Leveraging Large Language Models and Survival Analysis for Early Prediction of Chemotherapy Outcomes

2026-03-12

AI Summary

This study addresses the high cost and severe side effects of chemotherapy, as well as the scarcity of real-world data with clearly defined phenotypic and treatment response labels, which hinder early efficacy prediction. To overcome these challenges, the work proposes a novel approach that integrates large language models with medical ontologies to automatically extract standardized chemotherapy regimens, patient phenotypes, and treatment outcome labels from electronic health records, with regimen accuracy validated against NCCN/NIH guidelines. A random survival forest model is then employed to predict chemotherapy outcomes across multiple cancer types. In a breast cancer cohort, the model achieved a C-index of 0.73, with classification accuracy and F1 scores exceeding 70% at specific time points, and calibration curves confirming reliability. This framework substantially alleviates data sparsity and supports cross-cancer personalized treatment decisions.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Chemotherapy for cancer treatment is costly and accompanied by severe side effects, highlighting the critical need for early prediction of treatment outcomes to improve patient management and informed decision-making. Predictive models for chemotherapy outcomes using real-world data face challenges, including the absence of explicit phenotypes and treatment outcome labels such as cancer progression and toxicity. This study addresses these challenges by employing Large Language Models (LLMs) and ontology-based techniques for phenotypes and outcome label extraction from patient notes. We focused on one of the most frequently occurring cancers, breast cancer, due to its high prevalence and significant variability in patient response to treatment, making it a critical area for improving predictive modeling. The dataset included features such as vitals, demographics, staging, biomarkers, and performance scales. Drug regimens and their combinations were extracted from the chemotherapy plans in the EMR data and shortlisted based on NCCN guidelines, verified with NIH standards, and analyzed through survival modeling. The proposed approach significantly reduced phenotypes sparsity and improved predictive accuracy. Random Survival Forest was used to predict time-to-failure, achieving a C-index of 73%, and utilized as a classifier at a specific time point to predict treatment outcomes, with accuracy and F1 scores above 70%. The outcome probabilities were validated for reliability by calibration curves. We extended our approach to four other cancer types. This research highlights the potential of early prediction of treatment outcomes using LLM-based clinical data extraction enabling personalized treatment plans with better patient outcomes.

Problem

• chemotherapy outcomes
• early prediction
• real-world data
• phenotype extraction
• treatment response

Innovation

• Large Language Models
• Survival Analysis
• Phenotype Extraction
• Real-World Data
• Chemotherapy Outcome Prediction