Natural Language Processing of Social Media as Screening for Suicide Risk

Implementation Determinants and Outcomes of a Technology-Enabled Service Targeting Suicide Risk in High Schools: Mixed Methods Study

BACKGROUND

Technology-enabled services (TESs), which integrate human service and digital components, are popular strategies to increase the reach and impact of mental health interventions, but large-scale implementation of TESs has lagged behind their potential.

OBJECTIVE

This study applied a mixed qualitative and quantitative approach to gather input from multiple key user groups (students and educators) and to understand the factors that support successful implementation (implementation determinants) and implementation outcomes of a TES for universal screening, ongoing monitoring, and support for suicide risk management in the school setting.

METHODS

A total of 111 students in the 9th to 12th grade completed measures regarding implementation outcomes (acceptability, feasibility, and appropriateness) via an open-ended survey. A total of 9 school personnel (school-based mental health clinicians, nurses, and administrators) completed laboratory-based usability testing of a dashboard tracking the suicide risk of students, quantitative measures, and qualitative interviews to understand key implementation outcomes and determinants. School personnel were presented with a series of scenarios and common tasks focused on the basic features and functions of the dashboard. Directed content analysis based on the Consolidated Framework for Implementation Research was used to extract multilevel determinants (ie, the barriers or facilitators at the levels of the outer setting, inner setting, individuals, intervention, and implementation process) related to positive implementation outcomes of the TES.

RESULTS

Overarching themes related to implementation determinants and outcomes suggest that both student and school personnel users view TESs for suicide prevention as moderately feasible and acceptable based on the Acceptability of Intervention Measure and Feasibility of Intervention Measure and as needing improvements in usability based on the System Usability Scale. Qualitative results suggest that students and school personnel view passive data collection based on social media data as a relative advantage to the current system; however, the findings indicate that the TES and the school setting need to address issues of privacy, integration into existing workflows and communication patterns, and options for individualization for student-centered care.

CONCLUSIONS

Innovative suicide prevention strategies that rely on passive data collection in the school context are a promising and appealing idea. Usability testing identified key issues for revision to facilitate widespread implementation.

Detection of Suicidal Ideation on Social Media: Multimodal, Relational, and Behavioral Analysis

Background

Suicide risk assessment usually involves an interaction between doctors and patients. However, a significant number of people with mental disorders receive no treatment for their condition due to the limited access to mental health care facilities; the reduced availability of clinicians; the lack of awareness; and stigma, neglect, and discrimination surrounding mental disorders. In contrast, internet access and social media usage have increased significantly, providing experts and patients with a means of communication that may contribute to the development of methods to detect mental health issues among social media users.

Objective

This paper aimed to describe an approach for the suicide risk assessment of Spanish-speaking users on social media. We aimed to explore behavioral, relational, and multimodal data extracted from multiple social platforms and develop machine learning models to detect users at risk.

Methods

We characterized users based on their writings, posting patterns, relations with other users, and images posted. We also evaluated statistical and deep learning approaches to handle multimodal data for the detection of users with signs of suicidal ideation (suicidal ideation risk group). Our methods were evaluated over a dataset of 252 users annotated by clinicians. To evaluate the performance of our models, we distinguished 2 control groups: users who make use of suicide-related vocabulary (focused control group) and generic random users (generic control group).

Results

We identified significant statistical differences between the textual and behavioral attributes of each of the control groups compared with the suicidal ideation risk group. At a 95% CI, when comparing the suicidal ideation risk group and the focused control group, the number of friends (P=.04) and median tweet length (P=.04) were significantly different. The median number of friends for a focused control user (median 578.5) was higher than that for a user at risk (median 372.0). Similarly, the median tweet length was higher for focused control users, with 16 words against 13 words of suicidal ideation risk users. Our findings also show that the combination of textual, visual, relational, and behavioral data outperforms the accuracy of using each modality separately. We defined text-based baseline models based on bag of words and word embeddings, which were outperformed by our models, obtaining an increase in accuracy of up to 8% when distinguishing users at risk from both types of control users.

Conclusions

The types of attributes analyzed are significant for detecting users at risk, and their combination outperforms the results provided by generic, exclusively text-based baseline models. After evaluating the contribution of image-based predictive models, we believe that our results can be improved by enhancing the models based on textual and relational features. These methods can be extended and applied to different use cases related to other mental disorders.

Using Language Processing and Speech Analysis for the Identification of Psychosis and Other Disorders

Increasingly, data-driven methods have been implemented to understand psychopathology. Language is the main source of information in psychiatry and represents "big data" at the level of the individual. Language and behavior are amenable to computational natural language processing (NLP) analytics, which may help operationalize the mental status examination. In this review, we highlight the application of NLP to schizophrenia and its risk states as an exemplar of its use, operationalizing tangential and concrete speech as reductions in semantic coherence and syntactic complexity, respectively. Other clinical applications are reviewed, including forecasting suicide risk and detecting intoxication. Challenges and future directions are discussed, including biomarker development, harmonization, and application of NLP more broadly to behavior, including intonation/prosody, facial expression and gesture, and the integration of these in dyads and during discourse. Similar NLP analytics can also be applied beyond humans to behavioral motifs across species, important for modeling psychopathology in animal models. Finally, clinical neuroscience can inform the development of artificial intelligence.

AI in mental health

With the advent of digital approaches to mental health, modern artificial intelligence (AI), and machine learning in particular, is being used in the development of prediction, detection and treatment solutions for mental health care. In terms of treatment, AI is being incorporated into digital interventions, particularly web and smartphone apps, to enhance user experience and optimise personalised mental health care. In terms of prediction and detection, modern streams of abundant data mean that data-driven AI methods can be employed to develop prediction/detection models for mental health conditions. In particular, an individual's 'digital exhaust', the data gathered from their numerous personal digital device and social media interactions, can be mined for behavioural or mental health insights. Language, long considered a window into the human mind, can now be quantitatively harnessed as data with powerful computer-based natural language processing to also provide a method of inferring mental health. Furthermore, natural language processing can also be used to develop conversational agents used for therapeutic intervention.

A day in the life of a psychiatrist in 2050: where will the algorithm take us?

Digital phenotyping (such as using live data from personal digital devices on sleep, activity and social media interactions) to monitor and interpret people's current mental state is a newly emerging development in psychiatry. This article offers an imaginary insight into its future potential for both psychiatrist and patient.

Anomalies in language as a biomarker for schizophrenia

PURPOSE OF REVIEW

After more than a century of neuroscience research, reproducible, clinically relevant biomarkers for schizophrenia have not yet been established. This article reviews current advances in evaluating the use of language as a diagnostic or prognostic tool in schizophrenia.

RECENT FINDINGS

The development of computational linguistic tools to quantify language disturbances is rapidly gaining ground in the field of schizophrenia research. Current applications are the use of semantic space models and acoustic analyses focused on phonetic markers. These features are used in machine learning models to distinguish patients with schizophrenia from healthy controls or to predict conversion to psychosis in high-risk groups, reaching accuracy scores (generally ranging from 80 to 90%) that exceed clinical raters. Other potential applications for a language biomarker in schizophrenia are monitoring of side effects, differential diagnostics and relapse prevention.

SUMMARY

Language disturbances are a key feature of schizophrenia. Although in its early stages, the emerging field of research focused on computational linguistics suggests an important role for language analyses in the diagnosis and prognosis of schizophrenia. Spoken language as a biomarker for schizophrenia has important advantages because it can be objectively and reproducibly quantified. Furthermore, language analyses are low-cost, time efficient and noninvasive in nature.

Deep learning in mental health outcome research: a scoping review

Mental illnesses, such as depression, are highly prevalent and have been shown to impact an individual's physical health. Recently, artificial intelligence (AI) methods have been introduced to assist mental health providers, including psychiatrists and psychologists, for decision-making based on patients' historical data (e.g., medical records, behavioral data, social media usage, etc.). Deep learning (DL), as one of the most recent generation of AI technologies, has demonstrated superior performance in many real-world applications ranging from computer vision to healthcare. The goal of this study is to review existing research on applications of DL algorithms in mental health outcome research. Specifically, we first briefly overview the state-of-the-art DL techniques. Then we review the literature relevant to DL applications in mental health outcomes. According to the application scenarios, we categorize these relevant articles into four groups: diagnosis and prognosis based on clinical data, analysis of genetics and genomics data for understanding mental health conditions, vocal and visual expression data analysis for disease detection, and estimation of risk of mental illness using social media data. Finally, we discuss challenges in using DL algorithms to improve our understanding of mental health conditions and suggest several promising directions for their applications in improving mental health diagnosis and treatment.

A Sequential Emotion Approach for Diagnosing Mental Disorder on Social Media

Mental disorder has been affecting numerous individuals; however, mental health care is in a passive state where only a minority of individuals actively seek professional help. Due to the rapid development of social networks, individuals accustomed to expressing their raw feelings on social media include patients who are suffering great pain from mental disorders. To distinguish individuals who merely feel sad and others who have mental disorders, the symptoms of mental disorder are taken into consideration. These symptoms constantly arise as a regular pattern like shifting of emotions or repeating of one representative emotion during a certain time. We proposed a Mental Disorder Identification Model (MDI-Model) to identify the four most commonly occurring mental disorders in the world: anxiety disorder, bipolar disorder, depressive disorder, and obsessive-compulsive disorder (OCD). The MDI-Model compares the sequential emotion pattern from users to identify mental disorders to detect those who are in a high risk. Tweets of diagnosed mental disorder users were analyzed to evaluate the accuracy of the MDI-Model, furthermore, the tweets of users from six different occupations were analyzed to verify the precision and predict the tendency of mental disorder among the different occupations. Results show that the MDI-Model can efficiently diagnose users with high precision in different mental statuses as severe, moderate, and mild stage, or tendency of mental disorder and mentally healthy status.

Suicide on Facebook-the tales of unnoticed departure in Bangladesh

Facebook has transformed social communication and offers the opportunity to share personal thoughts to people including suicide ideas, plans and attempts. Suicide after Facebook posts has been reported in different parts of the world and it has become a potential area of research for suicide prevention. The analysis of Facebook posts prior to suicide or Facebook live streaming may help in understanding the etiological factors, patterns of communication and possible prevention approaches for a particular community. However, there is a dearth of evidence about suicide incidents after Facebook posts and Facebook live streaming in low and middle-income countries. This study aims to explore the trends and phenomena of suicide after Facebook posts and live streaming in Bangladesh. We conducted an online search using the Google, Facebook and five daily online newspaper archives from 15th August to 15th September 2019. Two research assistants independently conducted the initial searching to find out people who committed suicide after Facebook posts or live streamed suicide in Bangladesh and documented 21 cases. After further evaluation of each of the 21 cases we confirmed 19 cases that met the selection criteria. All of them were under 35-years of age. We observed sucide after Facebook posts were more common in male(78%) e and students. Hanging was the most frequently used method of suicide followed by poisoning. Their Facebook posts and livestream videos indicated relationship problems, academic stress and mental disorders were the common stressors for their suicide. This study lays the foundation for the future researchers to work on suicidal posts on Facebook in Bangladesh and develop culture-specific, real-time suicide preventive systems using a social media platform.

Detecting relapse in youth with psychotic disorders utilizing patient-generated and patient-contributed digital data from Facebook

Although most patients who experience a first-episode of psychosis achieve remission of positive psychotic symptoms, relapse is common. Existing relapse evaluation strategies are limited by their reliance on direct and timely contact with professionals, and accurate reporting of symptoms. A method by which to objectively identify early relapse warning signs could facilitate swift intervention. We collected 52,815 Facebook posts across 51 participants with recent onset psychosis (mean age = 23.96 years; 70.58% male) and applied anomaly detection to explore linguistic and behavioral changes associated with psychotic relapse. We built a one-class classification model that makes patient-specific personalized predictions on risk to relapse. Significant differences were identified in the words posted to Facebook in the month preceding a relapse hospitalization compared to periods of relative health, including increased usage of words belonging to the swear (p < 0.0001, Wilcoxon signed rank test), anger (p < 0.001), and death (p < 0.0001) categories, decreased usage of words belonging to work (p = 0.00579), friends (p < 0.0001), and health (p < 0.0001) categories, as well as a significantly increased use of first (p < 0.0001) and second-person (p  < 0.001) pronouns. We additionally observed a significant increase in co-tagging (p < 0.001) and friending (p < 0.0001) behaviors in the month before a relapse hospitalization. Our classifier achieved a specificity of 0.71 in predicting relapse. Results indicate that social media activity captures objective linguistic and behavioral markers of psychotic relapse in young individuals with recent onset psychosis. Machine-learning models were capable of making personalized predictions of imminent relapse hospitalizations at the patient-specific level.

Recent Advances in Using Natural Language Processing to Address Public Health Research Questions Using Social Media and ConsumerGenerated Data

OBJECTIVE

We present a narrative review of recent work on the utilisation of Natural Language Processing (NLP) for the analysis of social media (including online health communities) specifically for public health applications.

METHODS

We conducted a literature review of NLP research that utilised social media or online consumer-generated text for public health applications, focussing on the years 2016 to 2018. Papers were identified in several ways, including PubMed searches and the inspection of recent conference proceedings from the Association of Computational Linguistics (ACL), the Conference on Human Factors in Computing Systems (CHI), and the International AAAI (Association for the Advancement of Artificial Intelligence) Conference on Web and Social Media (ICWSM). Popular data sources included Twitter, Reddit, various online health communities, and Facebook.

RESULTS

In the recent past, communicable diseases (e.g., influenza, dengue) have been the focus of much social media-based NLP health research. However, mental health and substance use and abuse (including the use of tobacco, alcohol, marijuana, and opioids) have been the subject of an increasing volume of research in the 2016 - 2018 period. Associated with this trend, the use of lexicon-based methods remains popular given the availability of psychologically validated lexical resources suitable for mental health and substance abuse research. Finally, we found that in the period under review "modern" machine learning methods (i.e. deep neural-network-based methods), while increasing in popularity, remain less widely used than "classical" machine learning methods.

Patients' willingness to share digital health and non-health data for research: a cross-sectional study

BACKGROUND

Patients generate large amounts of digital data through devices, social media applications, and other online activities. Little is known about patients' perception of the data they generate online and its relatedness to health, their willingness to share data for research, and their preferences regarding data use.

METHODS

Patients at an academic urban emergency department were asked if they would donate any of 19 different types of data to health researchers and were asked about their views on data types' health relatedness. Factor analysis was used to identify the structure in patients' perceptions of willingness to share different digital data, and their health relatedness.

RESULTS

Of 595 patients approached 206 agreed to participate, of whom 104 agreed to share at least one types of digital data immediately, and 78% agreed to donate at least one data type after death. EMR, wearable, and Google search histories (80%) had the highest percentage of reported health relatedness. 72% participants wanted to know the results of any analysis of their shared data, and half wanted their healthcare provider to know.

CONCLUSION

Patients in this study were willing to share a considerable amount of personal digital data with health researchers. They also recognize that digital data from many sources reveal information about their health. This study opens up a discussion around reconsidering US privacy protections for health information to reflect current opinions and to include their relatedness to health.

A Year of Papers Using Biomedical Texts: Findings from the Section on Natural Language Processing of the IMIA Yearbook

OBJECTIVES

To analyze the content of publications within the medical Natural Language Processing (NLP) domain in 2018.

METHODS

Automatic and manual pre-selection of publications to be reviewed, and selection of the best NLP papers of the year. Analysis of the important issues.

RESULTS

Two best papers have been selected this year. One dedicated to the generation of multi- documents summaries and another dedicated to the generation of imaging reports. We also proposed an analysis of the content of main research trends of NLP publications in 2018.

CONCLUSIONS

The year 2018 is very rich with regard to NLP issues and topics addressed. It shows the will of researchers to go towards robust and reproducible results. Researchers also prove to be creative for original issues and approaches.

Crisis support-seeking behavior and temperature in the United States: Is there an association in young adults and adolescents?

BACKGROUND

Mounting evidence demonstrates the relationship between high temperatures and adverse mental health outcomes. Yet, no study has examined the influence of temperature on crisis support-seeking behavior among youth in large urban areas.

METHODS

Crisis Text Line (CTL) is a text messaging service that provides crisis interventions for support-seeking individuals for a range of mental-health outcomes in the United States. We applied a distributed lag non-linear modeling technique to assess the short-term impacts of daily maximum and minimum temperature on crisis-related events in four metropolitan locations in the USA.

RESULTS

There were multiple positive associations in three of the four study locations that demonstrate crisis help-seeking behavior increased during anomalously warm conditions.

CONCLUSIONS

This study suggests that there is a significant association between high minimum or maximum temperatures and crisis help-seeking behaviors in young adults and adolescents in urban areas in the United States.

Short-term prediction of suicidal thoughts and behaviors in adolescents: Can recent developments in technology and computational science provide a breakthrough?

BACKGROUND

Suicide is one of the leading causes of death among adolescents, and developing effective methods to improve short-term prediction of suicidal thoughts and behaviors (STBs) is critical. Currently, the most robust predictors of STBs are demographic or clinical indicators that have relatively weak predictive value. However, there is an emerging literature on short-term prediction of suicide risk that has identified a number of promising candidates, including (but not limited to) rapid escalation of: (a) emotional distress, (b) social dysfunction (e.g., bullying, rejection), and (c) sleep disturbance. However, these prior studies are limited in two critical ways. First, they rely almost entirely on self-report. Second, most studies have not focused on assessment of these risk factors using intensive longitudinal assessment techniques that are able to capture the dynamics of changes in risk states at the individual level.

METHOD

In this paper we explore how to capitalize on recent developments in real-time monitoring methods and computational analysis in order to address these fundamental problems.

RESULTS

We now have the capacity to use: (a) smartphone, wearable computing, and smart home technology to conduct intensive longitudinal assessments monitoring of putative risk factors with minimal participant burden and (b) modern computational techniques to develop predictive algorithms for STBs. Current research and theory on short-term risk processes for STBs, combined with the emergent capabilities of new technologies, suggest that this is an important research agenda for the future.

LIMITATIONS

Although these approaches have enormous potential to create new knowledge, the current empirical literature is limited. Moreover, passive monitoring of risk for STBs raises complex ethical issues that will need to be resolved before large scale clinical applications are feasible.

CONCLUSIONS

Smartphone, wearable, and smart home technology may provide one point of access that might facilitate both early identification and intervention implementation, and thus, represents a key area for future STB research.