore, this study aimed to follow an unsupervised clustering approach, where prior knowledge is not required (tabula rasa). More specifically, 268 hospitalized patients at the First Propaedeutic Department of Internal Medicine of AHEPA University Hospital of Thessaloniki were assessed in terms of 40 clinical variables (numerical and categorical), leading to a high-dimensionality dataset. Dimensionality reduction was performed by applying a principal component analysis (PCA) on the numerical part of the dataset and a multiple correspondence analysis (MCA) on the categorical part of the dataset. Then, the Bayesian information criterion (BIC) was applied to Gaussian mixture models (GMM) in order to identify the optimal number of clusters under which the best grouping of patients occurs. The proposed methodology identified four clusters of patients with similar clinical characteristics. The analysis revealed a cluster of asymptomatic patients that resulted in death at a rate of 23.8%. This striking result forces us to reconsider the relationship between the severity of COVID-19 clinical symptoms and the patient’s mortality.

Highlights

Disease outbreaks have overwhelmed humanity several times throughout world history
The aim of this study is to propose a methodology that infers clusters of COVID-19 infected patients with deep correlations and underlying common characteristics, and extracts knowledge based on their features
The algorithm was entirely programmed in Python 3.8 with the use of Numpy and Pandas libraries for mathematical operations and table manipulation, Scikit Learn and Pandas libraries for mathematical operations and table manipulation, Scikit Learn for for data normalization and unsupervised clustering algorithms, Prince library for the data normalization and unsupervised clustering algorithms, Prince library for the principrincipal component analysis and Matplotlib for visualization

Summary

Introduction

Disease outbreaks have overwhelmed humanity several times throughout world history. The latest one, a new type of coronavirus, SARS-CoV-2, appeared in China during the last months of 2019, and rapidly spread worldwide [1]. Health Organization (WHO) appointed the name COVID-19 to the disease that was caused from this kind of coronavirus, and in March 2020, declared COVID-19 a global pandemic [2]. As a result of the international concern, public health and all scientific interests focused on the global fight against the pandemic [3]. COVID-19 is a disease with multiple clinical manifestations of different severity and in a number of cases, with rapid development [4]. The development of the disease is not predictable, and the prompt identification of severe patient cases is of great importance, due to the chance of early intervention, especially in patients with lethal outcomes. The financial poverty of the health systems increased mortality during the COVID-19 pandemic [8]

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