{
  "abstract": "Chronic care requires sequential treatment under competing biomarker, safety, and cost constraints, yet clinical goal structures differ across diseases. This version asks whether one physiology-informed reinforcement-learning paradigm can be written once and then instantiated for three goal structures-- treat-to-target, multi-marker cruise, and bounded cycle completion--without a disease-specific policy architecture. Gout, chronic kidney disease, and PCOS-mediated fertility treatment are the three instances. Training is behaviour cloning followed by proximal policy optimization on 500 simulated trajectories per disease. Evaluation uses paired seeds (N = 50 primary; N = 500 bootstrap 95% confidence intervals), multi-seed offsets 42-51, ablation, and out-of-distribution profiles.\n\nPCOS (cycle completion): PPO 72.0% against the doctor baseline 54.0% at N = 50; at N = 500, PPO 69.8% [65.6, 73.8] against doctor 52.8% [48.8, 57.2]. Gout (treat-to-target): PPO 88.0% against 90.0% (McNemar p = 1.0). CKD (cruise): doctor 32.0%, BC/PPO 38.0%.\n\nThis v2 withdraws three bibliographic coordinates that appeared in v1 and that do not match any recoverable source. The withdrawn comparison is replaced by a NOR-Gout calibration at weeks 4, 8 and 12 that belongs to the recalibrated kernel of the parallel journal manuscript. Shared BC[->]PPO training is instantiated across the three goal types without cross-disease weight sharing. Prospective validation remains necessary.\n\nHighlightsO_LIThree goal types are hosted as different success tests, not as a claimed clinical taxonomy. C_LIO_LIPCOS cycle completion +18 percentage points against the doctor baseline at N = 50. C_LIO_LIGout remains non-inferior to the guideline-mimicking doctor in the v1 evaluation table. C_LIO_LINOR-Gout weeks 4/8/12 belong to the recalibrated kernel, not Section 3. C_LIO_LIBehaviour-cloning warm-start remains necessary under the gout kernel used here. C_LI",
  "abstract_shown": true,
  "affiliations": [
    "Guangdong Provincial Hospital of Chinese Medicine, Hainan Hospital"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "R. Wang"
    },
    {
      "name": "H. Chen"
    },
    {
      "name": "Y. Wu"
    },
    {
      "name": "Z. Li"
    },
    {
      "name": "R. Shen"
    },
    {
      "name": "F. He"
    },
    {
      "name": "S. Zhao"
    },
    {
      "name": "N. Zheng"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/e2r43znwrp5vm.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.07.03.26357209",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.07.03.26357209v2.full",
  "id": "e2r43znwrp5vm",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.07.03.26357209v2",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "health informatics"
    ],
    [
      "openalex",
      "1702|Machine Learning in Healthcare"
    ]
  ],
  "openalex": "W7167495421",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.07.03.26357209v2.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-07-03",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/e2r43znwrp5vm.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:01Z",
      "native_id": "10.64898/2026.07.03.26357209",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.07.03.26357209v2"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:health informatics",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Reinforcement Learning for Chronic Care Pathway Optimization: A Unified Framework across Three Clinical Goal Types",
  "title_text": "Reinforcement Learning for Chronic Care Pathway Optimization: A Unified Framework across Three Clinical Goal Types",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:01Z",
  "url": "https://research.draconitic.com/paper/e2r43znwrp5vm",
  "venue": "medRxiv",
  "versions": []
}