Enhancing health assessments with large language models: A methodological approach

Abstract

Health assessments have long been a significant research topic within the field of health psychology. By analyzing the results of subject scales, these assessments effectively evaluate physical and mental health status. Traditional methods, based on statistical analysis, are limited in accuracy due to their reliance on linear scoring methods. Meanwhile, machine learning approaches, despite their potential, have not been widely adopted due to their poor interpretability and dependence on large amounts of training data. Recently, large language models (LLMs) have gained widespread attention for their powerful natural language understanding capabilities, offering a viable solution to these issues. This study investigates the application of LLMs in enhancing physical and mental health assessments, introducing ScaleLLM. ScaleLLM employs language and knowledge alignment to turn LLMs into expert evaluators for health psychology scales. Experimental results indicate that ScaleLLM can improve the accuracy and interpretability of health assessments.

Keywords: health assessment; interpretability of diagnostic models; large language model.