  Objective. The impact of social determinants of health (SDoH) on patients'
healthcare quality and the disparity is well-known. Many SDoH items are not
coded in structured forms in electronic health records. These items are often
captured in free-text clinical notes, but there are limited methods for
automatically extracting them. We explore a multi-stage pipeline involving
named entity recognition (NER), relation classification (RC), and text
classification methods to extract SDoH information from clinical notes
automatically.
  Materials and Methods. The study uses the N2C2 Shared Task data, which was
collected from two sources of clinical notes: MIMIC-III and University of
Washington Harborview Medical Centers. It contains 4480 social history sections
with full annotation for twelve SDoHs. In order to handle the issue of
overlapping entities, we developed a novel marker-based NER model. We used it
in a multi-stage pipeline to extract SDoH information from clinical notes.
  Results. Our marker-based system outperformed the state-of-the-art span-based
models at handling overlapping entities based on the overall Micro-F1 score
performance. It also achieved state-of-the-art performance compared to the
shared task methods.
  Conclusion. The major finding of this study is that the multi-stage pipeline
effectively extracts SDoH information from clinical notes. This approach can
potentially improve the understanding and tracking of SDoHs in clinical
settings. However, error propagation may be an issue, and further research is
needed to improve the extraction of entities with complex semantic meanings and
low-resource entities using external knowledge.
