The paper "Research on the Application of Semantic Network in Disease Diagnosis Prompts Based on Medical Corpus" by Yufeng Li, Weimin Wang, Xu Yan, Min Gao, and MingXuan Xiao explores the use of a Disease-Symptom Semantic Network (DSSN) to reduce misdiagnosis rates in medical practice. The study addresses the issue of misdiagnosis, which is a significant cause of adverse events in outpatient clinics, often due to similar and indistinguishable symptoms. The authors propose merging symptom words of related diseases, building an ontology based on semantic relationships between symptoms, and associating the association between diseases and symptoms. The DSSN is constructed by integrating disease ontology, symptom ontology, and differential diagnosis knowledge. The process involves corpus acquisition from authoritative medical sources, identification of symptom vocabulary, computation of semantic similarity, and formation of a refined symptom ontology. The network includes 1001 disease words, 2380 symptom words, and 3381 concepts, encapsulating disease-symptom associations, easily misdiagnosed connections, and differential diagnosis knowledge. The DSSN is evaluated through an example in medical diagnosis, specifically for appendicitis, where it provides visual representations of symptoms and potential misdiagnosed diseases, aiding clinicians in making accurate diagnoses. The study concludes that the DSSN can effectively prompt against misdiagnoses, enhancing diagnostic accuracy and reducing misdiagnosis probabilities. Future research will focus on enriching the DSSN with additional biomedical features and developing tools for broader applications in clinical diagnosis.The paper "Research on the Application of Semantic Network in Disease Diagnosis Prompts Based on Medical Corpus" by Yufeng Li, Weimin Wang, Xu Yan, Min Gao, and MingXuan Xiao explores the use of a Disease-Symptom Semantic Network (DSSN) to reduce misdiagnosis rates in medical practice. The study addresses the issue of misdiagnosis, which is a significant cause of adverse events in outpatient clinics, often due to similar and indistinguishable symptoms. The authors propose merging symptom words of related diseases, building an ontology based on semantic relationships between symptoms, and associating the association between diseases and symptoms. The DSSN is constructed by integrating disease ontology, symptom ontology, and differential diagnosis knowledge. The process involves corpus acquisition from authoritative medical sources, identification of symptom vocabulary, computation of semantic similarity, and formation of a refined symptom ontology. The network includes 1001 disease words, 2380 symptom words, and 3381 concepts, encapsulating disease-symptom associations, easily misdiagnosed connections, and differential diagnosis knowledge. The DSSN is evaluated through an example in medical diagnosis, specifically for appendicitis, where it provides visual representations of symptoms and potential misdiagnosed diseases, aiding clinicians in making accurate diagnoses. The study concludes that the DSSN can effectively prompt against misdiagnoses, enhancing diagnostic accuracy and reducing misdiagnosis probabilities. Future research will focus on enriching the DSSN with additional biomedical features and developing tools for broader applications in clinical diagnosis.