Agent-Based Feature Generation from Clinical Notes for Outcome Prediction

• URL: http://arxiv.org/abs/2508.01956v1
• Date: Sun, 03 Aug 2025 23:45:18 GMT
• Title: Agent-Based Feature Generation from Clinical Notes for Outcome Prediction
• Authors: Jiayi Wang, Jacqueline Jil Vallon, Neil Panjwani, Xi Ling, Sushmita Vij, Sandy Srinivas, John Leppert, Mark K. Buyyounouski, Mohsen Bayati,
• Abstract summary: We introduce SNOW, a modular multi-agent system powered by large language models (LLMs) that autonomously generates structured clinical features from unstructured notes without human intervention.<n>We evaluated SNOW against manual CFG, clinician-guided LLM approaches, and RFG methods for predicting 5-year prostate cancer recurrence in 147 patients from Stanford Healthcare.
• Score: 4.5134876868616765
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Electronic health records (EHRs) contain rich unstructured clinical notes that could enhance predictive modeling, yet extracting meaningful features from these notes remains challenging. Current approaches range from labor-intensive manual clinician feature generation (CFG) to fully automated representational feature generation (RFG) that lack interpretability and clinical relevance. Here we introduce SNOW (Scalable Note-to-Outcome Workflow), a modular multi-agent system powered by large language models (LLMs) that autonomously generates structured clinical features from unstructured notes without human intervention. We evaluated SNOW against manual CFG, clinician-guided LLM approaches, and RFG methods for predicting 5-year prostate cancer recurrence in 147 patients from Stanford Healthcare. While manual CFG achieved the highest performance (AUC-ROC: 0.771), SNOW matched this performance (0.761) without requiring any clinical expertise, significantly outperforming both baseline features alone (0.691) and all RFG approaches. The clinician-guided LLM method also performed well (0.732) but still required expert input. SNOW's specialized agents handle feature discovery, extraction, validation, post-processing, and aggregation, creating interpretable features that capture complex clinical information typically accessible only through manual review. Our findings demonstrate that autonomous LLM systems can replicate expert-level feature engineering at scale, potentially transforming how clinical ML models leverage unstructured EHR data while maintaining the interpretability essential for clinical deployment.

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