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Multi-modal osteoporosis layered early warning method and system

An osteoporosis, multi-modal technology, applied in character and pattern recognition, instruments, calculations, etc., can solve the problems that affect the accuracy of osteoporosis recognition, single input feature parameters, and do not consider the relationship between osteoporosis recognition, etc. To achieve the effect of improving generalization ability, reducing feature dimension, and improving model performance

Pending Publication Date: 2020-10-09
SHANDONG UNIV
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

At present, the research on the identification of osteoporosis mainly uses the existing imaging data, and the input characteristic parameters are single. However, the actual identification of osteoporosis is also related to parameters other than imaging data.
At present, only a single imaging data is used, and the relationship between other relevant feature information and osteoporosis identification is not considered, which affects the identification accuracy of osteoporosis

Method used

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  • Multi-modal osteoporosis layered early warning method and system
  • Multi-modal osteoporosis layered early warning method and system
  • Multi-modal osteoporosis layered early warning method and system

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0038] combine figure 1 , the principle of a multimodal osteoporosis layered early warning method in this embodiment is as follows:

[0039] Step 1: Receive three layers of input features, which are individual information, laboratory test indicators and lumbar CT image features;

[0040] Step 2: Perform data cleaning, significance and correlation detection, and data standardization preprocessing on the three-layer input features;

[0041] Step 3: Select the optimal input features of each layer from the preprocessed three-layer input features and form an input feature set, and output the osteoporosis early warning results through the multimodal osteoporosis hierarchical early warning model;

[0042] Among them, the multi-modal osteoporosis layered early warning model is a SVM (Support Vector Machine, Support Vector Machine) classifier, and the optimization process of the SVM classifier is: using the genetic algorithm to simultaneously optimize the hyperparameters and select th...

Embodiment 2

[0121] The present embodiment provides a multimodal osteoporosis layered early warning system, which includes:

[0122] (1) Input feature receiving module, which is used to receive three layers of input features, the three layers of input features are individual information, laboratory examination indicators and lumbar spine CT image features respectively.

[0123] The individual information includes age, gender, height, weight, diastolic blood pressure, systolic blood pressure, pulse pressure, and whether menopausal.

[0124] The laboratory examination indicators include a complete set of blood routine and biochemical tests, wherein the blood routine indicators include white blood cells, neutrophil ratio, neutrophil count, lymphocyte ratio, lymphocyte count, monocyte count, monocyte ratio , erythrocyte, hemoglobin, hematocrit, platelet count, platelet hematocrit, erythrocyte sedimentation rate; a full set of biochemistry including alanine aminotransferase, aspartate aminotran...

Embodiment 3

[0156] This embodiment provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the steps in the multimodal osteoporosis layered early warning method described in the first embodiment above are implemented.

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Abstract

The invention provides a multi-modal osteoporosis layered early warning method and system. The multi-modal osteoporosis layered early warning method comprises the following steps: receiving three layers of input characteristics, wherein the three layers of input characteristics are respectively individual information, laboratory examination indexes and lumbar vertebra CT image characteristics; performing data cleaning, significance and correlation detection and data standardization preprocessing on the three layers of input features; screening out optimal input features of each layer from thethree layers of preprocessed input features, forming an input feature set, and outputting an osteoporosis early warning result through a multi-mode osteoporosis layered early warning model, wherein the multi-modal osteoporosis layered early warning model is an SVM classifier, and the optimization process of the SVM classifier is that a genetic algorithm is used for carrying out hyper-parameter optimization and optimal input feature selection of each layer at the same time.

Description

technical field [0001] The invention belongs to the field of medical data classification, and in particular relates to a multimodal osteoporosis layered early warning method and system. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] Primary osteoporosis is a common skeletal disease characterized by decreased bone mass and destroyed bone microarchitecture, which mostly occurs in the elderly, especially postmenopausal women. With the acceleration of population aging process, the incidence of osteoporosis is increasing day by day, and it has become the third largest chronic disease in the world after cardiovascular disease and diabetes. Osteoporosis is also known as the "silent killer". There are no obvious symptoms in the early stage of the disease, and it is generally not discovered until the patient suffers from a fragility fracture, whi...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16H50/70G16H50/30G06K9/62
CPCG16H50/70G16H50/30G06F18/2411Y02A90/10
Inventor 姬冰刘力瑜司萌马鹤成丛梦琳徐全政
Owner SHANDONG UNIV
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