ArticleClinical nutrition ESPEN2022
Machine learning approach for automated predicting of COVID-19 severity based on clinical and paraclinical characteristics: Serum levels of zinc, calcium, and vitamin D.
Article in Clinical nutrition ESPEN, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
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- Generation of tissues outside the field of view (FOV) of radiation therapy simulation imaging based on machine learning and patient body outline (PBO).Radiation oncology (London, England) · 2024Article
- Assessment of Some Risk Factors and Biological Predictors in the Post COVID-19 Syndrome in Asthmatic Patients.Journal of personalized medicine · 2023Article
- Multi-parametric radiomics of conventional T1 weighted and susceptibility-weighted imaging for differential diagnosis of idiopathic Parkinson's disease and multiple system atrophy.BMC medical imaging · 2023Article
- Using machine learning-based radiomics to differentiate between glioma and solitary brain metastasis from lung cancer and its subtypes.Discover oncology · 2023Article
- A radiomics based approach using adrenal gland and periadrenal fat CT images to allocate COVID-19 health care resources fairly.BMC medical imaging · 2023Article
- Development and external validation of the multichannel deep learning model based on unenhanced CT for differentiating fat-poor angiomyolipoma from renal cell carcinoma: a two-center retrospective study.Journal of cancer research and clinical oncology · 2023Article
- Comparative analysis of batch correction methods for FDG PET/CT using metabolic radiogenomic data of lung cancer patients.Scientific reports · 2023Article
- The value of radiomics-based CT combined with machine learning in the diagnosis of occult vertebral fractures.BMC musculoskeletal disorders · 2023Article
- Radiation pneumonia predictive model for radiotherapy in esophageal carcinoma patients.BMC cancer · 2023Article
- Comparison of the diagnostic efficacy of mathematical models in distinguishing ultrasound imaging of breast nodules.Scientific reports · 2023Article
- Predicting overall survival in chordoma patients using machine learning models: a web-app application.Journal of orthopaedic surgery and research · 2023Article
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9 authors.
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Abstract
BACKGROUND &
aimsConsidering that no standard therapy has yet been found for the novel coronavirus disease (COVID-19), identifying severe cases as early as possible, and such that treatment procedures can be escalated seems necessary. Hence, the present study aimed to develop a machine learning (ML) approach for automated severity assessment of COVID-19 based on clinical and paraclinical characteristics like serum levels of zinc, calcium, and vitamin D.
methodsIn this analytical cross-sectional study which was conducted from May 2020 to May 2021, clinical and paraclinical data sets of COVID-19-positive patients with known outcomes were investigated by combining statistical comparison and correlation methods with ML algorithms, including Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM).
resultsOur work revealed that some patients' characteristics including age, gender, cardiovascular diseases as an underlying condition, and anorexia as disease symptoms, and also some parameters which are measurable in blood samples including FBS and serum levels of calcium are factors that can be considered in predicting COVID-19 severity. In this regard, we developed ML predictive models that indicated accuracy and precision scores >90% for disease severity prediction. The SVM algorithm indicated better results than other algorithms by having a precision of 95.5%, recall of 94%, F1 score of 94.8%, the accuracy of 95%, and AUC of 94%.
conclusionsOur results indicated that clinical and paraclinical features like calcium serum levels can be used for automated severity assessment of COVID-19.
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