Background: The optimal treatment for gender dysphoria is medical intervention, but many transgender and nonbinary people face significant treatment barriers when seeking help for gender dysphoria. When untreated, gender dysphoria is associated with depression, anxiety, suicidality, and substance misuse. Technology-delivered interventions for transgender and nonbinary people can be used discretely, safely, and flexibly, thereby reducing treatment barriers and increasing access to psychological interventions to manage distress that accompanies gender dysphoria. Technology-delivered interventions are beginning to incorporate machine learning (ML) and natural language processing (NLP) to automate intervention components and tailor intervention content. A critical step in using ML and NLP in technology-delivered interventions is demonstrating how accurately these methods model clinical constructs. Objective: This study aimed to determine the preliminary effectiveness of modeling gender dysphoria with ML and NLP, using transgender and nonbinary people’s social media data. Methods: Overall, 6 ML models and 949 NLP-generated independent variables were used to model gender dysphoria from the text data of 1573 Reddit (Reddit Inc) posts created on transgender- and nonbinary-specific web-based forums. After developing a codebook grounded in clinical science, a research team of clinicians and students experienced in working with transgender and nonbinary clients used qualitative content analysis to determine whether gender dysphoria was present in each Reddit post (ie, the dependent variable). NLP (eg, n-grams, Linguistic Inquiry and Word Count, word embedding, sentiment, and transfer learning) was used to transform the linguistic content of each post into predictors for ML algorithms. A k-fold cross-validation was performed. Hyperparameters were tuned with random search. Feature selection was performed to demonstrate the relative importance of each NLP-generated independent variable in predicting gender dysphoria. Misclassified posts were analyzed to improve future modeling of gender dysphoria. Results: Results indicated that a supervised ML algorithm (ie, optimized extreme gradient boosting [XGBoost]) modeled gender dysphoria with a high degree of accuracy (0.84), precision (0.83), and speed (1.23 seconds). Of the NLP-generated independent variables, Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) clinical keywords (eg, dysphoria and disorder) were most predictive of gender dysphoria. Misclassifications of gender dysphoria were common in posts that expressed uncertainty, featured a stressful experience unrelated to gender dysphoria, were incorrectly coded, expressed insufficient linguistic markers of gender dysphoria, described past experiences of gender dysphoria, showed evidence of identity exploration, expressed aspects of human sexuality unrelated to gender dysphoria, described socially based gender dysphoria, expressed strong affective or cognitive reactions unrelated to gender dysphoria, or discussed body image. Conclusions: Findings suggest that ML- and NLP-based models of gender dysphoria have significant potential to be integrated into technology-delivered interventions. The results contribute to the growing evidence on the importance of incorporating ML and NLP designs in clinical science, especially when studying marginalized populations.

Background: Gay, bisexual, and other sexual minority men have expressed the acceptability of patient portals as tools for supporting HIV prevention behaviors, including facilitating disclosure of HIV and other sexually transmitted infection (STI/HIV) laboratory test results to sex partners. However, these studies, in which Black or African American sexual minority men were undersampled, failed to determine the relationship of reported history of discussing HIV results with sex partners and anticipated willingness to disclose web-based STI/HIV test results using a patient portal. Objective: Among a sample of predominantly Black sexual minority men, this study aimed to (1) determine preferences for patient portal use for HIV prevention and (2) test the associations between reported history of discussing HIV results and anticipated willingness to disclose web-based STI/HIV test results with most recent main and nonmain partners using patient portals. Methods: Data come from audio-computer self-assisted interview survey data collected during the 3-month visit of a longitudinal cohort study. Univariate analysis assessed patient portal preferences by measuring the valuation rankings of several portal features. Multiple Poisson regression models with robust error variance determined the associations between history of discussing HIV results and willingness to disclose those results using web-based portals by partner type, and to examine criterion validity of the enhancing dyadic communication (EDC) scale to anticipated willingness. Results: Of the 245 participants, 71% (n=174) were Black and 22% (n=53) were White. Most participants indicated a willingness to share web-based STI/HIV test results with their most recent main partner. Slightly fewer, nonetheless a majority, indicated a willingness to share web-based test results with their most recent nonmain partner. All but 2 patient portal features were valued as high or moderately high priority by >80% of participants. Specifically, tools to help manage HIV (n=183, 75%) and information about pre- and postexposure prophylaxis (both 71%, n=173 and n=175, respectively) were the top-valuated features to include in patient portals for HIV prevention. Discussing HIV test results was significantly associated with increased prevalence of willingness to disclose web-based test results with main (adjusted prevalence ratio [aPR] 1.46, 95% CI 1.21-1.75) and nonmain partners (aPR 1.54, 95% CI 1.23-1.93). Conclusions: Our findings indicate what features Black sexual minority men envision may be included in the patient portal’s design to optimize HIV prevention, further supporting the criterion validity of the EDC scale. Efforts should be made to support Black sexual minority men’s willingness to disclose STI/HIV testing history and status with partners overall as it is associated significantly with a willingness to disclose testing results digitally via patient portals. Future studies should consider discussion behaviors regarding past HIV test results with partners when tailoring interventions that leverage patient portals in disclosure events.