Transforming SDOH Screening: Towards a General Framework for Transformer-based Prediction of Social Determinants of Health

Social Determinants of Health (SDOH) play a crucial role in healthcare outcomes, yet identifying them from unstructured patient data remains a challenge. This research explores the potential of Large Language Models (LLMs) for automated SDOH identification from patient notes. We propose a general framework for SDOH screening that is simple and straightforward. We leverage existing SDOH datasets, adapting and combining them to create a more comprehensive benchmark for this task, addressing the research gap of limited datasets. Using the benchmark and proposed framework, we conclude by conducting several preliminary experiments exploring and comparing promising LLM system implementations. Our findings highlight the potential of LLMs for automated SDOH screening while emphasizing the need for more robust datasets and evaluation frameworks.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Computer Science & Applications

Department dc:contributor.department

Computer Science and#38; Applications

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2024

Author dc:creator

• King III, Kenneth Hale

Chair dc:contributor.committeechair

• Gracanin, Denis

Committee members dc:contributor.committeemember

• Luther, Kurt
• Azab, Mohamed Mahmoud Mahmoud

• Generative AI
• Large Language Models
• Social Determinants of Health

Statement dc:rights

• Creative Commons Attribution 4.0 International

Licence dc:rights.uri

• http://creativecommons.org/licenses/by/4.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:41355

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/121097