"Let Me Tell You About Your Mental Health!"

Gaur, Manas; Kursuncu, Ugur; Alambo, Amanuel; Sheth, Amit P.; Daniulaityte, Raminta; Thirunarayan, Krishnaprasad; Pathak, Jyotishman

doi:10.1145/3269206.3271732

Cited by 62 publications

(20 citation statements)

References 28 publications

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“…The use of social media [ 64 , 165 , 211 ], sensors [ 23 , 27 , 128 , 168 ], and other technology interaction data [ 154 , 222 ] has been described as allowing for the "non-burdensome," "unobtrusive," or "passive" assessment of peoples" mental health. These systems were suggested to enable "honest sharing of mental health concerns" (p. 754) [ 64 ] and to provide "natural data" as it is "generated by individuals in the normal course of their lives" (p. 10655) [ 133 ]. Sensor data was particularly valued for enabling the automatic, longer-term tracking of a person's mental health-related behaviors [ 44 ].…”

Section: Easy Timely Unobtrusivementioning

confidence: 99%

“…Due to their data sampling approach (described below), many of the social media papers did not specify any "user" numbers; and instead report the "total number of posts and comments" that were analyzed (e.g ., 3000 [ 89 ], 4026 [ 57 ], 5000 [ 133 ], 7410 [ 141 ], 113,337 [ 165 ]). Contrary to this, data that is accessed as part of existing datasets and health records predominantly included information about "patients," or "people with a mental health condition" (n = 18); and to a lesser extent individuals without a clinical mental health diagnosis (n = 5) such as: mobile phone users [ 176 , 177 ], students and workers [ 128 , 135 ], social media users [ 64 ]. Table 2 further outlines the number of people that were included in the respective studies.…”

Section: Source and Scale Of Mental Health Data ML Algorithms Build mentioning

confidence: 99%

“…The BiAffect mobile phone and Depresjon dataset were used to access acceleration data of people with depression [ 62 ] and bipolar conditions [ 27 ], while the English Longitudinal Study of Ageing (ELSA) provided psychological and mental health data on older adults as indicators of depression [ 209 ]. Finally, a few papers reported on the re-use of previously collected user data in the context of a commercial wellness platform [ 152 ], for social media analysis [ 64 ], and mood or well-being research (e.g . , Emotion Sense [ 176 , 177 ], SNAPSHOT 7 [ 135 ], and StudentLife 8 [ 128 ]).…”

Section: Access To Pre-existing Mental Health Data As An Alternative mentioning

confidence: 99%

“…In a few instances of regression tasks 9 [ 27 , 112 , 122 , 128 , 155 , 193 ], metrics of mean error (e.g., MSE, MAE, RMSE, SMAPE) were applied [ 50 , 62 , 112 , 155 , 179 ] to reveal any unexpected values, sensitivities towards outliers, and risks of over-or underestimating false predictions [ 51 ]. Individual works also applied more specific metrics to evaluate multi-dimensional classification (i.e., using Hamming score, Hamming Loss, Exact-match [ 136 ]); or the confidence [ 139 ], coherence [ 64 ], and completeness [ 45 ] of clustering outcomes (e.g . , using WCSS, Dunn Index, DB Index, or Silhouette Index to assess similarity within, and separation between, clusters [ 179 ]).…”

Section: Performance Evaluation Of ML Models: Common Techniques and Pmentioning

confidence: 99%

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Machine Learning in Mental Health

Thieme

Belgrave

Doherty

2020

ACM Trans. Comput.-Hum. Interact.

237

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High prevalence of mental illness and the need for effective mental health care, combined with recent advances in AI, has led to an increase in explorations of how the field of machine learning (ML) can assist in the detection, diagnosis and treatment of mental health problems. ML techniques can potentially offer new routes for learning patterns of human behavior; identifying mental health symptoms and risk factors; developing predictions about disease progression; and personalizing and optimizing therapies. Despite the potential opportunities for using ML within mental health, this is an emerging research area, and the development of effective ML-enabled applications that are implementable in practice is bound up with an array of complex, interwoven challenges. Aiming to guide future research and identify new directions for advancing development in this important domain, this article presents an introduction to, and a systematic review of, current ML work regarding psycho-socially based mental health conditions from the computing and HCI literature. A quantitative synthesis and qualitative narrative review of 54 papers that were included in the analysis surfaced common trends, gaps, and challenges in this space. Discussing our findings, we (i) reflect on the current state-of-the-art of ML work for mental health, (ii) provide concrete suggestions for a stronger integration of human-centered and multidisciplinary approaches in research and development, and (iii) invite more consideration of the potentially far-reaching personal, social, and ethical implications that ML models and interventions can have, if they are to find widespread, successful adoption in real-world mental health contexts.

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Section: Easy Timely Unobtrusivementioning

confidence: 99%

Section: Source and Scale Of Mental Health Data ML Algorithms Build mentioning

confidence: 99%

Section: Access To Pre-existing Mental Health Data As An Alternative mentioning

confidence: 99%

Section: Performance Evaluation Of ML Models: Common Techniques and Pmentioning

confidence: 99%

See 2 more Smart Citations

Machine Learning in Mental Health

Thieme

Belgrave

Doherty

2020

ACM Trans. Comput.-Hum. Interact.

237

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“…The gold standardusing human judgesis expensive and laborious to obtain (Roder, Both & Hinneburg, 2015). Following Gaur et al, (2018), the current paper thus turned to using topic coherence, an automated quantitative measure of topic understandability (Roder et al, 2015), to assist in the selection of k. The coherence measure used in the current study was Cv, which was proposed and…”

Section: Future Directionsmentioning

confidence: 99%

A quantitative exploration of repeated advice-seeking on Reddit's r/relationships

Kang¹

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Building trustworthy NeuroSymbolic AI Systems: Consistency, reliability, explainability, and safety

Gaur,

Sheth

2024

AI Magazine

Self Cite

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Explainability and Safety engender trust. These require a model to exhibit consistency and reliability. To achieve these, it is necessary to use and analyze data and knowledge with statistical and symbolic AI methods relevant to the AI application––neither alone will do. Consequently, we argue and seek to demonstrate that the NeuroSymbolic AI approach is better suited for making AI a trusted AI system. We present the CREST framework that shows how Consistency, Reliability, user‐level Explainability, and Safety are built on NeuroSymbolic methods that use data and knowledge to support requirements for critical applications such as health and well‐being. This article focuses on Large Language Models (LLMs) as the chosen AI system within the CREST framework. LLMs have garnered substantial attention from researchers due to their versatility in handling a broad array of natural language processing (NLP) scenarios. As examples, ChatGPT and Google's MedPaLM have emerged as highly promising platforms for providing information in general and health‐related queries, respectively. Nevertheless, these models remain black boxes despite incorporating human feedback and instruction‐guided tuning. For instance, ChatGPT can generate unsafe responses despite instituting safety guardrails. CREST presents a plausible approach harnessing procedural and graph‐based knowledge within a NeuroSymbolic framework to shed light on the challenges associated with LLMs.

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"Let Me Tell You About Your Mental Health!"

Cited by 62 publications

References 28 publications

Machine Learning in Mental Health

Machine Learning in Mental Health

A quantitative exploration of repeated advice-seeking on Reddit's r/relationships

Building trustworthy NeuroSymbolic AI Systems: Consistency, reliability, explainability, and safety

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