{
  "annotation_id": "6bcf4ba1-51a5-41c6-b0b1-b2b2e00e08f5",
  "date_created": "2026-02-17T05:53:12.052000Z",
  "date_modified": "2026-02-17T05:53:12.052000Z",
  "file_hash": "51db3b2322b62742084aa58440895a3726695eac3eb79d64e4596ae648aac226",
  "private": false,
  "record": {
    "abstract": "Clinical notes hold rich yet unstructured details about diagnoses, treatments, and outcomes that are vital to precision medicine but hard to exploit at scale. We introduce a method that represents each patient as a matrix built from aggregated embeddings of all their notes, enabling robust patient similarity computation based on their latent low-rank representations. Using clinical notes of 4,267 Czech breast-cancer patients and expert similarity labels from Masaryk Memorial Cancer Institute, we evaluate several matrix-based similarity measures and analyze their strengths and limitations across different similarity facets, such as clinical history, treatment, and adverse events. The results demonstrate the usefulness of the presented method for downstream tasks, such as personalized therapy recommendations or toxicity warnings.",
    "arxiv_id": "2601.07385",
    "authors": [
      "Petr Zelina",
      "Marko \u0158eh\u00e1\u010dek",
      "Jana Hal\u00e1mkov\u00e1",
      "Lucia Bohovicov\u00e1",
      "Martin Rusinko",
      "V\u00edt Nov\u00e1\u010dek"
    ],
    "categories": [
      "cs.LG"
    ],
    "doi": "10.1007/978-3-032-02551-7_13",
    "journal_ref": "Text, Speech, and Dialogue. TSD 2025. Lecture Notes in Computer Science(), vol 16030. Springer, Cham",
    "license": "http://creativecommons.org/licenses/by-sa/4.0/",
    "title": "Computing patient similarity based on unstructured clinical notes",
    "url": "https://arxiv.org/abs/2601.07385",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "f0bada9a-d6fb-40e9-877c-2bcdd609c8fa",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}