Developing a social vulnerability index from real-world electronic health records using natural language processing

Background: Social vulnerability is associated with adverse health outcomes, but existing social vulnerability indices often rely on cohort-based and self-reported data, potentially limiting their population-level applicability. Therefore, this study aimed to develop an SVI from routinely collected electronic health records (EHRs) using natural language processing (NLP) and examine its association with mortality and healthcare utilization. Method: EHR data from 193,629 individuals aged 35 to102 from the Wellbeing Services County of Central Finland between years 2010 and 2023 were used in this

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