Unlocking the power of quantum computing in biomedical NLP for lung cancer diagnosis

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dc.contributor.authorKesiku, Cyrille
dc.contributor.authorGarcía-Zapirain, Begoña
dc.contributor.authorElmaghraby, Adel
dc.date.accessioned2026-05-21T10:13:14Z
dc.date.available2026-05-21T10:13:14Z
dc.date.issued2026-06-01
dc.date.updated2026-05-21T10:13:14Z
dc.description.abstractLung cancer remains the leading cause of cancer-related mortality worldwide, underscoring the urgent need for accurate, efficient, and interpretable early detection methods. Existing benchmark large language models (LLMs) like ClinicalBERT and BioBERT, despite their advancements in biomedical text analysis, face critical limitations including high computational costs, limited interpretability, and reliance on extensive annotated datasets hindering their clinical integration. To address these challenges, we propose the Hybrid Attention Quantum Long Short-Term Memory-Attention (A-QLSTM-A) model, a novel quantum-classical framework that combines quantum variational circuits with LSTM networks and dual attention mechanisms. This design enhances feature extraction, improves interpretability, and offers a more efficient architectural pathway. Evaluated on MIMIC-III discharge summaries and MIMIC-IV chest radiography reports, A-QLSTM-A achieved 98.32% and 83.67% accuracy, respectively, surpassing existing models. This study establishes a new benchmark for scalable, interpretable AI in precision medicine, offering a promising tool for early lung cancer detection and clinical decision support.en
dc.identifier.citationKesiku, C. Y., Garcia-Zapirain, B., & Elmaghraby, A. S. (2026). Unlocking the power of quantum computing in biomedical NLP for lung cancer diagnosis. Quantum Machine Intelligence, 8(1). https://doi.org/10.1007/S42484-026-00392-7
dc.identifier.doi10.1007/S42484-026-00392-7
dc.identifier.eissn2524-4914
dc.identifier.issn2524-4906
dc.identifier.urihttps://hdl.handle.net/20.500.14454/6036
dc.language.isoeng
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.rights© The Author(s) 2026, modified publication 2026
dc.subject.otherLung cancer
dc.subject.otherNatural Language Processing(NLP)
dc.subject.otherNeural network
dc.subject.otherQuantum computing
dc.subject.otherQuantum NLP
dc.titleUnlocking the power of quantum computing in biomedical NLP for lung cancer diagnosisen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.issue1
oaire.citation.titleQuantum Machine Intelligence
oaire.citation.volume8
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
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