{
  "annotation_id": "83871818-5947-4391-ae04-4025f9aaccd2",
  "date_created": "2026-02-17T05:53:05.134000Z",
  "date_modified": "2026-02-17T05:53:05.134000Z",
  "file_hash": "233a85d9455433f1f3ae4ec350c8419ed10ad961aa7c376f453623e70428b25b",
  "private": false,
  "record": {
    "abstract": "In the context of personalized medicine, machine learning algorithms are growing in popularity. These algorithms require substantial information, which can be acquired effectively through the usage of previously gathered data. Open data and the utilization of synthetization techniques have been proposed to address this. In this paper, we propose and evaluate alternative approach that uses additional simulated data based on summary statistics published in the literature. The simulated data are used to pretrain random forests, which are afterwards fine-tuned on a real dataset. We compare the predictive performance of the new approach to random forests trained only on the real data. A Monte Carlo Cross Validation (MCCV) framework with 100 iterations was employed to investigate significance and stability of the results. Since a first study yielded inconclusive results, a second study with improved methodology (i.e., systematic information extraction and different prediction outcome) was conducted. In Study 1, some pretrained random forests descriptively outperformed the standard random forest. However, this improvement was not significant (t(99) = 0.89, p = 0.19). Contrary to expectations, in Study 2 the random forest trained only with the real data outperformed the pretrained random forests. We conclude with a discussion of challenges, such as the scarcity of informative publications, and recommendations for future research.",
    "arxiv_id": "2601.06159",
    "authors": [
      "Niklas Jacobs",
      "Manuel C. Voelkle",
      "Norbert Kathmann",
      "Kevin Hilbert"
    ],
    "categories": [
      "cs.LG"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "Can we Improve Prediction of Psychotherapy Outcomes Through Pretraining With Simulated Data?",
    "url": "https://arxiv.org/abs/2601.06159",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "61ec6719-95fe-440f-a430-6426ed491cef",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}