Dealing With Missing, Imbalanced, and Sparse Features During the Development of a Prediction Model for Sudden Death Using Emergency Medicine Data: Machine Learning Approach

BACKGROUND: In emergency departments (EDs), early diagnosis and timely rescue, which are supported by prediction modes using ED data, can increase patients’ chances of survival. Unfortunately, ED data usually contain missing, imbalanced, and sparse features, which makes it challenging to build early...

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Detalles Bibliográficos
Autores principales: Chen, Xiaojie, Chen, Han, Nan, Shan, Kong, Xiangtian, Duan, Huilong, Zhu, Haiyan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2023
Materias:
Original Paper
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9898833/
https://www.ncbi.nlm.nih.gov/pubmed/36662548
http://dx.doi.org/10.2196/38590

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9898833/
https://www.ncbi.nlm.nih.gov/pubmed/36662548
http://dx.doi.org/10.2196/38590

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