Multimodal electronic health record (EHR) data can offer a holistic
assessment of a patient's health status, supporting various predictive
healthcare tasks. Recently, several studies have embraced the multitask
learning approach in the healthcare domain, exploiting the inherent
correlations among clinical tasks to predict multiple outcomes simultaneously.
However, existing methods necessitate samples to possess complete labels for
all tasks, which places heavy demands on the data and restricts the flexibility
of the model. Meanwhile, within a multitask framework with multimodal inputs,
how to comprehensively consider the information disparity among modalities and
among tasks still remains a challenging problem. To tackle these issues, a
unified healthcare prediction model, also named by \textbf{FlexCare}, is
proposed to flexibly accommodate incomplete multimodal inputs, promoting the
adaption to multiple healthcare tasks. The proposed model breaks the
conventional paradigm of parallel multitask prediction by decomposing it into a
series of asynchronous single-task prediction. Specifically, a task-agnostic
multimodal information extraction module is presented to capture decorrelated
representations of diverse intra- and inter-modality patterns. Taking full
account of the information disparities between different modalities and
different tasks, we present a task-guided hierarchical multimodal fusion module
that integrates the refined modality-level representations into an individual
patient-level representation. Experimental results on multiple tasks from
MIMIC-IV/MIMIC-CXR/MIMIC-NOTE datasets demonstrate the effectiveness of the
proposed method. Additionally, further analysis underscores the feasibility and
potential of employing such a multitask strategy in the healthcare domain. The
source code is available at this https URL

提出了一种名为 FlexCare 的统一医疗预测模型，可以灵活地处理不完整的多模态输入，改善对多个医疗任务的适应性。该模型通过将多任务预测分解为一系列异步的单任务预测来突破传统的并行多任务预测范式，并通过引入多模态信息提取模块和多模态融合模块，在考虑不同模态之间和不同任务之间的信息差异的基础上实现对全面多模态数据的利用。通过在多个任务上进行实验，并进一步分析，验证了该多任务策略在医疗领域的可行性和潜力。