{
  "annotation_id": "3c8a6f38-49a4-48b5-bc70-bddf9abef3e5",
  "date_created": "2026-02-17T05:53:12.259000Z",
  "date_modified": "2026-02-17T05:53:12.259000Z",
  "file_hash": "4ef87cbc6e07dfdf24e38ddbaf4995fd7d4f1d83b1b3705648dc581d777fbf4a",
  "private": false,
  "record": {
    "abstract": "We present a novel framework for analyzing intracranial pressure monitoring data by applying interpretability principles. Intracranial pressure monitoring data was collected from 60 patients at Johns Hopkins. The data was segmented into individual cardiac cycles. A convolutional neural network was trained to classify each cardiac cycle into one of seven body positions. Neural network attention was extracted and was used to identify regions of interest in the waveform. Further directions for exploration are identified. This framework provides an extensible method to further understand the physiological and clinical underpinnings of the intracranial pressure waveform, which could lead to better diagnostic capabilities for intracranial pressure monitoring.",
    "arxiv_id": "2601.07691",
    "authors": [
      "Jonathan D. Socha",
      "Seyed F. Maroufi",
      "Dipankar Biswas",
      "Richard Um",
      "Aruna S. Rao",
      "Mark G. Luciano"
    ],
    "categories": [
      "q-bio.QM",
      "cs.LG"
    ],
    "license": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
    "title": "A Framework for Feature Discovery in Intracranial Pressure Monitoring Data Using Neural Network Attention",
    "url": "https://arxiv.org/abs/2601.07691",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "58ad4485-f716-43d2-9ab8-8f9f20eae6b6",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}