ArXiv preprint
Employee well-being is a critical concern in the contemporary workplace, as highlighted by the American Psychological Association's 2021 report, indicating that 71% of employees experience stress or tension. This stress contributes significantly to workplace attrition and absenteeism, with 61% of attrition and 16% of sick days attributed to poor mental health. A major challenge for employers is that employees often remain unaware of their mental health issues until they reach a crisis point, resulting in limited utilization of corporate well-being benefits. This research addresses this challenge by presenting a groundbreaking stress detection algorithm that provides real-time support preemptively. Leveraging automated chatbot technology, the algorithm objectively measures mental health levels by analyzing chat conversations, offering personalized treatment suggestions in real-time based on linguistic biomarkers. The study explores the feasibility of integrating these innovations into practical learning applications within real-world contexts and introduces a chatbot-style system integrated into the broader employee experience platform. This platform, encompassing various features, aims to enhance overall employee well-being, detect stress in real time, and proactively engage with individuals to improve support effectiveness, demonstrating a 22% increase when assistance is provided early. Overall, the study emphasizes the importance of fostering a supportive workplace environment for employees' mental health.
Mental health challenges pose considerable global burdens on individuals and communities. Recent data indicates that more than 20% of adults may encounter at least one mental disorder in their lifetime. On the one hand, the advancements in large language models have facilitated diverse applications, yet a significant research gap persists in understanding and enhancing the potential of large language models within the domain of mental health. On the other hand, across various applications, an outstanding question involves the capacity of large language models to comprehend expressions of human mental health conditions in natural language. This study presents an initial evaluation of large language models in addressing this gap. Due to this, we compare the performance of Llama-2 and ChatGPT with classical Machine as well as Deep learning models. Our results on the DAIC-WOZ dataset show that transformer-based models, like BERT or XLNet, outperform the large language models.
Depression and anxiety are the two most prevalent mental health disorders worldwide, impacting the lives of millions of people each year. In this work, we develop and evaluate a multilabel, multidimensional deep neural network designed to predict PHQ-4 scores based on individuals written text. Our system outperforms random baseline metrics and provides a novel approach to how we can predict psychometric scores from written text. Additionally, we explore how this architecture can be applied to analyse social media data.
26th AIAI
Almost 50% of cases of major depressive disorder go undiagnosed. In this paper, we propose a passive diagnostic system that combines the areas of clinical psychology, machine learning and conversational dialogue systems. We have trained a dialogue system, powered by sequence-to-sequence neural networks that can have a real-time conversation with individuals. In tandem, we have developed specific machine learning classifiers that monitor the conversation and predict the presence or absence of certain crucial depression symptoms. This would facilitate real-time instant crisis support for those suffering from depression. Our evaluation metrics have suggested this could be a positive future direction of research in both developing more human like chatbots and identifying depression in written text. We hope this work may additionally have practical implications in the area of crisis support services for mental health organisations.
