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				<full_title>Journal of Artificial Intelligence for Healthcare Systems</full_title>
				<abbrev_title>J. Artif. Intell. Healthc. Syst.</abbrev_title>
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					<year>2026</year>
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					<volume>5</volume>
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				<issue>1</issue>
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					<title>Explainable Gradient Boosting Machine for Predicting Postpartum Hemorrhage Risk Using Intrapartum Electronic Fetal Monitoring, Maternal Vital Signs, and Labor Progression Data</title>
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            <given_name>Fatima</given_name>
            <surname>Al-Zahra</surname>
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            <given_name>Amina</given_name>
            <surname>El Idrissi</surname>
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					<year>2026</year>
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					  <unstructured_citation>Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, et al. LightGBM: a highly efficient gradient boosting decision tree. Adv Neural Inf Process Syst. 2017;30:3146-54.</unstructured_citation>
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