Context-Specific Feature Augmentation for Improving Social Determinants of Health Extraction

Social determinants of health (SDoH) factors such as poverty, social environment, and unemployment are known to profoundly impact health outcomes. However, extracting SDoH from the electronic health records (EHR) is a challenge due to the unstructured nature of clinical narratives that encode them. To address this, several approaches ranging from rule-based natural language processing to large language models have been proposed in the literature. Despite significant advances, the existing SDoH extraction approaches are not robust to the noise present in clinical notes or discharge summaries and thus yield unsatisfactory performance. In other words, the noisy information in clinical notes leads to the generation of low-quality feature representations of medical concepts that severely impacts the performance of SDoH extraction.In this paper, we propose a novel approach that augments EHR discharge summaries with context-specific semantic knowledge from biomedical literature to generate robust feature representations needed for accurate SDoH extraction. Specifically, our approach identifies key contextual information (e.g., symptoms, diseases, and medications) from EHR discharge summaries and retrieves relevant scientific articles to generate additional semantic context for SDoH classifier. Moreover, to effectively fuse complementary information from both EHR discharge summaries and biomedical literature, we propose a new feature infusion strategy that adaptively fuses feature representations based on their contextual relevance. Experimental results on the benchmark MIMIC-SDoH dataset demonstrate that the proposed approach significantly outperforms baseline algorithms and highlight the role of context-specific feature augmentation in enhancing the accuracy of SDoH extraction.