149 research outputs found
Envisioning a Decolonial Digital Mental Health
The field of digital mental health is making strides in the application
of technology to broaden access to care. We critically examine how
these technology-mediated forms of care might amplify historical
injustices, and erase minoritized experiences and expressions of
mental distress and illness. We draw on decolonial thought and critiques of identity-based algorithmic bias to analyze the underlying
power relations impacting digital mental health technologies today,
and envision new pathways towards a decolonial digital mental
health. We argue that a decolonial digital mental health is one that
centers lived experience over rigid classification, is conscious of
structural factors that infuence mental wellbeing, and is fundamentally designed to deter the creation of power differentials that
prevent people from having agency over their care. Stemming from
this vision, we make recommendations for how researchers and designers can support more equitable futures for people experiencing
mental distress and illness
Identifying Depressive Symptoms from Tweets: Figurative Language Enabled Multitask Learning Framework
Existing studies on using social media for deriving mental health status of users focus on the depression detection task. However, for case management and referral to psychiatrists, healthcare workers require practical and scalable depressive disorder screening and triage system. This study aims to design and evaluate a decision support system (DSS) to reliably determine the depressive triage level by capturing fine-grained depressive symptoms expressed in user tweets through the emulation of Patient Health Questionnaire-9 (PHQ-9) that is routinely used in clinical practice. The reliable detection of depressive symptoms from tweets is challenging because the 280-character limit on tweets incentivizes the use of creative artifacts in the utterances and figurative usage contributes to effective expression. We propose a novel BERT based robust multi-task learning framework to accurately identify the depressive symptoms using the auxiliary task of figurative usage detection. Specifically, our proposed novel task sharing mechanism, co-task aware attention, enables automatic selection of optimal information across the BERT layers and tasks by soft-sharing of parameters. Our results show that modeling figurative usage can demonstrably improve the model\u27s robustness and reliability for distinguishing the depression symptoms
Mental-LLM: Leveraging Large Language Models for Mental Health Prediction via Online Text Data
Advances in large language models (LLMs) have empowered a variety of
applications. However, there is still a significant gap in research when it
comes to understanding and enhancing the capabilities of LLMs in the field of
mental health. In this work, we present the first comprehensive evaluation of
multiple LLMs, including Alpaca, Alpaca-LoRA, FLAN-T5, GPT-3.5, and GPT-4, on
various mental health prediction tasks via online text data. We conduct a broad
range of experiments, covering zero-shot prompting, few-shot prompting, and
instruction fine-tuning. The results indicate a promising yet limited
performance of LLMs with zero-shot and few-shot prompt designs for the mental
health tasks. More importantly, our experiments show that instruction
finetuning can significantly boost the performance of LLMs for all tasks
simultaneously. Our best-finetuned models, Mental-Alpaca and Mental-FLAN-T5,
outperform the best prompt design of GPT-3.5 (25 and 15 times bigger) by 10.9%
on balanced accuracy and the best of GPT-4 (250 and 150 times bigger) by 4.8%.
They further perform on par with the state-of-the-art task-specific language
model. We also conduct an exploratory case study on LLMs' capability on the
mental health reasoning tasks, illustrating the promising capability of certain
models such as GPT-4. We summarize our findings into a set of action guidelines
for potential methods to enhance LLMs' capability for mental health tasks.
Meanwhile, we also emphasize the important limitations before achieving
deployability in real-world mental health settings, such as known racial and
gender bias. We highlight the important ethical risks accompanying this line of
research
Attention-Based LSTM for Psychological Stress Detection from Spoken Language Using Distant Supervision
We propose a Long Short-Term Memory (LSTM) with attention mechanism to
classify psychological stress from self-conducted interview transcriptions. We
apply distant supervision by automatically labeling tweets based on their
hashtag content, which complements and expands the size of our corpus. This
additional data is used to initialize the model parameters, and which it is
fine-tuned using the interview data. This improves the model's robustness,
especially by expanding the vocabulary size. The bidirectional LSTM model with
attention is found to be the best model in terms of accuracy (74.1%) and
f-score (74.3%). Furthermore, we show that distant supervision fine-tuning
enhances the model's performance by 1.6% accuracy and 2.1% f-score. The
attention mechanism helps the model to select informative words.Comment: Accepted in ICASSP 201
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