{
  "abstract": "Clinical interventions for cancer treatment toxicities can reduce morbidity, mortality, and healthcare cost, if high risk patients can be identified. We developed SPARC, a transformer-based foundation model, using real-world, longitudinal, routinely collected health measurements, to predict adverse event risk. Pre-training and fine-tuning SPARC on 101,920,379 measurements from 224,047 patients with cancer led to state-of-the-art performance at predicting cancer-related adverse events on (i) held-out data from the development cohort, (ii) a prospective validation cohort from the same center (Npatients=23,126), (iii) multiple publicly available external datasets (Npatients=26,012), and (iv) external, multi-hospital cohorts (Npatients=562,620, 206,840). SPARC generalized to multiple external centers and identified patients most likely to benefit from an intervention (G-CSF). Pooled multi-institution, more than single-institution, fine-tuning improved generalizability. We release SPARC as a community resource. Our results highlight the utility of AI time series models in oncologic management.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 14,
  "authors": [
    {
      "name": "Chenlian Fu",
      "orcid": "0000-0002-2755-4359"
    },
    {
      "name": "Jie (Jennifer) Yu"
    },
    {
      "name": "Ruian (Ian) Shi"
    },
    {
      "name": "Michele Waters"
    },
    {
      "name": "Mitchell Pavlak"
    },
    {
      "name": "Andrea Arfe"
    },
    {
      "name": "Sohrab P. Shah"
    },
    {
      "name": "Alexis Battle"
    },
    {
      "name": "Kenneth L. Kehl"
    },
    {
      "name": "Brian M. Bot"
    },
    {
      "name": "US Cancer AI Alliance (CAIA)"
    },
    {
      "name": "Nikolaus Schultz"
    },
    {
      "name": "Quaid Morris"
    },
    {
      "name": "Justin Jee"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/b11magd0pj5s2.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.22.26364879",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.10.22.26364879v1.full",
  "id": "b11magd0pj5s2",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.10.22.26364879v1",
  "language": "en",
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Article"
    ],
    [
      "medrxiv",
      "oncology"
    ],
    [
      "openalex",
      "1702|Machine Learning in Healthcare"
    ]
  ],
  "openalex": "W7223673757",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.10.22.26364879v1.full.pdf",
  "pmcid": "PMC13734056",
  "pmid": "42990522",
  "published": "2026-10-22",
  "publisher": "Cold Spring Harbor Laboratory Preprints",
  "ris": "https://research.draconitic.com/paper/b11magd0pj5s2.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:47Z",
      "native_id": "10.64898/2026.10.22.26364879",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.10.22.26364879v2"
    },
    {
      "first_seen": "2026-11-08T12:23:42Z",
      "native_id": "PMC13734056",
      "source": "pmc",
      "url": "https://doi.org/10.64898/2026.10.22.26364879"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:oncology",
      "subfield": "oncology"
    }
  ],
  "title": "Longitudinal AI Models Predict Cancer Adverse Events",
  "title_text": "Longitudinal AI Models Predict Cancer Adverse Events",
  "type": "preprint",
  "updated_at": "2026-11-08T12:23:42Z",
  "url": "https://research.draconitic.com/paper/b11magd0pj5s2",
  "venue": "medRxiv",
  "versions": []
}