Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Oct 11;19(20):13016.
doi: 10.3390/ijerph192013016.

Application of a Decision Tree Model to Predict the Outcome of Non-Intensive Inpatients Hospitalized for COVID-19

Massimo Giotta  1 , Paolo Trerotoli  2 , Vincenzo Ostilio Palmieri  3 , Francesca Passerini  3 , Piero Portincasa  3 , Ilaria Dargenio  1 , Jihad Mokhtari  4 , Maria Teresa Montagna  2 , Danila De Vito  4
Affiliations

Application of a Decision Tree Model to Predict the Outcome of Non-Intensive Inpatients Hospitalized for COVID-19

Massimo Giotta et al. Int J Environ Res Public Health. .

Abstract

Many studies have identified predictors of outcomes for inpatients with coronavirus disease 2019 (COVID-19), especially in intensive care units. However, most retrospective studies applied regression methods to evaluate the risk of death or worsening health. Recently, new studies have based their conclusions on retrospective studies by applying machine learning methods. This study applied a machine learning method based on decision tree methods to define predictors of outcomes in an internal medicine unit with a prospective study design. The main result was that the first variable to evaluate prediction was the international normalized ratio, a measure related to prothrombin time, followed by immunoglobulin M response. The model allowed the threshold determination for each continuous blood or haematological parameter and drew a path toward the outcome. The model's performance (accuracy, 75.93%; sensitivity, 99.61%; and specificity, 23.43%) was validated with a k-fold repeated cross-validation. The results suggest that a machine learning approach could help clinicians to obtain information that could be useful as an alert for disease progression in patients with COVID-19. Further research should explore the acceptability of these results to physicians in current practice and analyze the impact of machine learning-guided decisions on patient outcomes.

Keywords: COVID-19; clinical aspect; haematochemical parameters; machine learning; prediction; prognostic markers.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Percentage distribution of patients by length of stay in days and outcome.
Figure 2
Figure 2
Variables’ importance (attribute usage) for the training decision tree model C5.0.
Figure 3
Figure 3
Graphical representation of the decision tree model built on the training dataset with the predicted outcome and the fitting goodness (probability of correct outcome and its complementary value).
Figure 4
Figure 4
Receiver operating characteristic curve to evaluate the fitting of the model.
See this image and copyright information in PMC

References

    1. Lu R., Zhao X., Li J., Niu P., Yang B., Wu H., Wang W., Song H., Huang B., Zhu N., et al. Genomic Characterisation and Epidemiology of 2019 Novel Coronavirus: Implications for Virus Origins and Receptor Binding. Lancet. 2020;395:565–574. doi: 10.1016/S0140-6736(20)30251-8. - DOI - PMC - PubMed
    1. A Novel Coronavirus Genome Identified in a Cluster of Pneumonia Cases—Wuhan, China 2019−2020. [(accessed on 7 March 2022)]. Available online: https://weekly.chinacdc.cn/en/article/id/a3907201-f64f-4154-a19e-4253b45.... - PMC - PubMed
    1. Iser B.P.M., Sliva I., Raymundo V.T., Poleto M.B., Schuelter-Trevisol F., Bobinski F. Suspected COVID-19 Case Definition: A Narrative Review of the Most Frequent Signs and Symptoms among Confirmed Cases. Epidemiol. Serv. Saúde. 2020;29 doi: 10.5123/S1679-49742020000300018. - DOI - PubMed
    1. Ejaz H., Alsrhani A., Zafar A., Javed H., Junaid K., Abdalla A.E., Abosalif K.O.A., Ahmed Z., Younas S. COVID-19 and Comorbidities: Deleterious Impact on Infected Patients. J. Infect. Public Health. 2020;13:1833–1839. doi: 10.1016/j.jiph.2020.07.014. - DOI - PMC - PubMed
    1. Alizadehsani R., Alizadeh Sani Z., Behjati M., Roshanzamir Z., Hussain S., Abedini N., Hasanzadeh F., Khosravi A., Shoeibi A., Roshanzamir M., et al. Risk Factors Prediction, Clinical Outcomes, and Mortality in COVID-19 Patients. J. Med. Virol. 2021;93:2307–2320. doi: 10.1002/jmv.26699. - DOI - PMC - PubMed

Substances