School of Medicine

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  • Fateme Nateghi Haredasht

    Fateme Nateghi Haredasht

    Postdoctoral Scholar, Biomedical Informatics

    BioI am a postdoctoral scholar at the Stanford Center for Biomedical Informatics Research. I earned my PhD in Biomedical Sciences from KU Leuven, Belgium. My research during my PhD program focused on machine learning applications in healthcare, particularly in the field of survival analysis.

    My doctoral thesis was centered on the development of predictive models for critically ill patients with acute kidney injury (AKI). By leveraging electronic health record (EHR) data, we created personalized risk profiles for AKI survivors upon ICU discharge, leading to tailored follow-up plans. Additionally, we developed machine learning-based models to predict outcomes post-AKI, including progression to chronic kidney disease (CKD) and mortality.

    In another study, we investigated the utilization of unlabeled data to enhance the accuracy of survival time predictions. By integrating partial supervision from censored data within a semi-supervised wrapper approach, we consistently achieved superior results. This approach has the potential to significantly improve survival outcome predictions, offering valuable insights for clinical decision-making.

    In my current role at Stanford, I continue to advance the integration of machine learning in healthcare, collaborating with experts to improve patient care and outcomes.

  • Madelena Ng

    Madelena Ng

    Postdoctoral Scholar, Biomedical Informatics

    BioMadelena is a postdoctoral scholar at the Stanford Center for Biomedical Informatics Research (BMIR). Her research aims to illuminate the evolving ethical and practical challenges among digital and emerging technologies (e.g., web- and app-based population health research, clinical AI solutions, blockchain for health data). Her work in the Boussard Lab focuses on discerning key factors for clinical AI solutions to flourish in practice—from the readiness of the datasets for machine learning research to the operational principles that are required for successful clinical deployment.