Detecting the Clinical Features of Difficult-to-Treat Depression using Synthetic Data from Large Language Models

Difficult-to-treat depression (DTD) has been proposed as a broader and more clinically comprehensive perspective on a person's depressive disorder where despite treatment, they continue to experience significant burden. We sought to develop a Large Language Model (LLM)-based tool capable of interrogating routinely-collected, narrative (free-text) electronic health record (EHR) data to locate published prognostic factors that capture the clinical syndrome of DTD. In this work, we use LLM-generated synthetic data (GPT3.5) and a Non-Maximum Suppression (NMS) algorithm to train a BERT-based …

clinical cs.cl data depression electronic experience features free health language language model language models large language large language model large language models llm narrative person perspective synthetic synthetic data text tool treatment