Machine learning and natural language processing in mental health: a systematic review

Glaz, Aziliz Le; [], Aziliz; Berrouiguet, Sofian; Walter, Michel; [], Michel

doi:10.26226/morressier.5d1a036c57558b317a13fd8f

Cited by 16 publications

(20 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years it has become evident that the data fingerprint produced by an individual contains information relevant for the early diagnosis and prognosis of several neuropsychiatric disorders (Meyer-Lindenberg, 2018;Sigman et al, 2021). Speech produced under natural or ecological conditions is perhaps among the most useful streams of data that can be tapped for automated machine learning models of mental health, since it is produced in large amounts, it is cheap to obtain and analyze, and might reflect the emotional content of the speakers, as well as their ongoing thought processes (Garfield et al, 1992;Le Glaz et al, 2021). Based on these examples, we can speculate that the automated analysis of natural speech will consolidate into a valuable tool to assist the decision-making process of clinicians, and to optimize the design and implementation of therapy sessions assisted by psychedelic compounds, with the objective of maximizing therapeutic gain and reducing the likelihood of anxiety in the patients.…”

Section: Semantic Similarity Parallels Neurochemical and Pharmacological Similaritymentioning

confidence: 99%

Natural Language as a Window into the Subjective Effects and Neurochemistry of Psychedelic Drugs

Tagliazucchi¹

2021

Preprint

View full text Add to dashboard Cite

Psychedelics are drugs capable of eliciting profound alterations in the subjective experience of the users, sometimes with long-lasting consequences. Because of this, psychedelic research tends to focus on human subjects, given their capacity to construct detailed narratives about the contents of their consciousness experiences. In spite of its relevance, the interaction between serotonergic psychedelics and language production is comparatively understudied in the recent literature. This review is focused on two aspects of this interaction: how the acute effects of psychedelic drugs impact on speech organization regardless of its semantic content, and how to characterize the subjective effects of psychedelic drugs by analyzing the semantic content of written retrospective reports. We show that the computational characterization of language production is an emergent powerful tool to predict the therapeutic outcome of individual experiences, relate the effects elicited by psychedelics with those associated with other altered states of consciousness, draw comparisons between the psychedelic state and the symptomatology of certain psychiatric disorders, and investigate the neurochemical profile and mechanism of action of different psychedelic drugs. We conclude that researchers studying psychedelics can considerably expand the range of their potential scientific conclusions by analyzing brief interviews obtained before, during and after the acute effects. Finally, we list a series of questions and open problems that should be addressed to further consolidate this approach.

show abstract

Section: Semantic Similarity Parallels Neurochemical and Pharmacological Similaritymentioning

confidence: 99%

Natural Language as a Window into the Subjective Effects and Neurochemistry of Psychedelic Drugs

Tagliazucchi¹

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…NLP models have been used to detect suicidal thoughts in clinical notes, forecast suicide risk online, and mine for psychiatric self-disclosure on Twitter in recent years (Le Glaz et al, 2021). Both personal patient care and broader public health policy can benefit from these models.…”

Section: Supports Mental Healthmentioning

confidence: 99%

AI in Healthcare: medical and socio-economic benefits and challenges

Shaheen¹

2021

SSRN Journal

View full text Add to dashboard Cite

“…The data from non-traditional digital mental health support services offers a unique window into mental health needs and experiences of distress, and provides an opportunity to evaluate personalised support approaches. In particular, big data analysis techniques, such as natural language processing (NLP) and machine learning, allow us to examine, at scale, expressions of distress and user interactions with mental health services [6]. To date, the challenges involved in accessing relevant data sets have partly prevented a full exploration of the value of such techniques for mental health insights and service evaluation (see [7] for a notable exception).…”

Section: Introductionmentioning

confidence: 99%

Listening to mental health crisis needs at scale: using Natural Language Processing to understand and evaluate a mental health crisis text messaging service

Liu

Peach

Lawrance

et al. 2021

Preprint

View full text Add to dashboard Cite

The current mental health crisis is a growing public health issue requiring a large-scale response that cannot be met with traditional services alone. Digital support tools are proliferating, yet most are not systematically evaluated, and we know little about their users and their needs. Shout is a free mental health text messaging service run by the charity Mental Health Innovations, which provides support for individuals in the UK experiencing mental or emotional distress and seeking help. Here we study a large data set of anonymised text message conversations and post-conversation surveys compiled through Shout. This data provides an opportunity to hear at scale from those experiencing distress; to better understand mental health needs for people not using traditional mental health services; and to evaluate the impact of a novel form of crisis support. We use natural language processing (NLP) to assess the adherence of volunteers to conversation techniques and formats, and to gain insight into demographic user groups and their behavioural expressions of distress. Our textual analyses achieve accurate classification of conversation stages (weighted accuracy = 88%), behaviours (1-hamming loss = 95%) and texter demographics (weighted accuracy = 96%), exemplifying how the application of NLP to frontline mental health data sets can aid with post hoc analysis and evaluation of quality of service provision in digital mental health services.

show abstract

Machine learning and natural language processing in mental health: a systematic review

Cited by 16 publications

References 67 publications

Natural Language as a Window into the Subjective Effects and Neurochemistry of Psychedelic Drugs

Natural Language as a Window into the Subjective Effects and Neurochemistry of Psychedelic Drugs

AI in Healthcare: medical and socio-economic benefits and challenges

Listening to mental health crisis needs at scale: using Natural Language Processing to understand and evaluate a mental health crisis text messaging service

Contact Info

Product

Resources

About