@inproceedings{aich-parde-2022-really,
    title = "Are You Really Okay? A Transfer Learning-based Approach for Identification of Underlying Mental Illnesses",
    author = "Aich, Ankit  and
      Parde, Natalie",
    editor = "Zirikly, Ayah  and
      Atzil-Slonim, Dana  and
      Liakata, Maria  and
      Bedrick, Steven  and
      Desmet, Bart  and
      Ireland, Molly  and
      Lee, Andrew  and
      MacAvaney, Sean  and
      Purver, Matthew  and
      Resnik, Rebecca  and
      Yates, Andrew",
    booktitle = "Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology",
    month = jul,
    year = "2022",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2022.clpsych-1.8/",
    doi = "10.18653/v1/2022.clpsych-1.8",
    pages = "89--104",
    abstract = "Evidence has demonstrated the presence of similarities in language use across people with various mental health conditions. In this work, we investigate these correlations both in terms of literature and as a data analysis problem. We also introduce a novel state-of-the-art transfer learning-based approach that learns from linguistic feature spaces of previous conditions and predicts unknown ones. Our model achieves strong performance, with F1 scores of 0.75, 0.80, and 0.76 at detecting depression, stress, and suicidal ideation in a first-of-its-kind transfer task and offering promising evidence that language models can harness learned patterns from known mental health conditions to aid in their prediction of others that may lie latent."
}