Machine-Aided Self-diagnostic Prediction Models for Polycystic Ovary Syndrome: Observational Study

BACKGROUND: Artificial intelligence and digital health care have substantially advanced to improve and enhance medical diagnosis and treatment during the prolonged period of the COVID-19 global pandemic. In this study, we discuss the development of prediction models for the self-diagnosis of polycys...

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Detalles Bibliográficos
Autores principales: Zigarelli, Angela, Jia, Ziyang, Lee, Hyunsun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2022
Materias:
Early Reports
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8965679/
https://www.ncbi.nlm.nih.gov/pubmed/35289757
http://dx.doi.org/10.2196/29967

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8965679/
https://www.ncbi.nlm.nih.gov/pubmed/35289757
http://dx.doi.org/10.2196/29967

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