An automatic matching method for ICD surgery and operation codes based on deep learning

An automatic matching and deep learning technology, applied in the medical field, can solve problems such as overfitting or underfitting, a large number of medical record reading work and code review work, and the inability to solve the problem of concept splitting of surgical descriptions, etc.

Active Publication Date: 2020-07-07
SHAN DONG MSUN HEALTH TECH GRP CO LTD
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  • Abstract
  • Description
  • Claims
  • Application Information

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

Method used

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  • An automatic matching method for ICD surgery and operation codes based on deep learning
  • An automatic matching method for ICD surgery and operation codes based on deep learning
  • An automatic matching method for ICD surgery and operation codes based on deep learning

Examples

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example 1

[0046] Surgery description input to the model: "Debridement and suture of head and face laceration"

[0047] The output of the model is thresholded by And do threshold truncation, after which the model output is less than The result becomes 0, greater than or equal to becomes 1, It is a real number between 0 and 1, and it is a hyperparameter, which is obtained by adjusting In order to optimize the matching performance of the ICD code in the verification data, and then obtain a value greater than , and find the code corresponding to the index, and backtrack the semantic space weight α, we can get:

[0048] "86.2201 Excisional debridement of skin wounds"

[0049] operation description head noodle department crack hurt clear create seam combine surgery Alpha 0.07 0.06 0.08 0.15 0.11 0.23 0.19 0.02 0.03 0.08

[0050] It can be seen that for the code 86.2201, the semantic space weight of the word "cleaning" is relatively h...

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Abstract

An automatic matching method for ICD surgery and operation codes based on deep learning, using a modular modeling method, each module only completes a relatively simple task, which greatly reduces the search space for model parameters and reduces the required amount of data. This method uses a bidirectional autoregressive language model to model the natural language sequence, uses each operation description and each ICD code to combine, calculates the semantic space weight between them, and uses the semantic space weight to reconstruct the operation description , and finally use the reconstructed surgery description to perform ICD code classification matching to solve the problem of concept splitting. In the calculation, the inherent hierarchical structure of ICD surgery and operation codes is also used for bidirectional autoregressive model modeling, incorporating business prior test knowledge. It solves the problems encountered in clinical practice, and can quickly and accurately perform ICD code matching.

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...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/901G06F16/903G06N3/04G06N3/08G16H10/60
CPCG16H10/60G06F16/90344G06F16/9027G06N3/084G06N3/048
Inventor 张述睿吴军樊昭磊张伯政张福鑫
Owner SHAN DONG MSUN HEALTH TECH GRP CO LTD
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