From the algorithm to the clinical interpretation of childbirth anxiety : analysis and explainability of obstetric predictive models based on psychological indicators

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      <subfield code="a">Recio Garcia, Juan A.</subfield>
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      <subfield code="a">From the algorithm to the clinical interpretation of childbirth anxiety</subfield>
      <subfield code="b">: analysis and explainability of obstetric predictive models based on psychological indicators</subfield>
      <subfield code="c">Juan A. Recio Garcia and Ana M. Martin Casado</subfield>
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      <subfield code="a">Anxiety during pregnancy constitutes a relevant factor that can significantly influence labor development. This study presents a novel approach based on explainable artificial intelligence to predict both the type and duration of labor using psychological indicators of anxiety prior to delivery. Employing data from 235 full-term pregnant women from two Spanish hospitals, we developed a multilayer perceptron model to classify eutocic and dystocic deliveries, achieving a capacity to identify 88\% of dystocic deliveries. Additionally, we implemented a regression model that predicts labor time with a mean error of 2 hours, correctly predicting 86% of cases with an error margin of less than 3 hours. The application of explainability techniques to the developed models allows for understanding the specific influence of each anxiety factor on labor development. These results demonstrate the potential of AI models to improve obstetric care and optimize healthcare resource allocation</subfield>
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      <subfield code="g">08/12/2025 Volume 28 Number  76 - December 2025 , p. 13 - 27</subfield>
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      <subfield code="t">Revista Iberoamericana de Inteligencia Artificial</subfield>
      <subfield code="d"> : IBERAMIA, Sociedad Iberoamericana de Inteligencia Artificial , 2018-</subfield>
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      <subfield code="u">https://journal.iberamia.org/index.php/intartif/article/view/2507/268</subfield>
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