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Accepted for/Published in: JMIR Formative Research

Date Submitted: Sep 23, 2022
Date Accepted: Jan 18, 2023
Date Submitted to PubMed: Jan 20, 2023

The final, peer-reviewed published version of this preprint can be found here:

Multidimensional Machine Learning for Assessing Parameters Associated With COVID-19 in Vietnam: Validation Study

Nguyen TT, Ho TC, Bui HTT, Ho LK, Ta VT

Multidimensional Machine Learning for Assessing Parameters Associated With COVID-19 in Vietnam: Validation Study

JMIR Form Res 2023;7:e42895

DOI: 10.2196/42895

PMID: 36668902

PMCID: 9937111

Multidimensional machine learning on 2173 COVID-19 patients in Vietnam: Retro-prospective Validation Study

  • Tue Trong Nguyen; 
  • Tu Cam Ho; 
  • Huong Thi Thu Bui; 
  • Lam Khanh Ho; 
  • Van Thanh Ta

ABSTRACT

Background:

Machine learning (ML) is a part of the Artificial Intelligence strategy. Its algorithms are imputed on Big Data sets to see patterns, learn from their results, and perform tasks autonomously without being instructed on how to address the problem. New diseases like Sars-Cov2 are important data stores for machine learning. Therefore, all relevant parameters should be explicitly quantified and modeled.

Objective:

The purpose of the study was to determine (a) the overall preclinical character; (b) the cumulative cutoff values and the risk ratio, and (c) the factors associated with severity by a unidimensional and multidimensional analysis on 2173 Sars-Cov2 patients.

Methods:

The machine learning study population consisted of 2173 patients (1587 mild and non-symptoms patients, 377 moderate patients, 209 severe patients). The status of the patients was recorded from September 2021 to March 2022.

Results:

The Covid19 Severity directly links with a significant correlation to Age, Score index of the chest X-ray, percentage and quantity of neutrophils, Albumin, C reactive protein, and ratio of Lymphocytes. Their important cut off values (from regression analysis) respectively are: 77.56 years old (the mild-moderate group), 5.53 (the mild-moderate group) and 10.51 (the moderate-severe group), 84.80% (the mild-moderate group) and 87.74%(the moderate-severe group), 11.77G/L (the moderate-severe group), 29.73g/L (the moderate-severe group), 7.46mg/dL (the mild-moderate group), 6.32% (the moderate-severe group). Their significant (p<0.0001) R score correlation with the severity of Covid19, are: 0.44, 0.52 and 0.52, 0.33 and 0.44, 0.42, -0.43, 0.40, -0.41. Their significant risk ratio (p<0.00001) from the meta-analysis, respectively are: 4.19 [3.58-4.95], 3.29 [2.76-3.92] and 3.03 [2.4023;3.8314], 3.18 [2.73-3.70] and 3.32 [2.6480;4.1529], 3.15 [2.6153;3.8025], 3.4[2.91-3.97], 0.46 [0.3650;0.5752] (p<0.00001), 0.34 [0.2743;0.4210]. The pair ALT – Leucocytes and Transferrin – Anion Chloride get the most important correlation shift. ALT – Leucocytes show the important negative link (R=-1, p<0.00001) in the mild group to the significant positive correlation in the moderate group (R=1, p<0.00001). Transferrin–anion Chloride has an important positive association (R=1, p<0.00001) in the mild group with a significant negative correlation in the moderate group (R=-0.59, p<0.00001). The network map and HCA show that the mild-moderate group, the closest neighbors with the Covid19 severity are ferritins, Age. Then there is C-reactive protein, SI of X-ray, Albumin, and Lactate dehydrogenase, which are the next close neighbors of these three factors. In the moderate-severe group, the closest neighbors with the Covid19 severity are Ferritin, Fibrinogen, Albumin, the quantity of Lymphocytes, SI of X-ray, white blood cells count, Lactate dehydrogenase, and quantity of neutrophils.

Conclusions:

Complete multidimensional study in 2173 Covid19 patients in Vietnam shows the whole picture of all the preclinical factors, which may become the clinical reference marker for surveillance and diagnostic management.


 Citation

Please cite as:

Nguyen TT, Ho TC, Bui HTT, Ho LK, Ta VT

Multidimensional Machine Learning for Assessing Parameters Associated With COVID-19 in Vietnam: Validation Study

JMIR Form Res 2023;7:e42895

DOI: 10.2196/42895

PMID: 36668902

PMCID: 9937111

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