DDxNet: a deep learning model for automatic interpretation of electronic health records, electrocardiograms and electroencephalograms

Effective patient care mandates rapid, yet accurate, diagnosis. With the abundance of non-invasive diagnostic measurements and electronic health records (EHR), manual interpretation for differential diagnosis has become time-consuming and challenging. This has led to wide-spread adoption of AI-power...

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Detalles Bibliográficos
Autores principales: Thiagarajan, Jayaraman J., Rajan, Deepta, Katoch, Sameeksha, Spanias, Andreas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7532141/
https://www.ncbi.nlm.nih.gov/pubmed/33009423
http://dx.doi.org/10.1038/s41598-020-73126-9

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7532141/
https://www.ncbi.nlm.nih.gov/pubmed/33009423
http://dx.doi.org/10.1038/s41598-020-73126-9

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