Severity detection of COVID-19 infection with machine learning of clinical records and CT images.
Technol Health Care
; 30(6): 1299-1314, 2022.
Artículo
en Inglés
| MEDLINE | ID: covidwho-2154631
ABSTRACT
BACKGROUND:
Coronavirus disease 2019 (COVID-19) is a deadly viral infection spreading rapidly around the world since its outbreak in 2019. In the worst case a patient's organ may fail leading to death. Therefore, early diagnosis is crucial to provide patients with adequate and effective treatment.OBJECTIVE:
This paper aims to build machine learning prediction models to automatically diagnose COVID-19 severity with clinical and computed tomography (CT) radiomics features.METHOD:
P-V-Net was used to segment the lung parenchyma and then radiomics was used to extract CT radiomics features from the segmented lung parenchyma regions. Over-sampling, under-sampling, and a combination of over- and under-sampling methods were used to solve the data imbalance problem. RandomForest was used to screen out the optimal number of features. Eight different machine learning classification algorithms were used to analyze the data.RESULTS:
The experimental results showed that the COVID-19 mild-severe prediction model trained with clinical and CT radiomics features had the best prediction results. The accuracy of the GBDT classifier was 0.931, the ROUAUC 0.942, and the AUCPRC 0.694, which indicated it was better than other classifiers.CONCLUSION:
This study can help clinicians identify patients at risk of severe COVID-19 deterioration early on and provide some treatment for these patients as soon as possible. It can also assist physicians in prognostic efficacy assessment and decision making.Palabras clave
Texto completo:
Disponible
Colección:
Bases de datos internacionales
Base de datos:
MEDLINE
Asunto principal:
COVID-19
Tipo de estudio:
Estudio observacional
/
Estudio pronóstico
Límite:
Humanos
Idioma:
Inglés
Revista:
Technol Health Care
Asunto de la revista:
Ingenieria Biomédica
/
Servicios de Salud
Año:
2022
Tipo del documento:
Artículo
País de afiliación:
THC-220321
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