Patient Representation Learning From Heterogeneous Data Sources and Knowledge Graphs Using Deep Collective Matrix Factorization: Evaluation Study

BACKGROUND: Patient representation learning aims to learn features, also called representations, from input sources automatically, often in an unsupervised manner, for use in predictive models. This obviates the need for cumbersome, time- and resource-intensive manual feature engineering, especially...

Descripción completa

Detalles Bibliográficos
Autores principales: Kumar, Sajit, Nanelia, Alicia, Mariappan, Ragunathan, Rajagopal, Adithya, Rajan, Vaibhav
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2022
Materias:
Original Paper
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8814927/
https://www.ncbi.nlm.nih.gov/pubmed/35049514
http://dx.doi.org/10.2196/28842

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8814927/
https://www.ncbi.nlm.nih.gov/pubmed/35049514
http://dx.doi.org/10.2196/28842

Ejemplares similares