  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.
