In clinical practice, one often needs to identify whether a patient is at
high risk of adverse outcomes after some key medical event; e.g., the
short-term risk of death after an admission for heart failure. This task,
however, remains challenging due to the complexity, variability, and
heterogeneity of longitudinal medical data, especially for individuals
suffering from chronic diseases like heart failure. In this paper, we introduce
Event-Based Contrastive Learning (EBCL) - a method for learning embeddings of
heterogeneous patient data that preserves temporal information before and after
key index events. We demonstrate that EBCL produces models that yield better
fine-tuning performance on critical downstream tasks including 30-day
readmission, 1-year mortality, and 1-week length of stay relative to other
representation learning methods that do not exploit temporal information
surrounding key medical events.

通过引入基于事件的对比学习（EBCL）方法，本文提出了一种用于学习保留关键指标事件之前和之后的异构患者数据的嵌入的方法，并证明相比不利用关键医学事件周围的时间信息的其他表示学习方法，EBCL 产生的模型在包括 30 天再住院、1 年死亡率和 1 周住院时间的关键后续任务的微调性能更好。

医学时间序列的事件对比学习

Event-Based Contrastive Learning for Medical Time Series

Measures to predict 30-day readmission are considered an important quality
factor for hospitals as accurate predictions can reduce the overall cost of
care by identifying high risk patients before they are discharged. While recent
deep learning-based studies have shown promising empirical results on
readmission prediction, several limitations exist that may hinder widespread
clinical utility, such as (a) only patients with certain conditions are
considered, (b) existing approaches do not leverage data temporality, (c)
individual admissions are assumed independent of each other, which is
unrealistic, (d) prior studies are usually limited to single source of data and
single center data. To address these limitations, we propose a multimodal,
modality-agnostic spatiotemporal graph neural network (MM-STGNN) for prediction
of 30-day all-cause hospital readmission that fuses multimodal in-patient
longitudinal data. By training and evaluating our methods using longitudinal
chest radiographs and electronic health records from two independent centers,
we demonstrate that MM-STGNN achieves AUROC of 0.79 on both primary and
external datasets. Furthermore, MM-STGNN significantly outperforms the current
clinical reference standard, LACE+ score (AUROC=0.61), on the primary dataset.
For subset populations of patients with heart and vascular disease, our model
also outperforms baselines on predicting 30-day readmission (e.g., 3.7 point
improvement in AUROC in patients with heart disease). Lastly, qualitative model
interpretability analysis indicates that while patients' primary diagnoses were
not explicitly used to train the model, node features crucial for model
prediction directly reflect patients' primary diagnoses. Importantly, our
MM-STGNN is agnostic to node feature modalities and could be utilized to
integrate multimodal data for triaging patients in various downstream resource
allocation tasks.

本研究提出的多模态、模态不可知时空图神经网络（MM-STGNN）结合了多模态病人纵向数据，用于预测 30 天内入院后出现的所有原因再次入院风险，并在两个独立中心的电子健康记录和胸部 X 光数据集中证明其预测效果优于现有的临床参考标准 LACE + 分数。