Method for predicting medical-seeking delay risk of gastric cancer patient

Abstract

The invention discloses a gastric cancer patient doctor-seeing delay risk prediction method, and belongs to the technical field of artificial intelligence, and the method comprises the following steps: obtaining doctor-seeing data of different gastric cancer patients, and respectively constructing prediction target variables and prediction features; selecting a significant prediction feature by using a significant feature objective function; feature grouping and label distribution are carried out based on the decision tree, and grouped labels with significant prediction features are obtained; constructing a gastric cancer patient doctor-seeing delay risk prediction model; constructing a prediction loss function; according to the prediction loss function, training a gastric cancer patient doctor-seeing delay risk prediction model based on the significant prediction feature data set to obtain a trained doctor-seeing delay risk prediction model; and on the basis of medical treatment data of a to-be-predicted gastric cancer patient, obtaining significant prediction features, and inputting the significant prediction features into the trained medical treatment delay risk prediction model to obtain a medical treatment delay risk result of the gastric cancer patient. The method solves the problem that the medical-seeking delay risk of the gastric cancer patient is difficult to accurately predict.

Description

Technical Field

[0001] The present invention belongs to the field of artificial intelligence technology, and in particular relates to a method for predicting the risk of delayed medical treatment for gastric cancer patients. Background Art

[0002] Gastric cancer has a high incidence and mortality rate. The prognosis of gastric cancer is closely related to the timing of diagnosis and treatment. Early diagnosis and treatment are key factors in improving the prognosis of gastric cancer patients. Delayed medical treatment will not only lead to poor prognosis of gastric cancer patients, affect the quality of life and survival time of patients, but also lead to increased medical expenses.

[0003] At present, the research on delayed medical treatment for gastric cancer patients mainly focuses on the current situation and influencing factors, and there is a lack of prediction of delayed medical treatment for gastric cancer patients. At the same time, due to the time and cost of collecting clinical data, the collected data on delayed medical treatment for gastric cancer patients are generally small. The factors that lead to delayed medical treatment for gastric cancer are complex. The existing prediction models are mostly based on logistic regression models, which are prone to overfitting problems on small data sets, resulting in insufficient model generalization, which is manifested as high accuracy on training data and low accuracy on test data. Although this problem can be alleviated to a certain extent by reducing model complexity and weight decay, these methods lack direct constraints on the feature space, making it difficult to achieve a balance between modeling ability and generalization ability, and it is difficult to meet the requirements for the accuracy of predicting the risk of delayed medical treatment for gastric cancer. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, the present invention provides a method for predicting the risk of delayed medical treatment for gastric cancer patients, which solves the problem that it is difficult to accurately predict the risk of delayed medical treatment for gastric cancer patients.

[0005] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is:

[0006] The present invention provides a method for predicting the risk of delayed medical treatment for gastric cancer patients, comprising the following steps:

[0007] S1. Obtain medical data of different gastric cancer patients and construct prediction target variables and prediction features respectively;

[0008] S2. According to the Lasso regression algorithm, a significant feature objective function is used to select significant prediction features from the prediction features, and a significant prediction feature data set is constructed;

[0009] S3, performing feature grouping and label assignment for each significant prediction feature in the significant prediction feature data set based on the decision tree to obtain a group label of the significant prediction feature;

[0010] S4. Construct a risk prediction model for delayed medical treatment in patients with gastric cancer;

[0011] S5. Construct a prediction loss function based on the grouping labels of significant prediction features;

[0012] S6. According to the prediction loss function, a risk prediction model for delayed medical treatment for gastric cancer patients is trained based on the significant prediction feature data set to obtain a trained risk prediction model for delayed medical treatment;

[0013] S7. Based on the medical treatment data of the gastric cancer patient to be predicted, obtain its significant prediction features, and input the significant prediction features into the trained medical treatment delay risk prediction model to obtain the medical treatment delay risk result of the gastric cancer patient.

[0014] The beneficial effects of the present invention are as follows: a method for predicting the risk of delayed medical treatment for gastric cancer patients provided by the present invention extracts prediction features from a variety of data that mainly affect the delay in medical treatment for gastric cancer patients based on the medical treatment data of different gastric cancer patients obtained, and uses the results that lead to the risk of delayed medical treatment as the prediction target variable, thereby fully considering the objective factors that affect the timely medical treatment of gastric cancer patients; the present invention obtains significant prediction features through Lasso algorithm and significant feature objective function screening, which provides a basis for reducing the overfitting effect on small data sets, thereby further ensuring the accuracy of predicting the risk of delayed medical treatment for gastric cancer patients; the present invention assigns grouping labels to each significant feature based on a decision tree, which is helpful to evaluate the feature space similarity of significant prediction features; the prediction loss function constructed by the present invention not only realizes direct constraints on the feature space, but also realizes a balance between modeling ability and generalization ability, effectively improving the training effect of the risk prediction model for delayed medical treatment for gastric cancer patients, so that the trained risk prediction model for delayed medical treatment can accurately predict the risk results of delayed medical treatment for gastric cancer patients.

[0015] Furthermore, the medical treatment data of gastric cancer patients in S1 include the time interval data from the first discovery of cancer symptoms to the first visit to the doctor, gender data, ethnicity data, age data, education level data, place of residence data, medical insurance type data, marital status data, per capita annual income data, spicy food data, long-term drinking data, relatives within three generations suffering from rectal cancer, previous cancer data, previous stomach disease data, regular physical examination data, family support score data, health literacy score data, anxiety score data, depression score data, medical coping style of facing score data, medical coping style of avoiding score data and medical coping style of giving in score data.

[0016] The beneficial effect of adopting the above-mentioned further scheme is as follows: the present invention provides medical treatment data items for gastric cancer patients, fully considering the direct phenomenon characteristics and influencing factors that lead to the risk of delayed medical treatment for gastric cancer patients, and providing a data basis for further screening of significant predictive features and accurately predicting the risk of delayed medical treatment for gastric cancer patients.

[0017] Furthermore, the S1 comprises the following steps:

[0018] S11, let the obtained medical treatment data of gastric cancer patients be N sets, where each patient has a unique set of medical treatment data, and N is a positive integer;

[0019] S12, extracting the time interval data from the first discovery of cancer symptoms to the first visit to the doctor from each medical treatment data as the time interval between the diagnosis and treatment;

[0020] S13, setting a prediction target variable according to the time interval between consultations;

[0021] The calculation expression of the predicted target variable is as follows:

[0022] ,

[0023] in, represents the i-th predicted target variable, represents the time interval between the first discovery of cancer-related symptoms by the i-th patient and the first visit to a medical institution for cancer, i is a positive integer;

[0024] S14. Combining the other data in the medical treatment data of the same patient except the time interval data from the first discovery of cancer symptoms to the first visit to the doctor as the corresponding prediction features of the patient.

[0025] The beneficial effect of adopting the above further scheme is: the present invention provides a method for constructing predictive target variables and predictive features based on the medical data of different gastric cancer patients, and realizes that the factors that may cause delayed medical treatment for gastric cancer patients are associated with the direct phenomenon characteristics of delayed medical treatment based on the medical data, thereby providing a basis for accurately predicting the risk of delayed medical treatment for gastric cancer patients.

[0026] Furthermore, the S2 comprises the following steps:

[0027] S21. Set feature weights based on the Lasso regression algorithm;

[0028] The calculation expression of the feature weight is as follows:

[0029] ,

[0030] in, represents the feature weight, Represents the 1-dimensional feature weight in the prediction feature, Represents the 2D feature weight in the prediction feature, represents the D-dimensional feature weight in the prediction feature, T represents transposition, ∈ represents belonging to, R D represents the prediction feature data set, where D represents the number of feature dimensions, D=21;

[0031] S22. Constructing a significant feature objective function based on feature weights;

[0032] The calculation expression of the significant feature objective function is as follows:

[0033] ,

[0034] , ,

[0035] in, represents the significant feature weight, represents the feature weight minimization operation, represents the salient feature objective function, represents the transpose of feature weights, represents the i-th prediction feature, Represents adjustable parameters, represents the feature weight of the d-dimensional feature in the prediction feature, | | represents the L1 norm, where d is a positive integer;

[0036] S23. According to the significant feature objective function, the features corresponding to the dimensions whose significant feature weights are not 0 are selected as significant prediction features, and a significant prediction feature data set is constructed.

[0037] The beneficial effect of adopting the above further scheme is: the significant feature objective function constructed by the present invention according to the Lasso regression algorithm, on the basis of the least squares method, adds the L1 norm regularization term, can extract the characteristic factors that are significantly related to the delay in medical treatment for gastric cancer patients through the feature weights, and construct the corresponding significant prediction feature data set, providing a basis for training a network model that can accurately predict the risk of delayed medical treatment for gastric cancer patients.

[0038] Furthermore, S3 comprises the following steps:

[0039] S31. Construct a feature classification binary tree according to the decision tree algorithm;

[0040] S32. Using a feature classification binary tree, perform feature grouping and label assignment for each significant prediction feature in the significant prediction feature data set to obtain a group label for the significant prediction feature.

[0041] The beneficial effect of adopting the above further scheme is as follows: the present invention provides a method for feature grouping and label classification for significant prediction features based on a decision tree, which helps to directly constrain the similarity of spatial features by assigning the same grouping labels to similar significant prediction features, thereby effectively improving the robustness of the model prediction even when the amount of medical data of gastric cancer patients is small, and accurately predicting the risk of delayed medical treatment for gastric cancer patients.

[0042] Furthermore, the risk prediction model for delayed medical treatment for gastric cancer patients in S4 comprises a first feature extraction module, a second feature extraction module, a third feature extraction module and a feature output module connected in sequence;

[0043] The first feature extraction module, the second feature extraction module and the third feature extraction module each include a linear mapping layer and a Relu activation layer; the feature output module includes a linear mapping layer and a Sigmoid activation layer.

[0044] The beneficial effect of adopting the above further scheme is: the present invention provides a risk prediction model for delayed medical treatment for gastric cancer patients. By constructing a neural network model with a simple structure, it can effectively reduce weight attenuation. On the basis of significant prediction features and prediction loss functions, it effectively ensures accurate prediction of the risk of delayed medical treatment for gastric cancer patients.

[0045] Furthermore, the calculation expression of the prediction loss function in S5 is as follows:

[0046] ,

[0047] ,

[0048] ,

[0049] ,

[0050] ,

[0051] Among them, L represents the prediction loss function, L CE represents the cross entropy loss function, Represents the feature space similarity loss weight coefficient, L FS represents the feature space similarity loss function, represents the risk prediction result of medical delay corresponding to the i-th significant prediction feature, The parameter indicating that the i-th significant predictive feature and the j-th significant predictive feature have the same grouping label, The exponential parameter representing the similarity between the i-th significant prediction feature and the j-th significant prediction feature, Indicates the variable adjustment amount, Parameter indicating that the i-th significant predictive feature and the k-th significant predictive feature have the same grouping label, represents the exponential parameter of the similarity between the i-th significant prediction feature and the k-th significant prediction feature, if represents if, g i represents the grouping label of the i-th significant prediction feature, g jrepresents the grouping label of the jth significant prediction feature, otherwise represents other cases, represents the similarity between the i-th significant prediction feature and the j-th significant prediction feature, f i represents the intermediate feature corresponding to the i-th significant prediction feature, f j represents the intermediate feature corresponding to the jth significant prediction feature, where j and k are both positive integers.

[0052] The beneficial effect of adopting the above further scheme is: the present invention provides a calculation method for predicting the instantaneous function, and a loss function constructed based on the degree of proximity between the risk prediction result and the predicted target value, and the similarity of the feature space. It can make the feature space more regular while ensuring the prediction accuracy, and make the model more robust to changes in input features, thereby improving the generalization ability of the model.

[0053] Further, the S6 comprises the following steps:

[0054] S61, inputting the significant prediction features in the significant prediction feature data set into a risk prediction model for delayed medical treatment for gastric cancer patients;

[0055] S62, extracting the input significant prediction feature by using the linear mapping layer in the first feature extraction module, and activating the input significant prediction feature by using the Relu activation layer in the first feature extraction module to obtain a first feature extraction result corresponding to the significant prediction feature;

[0056] S63, using the linear mapping layer in the second feature extraction module to perform feature extraction on the input first feature extraction result, and using the Relu activation layer in the second feature extraction module to activate it, so as to obtain a second feature extraction result corresponding to the significant prediction feature;

[0057] S64, using the linear mapping layer in the third feature extraction module to perform feature extraction on the input second feature extraction result, and using the Relu activation layer in the third feature extraction module to activate it, to obtain a third feature extraction result corresponding to the significant prediction feature as an intermediate feature;

[0058] S65, extracting features from the intermediate features using the linear mapping layer in the feature output module, and activating the intermediate features using the Sigmoid activation layer in the feature output module, to obtain risk prediction results corresponding to the significant prediction features as the risk results of delayed medical treatment for gastric cancer patients;

[0059] S66. Based on minimizing the prediction loss function, S61-S65 are repeatedly executed for the preset number of training times, and the parameters of the medical delay risk prediction model for gastric cancer patients are optimized through an adaptive momentum estimation algorithm to obtain a trained medical delay risk prediction model.

[0060] The beneficial effect of adopting the above further scheme is: the present invention provides a method for training a risk prediction model for delayed medical treatment for gastric cancer patients. Based on the prediction loss function provided by the present invention, the feature space can be effectively constrained during the model training process, thereby ensuring the balance between modeling ability and generalization ability, and providing a basis for accurately predicting the risk of delayed medical treatment for gastric cancer patients.

[0061] Other advantages of the present invention will be analyzed in more detail in subsequent embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0063] Figure 1 This is a flowchart of the steps of a method for predicting the risk of delayed medical treatment for gastric cancer patients in an embodiment of the present invention.

[0064] Figure 2 Schematic diagram of a risk prediction model for delayed medical treatment for gastric cancer patients in an embodiment of the present invention. DETAILED DESCRIPTION

[0065] The technical solutions in the embodiments of the present invention will be clearly and completely described below 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. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents the selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present invention.

[0066] like Figure 1 As shown, in one embodiment of the present invention, the present invention provides a method for predicting the risk of delayed medical treatment for gastric cancer patients, comprising the following steps:

[0067] S1. Obtain medical data of different gastric cancer patients and construct prediction target variables and prediction features respectively;

[0068] The medical treatment data of gastric cancer patients in S1 include the time interval data from the first discovery of cancer symptoms to the first visit to the doctor, gender data, ethnicity data, age data, education level data, place of residence data, medical insurance type data, marital status data, per capita annual income data, spicy food data, long-term drinking data, relatives within three generations suffering from rectal cancer, previous cancer data, previous stomach disease data, regular physical examination data, family support score data, health literacy score data, anxiety score data, depression score data, medical coping style of facing score data, medical coping style of avoiding score data and medical coping style of giving in score data.

[0069] In this embodiment, the medical treatment data indicators and value-taking rules of gastric cancer patients are shown in Table 1:

[0070] Table 1:

[0071]

[0072]

[0073] Based on the medical data indicators and related value-selection rules of gastric cancer patients, different influencing factors can be effectively distinguished and divided, thus providing a basis for improving the classification accuracy of significant predictive features.

[0074] The S1 comprises the following steps:

[0075] S11, let the obtained medical treatment data of gastric cancer patients be N sets, where each patient has a unique set of medical treatment data, and N is a positive integer;

[0076] S12, extracting the time interval data from the first discovery of cancer symptoms to the first visit to the doctor from each medical treatment data as the time interval between the diagnosis and treatment;

[0077] S13, setting a prediction target variable according to the time interval between consultations;

[0078] The calculation expression of the predicted target variable is as follows:

[0079] ,

[0080] in, represents the i-th predicted target variable, represents the time interval between the first discovery of cancer-related symptoms by the i-th patient and the first visit to a medical institution for cancer, i is a positive integer;

[0081] S14. Combining the other data in the medical treatment data of the same patient except the time interval data from the first discovery of cancer symptoms to the first visit to the doctor as the corresponding prediction features of the patient.

[0082] S2. According to the Lasso regression algorithm, a significant feature objective function is used to select significant prediction features from the prediction features, and a significant prediction feature data set is constructed;

[0083] The S2 comprises the following steps:

[0084] S21. Set feature weights based on the Lasso regression algorithm;

[0085] The calculation expression of the feature weight is as follows:

[0086] ,

[0087] in, represents the feature weight, Represents the 1-dimensional feature weight in the prediction feature, Represents the 2D feature weight in the prediction feature, represents the D-dimensional feature weight in the prediction feature, T represents transposition, ∈ represents belonging to, R D represents the prediction feature data set, where D represents the number of feature dimensions, D=21;

[0088] S22. Constructing a significant feature objective function based on feature weights;

[0089] The calculation expression of the significant feature objective function is as follows:

[0090] ,

[0091] , ,

[0092] in, represents the significant feature weight, represents the feature weight minimization operation, represents the salient feature objective function, represents the transpose of feature weights, represents the i-th prediction feature, Represents adjustable parameters, represents the feature weight of the d-dimensional feature in the prediction feature, | | represents the L1 norm, where d is a positive integer;

[0093] S23. According to the significant feature objective function, the features corresponding to the dimensions whose significant feature weights are not 0 are selected as significant prediction features, and a significant prediction feature data set is constructed.

[0094] In this embodiment, the calculation expression of the significant prediction feature in S23 is as follows:

[0095] ,

[0096] Among them, z i represents the i-th significant predictive feature, R M represents a significant prediction feature dataset, and M represents the number of feature dimensions with non-zero significant feature weights.

[0097] S3, performing feature grouping and label assignment for each significant prediction feature in the significant prediction feature data set based on the decision tree to obtain a group label of the significant prediction feature;

[0098] The S3 comprises the following steps:

[0099] S31. Construct a feature classification binary tree according to the decision tree algorithm;

[0100] In this embodiment, the feature classification binary tree includes one root node, several intermediate nodes and leaf nodes; when the significant prediction feature is input into the feature classification binary tree, the significant prediction feature starts from the root node, performs classification judgment when passing through the intermediate nodes, and finally reaches a leaf node. The significant prediction features reaching the same leaf node have high similarity. The solution of the present invention assigns the same grouping label to the significant prediction features with high similarity, thereby forming several data groups.

[0101] S32. Using a feature classification binary tree, perform feature grouping and label assignment for each significant prediction feature in the significant prediction feature data set to obtain a group label for the significant prediction feature.

[0102] In this embodiment, the calculation expression of the grouping label of the significant prediction feature is as follows:

[0103] ,

[0104] Among them, g i represents the grouping label of the i-th significant prediction feature, l represents the l-th leaf node, where l=1, 2, ..., L ’ , L ’ is the total number of leaf nodes.

[0105] S4. Construct a risk prediction model for delayed medical treatment in patients with gastric cancer;

[0106] like Figure 2 As shown, the risk prediction model for delayed medical treatment for gastric cancer patients in S4 includes a first feature extraction module, a second feature extraction module, a third feature extraction module and a feature output module connected in sequence;

[0107] The first feature extraction module, the second feature extraction module and the third feature extraction module each include a linear mapping layer and a Relu activation layer; the feature output module includes a linear mapping layer and a Sigmoid activation layer.

[0108] In this embodiment, the input dimension of the linear mapping layer of the first feature extraction module is M, and the output dimension is 24; the input dimension of the linear mapping layer of the second feature extraction module is 24, and the output dimension is 16; the input dimension of the linear mapping layer of the third feature extraction module is 16, and the output dimension is E, where E=8, which is the feature dimension extracted after passing through three layers of feature extraction modules; the input dimension of the linear mapping layer of the feature output module is E, and the output dimension is 1.

[0109] S5. Construct a prediction loss function based on the grouping labels of significant prediction features;

[0110] The calculation expression of the prediction loss function in S5 is as follows:

[0111] ,

[0112] ,

[0113] ,

[0114] ,

[0115] ,

[0116] Among them, L represents the prediction loss function, L CE represents the cross entropy loss function, Represents the feature space similarity loss weight coefficient, L FS represents the feature space similarity loss function, represents the risk prediction result of medical delay corresponding to the i-th significant prediction feature, The parameter indicating that the i-th significant predictive feature and the j-th significant predictive feature have the same grouping label, The exponential parameter representing the similarity between the i-th significant prediction feature and the j-th significant prediction feature, Indicates the variable adjustment amount, Parameter indicating that the i-th significant predictive feature and the k-th significant predictive feature have the same grouping label, represents the exponential parameter of the similarity between the i-th significant prediction feature and the k-th significant prediction feature, if represents if, g i represents the grouping label of the i-th significant prediction feature, g j represents the grouping label of the jth significant prediction feature, otherwise represents other cases, represents the similarity between the i-th significant prediction feature and the j-th significant prediction feature, f i represents the intermediate feature corresponding to the i-th significant prediction feature, fj represents the intermediate feature corresponding to the jth significant prediction feature, where j and k are both positive integers.

[0117] In this embodiment, the cross entropy loss function is used to supervise the closeness between the risk prediction result output by the model and the predicted target value, and the feature space similarity loss function is used to constrain the feature space of the neural network to improve the generalization ability of the model; in the feature space similarity loss function, the more similar the features with the same grouping labels are, the smaller the numerator is, and the smaller the corresponding loss value is; conversely, the more dissimilar the features with different grouping labels are, the larger the numerator is, and the larger the corresponding loss value is. Therefore, by minimizing the value of the feature space similarity loss function, features with the same grouping labels can be made more similar, thereby making the feature space more regular and making the model more robust to changes in input features, thereby improving the generalization ability of the model.

[0118] S6. According to the prediction loss function, a risk prediction model for delayed medical treatment for gastric cancer patients is trained based on the significant prediction feature data set to obtain a trained risk prediction model for delayed medical treatment;

[0119] The S6 comprises the following steps:

[0120] S61, inputting the significant prediction features in the significant prediction feature data set into a risk prediction model for delayed medical treatment for gastric cancer patients;

[0121] S62, extracting the input significant prediction feature by using the linear mapping layer in the first feature extraction module, and activating the input significant prediction feature by using the Relu activation layer in the first feature extraction module to obtain a first feature extraction result corresponding to the significant prediction feature;

[0122] S63, using the linear mapping layer in the second feature extraction module to perform feature extraction on the input first feature extraction result, and using the Relu activation layer in the second feature extraction module to activate it, so as to obtain a second feature extraction result corresponding to the significant prediction feature;

[0123] S64, using the linear mapping layer in the third feature extraction module to perform feature extraction on the input second feature extraction result, and using the Relu activation layer in the third feature extraction module to activate it, to obtain a third feature extraction result corresponding to the significant prediction feature as an intermediate feature;

[0124] S65, extracting features from the intermediate features using the linear mapping layer in the feature output module, and activating the intermediate features using the Sigmoid activation layer in the feature output module, to obtain risk prediction results corresponding to the significant prediction features as the risk results of delayed medical treatment for gastric cancer patients;

[0125] S66. Based on minimizing the prediction loss function, S61-S65 are repeatedly executed for the preset number of training times, and the parameters of the medical delay risk prediction model for gastric cancer patients are optimized through an adaptive momentum estimation algorithm to obtain a trained medical delay risk prediction model.

[0126] S7. Based on the medical treatment data of the gastric cancer patient to be predicted, obtain its significant prediction features, and input the significant prediction features into the trained medical treatment delay risk prediction model to obtain the medical treatment delay risk result of the gastric cancer patient.

[0127] The risk of delayed medical treatment for gastric cancer patients is reflected in whether the time from the first discovery of cancer symptoms to the first visit to the doctor exceeds 3 months. If it exceeds 3 months, there is a risk of delayed medical treatment. Otherwise, the medical treatment is timely and the risk of delayed medical treatment can be effectively avoided.

[0128] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for predicting the risk of delayed medical treatment in patients with gastric cancer, characterized in that: The steps include: S1. Obtain medical data of different gastric cancer patients and construct prediction target variables and prediction features respectively; S2. According to the Lasso regression algorithm, a significant feature objective function is used to select significant prediction features from the prediction features, and a significant prediction feature data set is constructed; S3, performing feature grouping and label assignment for each significant prediction feature in the significant prediction feature data set based on the decision tree to obtain a group label of the significant prediction feature; S4. Construct a risk prediction model for delayed medical treatment in patients with gastric cancer; S5. Construct a prediction loss function based on the grouping labels of significant prediction features; S6. According to the prediction loss function, a risk prediction model for delayed medical treatment for gastric cancer patients is trained based on the significant prediction feature data set to obtain a trained risk prediction model for delayed medical treatment; S7. Based on the medical data of the gastric cancer patients to be predicted, obtain their significant prediction features. The significant prediction feature is input into the trained medical delay risk prediction model to obtain the medical delay risk result of the gastric cancer patient.

2. The method for predicting the risk of delayed medical treatment for gastric cancer patients according to claim 1, characterized in that: The medical treatment data of gastric cancer patients in S1 include the time interval data from the first discovery of cancer symptoms to the first visit to the doctor, gender data, ethnicity data, age data, education level data, place of residence data, medical insurance type data, marital status data, per capita annual income data, spicy food data, long-term drinking data, relatives within three generations suffering from rectal cancer, previous cancer data, previous stomach disease data, regular physical examination data, family support score data, health literacy score data, anxiety score data, depression score data, medical coping style of facing score data, medical coping style of avoiding score data and medical coping style of giving in score data.

3. The method for predicting the risk of delayed medical treatment for gastric cancer patients according to claim 2, characterized in that: The S1 comprises the following steps: S11, let the obtained medical treatment data of gastric cancer patients be N sets, where each patient has a unique set of medical treatment data, and N is a positive integer; S12, extracting the time interval data from the first discovery of cancer symptoms to the first visit to the doctor from each medical treatment data as the time interval between the diagnosis and treatment; S13, setting a prediction target variable according to the time interval between consultations; The calculation expression of the predicted target variable is as follows: ， in, represents the i-th predicted target variable, represents the time interval between the first discovery of cancer-related symptoms by the i-th patient and the first visit to a medical institution for cancer, i is a positive integer; S14. Combining the other data in the medical treatment data of the same patient except the time interval data from the first discovery of cancer symptoms to the first visit to the doctor as the corresponding prediction features of the patient.

4. The method for predicting the risk of delayed medical treatment for gastric cancer patients according to claim 2, characterized in that: The S2 comprises the following steps: S21. Set feature weights based on the Lasso regression algorithm; The calculation expression of the feature weight is as follows: ， in, represents the feature weight, Represents the 1-dimensional feature weight in the prediction feature, Represents the 2D feature weight in the prediction feature, represents the D-dimensional feature weight in the prediction feature, T represents transposition, ∈ represents belonging to, R D represents the prediction feature data set, where D represents the number of feature dimensions, D=21; S22. Constructing a significant feature objective function based on feature weights; The calculation expression of the significant feature objective function is as follows: ， ， ， in, represents the significant feature weight, represents the feature weight minimization operation, represents the salient feature objective function, represents the transpose of feature weights, represents the i-th prediction feature, Represents adjustable parameters, represents the feature weight of the d-dimensional feature in the prediction feature, | | represents the L1 norm, where d is a positive integer; S23. According to the significant feature objective function, the features corresponding to the dimensions whose significant feature weights are not 0 are selected as significant prediction features, and a significant prediction feature data set is constructed.

5. The method for predicting the risk of delayed medical treatment for gastric cancer patients according to claim 4, characterized in that: The S3 comprises the following steps: S31. Construct a feature classification binary tree according to the decision tree algorithm; S32. Using a feature classification binary tree, perform feature grouping and label assignment for each significant prediction feature in the significant prediction feature data set to obtain a group label for the significant prediction feature.

6. The method for predicting the risk of delayed medical treatment for gastric cancer patients according to claim 5, characterized in that: The risk prediction model for delayed medical treatment for gastric cancer patients in S4 comprises a first feature extraction module, a second feature extraction module, a third feature extraction module and a feature output module connected in sequence; The first feature extraction module, the second feature extraction module and the third feature extraction module each include a linear mapping layer and a Relu activation layer; the feature output module includes a linear mapping layer and a Sigmoid activation layer.

7. The method for predicting the risk of delayed medical treatment for gastric cancer patients according to claim 6, characterized in that: The calculation expression of the prediction loss function in S5 is as follows: ， ， ， ， ， Among them, L represents the prediction loss function, L CE represents the cross entropy loss function, Represents the feature space similarity loss weight coefficient, L FS represents the feature space similarity loss function, represents the risk prediction result of medical delay corresponding to the i-th significant prediction feature, The parameter indicating that the i-th significant predictive feature and the j-th significant predictive feature have the same grouping label, The exponential parameter representing the similarity between the i-th significant prediction feature and the j-th significant prediction feature, Indicates the variable adjustment amount, Parameter indicating that the i-th significant predictive feature and the k-th significant predictive feature have the same grouping label, represents the exponential parameter of the similarity between the i-th significant prediction feature and the k-th significant prediction feature, if represents if, g i represents the grouping label of the i-th significant prediction feature, g j represents the grouping label of the jth significant prediction feature, otherwise represents other cases, represents the similarity between the i-th significant prediction feature and the j-th significant prediction feature, f i represents the intermediate feature corresponding to the i-th significant prediction feature, f j represents the intermediate feature corresponding to the jth significant prediction feature, where j and k are both positive integers.

8. The method for predicting the risk of delayed medical treatment for gastric cancer patients according to claim 7, characterized in that: The S6 comprises the following steps: S61, inputting the significant prediction features in the significant prediction feature data set into a risk prediction model for delayed medical treatment for gastric cancer patients; S62, extracting the input significant prediction feature by using the linear mapping layer in the first feature extraction module, and activating the input significant prediction feature by using the Relu activation layer in the first feature extraction module to obtain a first feature extraction result corresponding to the significant prediction feature; S63, using the linear mapping layer in the second feature extraction module to perform feature extraction on the input first feature extraction result, and using the Relu activation layer in the second feature extraction module to activate it, to obtain a second feature extraction result corresponding to the significant prediction feature; S64, using the linear mapping layer in the third feature extraction module to perform feature extraction on the input second feature extraction result, and using the Relu activation layer in the third feature extraction module to activate it, to obtain a third feature extraction result corresponding to the significant prediction feature as an intermediate feature; S65, extracting features from the intermediate features using the linear mapping layer in the feature output module, and activating the intermediate features using the Sigmoid activation layer in the feature output module, to obtain risk prediction results corresponding to the significant prediction features as the risk results of delayed medical treatment for gastric cancer patients; S66. Based on minimizing the prediction loss function, S61-S65 are repeatedly executed for the preset number of training times, and the parameters of the medical delay risk prediction model for gastric cancer patients are optimized through an adaptive momentum estimation algorithm to obtain a trained medical delay risk prediction model.

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