Frontiers in digital health · 2026
Machine learning for chemotherapy decision-making in breast cancer using large language model.
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Nabi Md Serajun, Yuden Dema, Choden Thinley Yeshey, Asiful Islam Saky S M, Bannah Hasanul, Hossen Md Sabbir, Fauzi Mohammad Faizal Ahmad, Karim Hezerul Bin Abdul
Аннотация
INTRODUCTION: Breast cancer chemotherapy decision-making remains challenging due to biological heterogeneity and variability in clinical practice. This study proposes a hybrid framework integrating machine learning (ML), causal reasoning, and large language models (LLMs) to improve treatment recommendations.
METHODS: Using the METABRIC dataset, eleven pre-treatment clinicopathologic variables were selected. A Random Forest classifier was developed and compared with baseline ML models. Individualized treatment benefit was estimated through inverse probability-weighted causal survival analysis, while GPT-4 was employed using few-shot prompting to generate clinical rationales.
RESULTS: The Random Forest achieved an AUC of 0.91, outperforming benchmark models. Causal analysis identified heterogeneous treatment benefits and patient groups where chemotherapy could potentially be deprioritized. GPT-4 showed moderate agreement with the Random Forest (Cohen's κ = 0.13) while consistently highlighting clinically relevant factors. Uplift-based ML policies outperformed treat-all and treat-none strategies, and GPT-4 improved interpretability through rationale-driven explanations.
DISCUSSION: By combining predictive ML, causal survival modeling, and LLM-based rationale generation, the proposed framework provides a promising approach for personalized and transparent chemotherapy decision support in oncology.
METHODS: Using the METABRIC dataset, eleven pre-treatment clinicopathologic variables were selected. A Random Forest classifier was developed and compared with baseline ML models. Individualized treatment benefit was estimated through inverse probability-weighted causal survival analysis, while GPT-4 was employed using few-shot prompting to generate clinical rationales.
RESULTS: The Random Forest achieved an AUC of 0.91, outperforming benchmark models. Causal analysis identified heterogeneous treatment benefits and patient groups where chemotherapy could potentially be deprioritized. GPT-4 showed moderate agreement with the Random Forest (Cohen's κ = 0.13) while consistently highlighting clinically relevant factors. Uplift-based ML policies outperformed treat-all and treat-none strategies, and GPT-4 improved interpretability through rationale-driven explanations.
DISCUSSION: By combining predictive ML, causal survival modeling, and LLM-based rationale generation, the proposed framework provides a promising approach for personalized and transparent chemotherapy decision support in oncology.