Using Topic Modeling to Detect and Describe Self-Injurious and Related Content on a Large-Scale Digital Platform

Investigating insomnia in United States deployed military forces: A topic modeling approach

STUDY OBJECTIVES

This retrospective study analyzed free-text clinical notes from medical encounters for insomnia among a sample of deployed US military personnel. Topic modeling, a natural language processing technique, was used to identify thematic patterns in the clinical notes that were potentially related to insomnia diagnosis.

METHODS

Clinical notes of patient clinical encounters coded for insomnia from the US Department of Defense Military Health System Theater Medical Data Store were analyzed. Following preprocessing of the free text in the clinical notes, topic modeling was employed to identify relevant underlying topics or themes in 32,864 unique patients. The machine-learned topics were validated using human-coded potential insomnia etiological issues.

RESULTS

A 12-topic model was selected based on quantitative metrics, interpretability, and coherence of terms comprising topics. The topics were assigned the following labels: personal/family history, stimulants, stress, family/relationships, other sleep disorders, depression, schedule/environment, anxiety, other medication, headache/concussion, pain, and medication refill. Validation of these topics (excluding the two medication topics) against their corresponding human-coded potential etiological issues showed strong agreement for the assessed topics.

CONCLUSIONS

Analysis of free-text clinical notes using topic modeling resulted in the identification of thematic patterns that largely mirrored known correlates of insomnia. These findings reveal multiple potential etiologies for deployment-related insomnia. The identified topics may augment electronic health record diagnostic codes and provide valuable information for sleep researchers and providers. As both civilian and military healthcare systems implement electronic health records, topic modeling may be a valuable tool for analyzing free-text data to investigate health outcomes.

The Promise of Affective Language for Identifying and Intervening on Psychopathology

We are in dire need of innovative tools for reducing the global burden of psychopathology. Emerging evidence suggests that analyzing language (i.e., the words people use) can grant insight into an individual's emotional experiences, their ability to regulate their emotions, and even their current experiences of psychopathology. As such, linguistic analyses of people's everyday word use may be a diagnostic marker of emotional well-being, and manipulating the words people use could foster adaptive emotion regulation and mental health. Given the ubiquity of language in everyday life, such language-based tools for measuring and intervening in emotion and mental health can advance how we identify and treat mental illnesses at a large scale. In this paper, I outline the promise of this approach and identify key problems we must solve if we are to make it a reality. In particular, I summarize evidence connecting language, emotion, and mental health for three key constructs: sentiment (i.e., the valence of one's language), linguistic distancing (i.e., using language to separate oneself from distressing stimuli), and emotion differentiation (i.e., using words to specifically identify one's emotions). I also identify open questions in need of attention for each of these constructs and this area of research as a whole. Overall, I believe the future is bright for the application of psycholinguistic approaches to mental health detection and intervention.

Dynamic suicide topic modelling: Deriving population-specific, psychosocial and time-sensitive suicide risk variables from Electronic Health Record psychotherapy notes

In the machine learning subfield of natural language processing, a topic model is a type of unsupervised method that is used to uncover abstract topics within a corpus of text. Dynamic topic modelling (DTM) is used for capturing change in these topics over time. The study deploys DTM on corpus of electronic health record psychotherapy notes. This retrospective study examines whether DTM helps distinguish closely matched patients that did and did not die by suicide. Cohort consists of United States Department of Veterans Affairs (VA) patients diagnosed with Posttraumatic Stress Disorder (PTSD) between 2004 and 2013. Each case (those who died by suicide during the year following diagnosis) was matched with five controls (those who remained alive) that shared psychotherapists and had similar suicide risk based on VA's suicide prediction algorithm. Cohort was restricted to patients who received psychotherapy for 9+ months after initial PTSD diagnoses (cases = 77; controls = 362). For cases, psychotherapy notes from diagnosis until death were examined. For controls, psychotherapy notes from diagnosis until matched case's death date were examined. A Python-based DTM algorithm was utilized. Derived topics identified population-specific themes, including PTSD, psychotherapy, medication, communication and relationships. Control topics changed significantly more over time than case topics. Topic differences highlighted engagement, expressivity and therapeutic alliance. This study strengthens groundwork for deriving population-specific, psychosocial and time-sensitive suicide risk variables.

Investigating online activity in UK adolescent mental health patients: a feasibility study using a natural language processing approach for electronic health records

OBJECTIVES

To assess the feasibility of using a natural language processing (NLP) application for extraction of free-text online activity mentions in adolescent mental health patient electronic health records (EHRs).

SETTING

The Clinical Records Interactive Search system allows detailed research based on deidentified EHRs from the South London and Maudsley NHS Foundation Trust, a large south London Mental Health Trust providing secondary and tertiary mental healthcare.

PARTICIPANTS AND METHODS

We developed a gazetteer of online activity terms and annotation guidelines, from 5480 clinical notes (200 adolescents, aged 11-17 years) receiving specialist mental healthcare. The preprocessing and manual curation steps of this real-world data set allowed development of a rule-based NLP application to automate identification of online activity (internet, social media, online gaming) mentions in EHRs. The context of each mention was also recorded manually as: supportive, detrimental or neutral in a subset of data for additional analysis.

RESULTS

The NLP application performed with good precision (0.97) and recall (0.94) for identification of online activity mentions. Preliminary analyses found 34% of online activity mentions were considered to have been documented within a supportive context for the young person, 38% detrimental and 28% neutral.

CONCLUSION

Our results provide an important example of a rule-based NLP methodology to accurately identify online activity recording in EHRs, enabling researchers to now investigate associations with a range of adolescent mental health outcomes.

Psycholinguistic changes in the communication of adolescent users in a suicidal ideation online community during the COVID-19 pandemic

Since the outbreak of the COVID-19 pandemic, increases in suicidal ideation and suicide attempts in adolescents have been registered. Many adolescents experiencing suicidal ideation turn to online communities for social support. In this retrospective observational study, we investigated the communication-language style, contents and user activity-in 7975 unique posts and 51,119 comments by N = 2862 active adolescent users in a large suicidal ideation support community (SISC) on the social media website reddit.com in the onset period of the COVID-19 pandemic. We found significant relative changes in language style markers for hopelessness such as negative emotion words (+ 10.00%) and positive emotion words (- 3.45%) as well as for social disengagement such as social references (- 8.63%) and 2nd person pronouns (- 33.97%) since the outbreak of the pandemic. Using topic modeling with Latent Dirichlet Allocation (LDA), we identified significant changes in content for the topics Hopelessness (+ 23.98%), Suicide Methods (+ 17.11%), Social Support (- 14.91%), and Reaching Out to users (- 28.97%). Changes in user activity point to an increased expression of mental health issues and decreased engagement with other users. The results indicate a potential shift in communication patterns with more adolescent users expressing their suicidal ideation rather than relating with or supporting other users during the COVID-19 pandemic.

Text mining for identifying the nature of online questions about non-suicidal self-injury

OBJECTIVE

The internet provides convenient access to information about non-suicidal self-injury (NSSI) owing to its accessibility and anonymity. This study aimed to explore the distribution of topics regarding NSSI posted on the internet and yearly trends in the derived topics using text mining.

METHODS

We searched for the keyword "non-suicidal self-injury" (Ja-Hae in Korean) in the Naver Q&A using the statistical package R. We analyzed 7893 NSSI-related questions posted between 2009 and 2018. Text mining was performed using latent Dirichlet allocation (LDA) on the dataset to determine associations between phrases and thus identify common themes in posts about NSSI.

RESULTS

In the LDA, we selected the following 10 most common topics: anger, family troubles, collecting information on NSSI, stress, concerns regarding NSSI scarring, ways to help a non-suicidal self-injurious friend, depression, medical advice, ways to perform or stop NSSI, and prejudices and thoughts regarding non-suicidal self-injurious people.

CONCLUSIONS

This study provides valuable information on the nature of NSSI questions posted online. In future research, developing websites that provide NSSI information and support or guidance on effectively communicating with NSSI is necessary.

Natural language processing applied to mental illness detection: a narrative review

Mental illness is highly prevalent nowadays, constituting a major cause of distress in people’s life with impact on society’s health and well-being. Mental illness is a complex multi-factorial disease associated with individual risk factors and a variety of socioeconomic, clinical associations. In order to capture these complex associations expressed in a wide variety of textual data, including social media posts, interviews, and clinical notes, natural language processing (NLP) methods demonstrate promising improvements to empower proactive mental healthcare and assist early diagnosis. We provide a narrative review of mental illness detection using NLP in the past decade, to understand methods, trends, challenges and future directions. A total of 399 studies from 10,467 records were included. The review reveals that there is an upward trend in mental illness detection NLP research. Deep learning methods receive more attention and perform better than traditional machine learning methods. We also provide some recommendations for future studies, including the development of novel detection methods, deep learning paradigms and interpretable models.

Annual Research Review: Translational machine learning for child and adolescent psychiatry

Children and adolescents could benefit from the use of predictive tools that facilitate personalized diagnoses, prognoses, and treatment selection. Such tools have not yet been deployed using traditional statistical methods, potentially due to the limitations of the paradigm and the need to leverage large amounts of digital data. This review will suggest that a machine learning approach could address these challenges and is designed to introduce new readers to the background, methods, and results in the field. A rationale is first introduced followed by an outline of fundamental elements of machine learning approaches. To provide an overview of the use of the techniques in child and adolescent literature, a scoping review of broad trends is then presented. Selected studies are also highlighted in order to draw attention to research areas that are closest to translation and studies that exhibit a high degree of experimental innovation. Limitations to the research, and machine learning approaches generally, are outlined in the penultimate section highlighting issues related to sample sizes, validation, clinical utility, and ethical challenges. Finally, future directions are discussed that could enhance the possibility of clinical implementation and address specific questions relevant to the child and adolescent psychiatry. The review gives a broad overview of the machine learning paradigm in order to highlight the benefits of a shift in perspective towards practically oriented statistical solutions that aim to improve clinical care of children and adolescents.

Themes, communities and influencers of online probiotics chatter: A retrospective analysis from 2009-2017

We build on recent examinations questioning the quality of online information about probiotic products by studying the themes of content, detecting virtual communities and identifying key influencers in social media using data science techniques. We conducted topic modelling (n = 36,715 tweets) and longitudinal social network analysis (n = 17,834 tweets) of probiotic chatter on Twitter from 2009-17. We used Latent Dirichlet Allocation (LDA) to build the topic models and network analysis tool Gephi for building yearly graphs. We identified the top 10 topics of probiotics-related communication on Twitter and a constant rise in communication activity. However the number of communities grew consistently to peak in 2014 before dipping and levelling off by 2017. While several probiotics industry actors appeared and disappeared during this period, the influence of one specific actor rose from a hub initially to an authority in the latter years. With multi-brand advertising and probiotics promotions mostly occupying the Twitter chatter, scientists, journalists, or policymakers exerted minimal influence in these communities. Consistent with previous research, we find that probiotics-related content on social media veers towards promotions and benefits. Probiotic industry actors maintain consistent presence on Twitter while transitioning from hubs to authorities over time; scientific entities assume an authoritative role without much engagement. The involvement of scientific, journalistic or regulatory stakeholders will help create a balanced informational environment surrounding probiotic products.

Review of Perfectionism Research From 1990 to 2019 Utilizing a Text-Mining Approach

Perfectionism is a personality characteristic that has been explored for its implications in mental health; reviews and meta-analyses were conducted to synthesize research findings. This study systemically synthesizes the perfectionism literature using a text-mining approach. Co-word analysis and Dirichlet Multinomial Regression topic modeling were performed on a total of 1,529 perfectionism abstracts published from 1990 to 2019. Analysis revealed that perfectionism research is closely connected with “disorder,” with “symptom” being the most frequently addressed issue. Topic-modeling results found a total of 15 topics represented perfectionism research of the past three decades. Most articles were published in psychology journals, with social and clinical psychology subdisciplines publishing perfectionism articles most frequently. There were overlaps in research topics by journal subdisciplines, while differences were also observed. This study provides a panoramic view of perfectionism literature and highlights frequently and infrequently explored areas that could be considered in future research endeavors.

Charting the development of emotion comprehension and abstraction from childhood to adulthood using observer-rated and linguistic measures

This study examined two facets of emotion development: emotion word comprehension (knowing the meaning of emotion words such as "anger" or "excitement") and emotion concept abstraction (representing emotions in terms of internal psychological states that generalize across situations). Using a novel emotion vocabulary assessment, we captured how a cross-sectional sample of participants aged 4-25 (N = 196) defined 24 emotions. Smoothing spline regression models suggested that emotion comprehension followed an emergent shape: Knowledge of emotion words increased across childhood and plateaued around age 11. Human coders rated the abstractness of participants' responses, and these ratings also followed an emergent shape but plateaued significantly later than comprehension, around age 18. An automated linguistic analysis of abstractness supported coders' perceptions of increased abstractness across age. Finally, coders assessed the definitional strategies participants used to describe emotions. Young children tended to describe emotions using concrete strategies such as providing example situations that evoked those emotions or by referring to physiological markers of emotional experiences. Whereas use of these concrete strategies decreased with age, the tendency to use more abstract strategies such as providing general definitions that delineated the causes and characteristics of emotions or by providing synonyms of emotion words increased with age. Overall, this work (a) provides a tool for assessing definitions of emotion terms, (b) demonstrates that emotion concept abstraction increases across age, and (c) suggests that adolescence is a period in which emotion words are comprehended but their level of abstraction continues to mature. (PsycInfo Database Record (c) 2020 APA, all rights reserved).