1. By reviewing your code and the details in the article, I can see that your workload is immense, however, the contribution of this article is incremental. My understanding is that it is essentially a combination of GraphRAG and GraphCare [1]. Furthermore, many key baselines were not cited. Since the authors mentioned that this paper focuses on RAG for EHR, some essential RAG algorithms should have been introduced, such as MedRetriever [2], and commonly used GraphRAG algorithms like KGRAG [3].
2. In the experiment or appendix section, I did not clearly see the formulas for Sensitivity and Specificity, nor were there any corresponding references, which is quite confusing to me. Moreover, using Accuracy as a metric in cases of highly imbalanced labels is unreasonable. For instance, in the MIMIC-III Mortality Prediction task, the positive rate is 5.42%. If I predict that all patients will survive, I can still achieve an accuracy of 94.58%. Previous works, such as GraphCare [1], have adopted AUROC and AUPRC as evaluation metrics.
3. The article is overly long and filled with detailed content, making it easy for readers to miss important points.
- [1] GraphCare: Enhancing Healthcare Predictions with Personalized Knowledge Graphs. ICLR 2024
- [2] MedRetriever: Target-driven interpretable health risk prediction via retrieving unstructured medical text. CIKM 2021
- [3] Biomedical knowledge graph-enhanced prompt generation for large language models. Arxiv 2023
1. By reviewing your code and the details in the article, I can see that your workload is immense, however, the contribution of this article is incremental. My understanding is that it is essentially a combination of GraphRAG and GraphCare [1]. Furthermore, many key baselines were not cited. Since the authors mentioned that this paper focuses on RAG for EHR, some essential RAG algorithms should have been introduced, such as MedRetriever [2], and commonly used GraphRAG algorithms like KGRAG [3].