#018 - Predicting hemodynamic significance of the PDA using multi-modal physiological data
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#018 - Predicting hemodynamic significance of the PDA using multi-modal physiological data
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In this episode of Fellows Friday, Rupa welcomes Giulia Lima, a third-year neonatal perinatal medicine fellow at Harvard and incoming Assistant Professor at the University of Miami. Giulia walks us through her fellowship research journey, from a large epidemiological study of premature infants with congenital heart disease through the Children's Hospital Neonatal Consortium, to her Marshall Klaus Award-winning work on optimal surgical timing. The centerpiece of the conversation is her machine learning model designed to predict hemodynamically significant PDA using continuous physiological data, including heart rate variability and diastolic blood pressure trends, without relying on echocardiography. Giulia also reflects on the power of mentorship and her vision for integrating artificial intelligence into real-time clinical decision-making in the NICU.
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