Lisiane Pruinelli, PhD, MS, RN, FAMIA

With more than 10 years of clinical experience in both multi-transplant organ coordination and information systems development and implementation, Dr. Pruinelli is part of a new generation of nursing informaticians focused on applied clinical informatics. Her expertise is on applying machine learning methods to longitudinal heterogeneous clinical data to investigate the trajectory of complex disease conditions, and its influence on health outcomes. Her work aims to identify the problems and targeted interventions toward better patient outcomes.

Dr. Pruinelli’s program of research leverages innovative applied clinical informatics tools and data science methods to investigate the trajectories of complex disease conditions, such as sepsis, solid organs transplantation, and pain management, to improve the patient care experience and increase the quality of health care delivery. She developed foundations for new personalized strategies for disease treatment and management over time, including addressing national initiatives for holistic models of care. Her scholarship reflects a strategic transition from an acute care nurse to a researcher, leveraging nursing informatics based on consistent formal education, previous clinical care experiences, and nursing science to build new knowledge. To guide her program of research, Dr. Pruinelli adopt the Applied Data Science Framework for Healthcare Leaders, a roadmap she developed, where shows the life-cycle process to build personalized models of care suitable of clinical applications. Similarly, with the heavily adoption of machine learning across her projects and the adoption of such models to lay the foundation for intelligent systems, she has been deeply involved in discussions around responsible Artificial Intelligence (AI). For more than 10 years she have mainly centered on using data for solid organ transplantation research. Besides survivorship, she has been investigating other factors that has impact on the overall health status and wellbeing of liver and kidney transplants patients, such as the health conditions amenable of change with targeted interventions. This work is a life-long project, due to the complexity of these patients and the multiples factors contributing to successful outcomes.

0000-0002-1046-6037

• Longitudinal Data Analysis
• Applied Clinical Informatics
• Artificial Intelligence
• Big data analytics
• Liver Transplantation
• Machine Learning
• Organ transplantation
• Renal Transplantation
• data science
• machine learning for predictive analytics