Abstract
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Authors: Timothy Wen, Audrey Kim, Lisa Bain, Kartik Venkatesh, Mark A. Clapp, Isabel Fulcher
Conference: Society for Maternal and Fetal Medicine Annual Pregnancy Meeting 2024
Key Findings: Small for Gestational Age (SGA) can be satisfactorily predicted using this clinically deployable machine learning model which can be implemented up until birth admission to improve resource utilization and team preparation in anticipation of birth.
Abstract
Authors: Timothy Wen, Audrey Kim, Lisa Bain, Kartik Venkatesh, Mark A. Clapp, Isabel Fulcher
Conference: Society for Maternal and Fetal Medicine Annual Pregnancy Meeting 2024
Key Findings: Small for Gestational Age (SGA) can be satisfactorily predicted using this clinically deployable machine learning model which can be implemented up until birth admission to improve resource utilization and team preparation in anticipation of birth.
Abstract
Authors: Timothy Wen, Audrey Kim, Lisa Bain, Kartik Venkatesh, Mark A. Clapp, Isabel Fulcher
Conference: Society for Maternal and Fetal Medicine Annual Pregnancy Meeting 2024
Key Findings: Small for Gestational Age (SGA) can be satisfactorily predicted using this clinically deployable machine learning model which can be implemented up until birth admission to improve resource utilization and team preparation in anticipation of birth.
Abstract
Authors: Timothy Wen, Audrey Kim, Lisa Bain, Kartik Venkatesh, Mark A. Clapp, Isabel Fulcher
Conference: Society for Maternal and Fetal Medicine Annual Pregnancy Meeting 2024
Key Findings: Small for Gestational Age (SGA) can be satisfactorily predicted using this clinically deployable machine learning model which can be implemented up until birth admission to improve resource utilization and team preparation in anticipation of birth.
Abstract
Authors: Timothy Wen, Audrey Kim, Lisa Bain, Kartik Venkatesh, Mark A. Clapp, Isabel Fulcher
Conference: Society for Maternal and Fetal Medicine Annual Pregnancy Meeting 2024
Key Findings: Small for Gestational Age (SGA) can be satisfactorily predicted using this clinically deployable machine learning model which can be implemented up until birth admission to improve resource utilization and team preparation in anticipation of birth.
Abstract
