Towards Holistic Disease Risk Prediction using Small Language Models
This work addresses the need for integrated multimodal reasoning in healthcare, offering a potential tool for medical practitioners, though it is incremental as it builds on existing language model capabilities.
The authors tackled the problem of holistic disease risk prediction by connecting small language models to multiple healthcare data sources, achieving competitive performance across 12 tasks in a multitask learning setup without surpassing specialized state-of-the-art methods.
Data in the healthcare domain arise from a variety of sources and modalities, such as x-ray images, continuous measurements, and clinical notes. Medical practitioners integrate these diverse data types daily to make informed and accurate decisions. With recent advancements in language models capable of handling multimodal data, it is a logical progression to apply these models to the healthcare sector. In this work, we introduce a framework that connects small language models to multiple data sources, aiming to predict the risk of various diseases simultaneously. Our experiments encompass 12 different tasks within a multitask learning setup. Although our approach does not surpass state-of-the-art methods specialized for single tasks, it demonstrates competitive performance and underscores the potential of small language models for multimodal reasoning in healthcare.