MedualTime: A Dual-Adapter Language Model for Medical Time Series-Text Multimodal Learning

TL;DR

MedualTime introduces a dual-adapter language model for medical multimodal learning, enabling flexible primary modality roles and achieving superior accuracy and transferability in medical time series-text tasks.

Contribution

It proposes a novel dual-adapter architecture that allows either modality to be primary, enhancing modality-specific learning and cross-modal interaction in medical multimodal models.

Findings

Achieves 8% accuracy improvement in supervised tasks

Attains 12% F1 score increase in medical data

Demonstrates effective transferability in few-shot learning

Abstract

The recent rapid advancements in language models (LMs) have garnered attention in medical time series-text multimodal learning. However, existing contrastive learning-based and prompt-based LM approaches tend to be biased, often assigning a primary role to time series modality while treating text modality as secondary. We classify these approaches under a temporal-primary paradigm, which may overlook the unique and critical task-relevant information embedded in text modality like clinical reports, thus failing to fully leverage mutual benefits and complementarity of different modalities. To fill this gap, we propose a novel textual-temporal multimodal learning paradigm that enables either modality to serve as the primary while being enhanced by the other, thereby effectively capturing modality-specific information and fostering cross-modal interaction. In specific, we design MedualTime, a language model composed of dual adapters to implement temporal-primary and textual-primary modeling simultaneously. Within each adapter, lightweight adaptation tokens are injected into the top layers of LM to encourage high-level modality fusion. The shared LM pipeline by dual adapters not only achieves adapter alignment but also enables efficient fine-tuning, reducing computational resources. Empirically, MedualTime demonstrates superior performance on medical data, achieving notable improvements of 8% accuracy and 12% F1 in supervised settings. Furthermore, MedualTime's transferability is validated by few-shot label transfer experiments from coarse-grained to fine-grained medical data. https://github.com/start2020/MedualTime