NSSC

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

verfasst von

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

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.)

Organisationseinheit(en)

Institut für Data Science

Externe Organisation(en)

Universidad Politécnica de Madrid (UPM)
Technische Informationsbibliothek (TIB) Leibniz-Informationszentrum Technik und Naturwissenschaften und Universitätsbibliothek
Hospital Universitario Puerta de Hierro de Majadahonda

Typ

Artikel

Journal

Medical and Biological Engineering and Computing

Band

63

Seiten

749–772

Anzahl der Seiten

24

ISSN

0140-0118

Publikationsdatum

03.2025

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Biomedizintechnik, Angewandte Informatik

Ziele für nachhaltige Entwicklung

SDG 3 – Gute Gesundheit und Wohlergehen

Elektronische Version(en)

https://doi.org/10.1007/s11517-024-03227-4 (Zugang: Offen)