Keywords
Artificial Intelligence
Maternal mortality
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Abstract
Objective: Identify effective approaches and evaluate the effectiveness of Artificial Intelligence (AI) in predicting postpartum hemorrhage (PPH). Methods: This is an integrative review, carried out in the PubMed, Scopus, Web of Science and IEEE databases, using the following descriptors: “postpartum hemorrhage”, “complications in pregnancy”, “artificial intelligence”, “machine learning” and “deep learning”. Studies published between 2018 and 2024 that included the general population with a clear definition of the diagnosis of PPH were included. Results: The final sample consisted of 6 articles, with a higher concentration of publications in 2022. The majority of studies were conducted in the USA, and the predominant research designs were cohort studies. It was found that the AI models showed high accuracy and sensitivity, standing out in the integration of multiple predictive factors for PPH, such as pre-gestational maternal weight, gestational age, gestational hypertension, characteristics of uterine contraction. Final considerations: This review highlights the relevance of AI models in predicting PPH, indicating their potential for clinical implementation. Although promising, these models require additional studies for improvement and validation in different contexts.
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