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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