Machine Learning: a new era for cardiovascular pregnancy physiology and cardio-obstetrics research DOI
Contessa A. Ricci, Benjamin Crysup, Nicole Phillips

et al.

AJP Heart and Circulatory Physiology, Journal Year: 2024, Volume and Issue: 327(2), P. H417 - H432

Published: June 7, 2024

The maternal cardiovascular system undergoes functional and structural adaptations during pregnancy postpartum to support increased metabolic demands of offspring placental growth, labor, delivery, as well recovery from childbirth. Thus, imposes physiological stress upon the system, in absence an appropriate response it imparts potential risks for complications adverse outcomes. proportion pregnancy-related deaths events has been steadily increasing, contributing high rates mortality. Despite advances physiology research, there is still no comprehensive understanding healthy pregnancies. Furthermore, current approaches prognosis are limited. Machine learning (ML) offers new effective tools investigating mechanisms involved development therapies. main goal this review summarize existing research that uses ML understand develop prediction models clinical application pregnant patients. We also provide overview platforms can be used comprehensively discuss interpretability outcomes, consequences model bias, importance ethical consideration use.

Language: Английский

Machine Learning: a new era for cardiovascular pregnancy physiology and cardio-obstetrics research DOI
Contessa A. Ricci, Benjamin Crysup, Nicole Phillips

et al.

AJP Heart and Circulatory Physiology, Journal Year: 2024, Volume and Issue: 327(2), P. H417 - H432

Published: June 7, 2024

The maternal cardiovascular system undergoes functional and structural adaptations during pregnancy postpartum to support increased metabolic demands of offspring placental growth, labor, delivery, as well recovery from childbirth. Thus, imposes physiological stress upon the system, in absence an appropriate response it imparts potential risks for complications adverse outcomes. proportion pregnancy-related deaths events has been steadily increasing, contributing high rates mortality. Despite advances physiology research, there is still no comprehensive understanding healthy pregnancies. Furthermore, current approaches prognosis are limited. Machine learning (ML) offers new effective tools investigating mechanisms involved development therapies. main goal this review summarize existing research that uses ML understand develop prediction models clinical application pregnant patients. We also provide overview platforms can be used comprehensively discuss interpretability outcomes, consequences model bias, importance ethical consideration use.

Language: Английский

Citations

0