Development of a large language model-based knowledge graph for chemotherapy-induced nausea and vomiting in breast...

Chemotherapy-induced nausea and vomiting (CINV) is a common adverse effect among breast cancer patients, significantly affecting quality of life. Existing evidence on the prevention, assessment, and management of this condition is fragmented and inconsistent. This study constructed a CINV knowledge graph using a large language model (LLM) to integrate nursing and medical evidence, thereby supporting systematic clinical decision-making.

A top-down approach was adopted. 1) Knowledge base preparation: Nine databases and eight guideline repositories were searched up to October 2024 to include guidelines, evidence summaries, expert consensuses, and systematic reviews screened by two researchers. 2) Schema design: Referring to the Unified Medical Language System, Systematized Nomenclature of Medicine - Clinical Terms, and the Nursing Intervention Classification, entity and relation types were defined to build the ontology schema. 3) LLM-based extraction and integration: Using the Qwen model under the CRISPE framework, named entity recognition, relation extraction, disambiguation, and fusion were conducted to generate triples and visualize them in Neo4j. Four expert rounds ensured semantic and logical consistency. Model performance was evaluated using precision, recall, F1-score, and 95 % confidence interval (95 %CI) in Python 3.11.

A total of 47 studies were included (18 guidelines, two expert consensuses, two evidence summaries, and 25 systematic reviews). The Qwen model extracted 273 entities and 289 relations; after expert validation, 238 entities and 242 relations were retained, forming 244 triples. The ontology comprised nine entity types and eight relation types. The F1-scores for named entity recognition and relation extraction were 82.97 (95 %CI: 0.820, 0.839) and 85.54 (95 %CI: 0.844, 0.867), respectively. The average node degree was 2.03, with no isolated nodes.

The LLM-based CINV knowledge graph achieved structured integration of nursing and medical evidence, offering a novel, data-driven tool to support clinical nursing decision-making and advance intelligent healthcare.