Machine learning models show promise for early high-risk pregnancy detection

High-risk pregnancies threaten maternal and fetal health; improved prediction could reduce preventable mortality and morbidity in resource-limited healthcare settings.

In the quiet arithmetic of pregnancy risk, a constellation of clinical signals often goes unread until it is too late — particularly in hospitals where resources are scarce and the margin for error is thin. Researchers in Iran have trained machine learning algorithms on real patient data to identify which pregnancies are most likely to end in serious complication, achieving 88 percent accuracy by learning to weigh pregnancy-specific measurements over demographic generalities. The work is less a technological triumph than a philosophical reorientation: an acknowledgment that pattern recognition

Machine learning models show promise for early high-risk pregnancy detection

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