  Objective: Evictions are important social and behavioral determinants of
health. Evictions are associated with a cascade of negative events that can
lead to unemployment, housing insecurity/homelessness, long-term poverty, and
mental health problems. In this study, we developed a natural language
processing system to automatically detect eviction status from electronic
health record (EHR) notes.
  Materials and Methods: We first defined eviction status (eviction presence
and eviction period) and then annotated eviction status in 5000 EHR notes from
the Veterans Health Administration (VHA). We developed a novel model, KIRESH,
that has shown to substantially outperform other state-of-the-art models such
as fine-tuning pre-trained language models like BioBERT and BioClinicalBERT.
Moreover, we designed a novel prompt to further improve the model performance
by using the intrinsic connection between the two sub-tasks of eviction
presence and period prediction. Finally, we used the Temperature Scaling-based
Calibration on our KIRESH-Prompt method to avoid over-confidence issues arising
from the imbalance dataset.
  Results: KIRESH-Prompt substantially outperformed strong baseline models
including fine-tuning the BioClinicalBERT model to achieve 0.74672 MCC, 0.71153
Macro-F1, and 0.83396 Micro-F1 in predicting eviction period and 0.66827 MCC,
0.62734 Macro-F1, and 0.7863 Micro-F1 in predicting eviction presence. We also
conducted additional experiments on a benchmark social determinants of health
(SBDH) dataset to demonstrate the generalizability of our methods.
  Conclusion and Future Work: KIRESH-Prompt has substantially improved eviction
status classification. We plan to deploy KIRESH-Prompt to the VHA EHRs as an
eviction surveillance system to help address the US Veterans' housing
insecurity.
