Ontology-Driven Self-Supervision for Adverse Childhood Experiences Identification Using Social Media Datasets

no code implementations • 24 Aug 2022 • Jinge Wu, Rowena Smith, Honghan Wu

In this paper, we present an ontology-driven self-supervised approach (derive concept embeddings using an auto-encoder from baseline NLP results) for producing a publicly available resource that would support large-scale machine learning (e. g., training transformer based large language models) on social media corpus.

Adverse Childhood Experiences Identification from Clinical Notes with Ontologies and NLP

no code implementations • 24 Aug 2022 • Jinge Wu, Rowena Smith, Honghan Wu

Adverse Childhood Experiences (ACEs) are defined as a collection of highly stressful, and potentially traumatic, events or circumstances that occur throughout childhood and/or adolescence.