Multi-Task Learning for Randomized Controlled Trials
نویسندگان
چکیده
A randomized controlled trial (RCT) is used to study the safety and efficacy of new treatments, by comparing patient outcomes an intervention group with a control group. Traditionally, RCTs rely on statistical analyses assess differences between treatment groups. However, such are generally not designed impact at individual level. In this paper, we explore machine learning models in conjunction RCT for personalized predictions depression intervention, where patients were longitudinally monitored wearable devices. We formulate individual-level groups from as multi-task (MTL) problem, propose novel MTL model specifically RCTs. Instead training separate groups, proposed trained both effectively enlarging dataset. develop hierarchical architecture aggregate data different sources longitudinal stages trial, which allows exploit commonalities capture two evaluated approach involving 106 depression, who receive integrated treatment. Our outperforms single-task traditional predictive performance, representing promising step utilizing collected precision medicine.
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ژورنال
عنوان ژورنال: Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies
سال: 2022
ISSN: ['2474-9567']
DOI: https://doi.org/10.1145/3534591