The Digital Biomarker Discovery Project: An Open Source Toolbox for Biosignal Analysis

About the presentation:
Digital health is rapidly expanding due to surging healthcare costs, deteriorating health outcomes, and the growing prevalence and accessibility of mobile health and wearable technologies. Data from mobile and wearable technologies can be transformed into digital biomarkers that act as indicators of health outcomes and can be used to diagnose and monitor a number of chronic diseases and conditions. The process of transforming data into digital biomarkers can be computationally expensive and evaluation methodology is poorly defined. Here, we provide a collaborative, standardized online space for digital biomarker development and validation through the open source The Digital Biomarker Discovery Project (DBDP.org). From accessing and preprocessing sensor data to development of statistical modeling, machine learning, and deep learning algorithms, the DBDP provides tools for each step of the digital biomarker discovery process. We envision the DBDP becoming a standard for the digital biomarker community and an epicenter of collaboration and exploration.
About the presenter:
Dr. Jessilyn Dunn is Assistant Professor of Biomedical Engineering and Biostatistics & Bioinformatics at Duke University, and Director of the BIG IDEAs Laboratory whose goal is to detect, treat, and prevent chronic and acute diseases through digital health innovation. She is PI of the CovIdentify study to detect and monitor COVID-19 using mobile health technologies, and PI of a Chan Zuckerberg Initiative grant to develop the DBDP, an open-source software platform for digital biomarker development. Dr. Dunn was an NIH Big Data to Knowledge (BD2K) Postdoctoral Fellow at Stanford and an NSF Graduate Research Fellow at Georgia Tech and Emory, as well as a visiting scholar at the US Centers for Disease Control and Prevention and the National Cardiovascular Research Institute in Madrid, Spain. Her work has been internationally recognized with media coverage from the NIH Director’s Blog to Wired, Time, and US News and World Report. To view more: bme.duke.edu/faculty/jessilyn...
Citations Referenced in the Presentation:
Bent, Lu, Kim, Dunn. Biosignal Compression Toolbox for Digital Biomarker Discovery. Sensors. (2021)
Bent, B., Goldstein, B.A., Kibbe, W.A. and Dunn, J.P., 2020. Investigating sources of inaccuracy in wearable optical heart rate sensors. NPJ digital medicine, 3(1), pp.1-9.