Clinical Hematochemical Parameters in Differential Diagnosis between Pediatric SARS-CoV-2 and Influenza Virus Infection: An Automated Machine Learning Approach

Abstract

Background: The influenza virus and the novel beta coronavirus (SARS-CoV-2) have similar transmission characteristics, and it is very difficult to distinguish them clinically. With the development of information technologies, novel opportunities have arisen for the application of intelligent software systems in disease diagnosis and patient triage. Methods: A cross-sectional study was conducted on 268 infants: 133 infants with a SARS-CoV-2 infection and 135 infants with an influenza virus infection. In total, 10 hematochemical variables were used to construct an automated machine learning model. Results: An accuracy range from 53.8% to 60.7% was obtained by applying support vector machine, random forest, k-nearest neighbors, logistic regression, and neural network models. Alternatively, an automated model convincingly outperformed other models with an accuracy of 98.4%. The proposed automated algorithm recommended a random tree model, a randomization-based ensemble method, as the most appropriate for the given dataset. Conclusions: The application of automated machine learning in clinical practice can contribute to more objective, accurate, and rapid diagnosis of SARS-CoV-2 and influenza virus infections in children.

Diagnostic value of routine blood tests in differentiating between SARS-CoV-2, influenza A, and RSV infections in hospitalized children: a retrospective study

“…AI’s ability to analyze data, recognize patterns, and process datasets may bolster the accuracy of risk evaluations. Amid the COVID-19 crisis, successful initiatives have employed automated machine learning to distinguish between influenza virus infections and SARS-CoV-2 [ 27 ], as well as promptly identifying COVID-19 in children [ 28 , 29 ]. Advances have also been made in utilizing decision tree models based on hemogram outcomes to distinguish between RSV and COVID-19 in infants [ 30 ].…”

Rapid Triage of Children with Suspected COVID-19 Using Laboratory-Based Machine-Learning Algorithms

“…RF overcomes these limitations by aggregating the prediction results of hundreds of individual trees [ 21 ]. Therefore, the RF model has been widely used during the COVID-19 pandemic for disease diagnosis [ 28 , 29 ], predicting patient outcomes [ 30 , 31 , 32 ], recommending hospitalization [ 33 ], processing of healthcare and travel data to identify COVID-infected people [ 34 ], etc.…”

Can mean platelet volume be a prognosis predictor in viral infections: An example of Covid-19

“…It is paramount to comprehend how the correlation between hematologic parameters such as MPV, PCT, and NLR may be applied in future clinical contexts. For instance, a study incorporating these biomarkers into various artificial intelligence algorithms revealed that the algorithm excelled in terms of diagnostic accuracy and triage speed [ 38 ]. Another study found that an automated testing device capable of sampling and evaluating results autonomously could offer rapid and effective results, particularly for screening large groups (e.g., healthcare workers) [ 39 ].…”

“…For instance, a study incorporating these biomarkers into various artificial intelligence algorithms revealed that the algorithm excelled in terms of diagnostic accuracy and triage speed [ 38 ]. Another study found that an automated testing device capable of sampling and evaluating results autonomously could offer rapid and effective results, particularly for screening large groups (e.g., healthcare workers) [ 39 ]. Additionally, it holds significant value to consider how these findings might be integrated into routine clinical practice.…”

“…Although it is diagnosed serologically, additional parameters are needed in addition to radiological imaging in treatment follow-up and prognosis. While rapid tests and PCR measurements hold diagnostic value, it is essential to acknowledge that they, in isolation, do not suffice for triage and prognostic follow-up [ 4 , 5 ]. Simultaneously, as the number of patients rapidly increases, the burden on laboratories can impede their functioning.…”