Detail publikace

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

MYŠKA, V. GENZOR, S. MEZINA, A. BURGET, R. MIZERA, J. ŠTÝBNAR, M. KOLAŘÍK, M. SOVA, M. DUTTA, M.

Originální název

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

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

personalised medication recommendation algorithms; artificial intelligence; post-COVID syndrome; prediction model; respiratory system; corticosteroids; eHealth

Autoři

MYŠKA, V.; GENZOR, S.; MEZINA, A.; BURGET, R.; MIZERA, J.; ŠTÝBNAR, M.; KOLAŘÍK, M.; SOVA, M.; DUTTA, M.

Vydáno

16. 5. 2023

Nakladatel

MDPI

ISSN

2075-4418

Periodikum

Diagnostics

Ročník

13

Číslo

10

Stát

Švýcarská konfederace

Strany od

1

Strany do

17

Strany počet

17

URL

https://www.mdpi.com/2075-4418/13/10/1755

Plný text v Digitální knihovně

http://hdl.handle.net/11012/212507

BibTex

@article{BUT183516,
  author="Vojtěch {Myška} and Samuel {Genzor} and Anzhelika {Mezina} and Radim {Burget} and Jan {Mizera} and Michal {Štýbnar} and Martin {Kolařík} and Milan {Sova} and Malay Kishore {Dutta}",
  title="Artificial-Intelligence-Driven Algorithms for Predicting Response to Corticosteroid Treatment in Patients with Post-Acute COVID-19",
  journal="Diagnostics",
  year="2023",
  volume="13",
  number="10",
  pages="1--17",
  doi="10.3390/diagnostics13101755",
  issn="2075-4418",
  url="https://www.mdpi.com/2075-4418/13/10/1755"
}