Natural language processing in mental health applications using non-clinical texts

Calvo, Rafael A.; Milne, David; Hussain, M. Sazzad; Christensen, Helen

doi:10.1017/s1351324916000383

Cited by 225 publications

(164 citation statements)

References 111 publications

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“…We developed a supervised machine learning algorithm to identify suicides related to LTC using the CME narratives; we treat this issue as a classification problem in trying to distinguish narratives associated with LTC from those that are not. 23 We used NLP techniques to convert the text narratives into computationally analyzable representations; these techniques are used to solve large-scale text classification problems, such as spam recognition, and are beginning to be used in health care. 24 , 25 The process of developing, training, iterating, and cross-validating the algorithm is summarized below and detailed in eFigure 1 in the Supplement .…”

Section: Methodsmentioning

confidence: 99%

Suicide Among Older Adults Living in or Transitioning to Residential Long-term Care, 2003 to 2015

Mezuk

Kalesnikava

et al. 2019

JAMA Netw Open

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Key Points Question How common is suicide in residential long-term care (LTC) settings, and is transitioning into or out of LTC associated with suicide among older adults? Findings In this cross-sectional epidemiologic study of 47 759 deaths in the National Violent Death Reporting System (NVDRS) (2003-2015), a natural language processing algorithm identified 1037 suicides among adults 55 years and older associated with LTC. The algorithm identified far more deaths associated with LTC in comparison with the existing NVDRS injury location codes. Meaning Suicide occurs among older adults living in or transitioning to LTC settings, and the mental health and well-being of older adults should be addressed in these settings.

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Section: Methodsmentioning

confidence: 99%

Suicide Among Older Adults Living in or Transitioning to Residential Long-term Care, 2003 to 2015

Mezuk

Kalesnikava

et al. 2019

JAMA Netw Open

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“…Advances in natural language processing and machine learning are likely to automate this process (Hirschberg & Manning, 2015). These techniques are increasingly being used in mental health to create treatments that can interact and adapt to individual people (Calvo, Milne, Hussain, & Christensen, 2017). …”

Section: Future Directionsmentioning

confidence: 99%

Ecological momentary interventions for depression and anxiety

2017

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Ecological momentary interventions (EMIs) are becoming more popular and more powerful resources for the treatment and prevention of depression and anxiety due to advances in technological capacity and analytic sophistication. Previous work has demonstrated that EMIs can be effective at reducing symptoms of depression and anxiety as well as related outcomes of stress and at increasing positive psychological functioning. In this review, we highlight the differences between EMIs and other forms of treatment due to the nature of EMIs to be deeply integrated into the fabric of people's day-to-day lives. EMIs require unique considerations in their design, deployment, and evaluation. Furthermore, given that EMIs have been advanced by changes in technologies and that the use of behavioral intervention technologies for mental health has been increasing, we discuss how technologies and analytics might usher in a new era of EMIs. Future EMIs might reduce user burden and increase intervention personalization and sophistication by leveraging digital sensors and advances in natural language processing and machine learning. Thus, although current EMIs are effective, the EMIs of the future might be more engaging, responsive, and adaptable to different people and different contexts.

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“…Neuroimaging data have also been provided many promising results, but this article focuses on non‐neuroimaging sensors such as voice. Finally, text obtained either from transcribed audio recordings, blogs, or social media has been used to detect many psychiatric disorders including psychotic, depressive, and anxiety disorders from morphological, syntactic, semantic, and discursive features (for reviews, see References ). These technologies thus provide opportunities for better assessment of mental health.…”

Section: Introductionmentioning

confidence: 99%

Automated assessment of psychiatric disorders using speech: A systematic review

Low

Bentley

Ghosh

2020

Laryngoscope Investig Oto

290

268

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Objective There are many barriers to accessing mental health assessments including cost and stigma. Even when individuals receive professional care, assessments are intermittent and may be limited partly due to the episodic nature of psychiatric symptoms. Therefore, machine‐learning technology using speech samples obtained in the clinic or remotely could one day be a biomarker to improve diagnosis and treatment. To date, reviews have only focused on using acoustic features from speech to detect depression and schizophrenia. Here, we present the first systematic review of studies using speech for automated assessments across a broader range of psychiatric disorders. Methods We followed the Preferred Reporting Items for Systematic Reviews and Meta‐Analysis (PRISMA) guidelines. We included studies from the last 10 years using speech to identify the presence or severity of disorders within the Diagnostic and Statistical Manual of Mental Disorders (DSM‐5). For each study, we describe sample size, clinical evaluation method, speech‐eliciting tasks, machine learning methodology, performance, and other relevant findings. Results 1395 studies were screened of which 127 studies met the inclusion criteria. The majority of studies were on depression, schizophrenia, and bipolar disorder, and the remaining on post‐traumatic stress disorder, anxiety disorders, and eating disorders. 63% of studies built machine learning predictive models, and the remaining 37% performed null‐hypothesis testing only. We provide an online database with our search results and synthesize how acoustic features appear in each disorder. Conclusion Speech processing technology could aid mental health assessments, but there are many obstacles to overcome, especially the need for comprehensive transdiagnostic and longitudinal studies. Given the diverse types of data sets, feature extraction, computational methodologies, and evaluation criteria, we provide guidelines for both acquiring data and building machine learning models with a focus on testing hypotheses, open science, reproducibility, and generalizability. Level of Evidence 3a

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Natural language processing in mental health applications using non-clinical texts

Cited by 225 publications

References 111 publications

Suicide Among Older Adults Living in or Transitioning to Residential Long-term Care, 2003 to 2015

Suicide Among Older Adults Living in or Transitioning to Residential Long-term Care, 2003 to 2015

Ecological momentary interventions for depression and anxiety

Automated assessment of psychiatric disorders using speech: A systematic review

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