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

J 2023

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

MYSKA, Vojtech, Samuel GENZOR, Anzhelika MEZINA, Radim BURGET, Jan MIZERA et. al.

Základní údaje

Originální název

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

Autoři

MYSKA, Vojtech (203 Česká republika, garant), Samuel GENZOR (203 Česká republika), Anzhelika MEZINA (203 Česká republika), Radim BURGET (203 Česká republika), Jan MIZERA (203 Česká republika), Michal STYBNAR (203 Česká republika), Martin KOLARIK (203 Česká republika), Milan SOVA (203 Česká republika, domácí) a Malay Kishore DUTTA

Vydání

Diagnostics, Basel, MDPI, 2023, 2075-4418

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

30203 Respiratory systems

Stát vydavatele

Švýcarsko

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 3.600 v roce 2022

Kód RIV

RIV/00216224:14110/23:00133302

Organizační jednotka

Lékařská fakulta

DOI

http://dx.doi.org/10.3390/diagnostics13101755

UT WoS

000998282700001

Klíčová slova anglicky

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

Štítky

14110215, rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 5. 4. 2024 09:21, Mgr. Tereza Miškechová

Anotace

V originále

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.

Citovat

MYSKA, Vojtech, Samuel GENZOR, Anzhelika MEZINA, Radim BURGET, Jan MIZERA, Michal STYBNAR, Martin KOLARIK, Milan SOVA a Malay Kishore DUTTA. Artificial-Intelligence-Driven Algorithms for Predicting Response to Corticosteroid Treatment in Patients with Post-Acute COVID-19. Diagnostics. Basel: MDPI, 2023, roč. 13, č. 10, s. 1-17. ISSN 2075-4418. Dostupné z: https://dx.doi.org/10.3390/diagnostics13101755.

@article{2367201,
   author = {Myska, Vojtech and Genzor, Samuel and Mezina, Anzhelika and Burget, Radim and Mizera, Jan and Stybnar, Michal and Kolarik, Martin and Sova, Milan and Dutta, Malay Kishore},
   article_location = {Basel},
   article_number = {10},
   doi = {http://dx.doi.org/10.3390/diagnostics13101755},
   keywords = {personalised medication recommendation algorithms; artificial intelligence; post-COVID syndrome; prediction model; respiratory system; corticosteroids; eHealth},
   language = {eng},
   issn = {2075-4418},
   journal = {Diagnostics},
   title = {Artificial-Intelligence-Driven Algorithms for Predicting Response to Corticosteroid Treatment in Patients with Post-Acute COVID-19},
   url = {https://www.mdpi.com/2075-4418/13/10/1755},
   volume = {13},
   year = {2023}
}

TY  - JOUR
ID  - 2367201
AU  - Myska, Vojtech - Genzor, Samuel - Mezina, Anzhelika - Burget, Radim - Mizera, Jan - Stybnar, Michal - Kolarik, Martin - Sova, Milan - Dutta, Malay Kishore
PY  - 2023
TI  - Artificial-Intelligence-Driven Algorithms for Predicting Response to Corticosteroid Treatment in Patients with Post-Acute COVID-19
JF  - Diagnostics
VL  - 13
IS  - 10
SP  - 1-17
EP  - 1-17
PB  - MDPI
SN  - 20754418
KW  - personalised medication recommendation algorithms
KW  - artificial intelligence
KW  - post-COVID syndrome
KW  - prediction model
KW  - respiratory system
KW  - corticosteroids
KW  - eHealth
UR  - https://www.mdpi.com/2075-4418/13/10/1755
N2  - 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.
ER  -

MYSKA, Vojtech, Samuel GENZOR, Anzhelika MEZINA, Radim BURGET, Jan MIZERA, Michal STYBNAR, Martin KOLARIK, Milan SOVA a Malay Kishore DUTTA. Artificial-Intelligence-Driven Algorithms for Predicting Response to Corticosteroid Treatment in Patients with Post-Acute COVID-19. \textit{Diagnostics}. Basel: MDPI, 2023, roč.~13, č.~10, s.~1-17. ISSN~2075-4418. Dostupné z: https://dx.doi.org/10.3390/diagnostics13101755.

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