本文提出SIFTING框架，结合大语言模型与结构化提示解决临床自由文本报告中信息提取不透明问题，应用于肺癌分期准确率达90%。
This study addresses the integration of genetic susceptibility and real-world behavioral dynamics to improve risk prediction for major depressive disorder (MDD). By combining polygenic risk scores (PRS), electronic health records, and longitudinal behavioral data from Fitbit wearable devices, the authors employ time-varying Cox regression models to examine the joint and interactive effects of PRS and dynamic behavioral features—such as daily step count and sleep stability—on MDD incidence. This work presents the first real-world implementation of a joint gene–digital-phenotype modeling framework, achieving an increase in model C-index from 0.637 to 0.705. Notably, behavioral factors exhibited stronger associations with MDD risk among individuals with high PRS, offering empirical support for genetically informed, personalized prevention strategies.
本文提出SIFTING框架，结合大语言模型与结构化提示解决临床自由文本报告中信息提取不透明问题，应用于肺癌分期准确率达90%。
This study addresses the integration of genetic susceptibility and real-world behavioral dynamics to improve risk prediction for major depressive disorder (MDD). By combining polygenic risk scores (PRS), electronic health records, and longitudinal behavioral data from Fitbit wearable devices, the authors employ time-varying Cox regression models to examine the joint and interactive effects of PRS and dynamic behavioral features—such as daily step count and sleep stability—on MDD incidence. This work presents the first real-world implementation of a joint gene–digital-phenotype modeling framework, achieving an increase in model C-index from 0.637 to 0.705. Notably, behavioral factors exhibited stronger associations with MDD risk among individuals with high PRS, offering empirical support for genetically informed, personalized prevention strategies.