Predicting infectious disease for biopreparedness and response: A systematic review of machine learning and deep learning approaches

The complex, unpredictable nature of pathogen occurrence has required substantial efforts to accurately predict infectious diseases (IDs). With rising popularity of Machine Learning (ML) and Deep Learning (DL) techniques combined with their unique ability to uncover connections between large amounts...

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Detalles Bibliográficos
Autores principales: Keshavamurthy, Ravikiran, Dixon, Samuel, Pazdernik, Karl T., Charles, Lauren E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Review Paper
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9582566/
https://www.ncbi.nlm.nih.gov/pubmed/36277100
http://dx.doi.org/10.1016/j.onehlt.2022.100439

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9582566/
https://www.ncbi.nlm.nih.gov/pubmed/36277100
http://dx.doi.org/10.1016/j.onehlt.2022.100439

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