GraphBEAT: Graph-based Bucketized Event Aggregation Over Time for Early Risk Stratification of Gastrointestinal Cancers
To address the problem, we introduce GraphBEAT (Graph-based Bucketized Event Aggregation over Time), a temporal graph representation learning framework that groups clinical events into time buckets and transforms observed features into patient-specific, temporally directed graphs to enable early risk stratification for gastrointestinal cancers.
ProofPaper ↗
Key points
- Longitudinal electronic health records (EHRs) contain complementary diagnostic and laboratory information about patient health.
- Each node in an EHR graph encodes feature identity, observed value, and temporal position of clinical events.
- GraphBEAT was trained and evaluated on multi-site longitudinal diagnosis codes and laboratory tests to predict pancreatic, liver, and intestinal cancer at 12-, 24-, and 36-month diagnostic lead times.
- These results support GraphBEAT as an accurate and computationally efficient approach for cancer risk stratification from sparse, heterogeneous longitudinal health records.
Sources (1)
- [1]GraphBEAT: Graph-based Bucketized Event Aggregation Over Time for Early Risk Stratification of Gastrointestinal CancersarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 02:55 PM
To address the problem, we introduce GraphBEAT (Graph-based Bucketized Event Aggregation over Time), a temporal graph representation learning framework that groups clinical events into time buckets and transforms observed features into patient-specific, temporally directed graphs to enable early risk stratification for gastrointestinal cancers.
Longitudinal electronic health records (EHRs) contain complementary diagnostic and laboratory information about patient health.
Extractive summary: sentences quoted from the sources.
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