  Clinical factors account only for a small portion, about 10-30%, of the
controllable factors that affect an individual's health outcomes. The remaining
factors include where a person was born and raised, where he/she pursued their
education, what their work and family environment is like, etc. These factors
are collectively referred to as Social Determinants of Health (SDoH). The
majority of SDoH data is recorded in unstructured clinical notes by physicians
and practitioners. Recording SDoH data in a structured manner (in an EHR) could
greatly benefit from a dedicated ontology of SDoH terms. Our research focuses
on extracting sentences from clinical notes, making use of such an SDoH
ontology (called SOHO) to provide appropriate concepts. We utilize recent
advancements in Deep Learning to optimize the hyperparameters of a Clinical
BioBERT model for SDoH text. A genetic algorithm-based hyperparameter tuning
regimen was implemented to identify optimal parameter settings. To implement a
complete classifier, we pipelined Clinical BioBERT with two subsequent linear
layers and two dropout layers. The output predicts whether a text fragment
describes an SDoH issue of the patient. We compared the AdamW, Adafactor, and
LAMB optimizers. In our experiments, AdamW outperformed the others in terms of
accuracy.

Artificial Intelligence;Computation and Language;Machine Learning