An open-source software platform for the development of digital biomarkers using mHealth and wearables.
Learn More at DBDP.org
The DBDP is created by the BIG IDEAS Lab at Duke University: http://dunn.pratt.duke.edu/ If you use the DBDP in your work, please cite the DBDP: dbdp.org, the following publication, and any references within the module you use.
Bent, B., Wang, K., Grzesiak, E., Jiang, C., Qi, Y., Jiang, Y., Cho, P., Zingler, K., Ogbeide, F.I., Zhao, A., Runge, R., Sim, I., Dunn, J. (2020). The Digital Biomarker Discovery Pipeline: An open source software platform for the development of digital biomarkers using mHealth and wearables data. Journal of Clinical and Translational Science, 1-28. doi:10.1017/cts.2020.511 (Link to Open Access Article)
Digital biomarkers are digitally collected data that are transformed into indicators of health outcomes. The BIG IDEAS Lab is developing digital biomarkers for a range of diseases and conditions using a variety of sensors.
We believe that not only data, but also computational pipelines and algorithms should operate by the FAIR principles (Findable, Accessible, Interoperable, and Reusable).
Digital biomarkers currently require extensive domain knowledge and computing skills. The purpose of the DBDP is to provide code sets, functions, and algorithms for the entire digital biomarker discovery pipeline to make discovering digital biomarkers more accessible. From the input of wearable sensor data to the development of machine learning and deep learning algorithms, we have provided an open source software resource for the digital biomarker community.
For general help with the DBDP, see our USER GUIDE. Please refer to specific DBDP modules for instructions for use.
The Digital Biomarker Discovery Resource Guide is available now!
The results of this method on the following wearable sensors:
| Module | Pipeline | Wearables currently supported | Languages | Status |
|---|---|---|---|---|
| Pre-processing | General | Basis Devices, Empatica E4, Garmin Vivosmart3, ECG, Non-specific | Python, R | Ongoing Development |
| Exploratory Data Analysis | General | Non-specific | Python, R | Ongoing Development |
| Glucose Variability | CGM (Dexcom), Support for other CGM | Python | Published package PyPi: cgmquantify | |
| Resting Heart Rate | Fitbit, Non-specific | R | Published | |
| Heart Rate Variability | Non-specific ECG, PPG | Python | Ongoing Development | |
| Sleep | Garmin vivosmart 3 | Python | In Development | |
| Mental Health | Actigraph, EEG | Python | In Development | |
| Human Activity Recognition | Empatica E4, Non-specific | Python | Ongoing Development |
For inclusion into the digital biomarkers discovery pipeline, please follow the instructions in the Instructions subdirectory and create an Issue.
Apache 2.0
Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.