Abstract
ObjectiveThe study aims to discuss the coding technology of international classification of diseases (ICD) and the application of embedded electronic medical record (EMR) system through clinical diagnosis selection and medical record information management by Artificial Intelligence Algorithm. MethodsAccording to the embedded medical record input interface, the embedded medical record information knowledge system is established, and the data are randomly extracted from the knowledge base. The data are randomly extracted from the EMR database to collect patient medical records and disease diagnosis code data on the first page of 8 clinical departments of endocrinology, oncology, obstetrics and gynecology, ophthalmology, burns, orthopedics, neurosurgery and cardiovascular medicine for statistical analysis. The natural language processing-bidirectional recurrent neural network (NLP-BIRNN) algorithm is used to optimize the medical records. ResultsThe coders are unfamiliar with the basic rule of the main diagnosis selection, and are unclear about the classification of disease codes. They do not code according to the main diagnosis principles. When there is a complication, the previous disease is mistaken as the main diagnosis. The coding of many kinds of diseases is not detailed and accurate. There is no merge coding for diseases that should be merged. The undiagnosed diseases are coded according to the diagnosed diseases. The diseases are not coded according to different conditions. The diseases are not coded according to the specific classification. The diseases are not coded according to cause. Postoperative complications codes are inaccurate. The filling of the disease diagnosis is incomplete. The name of the disease diagnosis site is inaccurate. Coding selection is too general. The solutions to these problems are as follows. Coders and medical staff should strengthen communication. The training of coders and medical staff in this aspect of knowledge should be strengthened. The corresponding incentive measures should be formulated to increase the importance of coders and medical staff on ICD coding technology. BIRNN is compared with convolutional neural network (CNN) and recurrent neural network (RNN) in accuracy, symptom precision, and symptom recall, and BIRNN proposed has higher values. Conclusionthe errors of coding staff and medical staff are reduced in the main diagnosis and coding, and the pathological language reading under artificial intelligence algorithm provides certain convenience for the diagnosis and treatment of diseases.
Published Version
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