Clinical predictive models often rely on patients electronic health records
(EHR), but integrating medical knowledge to enhance predictions and
decision-making is challenging. This is because personalized predictions
require personalized knowledge graphs (KGs), which are difficult to generate
from patient EHR data. To address this, we propose GraphCare, an open-world
framework that leverages external KGs to improve EHR-based predictions. Our
method extracts knowledge from large language models (LLMs) and external
biomedical KGs to generate patient-specific KGs, which are then used to train
our proposed Bi-attention AugmenTed BAT graph neural network GNN for healthcare
predictions. We evaluate GraphCare on two public datasets: MIMIC-III and
MIMIC-IV. Our method outperforms baseline models in four vital healthcare
prediction tasks: mortality, readmission, length-of-stay, and drug
recommendation, improving AUROC on MIMIC-III by average margins of 10.4%, 3.8%,
2.0%, and 1.5%, respectively. Notably, GraphCare demonstrates a substantial
edge in scenarios with limited data availability. Our findings highlight the
potential of using external KGs in healthcare prediction tasks and demonstrate
the promise of GraphCare in generating personalized KGs for promoting
personalized medicine.

提出了一种名为 GraphCare 的基于开放框架的方法，其结合了外部知识图和医疗健康记录，旨在提高医疗预测的精度。使用大型语言模型和外部生物医学知识图生成个性化图知识，并使用提出的双重注视增强的 BAT 图神经网络用于医疗保健预测。实验结果表明，GraphCare 方法在几个重要的医疗预测任务中的性能都优于基线模型，尤其是在数据稀缺的情况下表现优异。