Enhancing Lung Cancer Treatment Outcome Prediction through Semantic Feature Engineering Using Large Language Models
Published:Dec 25, 2025 05:00
•1 min read
•ArXiv ML
Analysis
This research paper presents a novel framework leveraging Large Language Models (LLMs) as Goal-oriented Knowledge Curators (GKC) to improve lung cancer treatment outcome prediction. The study addresses the challenges of sparse, heterogeneous, and contextually overloaded electronic health data. By converting laboratory, genomic, and medication data into task-aligned features, the GKC approach outperforms traditional methods and direct text embeddings. The results demonstrate the potential of LLMs in clinical settings, not as black-box predictors, but as knowledge curation engines. The framework's scalability, interpretability, and workflow compatibility make it a promising tool for AI-driven decision support in oncology, offering a significant advancement in personalized medicine and treatment planning. The use of ablation studies to confirm the value of multimodal data is also a strength.
Key Takeaways
- •LLMs can be effectively used as Goal-oriented Knowledge Curators (GKC) for feature engineering.
- •The GKC approach outperforms traditional methods in predicting lung cancer treatment outcomes.
- •The framework offers a scalable and interpretable solution for AI-driven decision support in oncology.
Reference
“By reframing LLMs as knowledge curation engines rather than black-box predictors, this work demonstrates a scalable, interpretable, and workflow-compatible pathway for advancing AI-driven decision support in oncology.”