Using artificial intelligence and longitudinal location data to differentiate persons who develop posttraumatic stress disorder following childhood trauma

Post-traumatic stress disorder (PTSD) is characterized by complex, heterogeneous symptomology, thus detection outside traditional clinical contexts is difficult. Fortunately, advances in mobile technology, passive sensing, and analytics offer promising avenues for research and development. The present study examined the ability to utilize Global Positioning System (GPS) data, derived passively from a smartphone across seven days, to detect PTSD diagnostic status among a cohort (N = 185) of high-risk, previously traumatized women. Using daily time spent away and maximum distance traveled from home as a basis for model feature engineering, the results suggested that diagnostic group status can be predicted out-of-fold with high performance (AUC = 0.816, balanced sensitivity = 0.743, balanced specificity = 0.8, balanced accuracy = 0.771). Results further implicate the potential utility of GPS information as a digital biomarker of the PTSD behavioral repertoire. Future PTSD research will benefit from application of GPS data within larger, more diverse populations

Utilizing Mixed Graphical Network Models to Explore Parent Psychological Symptoms and Their Centrality to Parent Mental Health in Households with High Child Screen Usage

Especially among adolescents, screens are being used more than ever. In conjunction with this trend, mental illness is increasingly prevalent among both adults and children, and parental psychological problems are shown to be associated with children\u27s TV watching, video watching, and gaming (Pulkki-Råback et al., 2022). This study aims to approach parent mental illness symptom by symptom to explore which specific symptoms are most central to parent psychological problems in households where children show high screen time behaviors. We draw from the Adolescent Brain Cognitive Development Study (ABCD Study®), a nationwide sample of 11,875 children aged 10-13 collected by the National Institute of Mental Health. We utilize Mixed Graphical Models (MGMs) on both polychoric and dichotomized data, using the Extended Bayesian Information Criterion to choose the best models. Within our polychoric data, we pinpoint “I feel worthless and inferior” as a symptom with both high bridge betweenness and strength between symptom communities within high screen time household networks. Within binary high child screen time networks, we find “I have trouble making decisions” as a parent symptom with high bridge strength and betweenness that is central to the overall structure of the network. Finally, we believe our approach could be more successfully applied to other psychological datasets with more nonzero responses to parent psychological symptoms to further illuminate parent symptoms that are important in households with high child screen time. Our analyses do not establish causality because our data is cross-sectional

Breaking the Silence: Leveraging Social Interaction Data to Identify High-Risk Suicide Users Online Using Network Analysis and Machine Learning

Social network-based exploration into the expression of suicidal thought and behavior on an online, pro-choice suicide forum. Files provide commented code and data utilized in analyses outlined in the companion manuscript

The Hidden Depths of Suicidal Discourse: Network Analysis and Natural Language Processing Unmask Uncensored Expression

Exploratory analysis of uncensored suicidal thought and behavior online using a combination of topic modeling and network analysis tools

Breaking the Silence: Leveraging Social Interaction Data to Identify High-Risk Suicide Users Online Using Network Analysis and Machine Learning

Suicidal thought and behavior (STB) is highly stigmatized and taboo. Prone to censorship, yet pervasive online, the development of uniquely insightful digital markers can be used to improve STB risk detection and aid a globally ailing population. Focusing on Sanctioned Suicide, an online pro-choice suicide forum, this work developed a social network-based operationalization of community engagement to derive 17 egocentric network features that captured the social dynamics of interaction within this uniquely uncensored community. Using network data generated from over 3.2 million unique interactions of N=192 individuals, n=48 of which were determined to be highest risk users (HRUs), a machine learning classification model was trained and validated to predict HRU status. Model prediction dynamics were analyzed using introspection techniques to uncover patterns in feature influence and highlight social phenomena. The model achieved an AUC=0.73 ([0.66, 0.8], 95% C.I.), suggesting that network-based socio-behavioral patterns of online interaction can signal heightened suicide risk. Transitivity, density, and in-degree centrality were among the most important features driving this performance. Their patterns of influence suggested that HRUs tended to be targets of social exchanges with lesser frequency and possessed egocentric networks with “small world” network properties. This research contributes to the modern efforts of data-driven suicidology through the implementation and assessment of an underutilized method on an unlikely data source. Findings support future incorporation of network-based social interaction features in STB research and highlight foci for further consideration within descriptive, predictive, and preventative pipelines

The hidden depths of suicidal discourse: Network analysis and natural language processing unmask uncensored expression

Background The socially unattractive and stigmatizing nature of suicidal thought and behavior (STB) makes it especially susceptible to censorship across most modern digital communication platforms. The ubiquitous integration of technology with day-to-day life has presented an invaluable opportunity to leverage unprecedented amounts of data to study STB, yet the complex etiologies and consequences of censorship for research within mainstream online communities render an incomplete picture of STB manifestation. Analyses targeting online written content of suicidal users in environments where fear of reproach is mitigated may provide novel insight into modern trends and signals of STB expression. Methods Complete written content of N  = 192 users, including n  = 48 identified as potential suicide completers/highest-risk users (HRUs), on the pro-choice suicide forum, Sanctioned Suicide, was modeled using a combination of lexicon-based topic modeling (EMPATH) and exploratory network analysis techniques to characterize and highlight prominent aspects of censorship-free suicidal discourse. Results Modeling of over 2 million tokens across 37,136 forum posts found higher frequency of positive emotion and optimism among HRUs, emphasis on methods seeking and sharing behaviors, prominence of previously undocumented jargon, and semantics related to loneliness and life adversity. Conclusion This natural language processing (NLP)- and network-driven exposé of online STB subculture uncovered trends that deserve further attention within suicidology as they may be able to bolster detection, intervention, and prevention of suicidal outcomes and exposures

Association of COVID19-induced anosmia and ageusia with depression and suicidal ideation

Background: Clinical reports from patients suffering from the novel coronavirus (COVID-19) reflect a high prevalence of sensory deprivation or loss pertaining to smell (dysosmia/anosmia) and/or taste (dysgeusia/ageusia). Given the importance of the senses to daily functioning and personal experience, the mental health consequences of these symptoms warrant further attention. Methods: A cohort of Reddit users posting within the /r/covid19positive subforum (N = 15,821) was leveraged to analyze instantaneous risk of transition to a state of suicidal ideation or depression using Cox proportional-hazards models. Risk transition was defined by posts made in suicide- or depression-related forums, or mentions of relevant phrases with and without mention of anosmia/ageusia in /r/covid19positive. Self-diagnosis of COVID-19 was also modeled as a separate and simultaneous predictor of mental health risk. Results: Mention of anosmia/ageusia was significantly associated with transition to a risk state. Users with a history of anosmia/ageusia-related posts and who self-identified as COVID-19 positive had 30% higher instantaneous risk relative to others. The highest increase in instantaneous risk of suicidal ideation or depression occurred more than 100 days after first posting in /r/covid19positive. Limitations: Use of self-diagnosed disease as well as a broad array of anosmia/ageusia-related terminology may entail both information bias and overestimates of symptom incidence. Conclusions: The specific effects of COVID-19 on the senses may have long-term implications for patient mental health well-being beyond the primary recovery period. Future work is needed to investigate the longitudinal mental health burden of residual COVID-19 symptom presentation

Profiling the Digital Mosaic of Uncensored Suicidal Thought and Behavior: A Theory-Driven Network Analysis of Online Written Expression

The highly stigmatizing and taboo nature of suicidal thought and behavior (STB) makes it especially prone to censorship across modern communication platforms, ultimately impacting breadth of phenomenological detection. Applying ideation-to-action models of suicide to uniquely naturalistic data sources may serve to deepen understanding of digital STB expression. STB written communication across N=839 posts on the pro-choice suicide forum, Sanctioned Suicide, was qualitatively coded through development of a 20-item inventory based on the Integrated Motivational-Volitional (IMV) model. Reliable items were modeled using network analysis techniques to highlight patterns in post content. Coping strain-related stressors and feelings of defeat/entrapment most frequently characterized posts. Recapitulating the IMV framework, network modeling implicated defeat/entrapment as the most central node among items. Further analysis of the estimated network structure highlighted three primary themes of STB expression: contexts of powerlessness, trauma-mediated alienation, and crowdsourced preparation. This research offered an analytical foray into the expression of unmitigated STB. Findings aligned with documented associations in the literature, establishing a baseline summary of STB content within a marginalized, at-risk community. Future work should focus on modeling detailed semantic and social engagement data within this extraordinary microcosm to further explore, describe, and develop digital profiles of STB risk

Distribution of predictive performance across individuals for each five factor-based outcome model.

(A) extraversion models; (B) agreeableness models; (C) conscientiousness models; (D) stability models; (E) openness to experience models. Reported R2 values represent the average R2 across all LOSO fold performances (N = 54) for each respective model.</p

Plots of idiographic model predictions.

(XLSX)</p