Artificial-Intelligence-Driven Algorithms for Predicting Response to Corticosteroid Treatment in Patients with Post-Acute COVID-19

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F65269705%3A_____%2F23%3A00078064" target="_blank" >RIV/65269705:_____/23:00078064 - isvavai.cz</a>

Alternative codes found

RIV/00216224:14110/23:00133302 RIV/61989592:15110/23:73619215 RIV/00216305:26220/23:PU148163 RIV/00098892:_____/23:10157945

Result on the web

<a href="https://www.mdpi.com/2075-4418/13/10/1755" target="_blank" >https://www.mdpi.com/2075-4418/13/10/1755</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/diagnostics13101755" target="_blank" >10.3390/diagnostics13101755</a>

Result language

angličtina

Original language name

Artificial-Intelligence-Driven Algorithms for Predicting Response to Corticosteroid Treatment in Patients with Post-Acute COVID-19

Original language description

Pulmonary fibrosis is one of the most severe long-term consequences of COVID-19. Corticosteroid treatment increases the chances of recovery; unfortunately, it can also have side effects. Therefore, we aimed to develop prediction models for a personalized selection of patients benefiting from corticotherapy. The experiment utilized various algorithms, including Logistic Regression, k-NN, Decision Tree, XGBoost, Random Forest, SVM, MLP, AdaBoost, and LGBM. In addition easily human-interpretable model is presented. All algorithms were trained on a dataset consisting of a total of 281 patients. Every patient conducted an examination at the start and three months after the post-COVID treatment. The examination comprised a physical examination, blood tests, functional lung tests, and an assessment of health state based on X-ray and HRCT. The Decision tree algorithm achieved balanced accuracy (BA) of 73.52%, ROC-AUC of 74.69%, and 71.70% F1 score. Other algorithms achieving high accuracy included Random Forest (BA 70.00%, ROC-AUC 70.62%, 67.92% F1 score) and AdaBoost (BA 70.37%, ROC-AUC 63.58%, 70.18% F1 score). The experiments prove that information obtained during the initiation of the post-COVID-19 treatment can be used to predict whether the patient will benefit from corticotherapy. The presented predictive models can be used by clinicians to make personalized treatment decisions.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

30218 - General and internal medicine

Project

<a href="/en/project/NU22-A-105" target="_blank" >NU22-A-105: Predicitve biomarkers of therapeutic response on COVID-19 therapy</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Diagnostics

ISSN

2075-4418

e-ISSN

2075-4418

Volume of the periodical

13

Issue of the periodical within the volume

10

Country of publishing house

CH - SWITZERLAND

Number of pages

17

Pages from-to

1755

UT code for WoS article

000998282700001

EID of the result in the Scopus database

2-s2.0-85160525296