Caitlin Dreisbach

Caitlin Dreisbach, PhD, RN is an Assistant Professor at the University of Rochester School of Nursing with a dual affiliation at the Goergen Institute for Data Science. She maintains active clinical practice on the high-risk obstetrics unit at Strong Memorial Hospital while leading interdisciplinary research at the intersection of nursing science and data analytics.

Her educational background includes a PhD in Nursing from the University of Virginia (2020), BSN from Johns Hopkins University (2013), and undergraduate studies at Cornell University. Her research interests span women's lifelong health trajectories, quantitative clinical assessment during pregnancy, microbiome dynamics, and social determinants of health in reproductive outcomes. She employs advanced data science techniques including natural language processing, machine learning, and microbiome analysis to address complex questions in maternal-child health.

Analysis of her 15 most recent publications reveals a strong focus on leveraging large-scale datasets (particularly the All of Us Research Program) to investigate pregnancy outcomes, symptom science, and chronic disease patterns. Key methodological themes include predictive modeling of birth outcomes, extraction of clinical data from unstructured notes, and analysis of microbiome-diet interactions during gestation.

Scientific awards and recognition include:

• Josephine Craytor Nurse Faculty Award (2024)
• Most Promising New Investigator (2023)
• Phyllis J. Verhonick Dissertation Award (2020)
• Wood Family Award for Data Science (2018)
• The Raven Society membership (2017)

Dr. Dreisbach serves as Principal Investigator for multiple funded projects including an NIH/NINR grant on neural network approaches to fetal weight estimation (2022-2025) and a Heilbrunn Foundation grant examining surgical weight management before pregnancy (2024-2026). Her lab (DreisbachLab) develops open-source tools for reproductive health research, including the GAP_Projection repository for birthweight prediction algorithms. She also contributes as a data science consultant on projects like the Significance of Symptom Onset-To-Angiography Time study at CTSI.

Her work bridges clinical practice, data science innovation, and nursing education through initiatives advancing data competencies for PhD students and exploring implications of generative AI in nursing science.