Using Machine Learning Algorithms to Develop a Clinical Decision-Making Tool for Covid-19 Inpatients
Dados Bibliográficos
The proposed probabilistic models were able to predict, using feature selected risk factors, the probability of the mentioned outcomes. Overall, our findings demonstrate reliable, multivariable, quantitative predictive models for four outcomes, which utilise readily available clinical information for COVID-19 adult inpatients. Further research is required to externally validate our models and demonstrate their utility as risk stratification and clinical decision-making tools
Bayesian network · Coronavirus disease 2019 (COVID-19 · Data mining · Decision tree · Infectious disease (medical specialty · Machine learning · Multivariable calculus · Outcome (game theory · Predictive modelling · Random forest · Retrospective cohort study · Computer Science · COVID-19 Clinical Research Studies · COVID-19 diagnosis using AI · Engineering · Machine Learning in Healthcare · Mathematics · Medicine · Artificial Intelligence · Internal Medicine
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| Obras citantes distintas | 4 |
|---|---|
| Citações por ano | 0,8 |
| Intervalo de citações | 2021 - 2024 (4) |
| Velocidade de citação | recent |
| Altamente citado | Não |
| Tipos de citação | Neutras: 4 |