The Intensive Care Unit (ICU) is one of the most important parts of a
hospital, which admits critically ill patients and provides continuous
monitoring and treatment. Various patient outcome prediction methods have been
attempted to assist healthcare professionals in clinical decision-making.
Existing methods focus on measuring the similarity between patients using deep
neural networks to capture the hidden feature structures. However, the
higher-order relationships are ignored, such as patient characteristics (e.g.,
diagnosis codes) and their causal effects on downstream clinical predictions.
In this paper, we propose a novel Hypergraph Convolutional Network that
allows the representation of non-pairwise relationships among diagnosis codes
in a hypergraph to capture the hidden feature structures so that fine-grained
patient similarity can be calculated for personalized mortality risk
prediction. Evaluation using a publicly available eICU Collaborative Research
Database indicates that our method achieves superior performance over the
state-of-the-art models on mortality risk prediction. Moreover, the results of
several case studies demonstrated the effectiveness of constructing graph
networks in providing good transparency and robustness in decision-making.

我们提出了一种新颖的超图卷积网络，可以捕捉隐藏的特征结构，计算个性化的死亡风险预测，评估表明我们的方法在死亡风险预测上优于现有模型，并且几个案例研究的结果证明了构建图网络在决策中提供良好的透明性和稳健性。

超图卷积网络用于精细化 ICU 病人相似性分析和风险预测

Hypergraph Convolutional Networks for Fine-grained ICU Patient  Similarity Analysis and Risk Prediction

Multitask deep learning has been applied to patient outcome prediction from
text, taking clinical notes as input and training deep neural networks with a
joint loss function of multiple tasks. However, the joint training scheme of
multitask learning suffers from inter-task interference, and diagnosis
prediction among the multiple tasks has the generalizability issue due to rare
diseases or unseen diagnoses. To solve these challenges, we propose a
hypernetwork-based approach that generates task-conditioned parameters and
coefficients of multitask prediction heads to learn task-specific prediction
and balance the multitask learning. We also incorporate semantic task
information to improves the generalizability of our task-conditioned multitask
model. Experiments on early and discharge notes extracted from the real-world
MIMIC database show our method can achieve better performance on multitask
patient outcome prediction than strong baselines in most cases. Besides, our
method can effectively handle the scenario with limited information and improve
zero-shot prediction on unseen diagnosis categories.

提出了一种基于超网络的方法，使用任务条件参数和多任务预测模型的系数来学习任务特定的预测并平衡多任务学习，将语义任务信息合并到模型中，以解决多任务学习中的干扰和泛化问题，并在真实世界的 MIMIC 数据库上表现出比强基线更好的多任务病人结果预测性能，在限制信息场景及对未见过的诊断类别问题，也能有效地应对。