GraphCare: Enhancing Healthcare Predictions with Personalized Knowledge
Graphs
- URL: http://arxiv.org/abs/2305.12788v3
- Date: Wed, 17 Jan 2024 18:12:47 GMT
- Title: GraphCare: Enhancing Healthcare Predictions with Personalized Knowledge
Graphs
- Authors: Pengcheng Jiang, Cao Xiao, Adam Cross, Jimeng Sun
- Abstract summary: textscGraphCare is an open-world framework that uses external knowledge graphs to improve EHR-based predictions.
Our method extracts knowledge from large language models (LLMs) and external biomedical KGs to build patient-specific KGs.
textscGraphCare surpasses baselines in four vital healthcare prediction tasks.
- Score: 44.897533778944094
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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 \textsc{GraphCare}, an
open-world framework that uses external KGs to improve EHR-based predictions.
Our method extracts knowledge from large language models (LLMs) and external
biomedical KGs to build patient-specific KGs, which are then used to train our
proposed Bi-attention AugmenTed (BAT) graph neural network (GNN) for healthcare
predictions. On two public datasets, MIMIC-III and MIMIC-IV, \textsc{GraphCare}
surpasses baselines in four vital healthcare prediction tasks: mortality,
readmission, length of stay (LOS), and drug recommendation. On MIMIC-III, it
boosts AUROC by 17.6\% and 6.6\% for mortality and readmission, and F1-score by
7.9\% and 10.8\% for LOS and drug recommendation, respectively. Notably,
\textsc{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
\textsc{GraphCare} in generating personalized KGs for promoting personalized
medicine.
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