Applications of novel state-of-the-art data science techniques, such as artificial intelligence/machine learning, multi-omics, and “big data” linkage analytics in Blood Banking and Transfusion Medicine (BB/TM), are still in their relative infancy. With advancements in the efficiency of computational methods and the availability of larger, more complex datasets, the time has come to leverage these novel techniques to drive innovation in the field and bring them into the mainstream. This educational session aims to give a problem-based approach for utilizing cutting-edge data science methods for clinical and research applications in BB/ TM. The session will provide a narrative review of the landscape of data science techniques in BB/TM, including applications for demand forecasting, blood supply chain management, and patient blood management. It will elaborate on real-world implementations of Omics and systems biology as examples for precision transfusion medicine. The session will expand on the role of BB/TM informatics in intelligent database design and curation for data-driven research. Examples of innovative applications of “big data” analytics and linkage analysis using large, nationally representative, multidimensional databases and registries will be explored with a focus on how they can be leveraged to produce meaningful research outcomes. Finally, the session will explore current gaps in BB/TM research and donor/ patient management that can be addressed with novel data science techniques, including artificial intelligence and machine learning, providing a blueprint for future adoption of computational techniques in the field.
All relevant financial relationships have been mitigated.
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AM23-SN-23-O: Novel Data Science Techniques in Transfusion Medicine and Cell Therapy: State-of-the-Art and Future Direction (Enduring) Evaluation