Xusong Bu1, Mingxia Zhang2, Zhan Zhang3, Qin Zhang4,5. 1. Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China. 2. Otorhinolaryngology Head & Neck Surgery, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China. 3. Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing, 100084, China. 4. Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China. qinzhang@tsinghua.edu.cn. 5. Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing, 100084, China. qinzhang@tsinghua.edu.cn.
Abstract
PURPOSE: Epistaxis is a common symptom and can be caused by various diseases, including nasal diseases, systemic diseases, etc. Many misdiagnosis and missed diagnosis of epistaxis are caused by lack of clinical knowledge and experience, especially some interns and the clinicans in primary hospitals. To help inexperienced clinicans improve their diagnostic accuracies of epistaxis, a computer-aided diagnostic system based on Dynamic Uncertain Causality Graph (DUCG) was designed in this study. METHODS: We build a visual epistaxis knowledge base based on medical experts' knowledge and experience. The knowledge base intuitively expresses the causal relationship among diseases, risk factors, symptoms, signs, laboratory checks, and image examinations. The DUCG inference algorithm well addresses the patients' clinical information with the knowledge base to deduce the currently suspected diseases and calculate the probability of each suspected disease. RESULT: The model can differentially diagnose 24 diseases with epistaxis as the chief complaint. A third-party verification was performed, and the total diagnostic precision was 97.81%. In addition, the DUCG-based diagnostic model was applied in Jiaozhou city and Zhongxian county, China, covering hundreds of primary hospitals and clinics. So far, the clinicians using the model have all agreed with the diagnostic results. The 432 real-world application cases show that this model is good for the differential diagnoses of epistaxis. CONCLUSION: The results show that the DUCG-based epistaxis diagnosis model has high diagnostic accuracy. It can assist primary clinicians in completing the differential diagnosis of epistaxis and can be accepted by clinicians.
PURPOSE: Epistaxis is a common symptom and can be caused by various diseases, including nasal diseases, systemic diseases, etc. Many misdiagnosis and missed diagnosis of epistaxis are caused by lack of clinical knowledge and experience, especially some interns and the clinicans in primary hospitals. To help inexperienced clinicans improve their diagnostic accuracies of epistaxis, a computer-aided diagnostic system based on Dynamic Uncertain Causality Graph (DUCG) was designed in this study. METHODS: We build a visual epistaxis knowledge base based on medical experts' knowledge and experience. The knowledge base intuitively expresses the causal relationship among diseases, risk factors, symptoms, signs, laboratory checks, and image examinations. The DUCG inference algorithm well addresses the patients' clinical information with the knowledge base to deduce the currently suspected diseases and calculate the probability of each suspected disease. RESULT: The model can differentially diagnose 24 diseases with epistaxis as the chief complaint. A third-party verification was performed, and the total diagnostic precision was 97.81%. In addition, the DUCG-based diagnostic model was applied in Jiaozhou city and Zhongxian county, China, covering hundreds of primary hospitals and clinics. So far, the clinicians using the model have all agreed with the diagnostic results. The 432 real-world application cases show that this model is good for the differential diagnoses of epistaxis. CONCLUSION: The results show that the DUCG-based epistaxis diagnosis model has high diagnostic accuracy. It can assist primary clinicians in completing the differential diagnosis of epistaxis and can be accepted by clinicians.