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    "story_id": "mp-2026-08-08-006",
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    "headline": "The Heart-Failure Feature Kept Its Evidence Trail",
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    "dek": "An automated pipeline built structured and rubric-scored variables from nine EHR tables, then linked each output back to its support.",
    "summary": "An automated pipeline built structured and rubric-scored variables from nine EHR tables, then linked each output back to its support.",
    "body_text": "The Nimblemind Multi-Agent System generated 132 structured and 70 rubric-scored aggregate features from nine electronic-health-record tables and attached evidence and rubric provenance. On 500 dummy patient records from one institution, adding the aggregates raised held-out AUROC from 0.895 to 0.963 for reduced-ejection-fraction phenotyping and from 0.870 to 0.910 for preserved-ejection-fraction phenotyping. The authors call for external validation; this was feature-engineering research, not a clinical diagnostic trial.",
    "why_it_matters": "An automated pipeline built structured and rubric-scored variables from nine EHR tables, then linked each output back to its support.",
    "limitations": [
      "The authors call for external validation; this was feature-engineering research, not a clinical diagnostic trial."
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    "tags": [
      "heart failure",
      "electronic health records",
      "clinical AI",
      "provenance"
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    "corrections": []
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  "sources": [
    {
      "source_id": "source-2026-08-08-006",
      "title": "arXiv preprint 2608.06366",
      "publisher": "arXiv",
      "url": "https://arxiv.org/abs/2608.06366",
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  "publisher": {
    "name": "The Machine Press",
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    "description": "A daily newspaper for the age of artificial intelligence."
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  "cite_this_report": {
    "title": "The Heart-Failure Feature Kept Its Evidence Trail",
    "publisher": "The Machine Press",
    "published_at": "2026-08-08T09:00:00.000-04:00",
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