Using machine learning of clinical data to diagnose COVID-19: a systematic review and meta-analysis.
BMC Med Inform Decis Mak
; 20(1): 247, 2020 09 29.
Article
in English
| MEDLINE | ID: covidwho-802031
ABSTRACT
BACKGROUND:
The recent Coronavirus Disease 2019 (COVID-19) pandemic has placed severe stress on healthcare systems worldwide, which is amplified by the critical shortage of COVID-19 tests.METHODS:
In this study, we propose to generate a more accurate diagnosis model of COVID-19 based on patient symptoms and routine test results by applying machine learning to reanalyzing COVID-19 data from 151 published studies. We aim to investigate correlations between clinical variables, cluster COVID-19 patients into subtypes, and generate a computational classification model for discriminating between COVID-19 patients and influenza patients based on clinical variables alone.RESULTS:
We discovered several novel associations between clinical variables, including correlations between being male and having higher levels of serum lymphocytes and neutrophils. We found that COVID-19 patients could be clustered into subtypes based on serum levels of immune cells, gender, and reported symptoms. Finally, we trained an XGBoost model to achieve a sensitivity of 92.5% and a specificity of 97.9% in discriminating COVID-19 patients from influenza patients.CONCLUSIONS:
We demonstrated that computational methods trained on large clinical datasets could yield ever more accurate COVID-19 diagnostic models to mitigate the impact of lack of testing. We also presented previously unknown COVID-19 clinical variable correlations and clinical subgroups.Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Pneumonia, Viral
/
Coronavirus Infections
/
Clinical Laboratory Techniques
/
Influenza, Human
/
Machine Learning
Type of study:
Diagnostic study
/
Prognostic study
/
Reviews
/
Systematic review/Meta Analysis
Limits:
Female
/
Humans
/
Male
Language:
English
Journal:
BMC Med Inform Decis Mak
Journal subject:
Medical Informatics
Year:
2020
Document Type:
Article
Affiliation country:
S12911-020-01266-z
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