R-classify: Extracting research papers’ relevant concepts from a controlled vocabulary

Aggarwal, Tanay; Salatino, Angelo A.; Osborne, Francesco; Motta, Enrico

doi:10.1016/j.simpa.2022.100444

Cited by 4 publications

(1 citation statement)

References 24 publications

(35 reference statements)

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“…The model found that having close contact with a confirmed COVID-19 case was a significant predictor of contracting the disease. This finding is consistent with the high transmission rate of the virus and underscores the importance of social distancing measures (Aggarwal et al, 2022). Additionally, the model identified male gender as a predictor of a positive test result, which aligns with the observed gender bias in COVID-19 cases seen in previous studies (Hall et al, 2020;Mark et al, 2020).…”

Section: Evaluation Of the Prediction Modelsupporting

confidence: 87%

Prediction of COVID-19 Diagnosis based on Symptoms

Malik

Aggarwal

2023

Preprint

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The COVID-19 pandemic has caused a significant global impact, creating a need for accurate prediction models. Such models can inform public health policies, guide resource allocation decisions, and assist individuals and society as a whole in decision-making. Several prediction models that combine multiple features to estimate infection risk have been developed using various data sources, such as case counts, testing rates, and demographic information. These models aim to assist healthcare professionals in triaging patients, especially in resource-limited settings. Our model accurately predicted COVID-19 test results using features such as sex, age 60 years, known contact with an infected person, and the presence of initial clinical symptoms. While no prediction model is perfect, they can provide valuable insights and contribute to the ongoing efforts to mitigate the impact of COVID-19.

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Section: Evaluation Of the Prediction Modelsupporting

confidence: 87%