MedAI #133: Precision Medicine: Knowledge-Informed Methods with Data Efficiency | Hairong Wang

Title: Advancing Precision Medicine: Knowledge-Informed Methods with Data Efficiency Speaker: Hairong Wang Abstract: In recent decades, machine learning (ML) has emerged as a promising tool for analyzing complex patterns from large datasets. The computational power and versatility of ML has enabled in-depth analysis of medical imaging, clinical, and molecular data, significantly enhancing diagnosis, prognosis, and treatment planning in healthcare. However, an intrinsic bottleneck exists in healthcare data acquisition, limited by the invasiveness or high expense of sample collection, the need for highly-specialized experts to create accurate labels, the rarity of some diseases in the population, and the difficulty in patient recruitment. In this talk, I will discuss my recent development on enhancing data efficiency in the context of precision medicine (PM). Motivated by the practical limitations, we developed knowledge-informed, data efficient algorithms to address the challenges of labeled sample size and implicit hierarchical knowledge integration, aiming for practical solutions in PM. These works have been applied in real-world contexts in collaboration with Columbia University Medical Center and Mayo Clinic, demonstrating considerable potential in boosting the accuracy, robustness, and interpretability of model outcomes. Speaker Bio: Hairong Wang is a final-year PhD candidate at School of Industrial and Systems Engineering at Georgia Institute of Technology. Her research mainly focuses on developing knowledge-informed, data and computational efficient machine learning models and algorithms, and pursuing their practical applications in addressing high-stake clinical challenges. Her research has been recognized with several awards, including the 2024 Georgia Tech Wally George Fellowship, first place in the 2024 IISE DAIS Student Data Analytics Competition, the 2023 INFORMS DMDA Workshop Student Paper Award, the 2023 Georgia Tech George Fellowship, and the 2022 Georgia Healthcare Information and Management Systems Society David Cowan Scholarship. Prior to joining Georgia Tech, she obtained her bachelor’s degree in mathematics from University of Oxford. ------ The MedAI Group Exchange Sessions are a platform where we can critically examine key topics in AI and medicine, generate fresh ideas and discussion around their intersection and most importantly, learn from each other. We will be having weekly sessions where invited speakers will give a talk presenting their work followed by an interactive discussion and Q&A. Our sessions are held every Monday from 1pm-2pm PST. To get notifications about upcoming sessions, please join our mailing list: https://mailman.stanford.edu/mailman/listinfo/medai_announce For more details about MedAI, check out our website: https://medai.stanford.edu. You can follow us on Twitter @MedaiStanford Organized by members of the Rubin Lab (http://rubinlab.stanford.edu) and Machine Intelligence in Medicine and Imaging (MI-2) Lab: - Nandita Bhaskhar (https://www.stanford.edu/~nanbhas) - Amara Tariq (https://www.linkedin.com/in/amara-tariq-475815158/) - Avisha Das (https://dasavisha.github.io/)