A group of LLM agents that collaboratively generate code for raw passive sensing data outperforms RAG on objective query accuracy, while remaining only moderately consistent across repeated runs.
Imputation Matters: A Deeper Look into an Overlooked Step in Longitudinal Health and Behavior Sensing Research
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abstract
Longitudinal passive sensing studies for health and behavior outcomes often have missing and incomplete data. Handling missing data effectively is thus a critical data processing and modeling step. Our formative interviews with researchers working in longitudinal health and behavior passive sensing revealed a recurring theme: most researchers consider imputation a low-priority step in their analysis and inference pipeline, opting to use simple and off-the-shelf imputation strategies without comprehensively evaluating its impact on study outcomes. Through this paper, we call attention to the importance of imputation. Using publicly available passive sensing datasets for depression, we show that prioritizing imputation can significantly impact the study outcomes -- with our proposed imputation strategies resulting in up to 31% improvement in AUROC to predict depression over the original imputation strategy. We conclude by discussing the challenges and opportunities with effective imputation in longitudinal sensing studies.
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GLOSS: Group of LLMs for Open-Ended Sensemaking of Passive Sensing Data for Health and Wellbeing
A group of LLM agents that collaboratively generate code for raw passive sensing data outperforms RAG on objective query accuracy, while remaining only moderately consistent across repeated runs.