Towards Holistic Disease Risk Prediction using Small Language Models

TL;DR

This paper presents a framework that uses small language models to integrate diverse healthcare data sources for simultaneous disease risk prediction, highlighting the potential of multimodal reasoning despite not outperforming specialized models.

Contribution

Introduces a multimodal framework connecting small language models to various healthcare data sources for multitask disease risk prediction.

Findings

Competitive performance across 12 tasks

Demonstrates potential of small language models for multimodal healthcare reasoning

Highlights the feasibility of multitask disease risk prediction

Abstract

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.