KerPrint: Local-Global Knowledge Graph Enhanced Diagnosis Prediction for Retrospective and Prospective Interpretations
نویسندگان
چکیده
While recent developments of deep learning models have led to record-breaking achievements in many areas, the lack sufficient interpretation remains a problem for specific applications, such as diagnosis prediction task healthcare. The previous knowledge graph(KG) enhanced approaches mainly focus on clinically meaningful representations, importance medical concepts, and even paths from inputs labels. However, it is infeasible interpret prediction, which needs consider different various relationships, time-effectiveness triples patient contexts. More importantly, retrospective prospective interpretations disease processes are valuable clinicians patients' confounding diseases. We propose KerPrint, novel KG approach tackle these problems. Specifically, we time-aware attention method solve decay over time trustworthy interpretation. also element-wise select candidate global using comprehensive representations local validate effectiveness our KerPrint through an extensive experimental study real-world dataset public dataset. results show that proposed not only achieves significant improvement knowledge-enhanced methods but gives interpretability both views.
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ژورنال
عنوان ژورنال: Proceedings of the ... AAAI Conference on Artificial Intelligence
سال: 2023
ISSN: ['2159-5399', '2374-3468']
DOI: https://doi.org/10.1609/aaai.v37i4.25667