NSSC

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

authored by: 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.)
Organisation(s): Institute of Data Science
External Organisation(s): Technical University of Madrid (UPM)
German National Library of Science and Technology (TIB)
Puerta de Hierro Majadahonda University Hospital
Type: Article
Journal: Medical and Biological Engineering and Computing
Volume: 63
Pages: 749–772
No. of pages: 24
ISSN: 0140-0118
Publication date: 03.2025
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Biomedical Engineering, Computer Science Applications
Sustainable Development Goals: SDG 3 - Good Health and Well-being
Electronic version(s): https://doi.org/10.1007/s11517-024-03227-4 (Access: Open)

BibTeX

@article{29d339e613d34f5ea7d813f4aa904f8e,
title = "NSSC: a neuro-symbolic AI system for enhancing accuracy of named entity recognition and linking from oncologic clinical notes",
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.)",
keywords = "Breast cancer, EHR, EL, LLM, NER, Neuro-symbolic",
author = "{\'A}lvaro Garc{\'i}a-Barrag{\'a}n and Ahmad Sakor and Vidal, {Maria Esther} and Ernestina Menasalvas and Gonzalez, {Juan Cristobal Sanchez} and Mariano Provencio and V{\'i}ctor Robles",
note = "Publisher Copyright: {\textcopyright} The Author(s) 2024.",
year = "2025",
month = mar,
doi = "10.1007/s11517-024-03227-4",
language = "English",
volume = "63",
pages = "749–772",
journal = "Medical and Biological Engineering and Computing",
issn = "0140-0118",
publisher = "Peter Peregrinus, Ltd.",
}