Building a predictive model to identify clinical indicators for COVID-19 using machine learning method.
Med Biol Eng Comput
; 60(6): 1763-1774, 2022 Jun.
Article
in English
| MEDLINE | ID: covidwho-1803060
ABSTRACT
Although some studies tried to identify risk factors for COVID-19, the evidence comparing COVID-19 and community-acquired pneumonia (CAP) is inconclusive, and CAP is the most common pneumonia with similar symptoms as COVID-19. We conducted a case-control study with 35 routine-collected clinical indicators and demographic factors to identify predictors for COVID-19 with CAP as controls. We randomly split the dataset into a training set (70%) and testing set (30%). We built Explainable Boosting Machine to select the important factors and built a decision tree on selected variables to interpret their relationships. The top five individual predictors of COVID-19 are albumin, total bilirubin, monocyte count, alanine aminotransferase, and percentage of monocyte with the importance scores ranging from 0.078 to 0.567. The top systematic predictors for COVID-19 are liver function, monocyte increasing, plasma protein, granulocyte, and renal function (importance scores ranging 0.009-0.096). We identified five combinations of important indicators to screen COVID-19 patients from CAP patients with differentiating abilities ranging 83.3-100%. An online predictive tool for our model was published. Certain clinical indicators collected routinely from most hospitals could help screen and distinguish COVID-19 from CAP. While further verification is needed, our findings and predictive tool could help screen suspected COVID-19 cases.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Pneumonia
/
COVID-19
Type of study:
Diagnostic study
/
Experimental Studies
/
Observational study
/
Prognostic study
/
Randomized controlled trials
/
Systematic review/Meta Analysis
Limits:
Humans
Language:
English
Journal:
Med Biol Eng Comput
Year:
2022
Document Type:
Article
Affiliation country:
S11517-022-02568-2
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