The purpose of write-missing diagnosis detection is to find diseases that have been clearly diagnosed from medical records but are missed in the discharge diagnosis. Unlike the definition of missed diagnosis, the write-missing diagnosis is clearly manifested in the medical record without further reasoning. The write-missing diagnosis is a common problem, often caused by physician negligence. The write-missing diagnosis will result in an incomplete diagnosis of medical records. While under DRG grouping, the write-missing diagnoses will miss important additional diagnoses (CC, MCC), thus affecting the correct rate of DRG enrollment. Under the circumstance that countries generally start to adopt DRG enrollment and payment, the problem of write-missing diagnosis is a common and serious problem. The current manual-based method is expensive due to the complex content of the full medical record. We think this problem is suitable to be solved as natural language processing. But to the best of our knowledge, no researchers have conducted research on this problem based on natural language processing methods. We propose a framework for solving the problem of write-missing diagnosis, which mainly includes three modules: disease recall module, disease context logic judgment module, and disease relationship comparison module. Through this framework, we verify that the problem of write-missing diagnosis can be solved well, and the results are interpretable. At the same time, we propose advanced solutions for the disease context logic judgment module and disease relationship comparison module, which have obvious advantages compared with the mainstream methods of the same type of problems. Finally, we verified the value of our proposed framework under DRG medical insurance payment in a tertiary hospital.

提出了一个基于自然语言处理(NLP)方法的框架，包括三个模块:疾病召回模块、疾病上下文逻辑判断模块和疾病关系比较模块，用于发现已经明确诊断但被漏诊未在出院诊断中的疾病，并且在DRG医疗保险支付中已验证其有效性。

如何使用深度学习从中国电子病历中检索缺失的附加诊断，用于 DRG