Informace o publikaci

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

Autoři

MYSKA Vojtech GENZOR Samuel MEZINA Anzhelika BURGET Radim MIZERA Jan STYBNAR Michal KOLARIK Martin SOVA Milan DUTTA Malay Kishore

Rok publikování 2023
Druh Článek v odborném periodiku
Časopis / Zdroj Diagnostics
Fakulta / Pracoviště MU

Lékařská fakulta

Citace
www https://www.mdpi.com/2075-4418/13/10/1755
Doi http://dx.doi.org/10.3390/diagnostics13101755
Klíčová slova personalised medication recommendation algorithms; artificial intelligence; post-COVID syndrome; prediction model; respiratory system; corticosteroids; eHealth
Popis 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.

Používáte starou verzi internetového prohlížeče. Doporučujeme aktualizovat Váš prohlížeč na nejnovější verzi.

Další info