{
  "abstract": "Drug-drug interactions (DDIs) are a leading cause of preventable adverse drug events, and the standard benchmark corpus for extracting them from biomedical text is severely im-balanced towards non-interacting pairs, making rare interaction types hard to learn. We asked whether combining focal loss, entity-aware transformer encoding, and feedback-guided feature fusion in a single architecture could improve minority-class DDI extraction under this imbalance while requiring less training data than prior methods. We propose FoLT-DMCNN-FBVL, which integrates a BiomedBERT backbone with entity marker injection, a multi-branch neural classifier, and feedback-based validation learning, trained on a small balanced subset of the SemEval- 2013 DDIExtraction corpus and evaluated on the full blind test set. FoLT-DMCNN-FBVL matched the most recent state-of-the-art result (statistically indistinguishable; nominal absolute+0.33%) and significantly outperformed the widely cited CNN-DDI baseline (absolute +4.32%), while using less than one-fifth of the available training data. These findings show that a doubly imbalance-aware transformer architecture can match or exceed current state-of-the-art DDI extraction performance with substantially greater data efficiency. Such architectures could support more scalable and cost-effective pharmacovigilance and clinical NLP pipelines in settings where annotated data for rare interaction types is limited.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "name": "Hiba Chanaa"
    },
    {
      "name": "El Habib Nfaoui"
    },
    {
      "name": "Chafik Boulealam"
    },
    {
      "name": "Chakir Loqman"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bgptxd2nb5m1n.bib",
  "cited_by": 0,
  "doi": "10.14569/ijacsa.2026.0170897",
  "fulltext_url": null,
  "id": "bgptxd2nb5m1n",
  "issns": [
    "2158-107X",
    "2156-5570"
  ],
  "landing_url": "https://hal.science/hal-05739949v1",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "hal",
      "0.info"
    ],
    [
      "hal",
      "0.math"
    ],
    [
      "hal",
      "0.scco"
    ],
    [
      "openalex",
      "1312|Biomedical Text Mining and Ontologies"
    ]
  ],
  "openalex": "W7204731091",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-06",
  "publisher": "The Science and Information Organization",
  "ris": "https://research.draconitic.com/paper/bgptxd2nb5m1n.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:29Z",
      "native_id": "hal-05739949",
      "source": "hal",
      "url": "https://hal.science/hal-05739949v1"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|Biomedical Text Mining and Ontologies",
      "subfield": "molecular-biology"
    }
  ],
  "title": "Focal-Loss Transformer with Feedback-Validation Learning for Imbalanced Drug Interaction Extraction",
  "title_text": "Focal-Loss Transformer with Feedback-Validation Learning for Imbalanced Drug Interaction Extraction",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:29Z",
  "url": "https://research.draconitic.com/paper/bgptxd2nb5m1n",
  "venue": "International journal of advanced computer science and applications (IJACSA)",
  "versions": []
}