Dr. Pruinelli Lab's work in transplantation is centered on leveraging advanced data science and AI techniques to improve outcomes for transplant patients.
In liver transplantation, her team is developing predictive models that can assess patient risk, optimize transplant timing, and enhance post-transplant care. By analyzing vast datasets, our project identifies key factors influencing transplant success, such as patient health trajectories and organ viability.
Similarly, this same approach is now being expanded to include kidney and pancreas transplantation and a better understanding of how data can inform better decision-making and transplantation success.
Our innovative approach aims to personalize treatment plans, reduce complications, and ultimately increase survival rates for transplant recipients, making significant contributions to the field of transplantation medicine.
Dr. Pruinelli's lab is dedicated to advancing nursing science through the innovative use of applied data science and artificial intelligence. By developing cutting-edge machine learning models, her team uncovers insights from large-scale health data, particularly in chronic disease management and transplantation. A key focus is on integrating demographic, clinical, and social data to personalize care and predict disease trajectories. Dr. Pruinelli is also committed to enhancing the role of AI in nursing, ensuring that data-driven approaches empower nurses to improve patient care, optimize health outcomes, and lead advancements in healthcare technology.
Under this project, Dr. Pruinelli collaborates with several stakeholders nationally and internationally to guide the future of nursing at the intersection of technology adoption and innovation.
Dr. Pruinelli's multidisciplinary collaboration focuses on transforming chronic disease management through the application of advanced data science techniques. By leveraging machine learning and AI, her research aims to model disease trajectories, helping to predict health outcomes and inform personalized care plans. Her work emphasizes the integration of large health data to tailor care strategies, improving patient outcomes and quality of life. Her efforts in this space seek to enhance how clinicians understand chronic disease progression, ultimately driving more effective, individualized care management for patients with complex conditions.
Specifically, her work in pain management focuses on identifying risk factors and creating tailored interventions to prevent chronic pain for specific subpopulations.