ADHD Research
Identifying ADHD Biotypes using Machine Learning
This is a project backed by a Stanford HAI seed research grant, aiming to aid ADHD diagnosis and biotype identification, a vastly unexplored territory usually clinically determined instead of data-driven. Our insight relies on applying machine learning on electrodermal activity (EDA) signals along with other bio-signals to find patterns, and if successful, this would make diagnosis accessible through simple wrist-worn sensors. I am a research assistant focusing on engineering a data pipeline for feature extractions and brainstorming unsupervised learning methods.
Timeline
Sep 2022 - Present
Given a small amount of incredibly rich, dense, and long time series data, how can we best extract the most important features and factors?
How can we automate the analysis pipeline and scale for the next round of the study? How can we organize and apply quality control to the data we have now?
For me, I wanted to get a taste of formal long-term, interdisciplinary research, while learning more data engineering and analysis tools and exploring bio-data and signal processing for the first time.
Outcome
Since this is a long-term research project involving multiple rounds of studies and analysis, my work is only the beginning to set up some ground work. Nonetheless, this was the most progress made in a while as commented by the lab PI.
Please reach out if you're interested in the details!
Data Pipeline