Assessing effects of adverse Social Determinants of Health (SDOH) in TTR V122l carriers via Structured data and Natural Language Processing (NLP) extraction, a comparison

Icahn School Of Medicine At Mount Sinai, New York NY

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Abstract

The research project has two goals. The first is to use natural language processing (NLP) to improve the identification of adverse social determinants of health (SDOH) in patients with a specific genetic mutation. This is important because many social factors are only captured in unstructured medical narratives, and NLP can help identify these factors more accurately than the current method of using specific codes. The second goal is to investigate whether adverse SDOH are associated with poor health outcomes in patients with this mutation. The researchers will look at patients' medical records to identify adverse SDOH and compare their health outcomes to those without adverse SDOH. The researchers hypothesize that adverse SDOH will be associated with worse outcomes, and that more adverse SDOH will be associated with even worse outcomes. However, the study has limitations, including relying on only one NLP tool and using a binary definition of adverse SDOH. Future studies may address these limitations by using different methodologies and more granular data.