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Zhongqi Yang, Mahkameh Rasouli, Neda Mohseni, Yong Huang et autres
Personalized models are essential in digital health because individuals exhibit substantial physiological and behavioral heterogeneity. Yet personalization is limited by scarce and noisy user-specific data. Most existing methods rely on population pretraining or data from similar users only, which can lead to …
Zhongqi Yang, Mahkameh Rasouli, Neda Mohseni, Yong Huang et autres
Personalized models are essential in digital health because individuals exhibit substantial physiological and behavioral heterogeneity. Yet personalization is limited by scarce and noisy user-specific data. Most existing methods rely on population pretraining or data from similar users only, which can lead to …
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Large Language Models (LLMs) excel at general-purpose reasoning by leveraging broad commonsense knowledge, but they remain limited in tasks requiring personalized reasoning over multifactorial personal data. This limitation constrains their applicability in domains such as healthcare, where decisions must adapt to individual …
us (code pays fourni par la source)
Yong Huang, Zhongqi Yang, Amir Rahmani
Sepsis is a leading cause of mortality in intensive care units (ICUs), yet existing research often relies on outdated datasets, non-reproducible preprocessing pipelines, and limited coverage of clinical interventions. We introduce MIMIC-Sepsis, a curated cohort and benchmark framework derived from the MIMIC-IV …
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MK Michael Cheung, Zhongqi Yang, Xinwei Zhai, Eugene Yujun Fu et autres
hk, cn (code pays fourni par la source)
Zhongqi Yang, Wenhang Ge, Yuqi Li, Jiaqi Chen et autres
Explorable 3D world generation from a single image or text prompt forms a cornerstone of spatial intelligence. Recent works utilize video model to achieve wide-scope and generalizable 3D world generation. However, existing approaches often suffer from a limited scope in the generated …
Large Language Models (LLMs) excel at general-purpose reasoning by leveraging broad commonsense knowledge, but they remain limited in tasks requiring personalized reasoning over multifactorial personal data. This limitation constrains their applicability in domains such as healthcare, where decisions must adapt to individual …
Ajan Subramanian, Zhongqi Yang, Iman Azimi, Amir M. Rahmani
Health monitoring systems have revolutionized mod-ern healthcare by enabling the continuous capture of physio-logical and behavioral data, essential for preventive measures and early intervention. Integrating this data with Large Lan-guage Models (LLMs) shows promise in delivering interactive health advice, but traditional methods …
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Yuning Wang, Zhongqi Yang, Iman Azimi, Amir M. Rahmani et autres
Wearable technology enables ubiquitous health monitoring where multivariate physiological and behavioral data can be captured over time. Such multivariate time series (MTS) data in healthcare applications needs technique to interpret the analysis results. However, existing deep learning models for MTS data analysis …
fi, us (code pays fourni par la source)
Ziyu Wang, Zhongqi Yang, Iman Azimi, Amir M. Rahmani
Mental health conditions, prevalent across various demographics, necessitate efficient monitoring to mitigate their adverse impacts on life quality. The surge in data-driven methodologies for mental health monitoring has underscored the importance of privacy-preserving techniques in handling sensitive health data. Despite strides in …
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Mahyar Abbasian, Zhongqi Yang, Elahe Khatibi, Pengfei Zhang et autres
Effective diabetes management is crucial for maintaining health in diabetic patients. Large Language Models (LLMs) have opened new avenues for diabetes management, facilitating their efficacy. However, current LLM-based approaches are limited by their dependence on general sources and lack of integration with …
us (code pays fourni par la source)
Ajan Subramanian, Zhongqi Yang, Iman Azimi, Amir M. Rahmani
Health monitoring systems have revolutionized modern healthcare by enabling the continuous capture of physiological and behavioral data, essential for preventive measures and early health intervention. While integrating this data with Large Language Models (LLMs) has shown promise in delivering interactive health advice, …
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