+DS IPLE: Biomedical Data Science and Machine Learning Applications in Healthcare

Recent technological advancements make it possible to closely and continuously monitor patients on multiple scales, both inside and outside of the clinic. These new technologies provide unprecedented opportunities for understanding and predicting health and disease but have also led to a deluge of biomedical data. In order to derive actionable health insights from these large volumes of data, a combination and biomedical data science and machine learning approaches are needed. In this talk, I will introduce the four major types of biomedical data (multi-omics, electronic health records, mobile sensor data, and imaging) and the opportunities and challenges in working with them. I will discuss applications where machine learning has generated novel uses for these data, including real-time illness detection outside of the clinic and clinical decision support models, and will explore how these new applications are revolutionizing medicine.

This session is part of the Duke + Data Science (+DS) program in-person learning experiences (IPLEs). To learn more, please visit https://plus.datascience.duke.edu/