Accepted for/Published in: JMIR Research Protocols
Date Submitted: Sep 3, 2024
Date Accepted: Dec 26, 2024
Applications of Natural Language Processing and Large Language Models for Social Determinants of Health: Protocol for a Systematic Review
ABSTRACT
Background:
In recent years, the intersection of Natural Language Processing (NLP) and public health has opened innovative pathways for investigating Social Determinants of Health (SDOH) in textual datasets. Despite the promise of NLP in the SDOH domain, the literature is dispersed across various disciplines, and there is a need to consolidate existing knowledge, identify knowledge gaps in the literature, and inform future research directions in this emerging field.
Objective:
This research protocol describes the systematic review to identify and highlight NLP techniques, including Large Language Models (LLMs), used for SDOH-related studies.
Methods:
A search strategy will be executed across PubMed, Web of Science, IEEE Xplore, Scopus, PsycINFO, HealthSource: Academic Nursing, and ACL Anthology to find studies published in English between 2014 and 2024. Three reviewers will independently screen the studies to avoid voting bias and any conflicts during the screening process will be resolved by two additional reviewers. We will further screen studies that cited the included studies (forward search). Following the title abstract and full-text screening, the characteristics and main findings of the included studies and resources will be tabulated, visualized, and summarized.
Results:
The search strategy has been formulated and run across the seven databases in August 2024. We expect the results to be submitted for peer-review publication in early 2025. At the time of submitting this protocol, the title and abstract screening was underway.
Conclusions:
This systematic review aims to provide a comprehensive study of existing research on the application of NLP for various SDOH tasks across multiple textual datasets. By rigorously evaluating the methodologies, tools, and outcomes of eligible studies, the review will identify gaps in current knowledge and suggest directions for future research in the form of specific research questions. The findings will be instrumental in guiding the development of more effective NLP models for SDOH, ultimately contributing to improved health outcomes and a better understanding of social determinants in diverse populations. Clinical Trial: PROSPERO 2024 CRD42024578082: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42024578082
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