Details
| Original language | English |
|---|---|
| Article number | 103985 |
| Pages (from-to) | 749–772 |
| Number of pages | 24 |
| Journal | Medical and Biological Engineering and Computing |
| Volume | 63 |
| Issue number | 3 |
| Early online date | 1 Nov 2024 |
| Publication status | Published - Mar 2025 |
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
ASJC Scopus subject areas
- Engineering(all)
- Biomedical Engineering
- Computer Science(all)
- Computer Science Applications
Sustainable Development Goals
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In: Medical and Biological Engineering and Computing, Vol. 63, No. 3, 103985, 03.2025, p. 749–772.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - NSSC
T2 - a neuro-symbolic AI system for enhancing accuracy of named entity recognition and linking from oncologic clinical notes
AU - García-Barragán, Álvaro
AU - Sakor, Ahmad
AU - Vidal, Maria Esther
AU - Menasalvas, Ernestina
AU - Gonzalez, Juan Cristobal Sanchez
AU - Provencio, Mariano
AU - Robles, Víctor
N1 - Publisher Copyright: © The Author(s) 2024.
PY - 2025/3
Y1 - 2025/3
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.)
AB - 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.)
KW - Breast cancer
KW - EHR
KW - EL
KW - LLM
KW - NER
KW - Neuro-symbolic
UR - http://www.scopus.com/inward/record.url?scp=85208104355&partnerID=8YFLogxK
U2 - 10.1007/s11517-024-03227-4
DO - 10.1007/s11517-024-03227-4
M3 - Article
VL - 63
SP - 749
EP - 772
JO - Medical and Biological Engineering and Computing
JF - Medical and Biological Engineering and Computing
SN - 0140-0118
IS - 3
M1 - 103985
ER -