Multimodal LLMs for health grounded in individual-specific data

Large language models (LLMs) have shown an impressive ability to solve tasks in a wide range of fields including health. Within the health domain, there are many data modalities that are relevant to an individual’s health status. To effectively solve tasks related to individual health, LLMs will need the ability to use a diverse set of features as context. However, the best way to encode and inject complex high-dimensional features into the input stream of an LLM remains an active area of research. Here, we explore the ability of a foundation LLM to estimate disease risk given health-related input features. First, we evaluate serialization of structured individual-level health data into text along with in context learning and prompt tuning approaches. We find that the LLM performs better than random in the zero-shot and few-shot cases, and has comparable and often equivalent performance to baseline after prompt tuning. Next, we propose a way to encode complex non-text data modalities into the token embedding space and then use this encoding to construct multimodal sentences. We show that this multimodal LLM achieves better or equivalent performance compared to baseline models. Overall, our results show the potential for using multi-modal LLMs grounded in individual health data to solve complex tasks such as risk prediction.

• Machine Intelligence

• Health & Bioscience