New Video Available: Quantitative Language And Facial Expression Phenotyping In Chronic Pain
Watch the full presentation now in the Media Center

Watch the full presentation now in the Media Center

In Episode 20 of the PURPOSE Professional Series, Paul Geha, MD, presents novel methodologies for converting unstructured patient narratives into quantitative, multidimensional digital phenotypes of chronic pain. Addressing the limitations of single value intensity scales and high burden questionnaires, Dr. Geha demonstrates how natural language processing and large language models extract standardized metrics directly from clinical interviews. By mapping patient language into semantic spaces alongside multimodal feature sets, his team establishes an objective framework to assess complex traits and differentiate overlapping clinical cohorts.

Key Learnings:

• High Dimensional Semantic Mapping: Learn how projecting patient narratives against predefined clinical anchor sentences quantifies subjective sensory, affective, and functional pain dimensions without relying on simple word counts.
  
  

• LLM Construct Extraction: Examine how zero shot prompting across non pain specific interview transcripts evaluates latent psychological constructs such as sense of agency deficit and narrative fragmentation, achieving high cross model reliability.
  
  

• Multimodal Phenotyping: Discover how combining semantic narrative vectors with facial emotion recognition and speech acoustic modeling yields a composite classification pipeline to discriminate distinct pain phenotypes and depressive comorbidities.