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KAMLN: A Knowledge-aware Multi-label Network for Lung Cancer Complication Prediction.
Summary
This study introduces a novel knowledge-aware multi-label network (KAMLN) to predict lung cancer surgery complications. The KAMLN improves prediction accuracy by considering causal relationships between complications, outperforming existing methods.
Area of Science:
- Medical Informatics
- Computational Biology
- Oncology
Background:
- Surgical resection is the primary curative treatment for early-stage lung cancer.
- Postoperative complications significantly impact patient outcomes and survival.
- Existing prediction models often overlook inter-complication causalities, limiting accuracy.
Purpose of the Study:
- To develop a novel approach for predicting postoperative complications in lung cancer patients.
- To leverage potential causal relationships between complications for enhanced prediction.
- To introduce the knowledge-aware multi-label network (KAMLN) for improved complication prediction.
Main Methods:
- Construction of a knowledge graph to represent causal relationships between complications.
- Development of a neural network (KAMLN) integrating this knowledge graph.
- Validation using data from 593 lung cancer patients.
Main Results:
- The KAMLN achieved a micro-AUC of 0.664±0.100, surpassing baseline methods.
- SHAP analysis identified lymph node dissection as a significant factor influencing multiple complications.
- The model demonstrated effective utilization of prior knowledge for prediction.
Conclusions:
- The knowledge-aware multi-label network (KAMLN) offers a more accurate and fine-grained approach to predicting lung cancer surgery complications.
- Incorporating causal relationships between complications enhances predictive performance.
- Identifying key surgical factors like lymph node dissection is crucial for mitigating risks.
