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ICD intelligent coding method based on deep learning and knowledge graph

A technology of knowledge graph and intelligent coding, which is applied in the direction of neural learning methods, based on specific mathematical models, biological neural network models, etc., can solve the problems of poor model textCNN effect and lack of diagnosis and merging modules, so as to improve the coding accuracy and reduce the The effect of reducing workload and reducing the frequency of communication

Pending Publication Date: 2021-12-10
北京雅丁信息技术有限公司
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AI Technical Summary

Problems solved by technology

[0008] 2. Missing diagnostic merge module
[0011] 3. The effect of the model textCNN is not good enough

Method used

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  • ICD intelligent coding method based on deep learning and knowledge graph
  • ICD intelligent coding method based on deep learning and knowledge graph

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Embodiment Construction

[0053] Embodiments of the present invention are described in detail below, examples of which are shown in the drawings, wherein the same or similar reference numerals designate the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the figures are exemplary and are intended to explain the present invention and should not be construed as limiting the present invention.

[0054] Several professional terms involved in the present invention are explained below.

[0055] DRG: Diagnosis Related Groups (DRG) is an important tool for measuring the quality and efficiency of medical services and paying for medical insurance. DRG is essentially a case combination classification scheme, that is, a system that divides patients into several diagnostic groups for management based on factors such as age, disease diagnosis, comorbidities, complications, treatment methods, disease severity, outcome, and resource ...

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Abstract

The invention provides an ICD intelligent coding method based on deep learning and a knowledge graph. The method comprises the following steps of acquiring the electronic medical record data and the medical advice item data; performing data standardization processing on the electronic medical record data and the medical advice item data to obtain the standardized data; constructing a BERT+BiLSTM+CRF training diagnosis name identification model, and identifying the diagnosis name of the standardized data by using the model; calculating a final ICD code of each diagnosis name based on a BERT model; combining the ICD codes of the diagnosis names; and based on a disease charging item knowledge graph, according to the charging medical advice of the current medical record, calculating the diagnosis which consumes the most medical resources at this time, and taking the diagnosis as the main diagnosis.

Description

technical field [0001] The present invention relates to the technical field of intelligent coding, in particular to an ICD intelligent coding method based on deep learning and knowledge graph. Background technique [0002] There are currently three main technical solutions for computer-aided coding: [0003] The first is the keyword search prompt scheme, which is similar to the keyword prompt in Baidu search. This scheme searches all ICD code names based on the diagnostic keywords input by the doctor, prompts the ICD name and code, and guides the coder to obtain the final code step by step. . [0004] The second is a rule-based coding system, which sets certain coding logic rules, triggers the rules under certain conditions, and prompts the correct coding. [0005] The third is based on AI intelligent coding scheme. Applying advanced natural language processing technology and deep learning model, without manual intervention, the correct ICD code is automatically generated...

Claims

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Application Information

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IPC IPC(8): G06F16/31G06F16/33G06F16/36G16H10/60G06N3/04G06N3/08G06N7/00
CPCG06F16/319G06F16/3344G06F16/367G16H10/60G06N3/08G06N7/01G06N3/044Y02A90/10
Inventor 张友书肖尚华程岚祝伟
Owner 北京雅丁信息技术有限公司
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