The past decade has seen an explosion in the amount of digital information stored in
electronic health records (EHRs). While primarily designed for archiving patient information …

Users suffering from mental health conditions often turn to online resources for support,
including specialized online support communities or general communities such as Twitter …

We propose a novel methodology to generate domain-specific large-scale question
answering (QA) datasets by re-purposing existing annotations for other NLP tasks. We …

Mental health is a significant and growing public health concern. As language usage can be
leveraged to obtain crucial insights into mental health conditions, there is a need for large …

Great research interests have been attracted to devise AI services that are able to provide
mental health support. However, the lack of corpora is a main obstacle to this research …

Mental health poses a significant challenge for an individual's well-being. Text analysis of
rich resources, like social media, can contribute to deeper understanding of illnesses and …

Data quality is crucial for training accurate, unbiased, and trustworthy machine learning
models as well as for their correct evaluation. Recent works, however, have shown that even …

Abstract The Human Phenotype Ontology (HPO) is a dictionary of> 15,000 clinical
phenotypic terms with defined semantic relationships, developed to standardize phenotypic …

Abstract Models of mental health based on natural language processing can uncover latent
signals of mental health from language. Models that indicate whether an individual is …

As an efficient approach to understand, generate, and process natural language texts,
research in natural language processing (NLP) has exhibited a rapid spread and wide …