Innovative Collaboration at the Forefront of Digital Health and Predictive Care

In an era where technology and healthcare are increasingly intertwined, a groundbreaking collaboration emerges from Florida Atlantic University (FAU), setting a new standard for the future of healthcare. This partnership, supported by seed funding from the Center for SMART Health (CSH), unites the Christine E. Lynn College of Nursing, the College of Engineering and Computer Science (CECS) at FAU, and the Memorial Healthcare System. The collaborative team includes Dr. Debarshi Datta, a Senior Research Fellow and expert in AI and machine learning; Dr. Laurie Martinez, an Assistant Professor with a focus on holistic nursing; Dean Safiya George, the Holli Rockwell Trubinsky Eminent Dean & Professor; Dr. Taghi M. Khoshgoftaar, a Motorola Professor of CECS specializing in data science; Dr. Javad Hashemi, Professor & Associate Dean for Research of CECS; Dr. David Newman, a Professor and Statistician; and from Memorial Healthcare System, Dr. Candice Sareli and Dr. Paula Eckardt, experts in medical research and infectious disease, respectively. Together, they provide a groundbreaking approach to integrating healthcare, technology, and data science for advanced patient care.

This initiative pioneers a unique approach to healthcare, where the empathetic and patient-centered ethos of nursing converges with the precision and analytical depth of engineering and computer science. The focus of their collaboration centers on employing artificial intelligence (AI) and machine learning (ML) to predict disease outcomes, particularly in the context of COVID-19, and to address broader health disparities. The team’s comprehensive research explores how predictive modeling can be utilized to anticipate patient treatment responses and disease trajectories, aiming to optimize healthcare delivery and patient outcomes.

The team’s efforts have led to significant advancements, such as the development of models to predict COVID-19 patient mortality and the identification of key factors influencing disease severity, demonstrating the power of combining healthcare knowledge with data science to tackle pressing health challenges. Their published works, including studies on the use of machine learning to predict COVID-19 mortality, highlight the impact of integrating data science with healthcare expertise. This research not only contributes to the scientific community but also offers practical applications for improving patient care and managing health crises more effectively.

As this collaboration continues to evolve, it sets a new standard for interdisciplinary research in healthcare, emphasizing the critical role of innovation, technology, and cross-disciplinary cooperation in addressing complex health challenges. It not only paves the way for future advancements in digital health but also exemplifies the power of collaborative endeavor in advancing the frontiers of nursing and medicine within healthcare.

 

Publications:

Datta, D., George Dalmida, S., Martinez, L., Newman, D., Hashemi, J., Khoshgoftaar, T. M., Shorten, C., Sareli, C., & Eckardt, P. Using Machine Learning to Identify Patient Characteristics to Predict Mortality of In-Patients with COVID-19 in South Florida. Frontiers in Digital Health, 5, 1193467.  https://doi.org/10.3389/fdgth.2023.1193467

Datta, D., George Dalmida, S., Martinez, L., Newman, D., Hashemi, J., Khoshgoftaar, T. M., Shorten, C., Sareli, C., & Eckardt, P. Using random forest classifier to identify important COVID-19 patient characteristics predicting mortality in South Florida, Proceedings of the 4th National Big Data Health Science Conference. BMC Proc 17 (Suppl 19), 32 (2023).  https://doi.org/10.1186/s12919-023-00281-y