Biomedical data, while abundant, remains underutilized, even today. During my time at the Auton Lab, we often explored the potential of using such data to predict adverse medical outcomes in order to give advance warning and help clinicians provide timely care. I had the great privilege of mentoring Willa Potosnak during her time as an undergraduate researcher in CMU’s Robotics Institute Summer Scholars (RISS) program. Her research focused on exploring the utility of biometric data collected before, during, and after coronary artery bypass grafting (CABG) surgery to predict whether or not patients are likely to experience postoperative renal failure. Posters for her work in RISS (Potosnak et al., 2020) and an abstract accepted at the Society of Thoracic Surgeons (STS) annual meeting (Potosnak et al., 2021) are available in the gallery, and I encourage you to explore her work further in those publications.

  1. Potosnak, W., Wertz, A., Miller, J. K., Kilic, A., Dufendach, K. A., & Dubrawski, A. (2020). Cardiothoracic Surgery Analysis for Predicting Acute Renal Failure Outcomes. In CMU Robotics Institute Summer Scholars.
  2. Potosnak, W., Dufendach, K. A., Wertz, A., Miller, K., Dubrawski, A., & Kilic, A. (2021). Continuous Intraoperative Data Analysis Using Machine Learning Reveals Multiple Parameters to Predict Post-CABG Renal Failure. The Society of Thoracic Surgeons Annual Meeting.