Abstract
Rising adolescent suicide rates underscore an urgent need for better detection of short-term risk in the days and hours leading up to attempts. Passive smartphone sensing of language offers a promising approach, yet its performance during vulnerable periods remains unclear. This case study examined five adolescents (3 male, 2 female) who were hospitalized for suicidal crises while enrolled in a smartphone sensing study. Participants contributed outgoing text entries over six months (M = 21,000/person), which were analyzed using natural language processing (NLP) to assess suicide-related content, sentiment, and topics (e.g., school, treatment). In addition, clinicians conducted qualitative reviews of the text entries to identify potential risk events. Results showed that 4 of 5 adolescents exhibited increased suicide-related language and negative sentiment during the 10 days prior to psychiatric hospitalization. Especially elevated suicide language was found within 5 days of hospitalization, while negative sentiment peaked between 5-10 days prior to hospitalization. These signals, however, also occurred outside of acute risk periods, highlighting the challenge of separating suicide risk from distress more generally. Clinical annotations revealed that suicidal thoughts and behaviors often co-occurred with NLP signals of suicide-related language, and topic models identified clinically relevant language related to substance use and psychiatric treatment. Clinical annotations of interpersonal conflict and school stressors were not identified by topic models. Discrepancies largely originated from the inability of NLP methods to infer context (e.g., text conversation history). Although smartphone language data showed low missingness and some sensitivity to acute crises, enhancing contextual analysis is essential for personalized risk detection.