Ontology-driven Self-supervision for Adverse Childhood Experiences Identification using Social Media Datasets

Jinge Wu, Jinge Wu, Rowena Smith, Honghan Wu

2022

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.

Paper Citation

in Harvard Style

Wu J., Smith R. and Wu H. (2022). Ontology-driven Self-supervision for Adverse Childhood Experiences Identification using Social Media Datasets. In Proceedings of the 1st Workshop on Scarce Data in Artificial Intelligence for Healthcare - Volume 1: SDAIH, ISBN 978-989-758-629-3, SciTePress, pages 5-10. DOI: 10.5220/0011531100003523

in Bibtex Style

@conference{sdaih22,
author={Jinge Wu and Rowena Smith and Honghan Wu},
title={Ontology-driven Self-supervision for Adverse Childhood Experiences Identification using Social Media Datasets},
booktitle={Proceedings of the 1st Workshop on Scarce Data in Artificial Intelligence for Healthcare - Volume 1: SDAIH,},
year={2022},
pages={5-10},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011531100003523},
isbn={978-989-758-629-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st Workshop on Scarce Data in Artificial Intelligence for Healthcare - Volume 1: SDAIH,
TI - Ontology-driven Self-supervision for Adverse Childhood Experiences Identification using Social Media Datasets
SN - 978-989-758-629-3
AU - Wu J.
AU - Smith R.
AU - Wu H.
PY - 2022
SP - 5
EP - 10
DO - 10.5220/0011531100003523
PB - SciTePress