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Foundation large language models (LLMs) have shown an impressive ability tosolve tasks across a wide range of fields including health. To effectivelysolve personalized health tasks, LLMs need the ability to ingest a diversity ofdata modalities that are relevant to an individual's health status. In thispaper, we take a step towards creating multimodal LLMs for health that aregrounded in individual-specific data by developing a framework (HeLM: HealthLarge Language Model for Multimodal Understanding) that enables LLMs to usehigh-dimensional clinical modalities to estimate underlying disease risk. HeLMencodes complex data modalities by learning an encoder that maps them into theLLM's token embedding space and for simple modalities like tabular data byserializing the data into text. Using data from the UK Biobank, we show thatHeLM can effectively use demographic and clinical features in addition tohigh-dimensional time-series data to estimate disease risk. For example, HeLMachieves an AUROC of 0.75 for asthma prediction when combining tabular andspirogram data modalities compared with 0.49 when only using tabular data.Overall, we find that HeLM outperforms or performs at parity with classicalmachine learning approaches across a selection of eight binary traits.Furthermore, we investigate the downstream uses of this model such as itsgeneralizability to out-of-distribution traits and its ability to powerconversations around individual health and wellness.

Multimodal LLMs for health grounded in individual-specific data