Výsledky vyhledávání - "Alicia Nanelia"

Akademický článek

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

Autor: Sajit Kumar, Alicia Nanelia, Ragunathan Mariappan, Adithya Rajagopal, Vaibhav Rajan

Publikováno v: JMIR Medical Informatics, Vol 10, Iss 1, p e28842 (2022)

BackgroundPatient 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-

Externí odkaz: https://doaj.org/article/31d5a543ae824c098bf8d0023d64e68a

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

Autor: Sajit Kumar, Alicia Nanelia, Ragunathan Mariappan, Adithya Rajagopal, Vaibhav Rajan

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

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::2f2ca835ff4c65e74eb4223d3f9f8680
https://doi.org/10.2196/preprints.28842

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

Autor: Ragunathan Mariappan, Vaibhav Rajan, Alicia Nanelia Tan Li Shi, Sajit Kumar, Adithya Rajagopal

Publikováno v: JMIR Medical Informatics. 10:e28842

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

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::938775c09372e3ce86b55680b16448a0
https://doi.org/10.2196/28842

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