School of Medicine
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Clinical Associate Professor, Radiation Oncology - Radiation Therapy
Current Research and Scholarly InterestsIn addition to my clinical research in head and neck and lung cancer, I work on the application of computer science and machine learning to cancer research. I develop tools for analyzing large datasets to improve outcomes and safety of cancer treatment. I developed a machine learning prognostic model using data from around 13,000 patients with metastatic cancer which performs better than traditional models and physicians [PubMed ID 33313792]. We recently completed a prospective randomized study in thousands of patients in which the model was used to help improve advance care planning conversations.
I also work on the methods underpinning observational and predictive modeling research. My open source nnet-survival software that allows use of neural networks for survival modeling has been used by researchers internationally. In collaboration with the Stanford Research Informatics Center, I examined how electronic medical record (EMR) survival outcome data compares to gold-standard data from a cancer registry [PubMed ID 35802836]. The EMR data captured less than 50% of deaths, a finding that affects many studies being published that use EMR outcomes data.
Iris C. Gibbs, MD, FACR, FASTRO
Professor of Radiation Oncology (Radiation Therapy) and, by courtesy, of Neurosurgery
Current Research and Scholarly InterestsDr. Gibbs is a board-certified radiation oncologist who specializes in the treatment of CNS tumors. Her research focuses on developing new radiation techniques to manage brain and spinal tumors in adults and children. Dr. Gibbs has gained worldwide acclaim for her expertise in Cyberknife robotic radiosurgery.