Predicting Mortality in COVID-19 Patients Using 6 Machine Learning Algorithms...21st International Conference on Informatics, Management, and Technology in Healthcare (ICIMTH), July 1-3, 2023, Athens, Greece.
In late 2019, COVID-19 appeared and has since spread worldwide as the new pandemic, causing more than 6 million deaths. In dealing with this global crisis, the contribution of Artificial Intelligence was also important through the possibilities of creating predictive models through Machine Learning...
| Publicado en: | Studies in Health Technology & Informatics Vol. 305; pp. 115 - 119 |
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| Autores principales: | , , , |
| Formato: | proceedings research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=164789446&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 164789446 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2023 vid: 305 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 164789446 164789446 164789446 10.3233/SHTI230437 164789446 ppf: 115 ppct: 4 formats: tig: atl: Predicting Mortality in COVID-19 Patients Using 6 Machine Learning Algorithms...21st International Conference on Informatics, Management, and Technology in Healthcare (ICIMTH), July 1-3, 2023, Athens, Greece. aug: au: KOURMPANIS, Nikolaos LIASKOS, Joseph ZOULIAS, Emmanouil MANTAS, John affil: Health Informatics Laboratory, Faculty of Nursing, National and Kapodistrian University of Athens, Athens, Greece. sug: subj: Prediction Models COVID-19 Mortality COVID-19 Prognosis Algorithms Utilization Clinical Prediction Rules Human Greece Male Female Logistic Regression Random Forest Decision Trees Precision Memory Patient Preference Disease Attributes Decision Making Congresses and Conferences Greece Male Female ab: In late 2019, COVID-19 appeared and has since spread worldwide as the new pandemic, causing more than 6 million deaths. In dealing with this global crisis, the contribution of Artificial Intelligence was also important through the possibilities of creating predictive models through Machine Learning algorithms, which are already successfully applied to solving a multitude of problems, for many scientific fields. This work aims to find the best model for predicting the mortality of patients with COVID-19, through the comparison of 6 classification algorithms, i.e. Logistic Regression, Decision Trees, Random Forest, eXtreme Gradient Boosting, Multi-Layer Perceptrons, K- Nearest Neighbors. We used a dataset containing more than 12 million cases which was cleansed, modified, and tested for each model. The best model is XGBoost (Precision: 0.93764, Recall: 0.95472, F1- score: 0.9113, AUC_ROC: 0.97855 and Runtime: 6.67306 sec), which is recommended for the prediction and priority treatment of patients with high mortality risk. pubtype: Academic Journal doctype: proceedings research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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