Automated ML Techniques for Predicting COVID-19 Mortality in the ICU
The COVID-19 infection is still a serious threat to public health and healthcare systems. Numerous practical machine learning applications have been investigated in this context to support clinical decision-making, forecast disease severity and admission to the intensive care unit, as well as to predict the demand for hospital beds, equipment, and staff in the future. We retrospectively analyzed demographics, and routine blood biomarkers from consecutive Covid-19 patients admitted to the intensive care unit (ICU) of a public tertiary hospital, during a 17-month period, relative to the outcome, in order to build a prognostic model. We used the Google Vertex AI platform, on the one hand, to evaluate its performance in predicting ICU mortality, and on the other hand to show the ease with which even non-experts can make prognostic models. The model's performance regarding the area under the receiver operating characteristic curve (AUC-ROC) was 0.955. The six highest-ranked predictors of mortality in the prognostic model were age, serum urea, platelets, C-reactive protein, hemoglobin, and SGOT.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:305 |
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| Enthalten in: |
Studies in health technology and informatics - 305(2023) vom: 29. Juni, Seite 517-520 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Sakagianni, Aikaterini [VerfasserIn] |
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| Links: |
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| Themen: |
Artificial intelligence |
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| Anmerkungen: |
Date Completed 03.07.2023 Date Revised 03.07.2023 published: Print Citation Status MEDLINE |
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| doi: |
10.3233/SHTI230547 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM358883415 |
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| 520 | |a The COVID-19 infection is still a serious threat to public health and healthcare systems. Numerous practical machine learning applications have been investigated in this context to support clinical decision-making, forecast disease severity and admission to the intensive care unit, as well as to predict the demand for hospital beds, equipment, and staff in the future. We retrospectively analyzed demographics, and routine blood biomarkers from consecutive Covid-19 patients admitted to the intensive care unit (ICU) of a public tertiary hospital, during a 17-month period, relative to the outcome, in order to build a prognostic model. We used the Google Vertex AI platform, on the one hand, to evaluate its performance in predicting ICU mortality, and on the other hand to show the ease with which even non-experts can make prognostic models. The model's performance regarding the area under the receiver operating characteristic curve (AUC-ROC) was 0.955. The six highest-ranked predictors of mortality in the prognostic model were age, serum urea, platelets, C-reactive protein, hemoglobin, and SGOT | ||
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| 700 | 1 | |a Koufopoulou, Christina |e verfasserin |4 aut | |
| 700 | 1 | |a Kalles, Dimitrios |e verfasserin |4 aut | |
| 700 | 1 | |a Loupelis, Evangelos |e verfasserin |4 aut | |
| 700 | 1 | |a Verykios, Vassilios S |e verfasserin |4 aut | |
| 700 | 1 | |a Feretzakis, Georgios |e verfasserin |4 aut | |
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