Using Large Language Models to Measure Symptom Severity in Patients At Risk for Schizophrenia
This work addresses the need for efficient and standardized symptom assessment in clinical practice for patients at risk for schizophrenia, representing an incremental improvement over existing methods.
The researchers tackled the problem of monitoring symptom severity in patients at clinical high risk for schizophrenia by using large language models (LLMs) to predict BPRS scores from clinical interview transcripts, achieving a median concordance of 0.84 and ICC of 0.73, which approaches human reliability.
Patients who are at clinical high risk (CHR) for schizophrenia need close monitoring of their symptoms to inform appropriate treatments. The Brief Psychiatric Rating Scale (BPRS) is a validated, commonly used research tool for measuring symptoms in patients with schizophrenia and other psychotic disorders; however, it is not commonly used in clinical practice as it requires a lengthy structured interview. Here, we utilize large language models (LLMs) to predict BPRS scores from clinical interview transcripts in 409 CHR patients from the Accelerating Medicines Partnership Schizophrenia (AMP-SCZ) cohort. Despite the interviews not being specifically structured to measure the BPRS, the zero-shot performance of the LLM predictions compared to the true assessment (median concordance: 0.84, ICC: 0.73) approaches human inter- and intra-rater reliability. We further demonstrate that LLMs have substantial potential to improve and standardize the assessment of CHR patients via their accuracy in assessing the BPRS in foreign languages (median concordance: 0.88, ICC: 0.70), and integrating longitudinal information in a one-shot or few-shot learning approach.