ICD surgery and operation code automatic matching method based on deep learning

Abstract

According to the ICD surgery and operation code automatic matching method based on deep learning, a modular modeling method is used. Each module only completes a relatively simple task, the search space of model parameters is greatly reduced, and the required data volume is reduced. The method comprises: using a two-way autoregressive language model to model a natural language sequence; combiningeach operation description with each ICD code; calculating a semantic space weight between the two; reconstructing the operation description by using the semantic space weight, performing ICD code classification matching by using the reconstructed operation description to solve the problem of concept splitting, performing bidirectional autoregression model modeling by using the inherent hierarchical structure of the ICD operation and the operation code in calculation, and fusing business priori knowledge. Problems encountered in clinic are solved, and ICD code matching can be rapidly and accurately carried out.

Description

technical field [0001] The invention relates to the field of medical technology, in particular to an ICD operation and operation code automatic matching method based on deep learning. Background technique [0002] The International Classification of Diseases Surgery and Operation Code (ICD-9-CM-3) is an important tool for the summary and statistics of hospital medical record information, and plays an important role in hospital medical treatment, research, and management. In actual clinical application, how to match the operation description entered by the medical personnel in the electronic medical record to the ICD operation and operation code is a time-consuming and labor-intensive task, requiring a lot of medical record reading work and code review work. Moreover, in the actual electronic medical record, the operation and operation description entered by the medical staff may be relatively short, that is, there are several surgical operation categories in a short descript...

AI Technical Summary

Problems solved by technology

In actual clinical application, how to match the operation description entered by the medical personnel in the electronic medical record to the ICD operation and operation code is a time-consuming and labor-intensive task, requiring a lot of medical record reading work and code review work

Moreover, in the actual electronic medical record, the operation and operation description entered by the medical staff may be relatively short, that is, there are several surgical operation categories in a short description, so how to conceptually split the operation description entered by the medical staff into the medical record and matching to standard ICD surgery and procedure codes is a lengthy and error-prone affair

However, general statistical learning, machine learning and deep learning classification models are often unable to cope with super-large-scale classification problems such as ICD coding, because the classification space is too large, and direct training using labeled data often results in serious overfitting or underfitting. And it is unable to solve the problem of conceptual separation of surgical descriptions. For example, the surgical description of "head and face laceration debridement and suture" needs to be split into two ICD surgery and operation codes, namely '86.2201 Excisional debridement of skin wounds' and '86.5900x006 skin suture', the general classification model cannot find a reasonable split method, and the general algorithm requires a large amount of labeled data. Under actual conditions, it is often difficult to obtain a large amount of labeled data due to various conditions. , and in clinical applications, because the error tolerance rate of medical work is relatively low, the errors caused by over-fitting and under-fitting of the model are unacceptable

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