-
Something wrong with this record ?
Artificial-Intelligence-Driven Algorithms for Predicting Response to Corticosteroid Treatment in Patients with Post-Acute COVID-19
V. Myska, S. Genzor, A. Mezina, R. Burget, J. Mizera, M. Stybnar, M. Kolarik, M. Sova, MK. Dutta
Status not-indexed Language English Country Switzerland
Document type Journal Article
Grant support
NU22-A-105
Ministry of Health of the Czech Republic
NLK
Directory of Open Access Journals
from 2011
PubMed Central
from 2011
Europe PubMed Central
from 2011
ProQuest Central
from 2011-01-01
Open Access Digital Library
from 2011-01-01
Open Access Digital Library
from 2011-01-01
ROAD: Directory of Open Access Scholarly Resources
from 2011
- Publication type
- Journal Article MeSH
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.
References provided by Crossref.org
- 000
- 00000naa a2200000 a 4500
- 001
- bmc23009761
- 003
- CZ-PrNML
- 005
- 20230725092218.0
- 007
- ta
- 008
- 230707s2023 sz f 000 0|eng||
- 009
- AR
- 024 7_
- $a 10.3390/diagnostics13101755 $2 doi
- 035 __
- $a (PubMed)37238239
- 040 __
- $a ABA008 $b cze $d ABA008 $e AACR2
- 041 0_
- $a eng
- 044 __
- $a sz
- 100 1_
- $a Myska, Vojtech $u Department of Telecommunications, Faculty of Electrical Engineering and Communications, Brno University of Technology, Technicka 12, 616 00 Brno, Czech Republic $1 https://orcid.org/0000000293642330
- 245 10
- $a Artificial-Intelligence-Driven Algorithms for Predicting Response to Corticosteroid Treatment in Patients with Post-Acute COVID-19 / $c V. Myska, S. Genzor, A. Mezina, R. Burget, J. Mizera, M. Stybnar, M. Kolarik, M. Sova, MK. Dutta
- 520 9_
- $a 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.
- 590 __
- $a NEINDEXOVÁNO
- 655 _2
- $a časopisecké články $7 D016428
- 700 1_
- $a Genzor, Samuel $u Department of Respiratory Medicine, University Hospital Olomouc and Faculty of Medicine and Dentistry, Palacky University Olomouc, I. P. Pavlova 6, 779 00 Olomouc, Czech Republic $1 https://orcid.org/0000000267305832
- 700 1_
- $a Mezina, Anzhelika $u Department of Telecommunications, Faculty of Electrical Engineering and Communications, Brno University of Technology, Technicka 12, 616 00 Brno, Czech Republic $1 https://orcid.org/0000000189656193
- 700 1_
- $a Burget, Radim, $u Department of Telecommunications, Faculty of Electrical Engineering and Communications, Brno University of Technology, Technicka 12, 616 00 Brno, Czech Republic $1 https://orcid.org/0000000318495390 $d 1982- $7 jo2015889385
- 700 1_
- $a Mizera, Jan $u Department of Respiratory Medicine, University Hospital Olomouc and Faculty of Medicine and Dentistry, Palacky University Olomouc, I. P. Pavlova 6, 779 00 Olomouc, Czech Republic $1 https://orcid.org/0000000289524243 $7 xx0238803
- 700 1_
- $a Stybnar, Michal $u Czech National e-Health Center, University Hospital Olomouc, I. P. Pavlova 6, 779 00 Olomouc, Czech Republic $1 https://orcid.org/0000000240994535
- 700 1_
- $a Kolarik, Martin $u Department of Telecommunications, Faculty of Electrical Engineering and Communications, Brno University of Technology, Technicka 12, 616 00 Brno, Czech Republic $1 https://orcid.org/0000000161586162
- 700 1_
- $a Sova, Milan $u Department of Respiratory Diseases and Tuberculosis, University Hospital Brno and Faculty of Medicine and Dentistry, Masaryk University Brno, Jihlavska 340/20, 625 00 Brno, Czech Republic $1 https://orcid.org/0000000285427841
- 700 1_
- $a Dutta, Malay Kishore $u Centre for Advanced Studies, Dr. A. P. J. Abdul Kalam Technical University, Jankipuram Vistar, Lucknow 226021, India $1 https://orcid.org/000000032462737X
- 773 0_
- $w MED00195450 $t Diagnostics (Basel, Switzerland) $x 2075-4418 $g Roč. 13, č. 10 (2023)
- 856 41
- $u https://pubmed.ncbi.nlm.nih.gov/37238239 $y Pubmed
- 910 __
- $a ABA008 $b sig $c sign $y p $z 0
- 990 __
- $a 20230707 $b ABA008
- 991 __
- $a 20230725092215 $b ABA008
- 999 __
- $a ok $b bmc $g 1958492 $s 1196025
- BAS __
- $a 3
- BAS __
- $a PreBMC-PubMed-not-MEDLINE
- BMC __
- $a 2023 $b 13 $c 10 $e 20230516 $i 2075-4418 $m Diagnostics $n Diagnostics $x MED00195450
- GRA __
- $a NU22-A-105 $p Ministry of Health of the Czech Republic
- LZP __
- $a Pubmed-20230707
