Predicting depression in adolescents using mobile and wearable sensors: multimodal machine learning–based exploratory study T Mullick, A Radovic, S Shaaban, A Doryab JMIR Formative Research 6 (6), e35807, 2022 | 30 | 2022 |
Objective measurement of hyperactivity using mobile sensing and machine learning: Pilot study O Lindhiem, M Goel, S Shaaban, KJ Mak, P Chikersal, J Feldman, ... JMIR Formative Research 6 (4), e35803, 2022 | 8 | 2022 |
An automated machine learning pipeline for monitoring and forecasting mobile health data A Bonaquist, M Grehan, O Haines, J Keogh, T Mullick, N Singh, ... 2021 Systems and Information Engineering Design Symposium (SIEDS), 1-6, 2021 | 7 | 2021 |
LemurDx: Using Unconstrained Passive Sensing for an Objective Measurement of Hyperactivity in Children with no Parent Input R Arakawa, K Ahuja, K Mak, G Thompson, S Shaaban, O Lindhiem, ... Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous …, 2023 | 4 | 2023 |
Framework for Ranking Machine Learning Predictions of Limited, Multimodal, and Longitudinal Behavioral Passive Sensing Data: Combining User-Agnostic and Personalized Modeling T Mullick, S Shaaban, A Radovic, A Doryab JMIR AI 3 (1), e47805, 2024 | | 2024 |