Virtually screening adults for depression, anxiety, and suicide risk using machine learning and language from an open-ended interview

Current screening techniques for depression, anxiety, and suicide rely on retrospective patient reports to standardized scales. But combining a natural language processing (NLP) and machine learning (ML) approach with qualitative screening shows promise. This new approach detects depression, anxiety, and suicide risk by analyzing a patient’s language during a 5-to-10-min open-ended interview.

This recent study evaluated the NLP/ML models and found they performed well in identifying depression, anxiety, and suicide risk. With a virtual platform, we can now screen for all three conditions simultaneously during a single brief interview.

While clinical applications are still uncertain, understanding the results of suicide risk classifications supports better clinical decisions. By improving patient-centeredness and detecting risk during the interview, healthcare providers can be better equipped to help those in need.

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