Summary of Kg-treat: Pre-training For Treatment Effect Estimation by Synergizing Patient Data with Knowledge Graphs, By Ruoqi Liu et al.
KG-TREAT: Pre-training for Treatment Effect Estimation by Synergizing Patient Data with Knowledge Graphs
by Ruoqi Liu, Lingfei Wu, Ping Zhang
First submitted to arxiv on: 6 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a novel framework, KG-TREAT, for treatment effect estimation (TEE) that combines large-scale observational patient data with biomedical knowledge graphs (KGs). The approach synergizes dual-focus KGs and deep bi-level attention synergy to enhance TEE. KG-TREAT also incorporates pre-training tasks to contextualize patient data and KGs. Experimental results show an average improvement of 7% in Area under the ROC Curve (AUC) and 9% in Influence Function-based Precision of Estimating Heterogeneous Effects (IF-PEHE) compared to existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a big problem in medicine, called treatment effect estimation. This is when doctors try to figure out how different treatments affect patients. Right now, the methods used are not very good because they rely on limited data and can’t handle complex patient information. The researchers created a new approach that uses lots of patient data and special knowledge graphs to help with this problem. They tested their method and it worked better than other methods. |
Keywords
* Artificial intelligence * Attention * Auc * Precision * Roc curve
