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Chest X-ray film diagnostic report anomaly detection method based on subject model

A technology for diagnostic reports and topic models, applied in medical reports, instruments, electrical and digital data processing, etc., can solve problems such as poor results, deviations in abnormal point detection, and can not be classified as abnormal, so as to alleviate the problem of data sparseness and alleviate The effect of feature sparse and rich feature information

Active Publication Date: 2020-05-12
KUNMING UNIV OF SCI & TECH
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AI Technical Summary

Problems solved by technology

The text description of the diagnosis report is free, and some images describe symptoms or diseases that appear less frequently, but they cannot be classified as abnormal, so the detection of abnormal points will be biased
The diagnostic report data is high-dimensional and sparse, and the traditional mapping function is used to perform contextual feature matching, which is not effective

Method used

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  • Chest X-ray film diagnostic report anomaly detection method based on subject model
  • Chest X-ray film diagnostic report anomaly detection method based on subject model
  • Chest X-ray film diagnostic report anomaly detection method based on subject model

Examples

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Embodiment 1

[0036] Embodiment 1: as figure 1 Shown, a kind of chest X-ray diagnosis report abnormality detection method based on topic model, the specific steps of described method are as follows:

[0037] Step1. Entity extraction of diagnosis report: The core content of diagnosis report is image description and diagnosis conclusion. According to the characteristics of diagnosis report itself, the entity extraction of diagnosis report based on LSTM-CRF model is proposed;

[0038] Step2, Entity Feature Extension Supplement: Extend and supplement the features of the entity extracted in Step1, add the nature of the diagnosis to the conclusion part, and match it with the symptom entity in the image description;

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Abstract

The invention relates to a chest X-ray film diagnostic report anomaly detection method based on a subject model, and belongs to the technical field of computer natural language processing. The methodcomprises the following steps: firstly, proposing entity extraction of a diagnosis report based on an LSTM-CRF model according to the characteristics of the diagnosis report; performing effective feature extension on the diagnosis report according to domain knowledge and a template to relieve the problem of data sparseness; obtaining instance theme distribution of two diagnosis reports, namely image description and diagnosis conclusion, by utilizing the improved LDA model; and detecting the abnormal diagnosis report by calculating and comparing whether instance theme distribution obtained by the image description entity and the diagnosis conclusion entity is matched or not. The method is high in detection accuracy.

Description

technical field [0001] The invention relates to a method for detecting abnormality in a chest X-ray diagnosis report based on a subject model, and belongs to the technical field of computer natural language processing. Background technique [0002] Chest X-ray is the preferred choice of chest examination for patients and plays an important role in the diagnosis and treatment of patients. Doctors write chest X-ray diagnosis reports based on their own experience and habits. The core content of the diagnosis report is the image description and diagnosis conclusion. These two parts are important references to assist doctors in diagnosis and patient treatment, and are also the key to abnormal detection in diagnosis reports. information. Doctors writing diagnostic reports are quite subjective, and they may misinterpret the content of image descriptions due to inexperience or fatigue, which may cause some diseases to be missed or misdiagnosed. In addition, the part of the image s...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16H15/00G06F40/295G06F40/30
CPCG16H15/00
Inventor 黄青松殷宁波尤诚诚刘利军冯旭鹏
Owner KUNMING UNIV OF SCI & TECH
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