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TRV-2026-0250Certified recordPeer-reviewed

Phenotyping antidepressant treatment response with deep learning in electronic health records

ABSTRACT Efficient, accurate phenotyping for antidepressant treatment response in electronic health records (EHRs) could facilitate precision psychiatry applications but remains a challenge. Increasingly, artificial intelligence methods using “deep learning” applied to clinical data have shown promise in complex classification problems. Here, we systematically evaluate the performance of eight deep-learning-based natural language processing models in classifying response to antidepressants in a large real-world…

Health · G Space — documented gain · certified 2026-07-17 · v1 · article view · machine-readable

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Deep-learning NLP models classified antidepressant treatment response as improved versus no evidence of improvement from routine EHR clinical notes with AUROC up to 0.88.

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Truvace Impact Record TRV-2026-0250, v1: “Phenotyping antidepressant treatment response with deep learning in electronic health records.” Truvace, 2026-07-17. /record/TRV-2026-0250 (accessed at citation time). sha256 cbc2944812dcbc65…

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