Tracing the Heart: An Evidence-Linked Pipeline for Heart-Failure Feature Engineering

• URL: http://arxiv.org/abs/2608.06366v1
• Date: Thu, 06 Aug 2026 17:57:37 GMT
• Title: Tracing the Heart: An Evidence-Linked Pipeline for Heart-Failure Feature Engineering
• Abstract summary: We developed an evidence-linked, rubric-grounded pipeline for automated heart-failure feature engineering.<n>nMAS generated 132 structured and 70 rubric-scored aggregated features, verified for structural integrity, rubric compliance, and provenance, and audited by a restricted LLM.<n>These results demonstrate the feasibility of automated, auditable feature engineering for complex cardiovascular EHR data.
• Score: 18.09162983861884
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Electronic health record (EHR) feature engineering is a major bottleneck in clinical research and AI, accounting for 39-45% of data scientists' workload. This is especially pronounced in heart failure, which affects an estimated 6.7 million U.S. adults and requires integrating fragmented EHR data with disease-specific, guideline-based clinical reasoning. Existing rule-based and large language model (LLM)-based approaches offer only partial automation with limited maintainability and evidence traceability. We developed the Nimblemind Multi-Agent System (nMAS), an evidence-linked, rubric-grounded pipeline for automated heart-failure feature engineering, and evaluated it on 500 dummy patient records from nine EHR source tables. nMAS generated 132 structured and 70 rubric-scored aggregated features, verified for structural integrity, rubric compliance, and provenance, and audited by a restricted LLM. Adding the aggregated features improved held-out AUROC from 0.895 to 0.963 for HFrEF and 0.870 to 0.910 for HFpEF phenotyping, and an independent LLM-based rubric assessment of evidence support and methodological soundness scored the features at 81.5% of maximum points. These results demonstrate the feasibility of automated, auditable feature engineering for complex cardiovascular EHR data, though evaluation was limited to a single-institution cohort and external validation is needed.

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