NSSC: a neuro-symbolic AI system for enhancing accuracy of named entity recognition and linking from oncologic clinical notes

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Autorschaft

  • Álvaro García-Barragán
  • Ahmad Sakor
  • Maria Esther Vidal
  • Ernestina Menasalvas
  • Juan Cristobal Sanchez Gonzalez
  • Mariano Provencio
  • Víctor Robles

Organisationseinheiten

Externe Organisationen

  • Universidad Politécnica de Madrid (UPM)
  • Scientific Data Management Group
  • Technische Informationsbibliothek (TIB) Leibniz-Informationszentrum Technik und Naturwissenschaften und Universitätsbibliothek
  • Hospital Universitario Puerta de Hierro de Majadahonda
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Details

OriginalspracheEnglisch
Seitenumfang24
FachzeitschriftMedical and Biological Engineering and Computing
Frühes Online-Datum1 Nov. 2024
PublikationsstatusElektronisch veröffentlicht (E-Pub) - 1 Nov. 2024

Abstract

Abstract: Accurate recognition and linking of oncologic entities in clinical notes is essential for extracting insights across cancer research, patient care, clinical decision-making, and treatment optimization. We present the Neuro-Symbolic System for Cancer (NSSC), a hybrid AI framework that integrates neurosymbolic methods with named entity recognition (NER) and entity linking (EL) to transform unstructured clinical notes into structured terms using medical vocabularies, with the Unified Medical Language System (UMLS) as a case study. NSSC was evaluated on a dataset of clinical notes from breast cancer patients, demonstrating significant improvements in the accuracy of both entity recognition and linking compared to state-of-the-art models. Specifically, NSSC achieved a 33% improvement over BioFalcon and a 58% improvement over scispaCy. By combining large language models (LLMs) with symbolic reasoning, NSSC improves the recognition and interoperability of oncologic entities, enabling seamless integration with existing biomedical knowledge. This approach marks a significant advancement in extracting meaningful information from clinical narratives, offering promising applications in cancer research and personalized patient care. Graphical abstract: (Figure presented.)

ASJC Scopus Sachgebiete

Ziele für nachhaltige Entwicklung

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NSSC: a neuro-symbolic AI system for enhancing accuracy of named entity recognition and linking from oncologic clinical notes. / García-Barragán, Álvaro; Sakor, Ahmad; Vidal, Maria Esther et al.
in: Medical and Biological Engineering and Computing, 01.11.2024.

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

García-Barragán, Á., Sakor, A., Vidal, M. E., Menasalvas, E., Gonzalez, J. C. S., Provencio, M., & Robles, V. (2024). NSSC: a neuro-symbolic AI system for enhancing accuracy of named entity recognition and linking from oncologic clinical notes. Medical and Biological Engineering and Computing. Vorabveröffentlichung online. https://doi.org/10.1007/s11517-024-03227-4
García-Barragán Á, Sakor A, Vidal ME, Menasalvas E, Gonzalez JCS, Provencio M et al. NSSC: a neuro-symbolic AI system for enhancing accuracy of named entity recognition and linking from oncologic clinical notes. Medical and Biological Engineering and Computing. 2024 Nov 1. Epub 2024 Nov 1. doi: 10.1007/s11517-024-03227-4
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abstract = "Abstract: Accurate recognition and linking of oncologic entities in clinical notes is essential for extracting insights across cancer research, patient care, clinical decision-making, and treatment optimization. We present the Neuro-Symbolic System for Cancer (NSSC), a hybrid AI framework that integrates neurosymbolic methods with named entity recognition (NER) and entity linking (EL) to transform unstructured clinical notes into structured terms using medical vocabularies, with the Unified Medical Language System (UMLS) as a case study. NSSC was evaluated on a dataset of clinical notes from breast cancer patients, demonstrating significant improvements in the accuracy of both entity recognition and linking compared to state-of-the-art models. Specifically, NSSC achieved a 33% improvement over BioFalcon and a 58% improvement over scispaCy. By combining large language models (LLMs) with symbolic reasoning, NSSC improves the recognition and interoperability of oncologic entities, enabling seamless integration with existing biomedical knowledge. This approach marks a significant advancement in extracting meaningful information from clinical narratives, offering promising applications in cancer research and personalized patient care. Graphical abstract: (Figure presented.)",
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AU - Sakor, Ahmad

AU - Vidal, Maria Esther

AU - Menasalvas, Ernestina

AU - Gonzalez, Juan Cristobal Sanchez

AU - Provencio, Mariano

AU - Robles, Víctor

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N2 - Abstract: Accurate recognition and linking of oncologic entities in clinical notes is essential for extracting insights across cancer research, patient care, clinical decision-making, and treatment optimization. We present the Neuro-Symbolic System for Cancer (NSSC), a hybrid AI framework that integrates neurosymbolic methods with named entity recognition (NER) and entity linking (EL) to transform unstructured clinical notes into structured terms using medical vocabularies, with the Unified Medical Language System (UMLS) as a case study. NSSC was evaluated on a dataset of clinical notes from breast cancer patients, demonstrating significant improvements in the accuracy of both entity recognition and linking compared to state-of-the-art models. Specifically, NSSC achieved a 33% improvement over BioFalcon and a 58% improvement over scispaCy. By combining large language models (LLMs) with symbolic reasoning, NSSC improves the recognition and interoperability of oncologic entities, enabling seamless integration with existing biomedical knowledge. This approach marks a significant advancement in extracting meaningful information from clinical narratives, offering promising applications in cancer research and personalized patient care. Graphical abstract: (Figure presented.)

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