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Building a predictive model to identify clinical indicators for COVID-19 using machine learning method.

Xinlei Deng¹, Han Li², Xin Liao³

¹Department of Environmental Health Sciences, School of Public Health, University at Albany, State University of New York, Rensselaer, NY, USA.

Medical & Biological Engineering & Computing

|April 26, 2022

Summary

This summary is machine-generated.

Routine clinical indicators can help differentiate COVID-19 from community-acquired pneumonia (CAP). Albumin, liver function, and monocyte counts are key predictors, aiding in early screening of suspected COVID-19 cases.

Keywords:

COVID-19 Community-acquired pneumonia Machine learning Predictor

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Area of Science:

Medical research
Infectious disease diagnostics
Clinical pathology

Background:

Distinguishing COVID-19 from community-acquired pneumonia (CAP) is challenging due to overlapping symptoms.
Existing research on risk factors for COVID-19 compared to CAP remains inconclusive.

Purpose of the Study:

To identify clinical indicators that predict COVID-19 in patients presenting with pneumonia symptoms.
To differentiate COVID-19 from CAP using routine clinical data.

Main Methods:

A case-control study utilizing 35 routine clinical and demographic factors.
Data split into training (70%) and testing (30%) sets.
Explainable Boosting Machine for feature selection and decision tree for relationship interpretation.

Main Results:

Top individual predictors for COVID-19 include albumin, total bilirubin, monocyte count, and alanine aminotransferase.
Systematic predictors involve liver function, monocyte levels, plasma protein, granulocyte count, and renal function.
Identified five indicator combinations with 83.3-100% ability to screen COVID-19 from CAP.

Conclusions:

Routine clinical indicators are valuable for screening and distinguishing COVID-19 from CAP.
An online predictive tool has been developed based on these findings.
Further verification is recommended, but the tool can assist in screening suspected COVID-19 cases.