Introduction

Predicting treatment outcomes for lung cancer remains a challenge due to the sparsity, heterogeneity, and information overload of real-world electronic health data. A team of researchers has developed a new framework that uses large language models to transform laboratory, genomic, and medication data into high-fidelity features to improve treatment outcome prediction.

Methodology

The new framework uses Large Language Models (LLMs) as Goal-oriented Knowledge Curators (GKC) to convert laboratory, genomic, and medication data into high-fidelity features. GKC produces task-aligned representations tailored to the prediction objective and operates as an offline preprocessing step that integrates naturally into hospital informatics pipelines.

Results

Results have been published on arXiv and show that the quality of semantic representation is a key determinant of predictive accuracy in sparse clinical data settings. The new framework demonstrates a scalable, interpretable, and workflow-compatible pathway for advancing AI-driven decision support in oncology.