Chronic Obstructive Pulmonary Disease Risk Assessment System Based on Nursing Information

Abstract

The present invention discloses a risk assessment system for chronic obstructive pulmonary disease based on nursing information, including a data acquisition module, a feature extraction module, a risk prediction module, and a risk assessment module for chronic obstructive pulmonary disease. The present invention belongs to the field of medical information processing, specifically referring to a risk assessment system for chronic obstructive pulmonary disease based on nursing information. This solution dynamically adjusts the receptive field size based on the change rate of nursing information, adaptively captures short-term fluctuations and long-term trends, reduces the over-concern for stable nursing data, improves the feature expression ability for key fluctuation regions, and thus improves the accuracy of risk assessment for chronic obstructive pulmonary disease; dynamically adjusts the penalty parameter according to the sample distribution, enhances the attention to highly risk patients, reduces the bias towards low-risk patients, introduces a logarithmic smoothing mechanism for low-risk patients to reduce noise interference, and introduces the inter-class distance in the objective function to avoid decision overlap between classes; thereby improving the risk assessment effect of chronic obstructive pulmonary disease.

Description

Technical Field

[0001] The present invention relates to the field of medical information processing, and specifically refers to a chronic obstructive pulmonary disease risk assessment system based on nursing information. Background Art

[0002] The chronic obstructive pulmonary disease risk assessment system is an intelligent and multi-dimensional data analysis tool that can capture the dynamic changes in the patient's health status in real time, combine historical static data and real-time monitoring data, and predict the patient's risk level. It overcomes the limitations of traditional assessment methods through innovative feature extraction and classification methods, provides more accurate predictions and personalized nursing services for patients, thereby improving the prognosis of patients and reducing the medical burden. However, in general, the chronic obstructive pulmonary disease risk assessment system has problems such as the separation and improper handling of the static and dynamic characteristics of patients, the improper capture of key fluctuations after nursing, which leads to one-sided and inaccurate assessment results; in general, the chronic obstructive pulmonary disease risk assessment system has problems such as improper handling of the class imbalance problem of nursing data, weak recognition ability for high-risk and low-risk patients, and sensitivity to noise, which leads to poor assessment effects. Summary of the Invention

[0003] In view of the above situation, to overcome the defects of the prior art, the present invention provides a chronic obstructive pulmonary disease risk assessment system based on nursing information. Aiming at the problems of the general chronic obstructive pulmonary disease risk assessment system, such as the separation and improper handling of the static and dynamic characteristics of patients, the improper capture of key fluctuations after nursing, which leads to one-sided and inaccurate assessment results, this solution dynamically adjusts the receptive field size based on the nursing information change rate, adaptively captures short-term fluctuations and long-term trends, and improves the ability to extract indicators in critical periods; uses the receptive field attention mechanism to generate attention weights, reduces the over-concern for stable nursing data, improves the feature expression ability for key fluctuation regions, and thus improves the accuracy of chronic obstructive pulmonary disease risk assessment; aiming at the problems of the general chronic obstructive pulmonary disease risk assessment system, such as improper handling of the class imbalance problem of nursing data, weak recognition ability for high-risk and low-risk patients, and sensitivity to noise, which leads to poor assessment effects, this solution dynamically adjusts the penalty parameter according to the sample distribution, enhances the attention to highly risk patients, reduces the bias towards low-risk patients, introduces a logarithmic smoothing mechanism for low-risk patients to reduce noise interference, and introduces the inter-class distance in the objective function to avoid decision overlap between classes, reducing the confusion between high-risk and medium-low-risk categories; thereby improving the chronic obstructive pulmonary disease risk assessment effect.

[0004] The technical solution adopted by the present invention is as follows: The chronic obstructive pulmonary disease risk assessment system based on nursing information provided by the present invention includes a data acquisition module, a feature extraction module, a risk prediction module, and a chronic obstructive pulmonary disease risk assessment module;

[0005] The data acquisition module acquires historical nursing data;

[0006] The feature extraction module adaptively extracts local features of key periods in the time series of nursing information by dynamically adjusting the receptive field size and receptive field attention mechanism, capturing short-term fluctuations and long-term trends;

[0007] The risk prediction module combines static features and multi-scale dynamic features, uses the attention mechanism and global feature splicing, comprehensively integrates local details and global trend information, and establishes a risk prediction model;

[0008] The chronic obstructive pulmonary disease risk assessment module realizes the risk assessment of chronic obstructive pulmonary disease for real-time nursing data based on the established risk prediction model.

[0009] Furthermore, in the data acquisition module, the historical nursing data includes patient static feature data, patient dynamic nursing data, patient physiological condition data, time, and risk assessment levels; the risk assessment levels include normal, low risk, medium risk, and high risk; the risk assessment level is used as a data label; and the collected data is subjected to data cleaning, data conversion, and standardization processing to obtain time series data of nursing information.

[0010] Furthermore, the feature extraction module specifically includes the following:

[0011] Local feature extraction; extracting local dependencies in the time series X of nursing information; using group convolution to extract local features , according to the feature distribution of the time series of nursing information, dynamically adjusting the size k of the receptive field to achieve adaptive group convolution; and generating attention weights based on the receptive field attention mechanism , expressed as: ; ; finally calibrating the local features, expressed as: ; where Softmax(·) is the Softmax function; ReLU(·) is the ReLU activation function; Norm(·) is the normalization operation; AvgPool(·) is the average pooling operation; is the group convolution operation; and are the minimum receptive field and the maximum receptive field respectively; is the average change rate of blood oxygen saturation; is the maximum change rate of blood oxygen saturation;

[0012] Multi-level feature fusion; extracting multi-scale features layer by layer for the feature , expressed as: ; generating attention weights based on the SE module , expressed as: ; Adjust the multi-scale features according to the weights, expressed as: where, are the multi-scale features extracted layer by layer; S is the number of scales, i is the scale index; Conv(·) is the convolution operation; is the convolution kernel size; is the attention score; are the multi-scale features after attention adjustment; are the static features is the weight matrix of is the bias term generated by the multi-layer perceptron MLP;

[0013] Global feature fusion; Concatenate the features of all scales, expressed as: ; Concatenate the static features with the dynamic features extracted from the time series to form the final feature vector , expressed as: where, , and are the features of different scales after weighted adjustment; Concat(·) is the concatenation operation; Attention(·) is the attention mechanism; Y is the concatenated feature.

[0014] Furthermore, the risk prediction module takes the fused feature vector Z generated by the feature extraction module as the input data, and performs risk classification based on the multi-class SVM. For each class, a binary SVM is trained to distinguish the current class from other classes to obtain the risk prediction model; specifically, it includes the following contents:

[0015] Define the objective function, expressed as: ; ; Dynamically adjust the penalty parameter according to the sample distribution, and set higher weights for the high-risk samples, expressed as: where, is the normal vector of the classification hyperplane; is the classification error, represents the deviation degree of the o-th sample from the classification result of the hyperplane; C is the penalty parameter; a is the control parameter; is the true label of the sample; is the sample feature vector; is the square of the L2 norm; l is the total number of samples; and are the normal vectors of the classification hyperplanes of class k1 and class y1 respectively;

[0016] Loss processing; Apply a high penalty to the misclassification of high-risk patients, expressed as: ;Apply a small penalty to the misclassification of low-risk patients and introduce a gradient smoothing mechanism to prevent the loss value from being too large due to noisy samples; expressed as: ; where L(·) is the loss function; Smooth(·) is the logarithmic smoothing function;

[0017] Objective function optimization; Update the weights using the gradient descent algorithm based on the gradient of the objective function, and the gradient is expressed as: ; Furthermore, predict the risk level of chronic obstructive pulmonary disease in patients, expressed as: ; where, is the gradient of the objective function; is the predicted risk level; T is the matrix transpose; sign(·) is the sign function; Z is the sample set; is the sample proportion of the category;

[0018] Classification determination; Set a maximum number of iterations and a classification threshold in advance, divide the data set into a training set and a test set, train the risk prediction model based on the training set, and when the loss converges or reaches the maximum number of iterations, the risk prediction model training is completed; When the prediction accuracy of the trained risk prediction model for the test set is higher than the classification threshold, the risk prediction model is established; otherwise, re-divide the data set and adjust the initial normal vector parameters to train the risk prediction model.

[0019] Furthermore, the chronic obstructive pulmonary disease risk assessment module is based on the established risk prediction model, collects patient care data in real time, processes it through the feature extraction module and inputs it into the risk prediction model, and uses the risk assessment level output by the risk prediction model as the assessment result. When the assessment result is moderate risk and high risk, give an early warning to the patient.

[0020] The beneficial effects achieved by the present invention using the above solution are as follows:

[0021] (1) Aiming at the problems of the general chronic obstructive pulmonary disease risk assessment system, such as the separation and improper handling of static and dynamic characteristics of patients, and the improper capture of key fluctuations after care, resulting in one-sided and inaccurate assessment results. This solution dynamically adjusts the receptive field size based on the change rate of care information, adaptively captures short-term fluctuations and long-term trends, and improves the ability to extract key period indicators; uses the receptive field attention mechanism to generate attention weights, reduces the over-concern for stable care data, improves the feature expression ability of key fluctuation regions, and thus improves the accuracy of chronic obstructive pulmonary disease risk assessment.

[0022] (2) Aiming at the problems existing in the general chronic obstructive pulmonary disease risk assessment system, such as improper handling of the class imbalance problem of nursing data, weak recognition ability for high-risk and low-risk patients, and sensitivity to noise, which lead to poor assessment effects. This solution dynamically adjusts the penalty parameter according to the sample distribution, enhances the attention to highly risky patients, reduces the bias towards low-risk patients, introduces a logarithmic smoothing mechanism for low-risk patients to reduce noise interference, and introduces the inter-class distance in the objective function to avoid decision overlap between classes and reduce the confusion between high-risk and medium-low-risk categories, thereby improving the chronic obstructive pulmonary disease risk assessment effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a schematic flowchart of the chronic obstructive pulmonary disease risk assessment system based on nursing information provided by the present invention;

[0024] Figure 2 It is a schematic flowchart of the risk prediction module.

[0025] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the present invention, and do not constitute a limitation to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0026] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0027] In the description of the present invention, it should be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", etc. indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation to the present invention.

[0028] Embodiment 1, refer to Figure 1 The chronic obstructive pulmonary disease risk assessment system based on nursing information provided by the present invention includes a data acquisition module, a feature extraction module, a risk prediction module, and a chronic obstructive pulmonary disease risk assessment module;

[0029] The data acquisition module acquires historical nursing data and sends the data to the feature extraction module;

[0030] The feature extraction module adaptively extracts local features of key time periods in the nursing information time series by dynamically adjusting the receptive field size and the receptive field attention mechanism, captures short-term fluctuations and long-term trends, and sends the data to the risk prediction module.

[0031] The risk prediction module combines static features and multi-scale dynamic features, uses the attention mechanism and global feature splicing to comprehensively integrate local details and global trend information, establishes a risk prediction model, and sends the data to the chronic obstructive pulmonary disease risk assessment module.

[0032] The chronic obstructive pulmonary disease risk assessment module realizes the risk assessment of chronic obstructive pulmonary disease for real-time nursing data based on the established risk prediction model.

[0033] Embodiment 2, refer to Figure 1 , based on the above embodiment, in the data acquisition module, the historical nursing data includes patient static feature data, patient dynamic nursing data, patient physiological condition data, time, and risk assessment level; the risk assessment level includes normal, low risk, medium risk, and high risk; the risk assessment level is used as the data label; and the collected data is subjected to data cleaning, data conversion, and standardization processing to obtain nursing information time series data; the static feature data includes smoking history, occupational exposure, family medical history, disease types of internal and surgical patients, and economic factors; the patient dynamic nursing data includes respiratory system indicators, blood oxygen saturation, cardiovascular indicators, exercise data, environmental risk factors, and sleep characteristics; the patient physiological condition data includes blood biochemical indicators and metabolic indicators.

[0034] Embodiment 3, refer to Figure 1 , based on the above embodiment, the feature extraction module specifically includes the following:

[0035] Local feature extraction; extracting local dependencies in the nursing information time series X, enhancing the feature extraction of index fluctuations in the key time period within 24 hours after nursing; using group convolution to extract local features , according to the feature distribution of the nursing information time series, dynamically adjusting the size k of the receptive field to achieve adaptive group convolution; and generating attention weights based on the receptive field attention mechanism , expressed as: ; ; finally calibrating the local features, expressed as: ; where Softmax(·) is the Softmax function; ReLU(·) is the ReLU activation function; Norm(·) is the normalization operation; AvgPool(·) is the average pooling operation; is the group convolution operation; and They are the minimum receptive field and the maximum receptive field respectively; is the average change rate of blood oxygen saturation; is the maximum change rate of blood oxygen saturation;

[0036] Multi-level feature fusion; The risk of chronic obstructive pulmonary disease is affected by multi-level features. Through multi-scale feature fusion, both local features and long-term trends are retained, providing comprehensive information for risk assessment; For features Extract multi-scale features layer by layer, which means: ; Generate attention weights based on the SE module which means: ; ; Adjust the multi-scale features according to the weights, which is expressed as: ; Among them, are the multi-scale features extracted layer by layer; S is the number of scales, i is the scale index; Conv(·) is the convolution operation; is the convolution kernel size; is the attention score; are the multi-scale features after attention adjustment; are the static features weight matrix; is the bias term generated by the multi-layer perceptron MLP;

[0037] Global feature fusion; Concatenate the features of all scales, which is expressed as: ; Concatenate the static features with the dynamic features extracted from the time series to form the final feature vector which is expressed as: ; Among them, , and are the features of different scales after weighted adjustment; Concat(·) is the concatenation operation; Attention(·) is the attention mechanism; Y is the concatenated feature.

[0038] By performing the above operations, aiming at the problems existing in the general chronic obstructive pulmonary disease risk assessment system, such as the separation and improper processing of the static and dynamic features of patients, and the improper capture of the key fluctuations after nursing, resulting in one-sided and inaccurate assessment results. This solution dynamically adjusts the receptive field size based on the change rate of nursing information, adaptively captures short-term fluctuations and long-term trends, and improves the ability to extract key period indicators; uses the receptive field attention mechanism to generate attention weights, reduces the over-concern for stable nursing data, improves the feature expression ability of key fluctuation regions, and thus improves the accuracy of chronic obstructive pulmonary disease risk assessment.

[0039] Example 4, refer to Figure 1 and Figure 2, based on the above embodiment, the risk prediction module uses the fused feature vector Z generated by the feature extraction module as input data, and performs risk classification based on multi-class SVM. For each class, a binary SVM is trained to distinguish the current class from other classes, obtaining a risk prediction model; specifically including the following:

[0040] Define the objective function, expressed as: ; ; Dynamically adjust the penalty parameter according to the sample distribution, setting a higher weight for high-risk samples, expressed as: ; Where is the normal vector of the classification hyperplane; is the classification error, represents the deviation degree of the o-th sample from the classification result of the hyperplane; C is the penalty parameter; a is the control parameter; is the true label of the sample; is the sample feature vector; is the square of the L2 norm; l is the total number of samples; and are the normal vectors of the classification hyperplanes of class k1 and class y1 respectively;

[0041] Loss processing; imposing a high penalty on misclassifying high-risk patients, expressed as: ; Imposing a small penalty on misclassifying low-risk patients and introducing a gradient smoothing mechanism to prevent the loss value from being too large due to noisy samples; expressed as: ; Where L(·) is the loss function; Smooth(·) is the logarithmic smoothing function;

[0042] Objective function optimization; updating the weights using the gradient descent algorithm based on the gradient of the objective function, the gradient is expressed as: ; Further predicting the risk level of patients with chronic obstructive pulmonary disease, expressed as: ; Where is the gradient of the objective function; is the predicted risk level; T is the matrix transpose; sign(·) is the sign function; Z is the sample set; is the sample proportion of the class;

[0043] Classification determination; preset the maximum number of iterations and the classification threshold, divide the data set into a training set and a test set, train the risk prediction model based on the training set, when the loss converges or reaches the maximum number of iterations, the risk prediction model training is completed; when the prediction accuracy of the trained risk prediction model for the test set is higher than the classification threshold, the risk prediction model is established, otherwise re-divide the data set and adjust the initial normal vector parameters to train the risk prediction model.

[0044] By performing the above operations, for the problems existing in the general chronic obstructive pulmonary disease risk assessment system, such as improper handling of the class imbalance problem of nursing data, weak recognition ability for high-risk and low-risk patients, sensitivity to noise, and thus poor assessment effect, this solution dynamically adjusts the penalty parameter according to the sample distribution, enhances the attention to highly risky patients, reduces the bias towards low-risk patients, introduces a logarithmic smoothing mechanism for low-risk patients to reduce noise interference, and introduces the inter-class distance in the objective function to avoid decision overlap between classes and reduce the confusion between high-risk and medium-low-risk classes; thereby improving the chronic obstructive pulmonary disease risk assessment effect.

[0045] Example Five. Refer to Figure 1 , this example is based on the above example. The chronic obstructive pulmonary disease risk assessment module is based on the established risk prediction model, which collects patients' nursing data in real time. After being processed by the feature extraction module, it is input into the risk prediction model, and the risk assessment level output by the risk prediction model is used as the assessment result. When the assessment result is moderate risk and high risk, the patient is given a warning.

[0046] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "including", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device.

[0047] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principle and spirit of the present invention.

[0048] The above describes the present invention and its implementation manners, and this description is not restrictive. What is shown in the drawings is only one of the implementation manners of the present invention, and the actual structure is not limited thereto. All in all, if those of ordinary skill in the art are inspired by it and design similar structural manners and embodiments without creative efforts without departing from the purpose of the present invention, they shall fall within the protection scope of the present invention.

Claims

1. A chronic obstructive pulmonary disease risk assessment system based on nursing information, characterized by: The system includes a data acquisition module, a feature extraction module, a risk prediction module and a COPD risk assessment module; The data collection module collects historical nursing data; The feature extraction module adaptively extracts local features of key periods in the nursing information time series by dynamically adjusting the receptive field size and the receptive field attention mechanism, capturing short-term fluctuations and long-term trends; The risk prediction module combines static features and multi-scale dynamic features, uses attention mechanism and global feature splicing, comprehensively integrates local details and global trend information, and establishes a risk prediction model; The chronic obstructive pulmonary disease risk assessment module implements chronic obstructive pulmonary disease risk assessment on real-time nursing data based on the established risk prediction model; The feature extraction module includes the following contents: local feature extraction; extracting local dependencies in the nursing information time series X; extracting local features using group convolution , according to the characteristic distribution of the nursing information time series, the size k of the receptive field is dynamically adjusted to achieve adaptive group convolution; and the attention weight is generated based on the receptive field attention mechanism, which is expressed as: ; ; Finally, calibrate the local features, expressed as: ; Among them, Softmax(·) is the Softmax function; ReLU(·) is the ReLU activation function; Norm(·) is the normalization operation; AvgPool(·) is the average pooling operation; is a group convolution operation; and are the minimum receptive field and the maximum receptive field respectively; is the average rate of change of blood oxygen saturation; is the maximum rate of change of blood oxygen saturation; The feature extraction module includes: multi-level feature fusion; Extract multi-scale features layer by layer, indicating: ; Generate attention weights based on SE module ,express: ; ; Adjust the multi-scale features according to the weights, expressed as: ;in, is the multi-scale feature extracted layer by layer; S is the number of scales, i is the scale index; Conv(·) is the convolution operation; is the convolution kernel size; is the attention score; is the multi-scale feature after attention adjustment; It is a static feature The weight matrix of It is the bias term generated by the multi-layer perceptron MLP.

2. The chronic obstructive pulmonary disease risk assessment system based on nursing information according to claim 1, characterized in that: The feature extraction module specifically includes the following contents: Local feature extraction; Multi-level feature fusion; Global feature fusion: concatenate features of all scales, expressed as: ; Static features Combined with the dynamic features extracted from the time series to form the final feature vector , expressed as: ;in, , and are the features of different scales after weighted adjustment; Concat(·) is the concatenation operation; Attention(·) is the attention mechanism; Y is the concatenated feature.

3. The chronic obstructive pulmonary disease risk assessment system based on nursing information according to claim 2, characterized in that: The risk prediction module uses the fused feature vector Z generated by the feature extraction module as input data, performs risk classification based on a multi-classification SVM, and for each category, trains a binary classification SVM to distinguish the current category from other categories to obtain a risk prediction model; specifically, it includes the following contents: Define the objective function, expressed as: ; ; Dynamically adjust the penalty parameter according to the sample distribution to set a higher weight for high-risk samples, expressed as: ;in, is the normal vector of the classification hyperplane; is the classification error, Indicates the degree of deviation of the oth sample from the hyperplane classification result; C is the penalty parameter; a is the control parameter; is the true label of the sample; is the sample feature vector; is the square of the L2 norm; l is the total number of samples; and are the normal vectors of the classification hyperplanes of category k1 and category y1 respectively; Loss handling; impose a high penalty on misclassification of high-risk patients, expressed as: ; A small penalty is imposed on misclassification of low-risk patients, and a gradient smoothing mechanism is introduced to prevent excessive loss values ​​due to noise samples; expressed as: ; Where L(·) is the loss function; Smooth(·) is the logarithmic smoothing function; Objective function optimization; Classification judgment; the maximum number of iterations and classification threshold are set in advance, the data set is divided into a training set and a test set, and the risk prediction model is trained based on the training set. When the loss converges or reaches the maximum number of iterations, the risk prediction model training is completed; when the prediction accuracy of the trained risk prediction model for the test set is higher than the classification threshold, the risk prediction model is established, otherwise the data set is re-divided and the initial normal vector parameters are adjusted to train the risk prediction model.

4. The chronic obstructive pulmonary disease risk assessment system based on nursing information according to claim 3, characterized in that: The objective function optimization is specifically as follows: based on the objective function gradient, the weight is updated using the gradient descent algorithm, and the gradient is expressed as: ; Then predict the patient's COPD risk level, expressed as: ;in, is the objective function gradient; is the predicted risk level; T is the matrix transpose; sign(·) is the sign function; Z is the sample set; is the sample proportion of the class.

5. The chronic obstructive pulmonary disease risk assessment system based on nursing information according to claim 4, characterized in that: In the data acquisition module, the historical nursing data includes patient static characteristic data, patient dynamic nursing data, patient physiological condition data, time and risk assessment level; The risk assessment levels include normal, low risk, medium risk and high risk; the risk assessment levels are used as data labels; and the collected data are cleaned, converted and standardized to obtain nursing information time series data.

6. The chronic obstructive pulmonary disease risk assessment system based on nursing information according to claim 5, characterized in that: The COPD risk assessment module is based on an established risk prediction model, collects patient care data in real time, and inputs the data into the risk prediction model after being processed by a feature extraction module. The risk assessment level output by the risk prediction model is used as the assessment result. When the assessment result is a moderate risk or a high risk, the patient is warned.

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