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The purpose of write-missing diagnosis detection is to find diseases thathave been clearly diagnosed from medical records but are missed in thedischarge diagnosis. Unlike the definition of missed diagnosis, thewrite-missing diagnosis is clearly manifested in the medical record withoutfurther reasoning. The write-missing diagnosis is a common problem, oftencaused by physician negligence. The write-missing diagnosis will result in anincomplete diagnosis of medical records. While under DRG grouping, thewrite-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 enrollmentand payment, the problem of write-missing diagnosis is a common and seriousproblem. The current manual-based method is expensive due to the complexcontent of the full medical record. We think this problem is suitable to besolved as natural language processing. But to the best of our knowledge, noresearchers have conducted research on this problem based on natural languageprocessing methods.  We propose a framework for solving the problem of write-missing diagnosis,which mainly includes three modules: disease recall module, disease contextlogic judgment module, and disease relationship comparison module. Through thisframework, we verify that the problem of write-missing diagnosis can be solvedwell, and the results are interpretable. At the same time, we propose advancedsolutions for the disease context logic judgment module and diseaserelationship comparison module, which have obvious advantages compared with themainstream methods of the same type of problems. Finally, we verified the valueof our proposed framework under DRG medical insurance payment in a tertiaryhospital.

How can Deep Learning Retrieve the Write-Missing Additional Diagnosis from Chinese Electronic Medical Record For DRG