Clinical Significance of Marital Status and Changes in Status Extracted from Unstructured Clinical Notes Using Ensembles of Off-the-Shelf Extraction Models.

Marital status and its dynamics significantly influence health outcomes. This study aims to demonstrate clinical utility of marital status and improve its extraction from electronic health records (EHR) by developing ensembles capable of using structured and unstructured EHR data. Four ensembles based on different voting rules (unanimous vote, majority vote, precision-based vote, and vote based on random forest) were assembled using structured marital status data, off-the-shelf CNN, and hybrid models, capable of detecting marital status from clinical notes, followed by BERT-based fine-tuning to increase recall.

Natural Language Processing and Social Determinants of Health in Mental Health Research: AI-Assisted Scoping Review.

Background: The use of natural language processing (NLP) in mental health research is increasing, with a wide range of applications and datasets being investigated.

Objective: This review aims to summarize the use of NLP in mental health research, with a special focus on the types of text datasets and the use of social determinants of health (SDOH) in NLP projects related to mental health.

Methods: The search was conducted in September 2024 using a broad search strategy in PubMed, Scopus, and CINAHL Complete.