{
  "abstract": "Accurate, physiologically consistent glucose forecasts could help people with type 1 diabetes respond to impending excursions. We applied our physiological-model-based neural network (PMB--NN) framework to glucose forecasting. A temporal convolutional network (TCN) used 180 min of history, constrained during training by a personalized four-parameter physiological model. The main PMB--NN combined forecast and neural--physiological agreement losses; a three-term variant additionally fitted physiological predictions to measured glucose. Data comprised 6--8 weeks of continuous glucose monitoring, pump, and meal records from 25 AZT1D participants. Person-specific models used chronological 70/10/20 train/validation/test splits and 30-, 60-, and 120-min horizons. Median mean absolute errors were 16.24, 25.00, and 30.20 mg/dL with basal insulin records and 15.38, 24.21, and 31.79 mg/dL before these records became available. In the latter setting, PMB--NN had the lowest median error at 30 and 60 min; it outperformed the physiological model alone in all six settings. In input-increase tests, glucose predictions changed in the expected direction for 85\\% of correction boluses and 100\\% of carbohydrate rescues, versus 57\\% for active insulin and 56\\% for active carbohydrates. Most standalone physiological fits showed locally distinguishable parameter effects, with glucose-related coefficients more stable under resampling than insulin- or carbohydrate-related coefficients. The three-term variant improved hyperglycaemia accuracy with basal records, but did not significantly change overall MAE and reduced directional plausibility in three tests. PMB--NN combines personalized forecasting with an explicit physiological model, allowing assessment of predictive accuracy, physiological response behavior, and participant-specific coefficients.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.15456",
  "author_total": 3,
  "authors": [
    {
      "name": "Yaowen Zhang"
    },
    {
      "name": "Shuhao Que"
    },
    {
      "name": "Ying Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/a453zt8nav5cg.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.15456",
  "fulltext_url": null,
  "id": "a453zt8nav5cg",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.15456",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "physics.med-ph"
    ],
    [
      "openalex",
      "2712|Diabetes Management and Research"
    ]
  ],
  "openalex": "W7221802950",
  "pdf_url": "https://arxiv.org/pdf/2610.15456",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-15",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/a453zt8nav5cg.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:50Z",
      "native_id": "2610.15456",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.15456"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.med-ph",
      "subfield": "biological-medical"
    }
  ],
  "title": "PMB-NN: Physiological-Model-Based Neural Network for Glucose Forecasting in Type 1 Diabetes",
  "title_text": "PMB-NN: Physiological-Model-Based Neural Network for Glucose Forecasting in Type 1 Diabetes",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:50Z",
  "url": "https://research.draconitic.com/paper/a453zt8nav5cg",
  "venue": "arXiv",
  "versions": []
}