  Objective: The 2022 n2c2 NLP Challenge posed identification of social
determinants of health (SDOH) in clinical narratives. We present three systems
that we developed for the Challenge and discuss the distinctive task
formulation used in each of the three systems. Materials and Methods: The first
system identifies target pieces of information independently using machine
learning classifiers. The second system uses a large language model (LLM) to
extract complete structured outputs per document. The third system extracts
candidate phrases using machine learning and identifies target relations with
hand-crafted rules. Results: The three systems achieved F1 scores of 0.884,
0.831, and 0.663 in the Subtask A of the Challenge, which are ranked third,
seventh, and eighth among the 15 participating teams. The review of the
extraction results from our systems reveals characteristics of each approach
and those of the SODH extraction task. Discussion: Phrases and relations
annotated in the task is unique and diverse, not conforming to the conventional
event extraction task. These annotations are difficult to model with limited
training data. The system that extracts information independently, ignoring the
annotated relations, achieves the highest F1 score. Meanwhile, LLM with its
versatile capability achieves the high F1 score, while respecting the annotated
relations. The rule-based system tackling relation extraction obtains the low
F1 score, while it is the most explainable approach. Conclusion: The F1 scores
of the three systems vary in this challenge setting, but each approach has
advantages and disadvantages in a practical application. The selection of the
approach depends not only on the F1 score but also on the requirements in the
application.
