2 - (SYM 61) An Application of Screenomics to Improve Real-time Suicide Risk Detection

Suicide risk is recognized as an increasingly dynamic and personalized process (Kaurin et al., 2022). However, traditional approaches to intensive time sampling (i.e., ecological momentary assessment; EMA) are often limited in the amount of information that can be obtained from a given individual on a particular day due to concerns of high participant burden. Screenomics (Ram et al., 2022), a specific digital phenotyping framework, unobstrusively captures screenshots from participants’ smartphones every five seconds, passively extracting near-continuous information. These data can then be utilized to indirectly identify suicide risk, in real-time, via generated and viewed text, which will serve to inform our understanding of the dynamics of suicide risk, as well as the implementation well-timed interventions.

78 participants with past-month active suicidal ideation (SI) completed 28 days of EMA (6 surveys/day) assessing momentary SI, as well as 35 days of screenshot capture via the ScreenLife Capture application (Yee et al., 2023). Across participants, average EMA compliance was 73%, and approximately 100,000 screenshots were collected per person. Data collection recently concluded. We detail preliminary findings among a subset of participants; a parallel approach will be applied to the full sample.

To extract text from the screenshots, we used EasyOCR – a tool designed to recognize text from images. Using the extracted text, we used several different approaches: simple word counts, applied dictionaries including LIWC and custom dictionaries (i.e., suicide; risk factor; Facebook; Text message), applied pre-trained transformer models (BERT) to extract sentiment scores and latent embeddings, and fine-tuned a transformer classification model on Reddit r/SuicideWatch and r/Depression.

Evaluated using 10-fold cross-validation, we found that using the custom dictionary scores as predictors in a logistic regression model predicting SI produced the highest AUCs (0.66), significantly improving upon the BERT models (AUC’s  0.60) and LIWC (0.53).

These findings highlight the first application of screenomics to serve as an indirect proxy of suicide risk. While the AUC’s do not approach those commonly seen in suicide prediction research, it is important to note that most prediction research uses self-report predictors to predict self-reported SI, whereas our prediction crosses modalities and is at the momentary level. Future directions of screenomics in suicide risk prediction will be explored.