Rapid and reliable digital phenotyping using computational modeling, machine learning, and mobile technology

Woo-Young Ahn

Machine learning has the potential to facilitate the development of computational methods that improve the measurement of cognitive and mental functioning, and adaptive design optimization (ADO) is a promising machine-learning method that might lead to rapid, precise, and reliable markers of individual differences. In this talk, I will first discuss the importance of reliability of (bio)markers. Then, I will present a series of studies that utilized ADO in the area of decision-making and for the development of ADO-based digital phenotypes for addiction and related behaviors. Lastly, I will introduce an open-source Python package, ADOpy, which we developed to increase the accessibility of ADO to even researchers who have limited background in Bayesian statistics or cognitive modeling.

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