Machine learning for suicidal ideation identification: A systematic literature review

Heckler, Wesllei Felipe; Carvalho, Juliano Varella de; Barbosa, Jorge Luís Victória

doi:10.1016/j.chb.2021.107095

Cited by 29 publications

(11 citation statements)

References 72 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(1) A trend plot will visualize the progression of log file data publications [ 44 ]. (2) A taxonomy flow chart will showcase the complex relationships between various theories in different research domains [ 45 ]. (3) A summary table is crafted to encapsulate the characteristics of each study.…”

Section: Methodsmentioning

confidence: 99%

Understanding the log file data from educational and psychological computer-based testing: A scoping review protocol

Chen,

Liu,

Mao

2024

PLoS ONE

View full text Add to dashboard Cite

With the advancement of computer-based testing, log file data has drawn considerable attention from researchers. Although emerging studies have begun to explore log file data, there is a gap in the exploitation of log file data for capturing and understanding participants’ cognitive processes. The debate on how to maximize insights from log file data has not yet reached a consensus. Therefore, we present this protocol for a scoping review that aims to characterize the application of log file data in current publications, including the data pre-processing techniques, analytical methodologies, and theoretical frameworks used by researchers. This review will also aim to illuminate how log file data can enhance psychological and educational assessments. Our findings will highlight the opportunities and challenges presented by log file data as an emerging and essential source of evidence for future advancements in psychological and educational assessment.

show abstract

Section: Methodsmentioning

confidence: 99%

Understanding the log file data from educational and psychological computer-based testing: A scoping review protocol

Chen,

Liu,

Mao

2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Comprehensive studies of learning-based techniques have been applied to digital data [6], medical record [7], complex and large (original) reports [8], social media text [9]- [11] and multimodal data [12]. Systematic studies and reviews towards this research field recapitulate the problem of demographic bias in data collection mechanism, managing consent of users for data, and theoretical underpinnings about human behavior in user-generated information [13]. Although such studies have made headway in human understanding of causes and perceptions associated with user-generated social media posts, automated identification of mental health related causes and perceptions is an area that has not yet come to fruition.…”

Section: A Current Position Of Communitymentioning

confidence: 99%

NLP as a Lens for Causal Analysis and Perception Mining to Infer Mental Health on Social Media

Garg¹,

Saxena²,

Naseem³

et al. 2023

Preprint

View full text Add to dashboard Cite

<p>Interactions among humans on social media often convey intentions behind their actions, yielding a psychological language resource for Mental Health Analysis (MHA) of online users. The success of Computational Intelligence Techniques (CIT) for inferring mental illness from such social media resources points to NLP as a lens for causal analysis and perception mining. However, we argue that more consequential and explainable research is required for optimal impact on clinical psychology practice and personalized mental healthcare. To bridge this gap, we posit two significant dimensions: (1) Causal analysis to illustrate a cause and effect relationship in the user generated text; (2) Perception mining to infer psychological perspectives of social effects on online users intentions. Within the scope of Natural Language Processing (NLP), we further explore critical areas of inquiry associated with these two dimensions, specifically through recent advancements in discourse analysis. This position paper guides the community to explore solutions in this space and advance the state of practice in developing conversational agents for inferring mental health from social media. We advocate for a more explainable approach toward modeling computational psychology problems through the lens of language as we observe an increased number of research contributions in dataset and problem formulation for causal relation extraction and perception enhancements while inferring mental states.</p>

show abstract

“…An increasing number of mobile solutions have been proposed in the literature for digital phenotyping [13]. Moreover, DL models have been intensively explored in combination with data from social media to detect suicidal ideation [67]. However, two research gaps remain: (1) there is a lack of solutions that deploy the models developed for suicidal ideation detection (different models have been developed but not deployed in solutions used in clinical settings) [23]; and (2) the passive monitoring of textual patterns is a too-little explored method in digital phenotyping studies [12,13].…”

Section: Contributions and Applicability Of The Boamentementioning

confidence: 99%

Boamente: A Natural Language Processing-Based Digital Phenotyping Tool for Smart Monitoring of Suicidal Ideation

Diniz

Fontenele²,

Oliveira³

et al. 2022

Healthcare

View full text Add to dashboard Cite

People at risk of suicide tend to be isolated and cannot share their thoughts. For this reason, suicidal ideation monitoring becomes a hard task. Therefore, people at risk of suicide need to be monitored in a manner capable of identifying if and when they have a suicidal ideation, enabling professionals to perform timely interventions. This study aimed to develop the Boamente tool, a solution that collects textual data from users’ smartphones and identifies the existence of suicidal ideation. The solution has a virtual keyboard mobile application that passively collects user texts and sends them to a web platform to be processed. The platform classifies texts using natural language processing and a deep learning model to recognize suicidal ideation, and the results are presented to mental health professionals in dashboards. Text classification for sentiment analysis was implemented with different machine/deep learning algorithms. A validation study was conducted to identify the model with the best performance results. The BERTimbau Large model performed better, reaching a recall of 0.953 (accuracy: 0.955; precision: 0.961; F-score: 0.954; AUC: 0.954). The proposed tool demonstrated an ability to identify suicidal ideation from user texts, which enabled it to be experimented with in studies with professionals and their patients.

show abstract

Machine learning for suicidal ideation identification: A systematic literature review

Cited by 29 publications

References 72 publications

Understanding the log file data from educational and psychological computer-based testing: A scoping review protocol

Understanding the log file data from educational and psychological computer-based testing: A scoping review protocol

NLP as a Lens for Causal Analysis and Perception Mining to Infer Mental Health on Social Media

Boamente: A Natural Language Processing-Based Digital Phenotyping Tool for Smart Monitoring of Suicidal Ideation

Contact Info

Product

Resources

About