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

ABSTRACT: 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. They have been shown to be associated
with increased risks of mental health diseases or other abnormal behaviours in
later lives. However, the identification of ACEs from textual data with Natural
Language Processing (NLP) is challenging because (a) there are no NLP ready ACE
ontologies; (b) there are few resources available for machine learning,
necessitating the data annotation from clinical experts; (c) costly annotations
by domain experts and large number of documents for supporting large machine
learning models. 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. This resource as well as the proposed approach
are aimed to facilitate the community in training transferable NLP models for
effectively surfacing ACEs in low-resource scenarios like NLP on clinical notes
within Electronic Health Records. The resource including a list of ACE ontology
terms, ACE concept embeddings and the NLP annotated corpus is available at
https://github.com/knowlab/ACE-NLP.
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. They have been shown to be associated
with increased risks of mental health diseases or other abnormal behaviours in
later lives. However, the identification of ACEs from textual data with Natural
Language Processing (NLP) is challenging because (a) there are no NLP ready ACE
ontologies; (b) there are few resources available for machine learning,
necessitating the data annotation from clinical experts; (c) costly annotations
by domain experts and large number of documents for supporting large machine
learning models. 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. This resource as well as the proposed approach
are aimed to facilitate the community in training transferable NLP models for
effectively surfacing ACEs in low-resource scenarios like NLP on clinical notes
within Electronic Health Records. The resource including a list of ACE ontology
terms, ACE concept embeddings and the NLP annotated corpus is available at
https://github.com/knowlab/ACE-NLP.
Ontology-Driven Self-Supervision for Adverse Childhood Experiences Identification Using Social Media Datasets
http://arxiv.org/abs/2208.11701v1