A diagnostic model for sudden cardiac death and a method for constructing the same

Abstract

The application relates to the medical field and provides a diagnosis model for cardiogenic sudden death and a construction method thereof, wherein the method comprises the following steps: collecting data samples, and constructing a training set based on the data samples, wherein the training set comprises diagnosis features, and the diagnosis features represent the data samples in the training set as cardiogenic sudden death; data interpolation and variable screening are performed on the diagnosis features in the training set; a logistic regression model is constructed based on the training set, wherein independent variables of the logistic regression model are the diagnosis features, and dependent variables of the logistic regression model are diagnosis results of samples corresponding to the diagnosis features; and a diagnosis calculator is constructed based on the logistic regression model, wherein the diagnosis calculator inputs received feature values into the logistic regression model to diagnose cardiogenic sudden death. The application can accurately diagnose cardiogenic sudden death, and solves the problem that the diagnosis result of cardiogenic sudden death is mainly dependent on the subjective experience of a forensic doctor, and the result is not accurate enough.

Description

Technical Field

[0001] The present invention relates to the field of medical technology, and in particular to a diagnostic model for sudden cardiac death and a construction method thereof. Background Art

[0002] Sudden cardiac death (SCD) is an unpredictable, acute, and natural death caused by cardiovascular disease. Approximately 5 million SCDs occur worldwide each year, accounting for approximately 50% of cardiovascular disease deaths. Due to its sudden and unpredictable nature, diagnosing SCD is a challenging task.

[0003] In current diagnostic practice, the diagnosis of sudden cardiac death in corpses is mainly based on the subjective experience of the forensic doctor after the autopsy. However, such results are highly accidental and may be affected by a variety of subjective and objective factors, resulting in inaccurate diagnostic results. Summary of the Invention

[0004] The present invention provides a diagnostic model for sudden cardiac death and a method for constructing the same, which is used to solve the problem that the current diagnosis of sudden cardiac death mainly relies on the subjective experience of forensic doctors to give diagnostic results, which is not accurate enough. The solution of the present application can construct a logistic regression model based on sample data, and obtain the input feature values ​​through a diagnostic calculator and call the logistic regression model, so as to accurately diagnose sudden cardiac death.

[0005] The present invention provides a method for constructing a diagnostic model for sudden cardiac death, comprising:

[0006] Collecting data samples and constructing a training set based on the data samples, wherein the training set includes diagnostic features, and the diagnostic features indicate that the data samples in the training set are sudden cardiac death;

[0007] performing data interpolation and variable screening on the diagnostic features in the training set;

[0008] Constructing a logistic regression model based on the training set, wherein the independent variable of the logistic regression model is the diagnostic feature, and the dependent variable of the logistic regression model is the diagnostic result of the sample corresponding to the diagnostic feature;

[0009] A diagnostic calculator is constructed based on the logistic regression model, and the diagnostic calculator inputs the received feature values ​​into the logistic regression model to diagnose sudden cardiac death.

[0010] According to the method for constructing a diagnostic model for sudden cardiac death provided by the present invention, the diagnostic features include anatomical data and pathological analysis results of the data sample;

[0011] The anatomical data includes heart weight of the data sample, and / or left ventricular wall thickness, and / or right ventricular wall thickness, and / or mitral valve circumference, and / or aortic valve circumference, and / or tricuspid valve circumference, and / or pulmonary valve circumference, and / or degree of stenosis of the left anterior descending artery, and / or degree of stenosis of the left circumflex artery, and / or degree of stenosis of the right coronary artery;

[0012] The pathological analysis results include myocardial infarction, and / or cardiomyopathy, and / or conduction system disease, and / or valvulitis, and / or epicarditis, and / or myocarditis, and / or endocarditis and pericarditis.

[0013] The diagnostic features also include the height of the data sample, and / or the thickness of the abdominal wall subcutaneous fat, and / or the gender, and / or the age.

[0014] According to the method for constructing a diagnostic model for sudden cardiac death provided by the present invention, the anatomical data includes the stenosis degree of the left anterior descending artery, the stenosis degree of the left circumflex artery, the stenosis degree of the right coronary artery, the left ventricular wall thickness and the pulmonary valve circumference of the data sample.

[0015] The pathological analysis results include myocardial infarction, cardiomyopathy, valvulitis, pericarditis, myocarditis, and conduction system lesions.

[0016] According to the method for constructing a diagnostic model for sudden cardiac death provided by the present invention, the diagnostic feature further includes the age of the data sample.

[0017] According to the method for constructing a diagnostic model for sudden cardiac death provided by the present invention, the data interpolation and variable screening of the diagnostic features in the training set include:

[0018] Using the Lost Forest interpolation method to interpolate data for the diagnostic features in the training set;

[0019] The LASSO regression method with ten-fold cross validation and the recursive feature elimination method based on random forest were used to screen the diagnostic features in the training set.

[0020] According to the method for constructing a diagnostic model for sudden cardiac death provided by the present invention, the diagnostic features in the training set are screened using a LASSO regression method with ten-fold cross-validation and a recursive feature elimination method based on random forests, comprising:

[0021] The diagnostic features in the training set are screened using the LASSO regression method with ten-fold cross validation to obtain a first variable set;

[0022] Using a recursive feature elimination method based on random forest to screen the diagnostic features in the training set to obtain a second variable set;

[0023] The intersection of the first variable set and the second variable set is taken to obtain a result of variable screening.

[0024] According to the method for constructing a diagnostic model for sudden cardiac death provided by the present invention, the diagnostic calculator inputs the received characteristic values ​​into the logistic regression model to diagnose sudden cardiac death, including:

[0025] Obtain the diagnostic characteristics of the body to be tested through the input box;

[0026] inputting the diagnostic characteristics of the corpse to be tested into the logistic regression model;

[0027] Calculating the diagnostic probability corresponding to the diagnostic feature using a Norman plot derived from the logistic regression model, wherein the diagnostic probability reflects the probability that the corpse to be tested is diagnosed with sudden cardiac death;

[0028] If the diagnosis probability corresponding to the diagnostic feature is higher than the pre-calculated critical probability value, it is determined that the corpse to be tested is suffering from sudden cardiac death.

[0029] According to the method for constructing a diagnostic model for sudden cardiac death provided by the present invention, the critical probability value is calculated as follows:

[0030] Calculating the Youden index of the training set;

[0031] The critical probability value is determined based on the maximum value of the Youden Index.

[0032] The present invention also provides a system for constructing a diagnostic model for sudden cardiac death, comprising:

[0033] A data acquisition module, configured to collect data samples and construct a training set based on the data samples, wherein the training set includes diagnostic features, and the diagnostic features indicate that the data samples in the training set are sudden cardiac death;

[0034] A data processing module, configured to perform data interpolation and variable screening on the diagnostic features in the training set;

[0035] A model building module, configured to build a logistic regression model based on the training set, wherein the independent variable of the logistic regression model is the diagnostic feature, and the dependent variable of the logistic regression model is the diagnostic result of the sample corresponding to the diagnostic feature;

[0036] The model diagnosis module is used to construct a diagnosis calculator based on the logistic regression model, and the diagnosis calculator inputs the received characteristic values ​​into the logistic regression model to diagnose sudden cardiac death.

[0037] The present invention also provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the method for constructing a diagnostic model for sudden cardiac death as described above is implemented.

[0038] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-mentioned methods for constructing a diagnostic model for sudden cardiac death.

[0039] The present invention also provides a computer program product, comprising a computer program, which, when executed by a processor, implements any of the above-mentioned methods for constructing a diagnostic model for sudden cardiac death.

[0040] In the construction method provided by the present invention, a training set can be constructed using data samples that characterize sudden cardiac death. By constructing a special training set in this way, the accuracy of the training process can be improved. In addition, data interpolation and variable screening can be used to improve the completeness of the data in the training set, simplify the training set, and thus simplify the difficulty of training. After the training set processing is completed, a logistic regression model and a diagnostic calculator can be constructed. After the diagnostic calculator obtains the various characteristic values ​​of the corpse input by the operator through a graphical user interface, it can be input into the logistic regression model. In the logistic regression model, a diagnosis result of sudden cardiac death of the corpse can be obtained based on the input various diagnostic characteristic values. In this process, forensic personnel do not need to make judgments based on experience, which basically avoids the phenomenon of inaccurate diagnostic results caused by external subjective and objective factors, and can significantly improve the diagnostic accuracy of sudden cardiac death. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0042] Figure 1 This is one of the flow charts of the method for constructing a diagnostic model for sudden cardiac death provided by the present invention;

[0043] Figure 2 is a schematic diagram of a Norman diagram provided by an embodiment of the present invention;

[0044] Figure 3 1 is a schematic structural diagram of a system for constructing a diagnostic model for sudden cardiac death provided by an embodiment of the present invention;

[0045] Figure 4 A schematic diagram of performance test results provided by an embodiment of the present invention;

[0046] Figure 5 A schematic diagram of the physical structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0047] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0048] Figure 1 This is one of the flow charts of the method for constructing a diagnostic model for sudden cardiac death provided by the present invention.

[0049] like Figure 1 As shown, this embodiment provides a method for constructing a diagnostic model for sudden cardiac death, comprising:

[0050] Step 101: Collect data samples and construct a training set based on the data samples, wherein the training set includes diagnostic features, and the diagnostic features indicate that the data samples in the training set are sudden cardiac death;

[0051] Step 102, performing data interpolation and variable screening on the diagnostic features in the training set;

[0052] Step 103: constructing a logistic regression model based on the training set, wherein the independent variable of the logistic regression model is the diagnostic feature, and the dependent variable of the logistic regression model is the diagnostic result of the sample corresponding to the diagnostic feature;

[0053] Step 104 : constructing a diagnosis calculator based on the logistic regression model, and inputting the received feature values ​​into the logistic regression model to diagnose sudden cardiac death.

[0054] In practice, the data sample in this embodiment may be a cause of death identification result obtained by forensic identification. Furthermore, the cause of death identification result of the data sample shows that the cause of death of the corpse is sudden cardiac death.

[0055] In practical applications, the data samples collected in this embodiment may be several death cause identification cases collected by the Forensic Identification Center of Sun Yat-sen University over the past period of time. The diagnostic features may include characteristic data closely related to the diagnosis of sudden cardiac death, such as the morphology and histological parameters of cardiac anatomy and demographic information. These diagnostic features can comprehensively summarize the various characteristics of sudden cardiac death, thereby making the trained logistic regression model more accurate.

[0056] The diagnostic features in this embodiment refer to features that can be used to diagnose sudden cardiac death, or features that are helpful for the diagnosis of sudden cardiac death.

[0057] Data interpolation refers to the process of inferring missing data values ​​through specific algorithms when the values ​​of certain data points are known. This operation can improve the integrity of the data. A complete training set can provide more comprehensive information, reduce analytical bias caused by incomplete data, and improve the reliability and accuracy of the constructed logistic regression model.

[0058] In practical applications, the constructed logistic regression model can use the diagnostic features of the training set as independent variables, and the dependent variable can be the diagnostic result of sudden cardiac death. Specifically, the diagnostic result can be the probability of dying from sudden cardiac death. For example, by inputting the various diagnostic features of a sample into the logistic regression model, the logistic regression model can determine that the probability of the sample dying from sudden cardiac death is 78%, or the probability is 12%, and so on.

[0059] The solution of this embodiment also constructs a diagnostic calculator, which can have a graphical user interface, through which the operator's input can be accepted. The forensic operator can enter various diagnostic features of the corpse to be tested in the diagnostic calculator. In actual applications, the graphical user interface of the diagnostic calculator can use components such as sliders, drop-down boxes, and buttons to indicate the diagnostic features that need to be entered. During implementation, the forensic operator can, based on actual conditions, enter only known diagnostic features into the diagnostic calculator when some diagnostic features are unknown, and make a conservative diagnosis of sudden cardiac death based on the entered diagnostic features. It should be noted that the more and more comprehensive the diagnostic features entered, the more accurate the diagnostic results will naturally be. However, this method in the solution of this embodiment can cope with various special situations. For example, in actual applications, case information is incomplete or the specimen is partially damaged, resulting in the inability to collect certain feature information. In this case, sudden cardiac death can also be diagnosed, thereby improving the scope of application of the diagnostic model.

[0060] The following describes how to use the diagnostic calculator:

[0061] First, enter the diagnostic features through the drop-down box and slider in the diagnostic calculator;

[0062] Click the "Predict" button in the diagnostic calculator to output the probability value of the corresponding sample being sudden cardiac death and its 95% confidence interval;

[0063] On the "Model Performance Overview" page of the diagnostic calculator, you can find the cutoff value closest to the output probability and determine the confidence level in diagnosing the sample as sudden cardiac death based on the parameters corresponding to the cutoff value. The corresponding parameters can include accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (PPV), and other data.

[0064] In addition, in the construction method provided in this embodiment, a training set can be constructed using data samples that characterize sudden cardiac death. By constructing a special training set in this way, the accuracy of the training process can be improved. In addition, the completeness of the data in the training set can be improved by data interpolation. After the training set processing is completed, a logistic regression model and a diagnostic calculator can be constructed. After the diagnostic calculator obtains the various feature values ​​of the corpse input by the operator through a graphical user interface, it can be input into the logistic regression model. In the logistic regression model, a diagnosis result of sudden cardiac death of the corpse can be obtained based on the input feature values. In this process, forensic personnel do not need to make judgments based on experience, which basically avoids the phenomenon of inaccurate diagnostic results caused by external subjective and objective factors, and can significantly improve the diagnostic accuracy of sudden cardiac death.

[0065] In an exemplary embodiment, the diagnostic features include anatomical data and pathological analysis results of the data sample;

[0066] The anatomical data includes heart weight of the data sample, and / or left ventricular wall thickness, and / or right ventricular wall thickness, and / or mitral valve circumference, and / or aortic valve circumference, and / or tricuspid valve circumference, and / or pulmonary valve circumference, and / or degree of stenosis of the left anterior descending artery, and / or degree of stenosis of the left circumflex artery, and / or degree of stenosis of the right coronary artery;

[0067] The pathological analysis results include myocardial infarction, and / or cardiomyopathy, and / or conduction system disease, and / or valvulitis, and / or epicarditis, and / or myocarditis, and / or endocarditis and pericarditis.

[0068] In practical applications, the degree of stenosis refers to the percentage of the stenotic portion of the lumen to the lumen, that is, the degree of stenosis = 1 - the percentage of the normal portion of the lumen to the lumen.

[0069] In the example embodiment, the diagnostic features further include height of the data sample, and / or, abdominal subcutaneous fat thickness, and / or, gender, and / or, age.

[0070] Left ventricular wall thickness (LVWT), right ventricular wall thickness (RVWT), circumference of mitral annulus (cMA), circumference of aortic annulus (cAA), circumference of tricuspid annulus (cTA), circumference of pulmonary annulus (cPA), and stenosis degree of left anterior descending branch (LAD), stenosis degree of left circumflex branch (LCX), stenosis degree of right coronary artery (RCA), abdominal subcutaneous fat thickness, gender, and age are all key information for diagnosing sudden cardiac death.

[0071] In addition, myocardial infarction (MI), cardiomyopathy (CM), conduction systematic lesions (CSL), and valvulitis, epicarditis, myocarditis, endocarditis, and pericarditis in the pathological analysis results are also within the diagnostic category of sudden cardiac death.

[0072] In the scheme of the example embodiment, the diagnostic features of sudden cardiac death can be more comprehensively summarized by the anatomical data, the pathological analysis result data, and the demographic data, and the accuracy of diagnosis can be improved.

[0073] In the example embodiment, the anatomical data includes stenosis degree of left anterior descending branch, stenosis degree of left circumflex branch, stenosis degree of right coronary artery, left ventricular wall thickness, and circumference of pulmonary annulus of the data sample.

[0074] The pathological analysis result includes myocardial infarction, cardiomyopathy, valvulitis, pericarditis, myocarditis, and conduction systematic lesions.

[0075] The demographic data includes age.

[0076] The anatomical data, pathological analysis results, and demographic data provided in this embodiment are certain more representative diagnostic features screened and determined from a number of diagnostic features. That is to say, the diagnostic features provided in this embodiment are more helpful for the diagnosis of sudden cardiac death, or in other words, the diagnostic features provided in this embodiment have a greater proportional weight when diagnosing sudden cardiac death.

[0077] The diagnostic features provided in this embodiment can assist in the diagnosis of sudden cardiac death.

[0078] In an exemplary embodiment, performing data interpolation and variable screening on the diagnostic features in the training set includes:

[0079] Using the Lost Forest interpolation method to interpolate data for the diagnostic features in the training set;

[0080] The LASSO regression method with ten-fold cross validation and the recursive feature elimination method based on random forest were used to screen the diagnostic features in the training set.

[0081] Random forest is an algorithm that integrates multiple trees through the idea of ​​ensemble learning. The basic unit is the decision tree, and its essence belongs to the ensemble learning method in machine learning. The essence of the random forest algorithm is a classifier integration algorithm based on the decision tree. Random forest randomizes the column variables and row observations of the data set to generate multiple classification numbers, and finally summarizes the results of multiple classification numbers. When performing data interpolation, the random forest algorithm reduces the amount of calculation while significantly improving the prediction accuracy compared to the traditional neural network algorithm. Moreover, the algorithm is insensitive to multicollinearity and is relatively robust to missing data and unbalanced data, and can adapt well to data sets with up to thousands of explanatory variables.

[0082] LASSO regression, short for Least Absolute Shrinkage and Selection Operator, is a penalized regression method. It adds an L1 regularization term to the loss function, shrinking some coefficients to zero, thereby performing feature selection and model simplification. The L1 regularization term penalizes the absolute value of the coefficients in the model, helping to reduce model complexity and prevent overfitting. Ten-fold cross-validation is a method for evaluating model performance. It divides the dataset into 10 parts, alternating between using 9 parts as training sets and 1 part as a test set, and performing 10 experiments. Each experiment yields a corresponding accuracy or error rate, and the average of these 10 results is taken as an estimate of the algorithm's accuracy. This method can reduce the risk of overfitting and improve the model's generalization ability.

[0083] In the solution of this embodiment, the data set is interpolated by the Lost Forest algorithm, and variable screening is completed by combining the recursive feature elimination method based on random forest and the LASSO regression method with ten-fold cross validation. The advantages of the two algorithms can be combined to ultimately improve the performance of the diagnostic model.

[0084] In an exemplary embodiment, the LASSO regression method with ten-fold cross validation and the recursive feature elimination method based on random forest are used to screen the diagnostic features in the training set, including:

[0085] The diagnostic features in the training set are screened using the LASSO regression method with ten-fold cross validation to obtain a first variable set;

[0086] Using a recursive feature elimination method based on random forest to screen the diagnostic features in the training set to obtain a second variable set;

[0087] The intersection of the first variable set and the second variable set is taken to obtain a result of variable screening.

[0088] In the scheme of this embodiment, when performing variable screening, the first variable set and the second variable set can be obtained based on the LASSO regression method of ten-fold cross validation and the recursive feature elimination method based on random forest, that is, the two algorithms are used to perform variable screening once, and then the results obtained by the two screenings are comprehensively summarized to obtain the final variable screening result. The method for summarizing the two algorithms can be to take the intersection, and the process of taking the intersection is equivalent to further variable screening, that is, the final variable screening result is the data screened by the two algorithms at the same time. This method can improve the stability of variable screening.

[0089] In an exemplary embodiment, the diagnostic calculator inputs the received feature values ​​into the logistic regression model to diagnose sudden cardiac death, including:

[0090] Obtaining diagnostic characteristics of the body to be tested;

[0091] inputting the diagnostic characteristics of the corpse to be tested into the logistic regression model;

[0092] Calculating the diagnostic probability corresponding to the diagnostic feature through a Norman plot in the logistic regression model, wherein the diagnostic probability reflects the probability that the corpse to be tested is diagnosed as suffering from sudden cardiac death;

[0093] If the diagnosis probability corresponding to the diagnostic feature is higher than the pre-calculated critical probability value, it is determined that the corpse to be tested is suffering from sudden cardiac death.

[0094] Figure 2 Schematic diagram of a Norman diagram provided by an embodiment of the present invention.

[0095] like Figure 2 As shown, the Norman diagram is a tool derived from the logistic regression model that uses images to calculate probabilities. It is a two-dimensional image. That is to say, the Norman diagram in this embodiment can intuitively represent the probability of the body being diagnosed with sudden cardiac death.

[0096] In an exemplary embodiment, the critical probability value is calculated as follows:

[0097] Calculating the Youden index of the training set;

[0098] The critical probability value is determined based on the maximum value of the Youden Index.

[0099] In implementation, the Youden index, also known as the accuracy index, is a method for evaluating the authenticity of a screening test. In practical applications, the Youden index is the sum of sensitivity and specificity minus one, which indicates the total ability of the screening method to detect true patients and non-patients. The larger the Youden index, the better the effect of the screening test and the greater its authenticity.

[0100] Sensitivity, also known as sensitivity, refers to the proportion of people who are actually sick that can be correctly identified as patients in the screening method; specificity refers to the proportion of people who are actually healthy that can be correctly identified as non-patients in the screening method.

[0101] In this embodiment, the critical probability value can be determined based on the calculated maximum value of the Youden index. The determined critical probability value can be 0.240, which is 24%. That is, the predicted probability higher than 24% through the Norman diagram is a high probability, and the predicted probability lower than 24% is a low probability.

[0102] The following describes a system for constructing a diagnostic model for sudden cardiac death provided by the present invention. The system for constructing a diagnostic model for sudden cardiac death described below and the method for constructing a diagnostic model for sudden cardiac death described above can refer to each other.

[0103] Figure 3 It is a structural diagram of a system for constructing a diagnostic model for sudden cardiac death provided by an embodiment of the present invention.

[0104] like Figure 3 As shown, the system for constructing a diagnostic model for sudden cardiac death provided in this embodiment includes:

[0105] A data acquisition module 301 is configured to collect data samples and construct a training set based on the data samples, wherein the training set includes diagnostic features, and the diagnostic features indicate that the data samples in the training set are sudden cardiac death;

[0106] A data processing module 302 is used to perform data interpolation and variable screening on the diagnostic features in the training set;

[0107] A model building module 303 is configured to build a logistic regression model based on the training set, wherein the independent variable of the logistic regression model is the diagnostic feature, and the dependent variable of the logistic regression model is the diagnostic result of the sample corresponding to the diagnostic feature;

[0108] The model diagnosis module 304 is configured to construct a diagnosis calculator based on the logistic regression model, and the diagnosis calculator inputs the received feature values ​​into the logistic regression model to perform a diagnosis of sudden cardiac death.

[0109] In an exemplary embodiment, the data processing module is further configured to: perform data interpolation on the diagnostic features in the training set using a Lost Forest interpolation method;

[0110] The LASSO regression method with ten-fold cross validation and the recursive feature elimination method based on random forest were used to screen the diagnostic features in the training set.

[0111] In an exemplary embodiment, the data processing module is further configured to: use a ten-fold cross-validation LASSO regression method to perform variable screening on the diagnostic features in the training set to obtain a first variable set;

[0112] Using a recursive feature elimination method based on random forest to screen the diagnostic features in the training set to obtain a second variable set;

[0113] The intersection of the first variable set and the second variable set is taken to obtain a result of variable screening.

[0114] In an exemplary embodiment, the model diagnosis module is further configured to: obtain diagnostic features of the body to be tested through the input module;

[0115] inputting the diagnostic characteristics of the corpse to be tested into the logistic regression model;

[0116] Calculating the diagnostic probability corresponding to the diagnostic feature using a Norman plot derived from the logistic regression model, wherein the diagnostic probability reflects the probability that the corpse to be tested is diagnosed with sudden cardiac death;

[0117] If the diagnosis probability corresponding to the diagnostic feature is higher than the pre-calculated critical probability value, it is determined that the corpse to be tested is suffering from sudden cardiac death.

[0118] In an exemplary embodiment, the model diagnosis module is further configured to: calculate the Youden index of the training set;

[0119] The critical probability value is determined based on the maximum value of the Youden Index.

[0120] The specific implementation method of the system for constructing a diagnostic model for sudden cardiac death provided in this embodiment can be implemented with reference to the above embodiments and will not be described in detail here.

[0121] The solution of this application also provides a method for evaluating the diagnostic performance of the diagnostic model, as shown below:

[0122] The training set can be constructed from 2,284 case samples collected by the Forensic Identification Center of Sun Yat-sen University between 2017 and 2023. The data are evaluated using the receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) and related statistical parameters (accuracy, sensitivity, and specificity) used in the external test set. The external test set consists of cases collected in recent years by forensic identification centers from five universities in four provinces, including Southern Medical University (741 cases), China Medical University (567 cases), Jinan University (216 cases), Xi'an Jiaotong University (181 cases), and Guizhou Medical University (178 cases).

[0123] Figure 4 A schematic diagram of performance test results provided by an embodiment of the present invention.

[0124] like Figure 4 As shown in the figure, the AUC of the Norman diagram diagnostic model in the Forensic Identification Center of Sun Yat-sen University (training set) was 0.894 (95% confidence interval [CI]: 0.879-0.908), indicating that it has high diagnostic performance in the training set. At the same time, the AUC of the Norman diagram in five external test sets including Southern Medical University, China Medical University, Jinan University, Xi'an Jiaotong University, and Guizhou Medical University were 0.871 (95% CI: 0.839-0.903), 0.930 (95% CI: 0.906-0.953), 0.904 (95% CI: 0.865-0.943), 0.878 (95% CI: 0.827-0.929), and 0.834 (95% CI: 0.757-0.912), respectively, indicating that the model has the characteristics of high robustness.

[0125] As shown in Table 1, when the Norman plot prediction probability cutoff value was 0.240, the model's accuracy, sensitivity, and specificity for sudden cardiac death were 0.820, 0.876, and 0.797 in the Sun Yat-sen University training set, indicating good diagnostic performance. Furthermore, the Norman plot achieved accuracy of 0.703, 0.824, 0.787, 0.729, and 0.798, respectively, in five external test sets from Southern Medical University, China Medical University, Jinan University, Xi'an Jiaotong University, and Guizhou Medical University. Sensitivities were 0.873, 0.947, 0.926, 0.936, and 0.738, respectively, and specificities were 0.655, 0.786, 0.741, 0.657, and 0.816, respectively, validating the model's generalizability.

[0126] Table 1 Statistical parameters of the Norman plot in the training set and the other five external test sets

[0127]

[0128] Four senior and four junior forensic pathologists were also invited to participate in human-machine performance comparisons and human-machine fusion experiments. As shown in Table 2, the Norman chart's performance in diagnosing sudden cardiac death, considering only cardiac lesions, was comparable to that of senior forensic pathologists with systemic lesions and significantly superior to that of junior pathologists. In the human-machine fusion experiment, as shown in Table 3, the Norman chart significantly improved the AUC for one senior pathologist and all junior pathologists, the sensitivity for two senior pathologists and three junior pathologists, and the specificity for two junior pathologists. Overall, the pathologists' AUC and sensitivity significantly improved before and after human-machine fusion (P = 0.004 and P = 0.01, respectively).

[0129] Table 2 Comparison of diagnostic performance between Norman chart and senior pathologists

[0130]

[0131]

[0132] Table 3 Comparison of performance before and after human-machine fusion

[0133]

[0134] Among them, P in Table 2 # The p-values ​​were obtained by Delong's test, and the p-values ​​were given based on McNemar's test.

[0135] The data in the table marked with * indicate that the calculated P value is less than 0.05, which means that there is a statistically significant difference.

[0136] P in Table 3 # The value is obtained by Delong's test on the ROC curve before and after human-computer integration, P § The values ​​are given by McNemar test for sensitivity and specificity before and after human-machine fusion.

[0137] In Tables 2 and 3, AUC stands for area under receiver-operating characteristic curve; Nom stands for nomogram; SP stands for senior pathologist; and JP stands for junior pathologist.

[0138] Figure 5 An example of a physical structure diagram of an electronic device is shown below. Figure 5 As shown, the electronic device may include: a processor 510, a communication interface 520, a memory 530, and a communication bus 540, wherein the processor 510, the communication interface 520, and the memory 530 communicate with each other via the communication bus 540. The processor 510 may call the logic instructions in the memory 530 to execute a method for constructing a diagnostic model for sudden cardiac death, the method comprising:

[0139] Collecting data samples and constructing a training set based on the data samples, wherein the training set includes diagnostic features, and the diagnostic features indicate that the data samples in the training set are sudden cardiac death;

[0140] performing data interpolation and variable screening on the diagnostic features in the training set;

[0141] Constructing a logistic regression model based on the training set, wherein the independent variable of the logistic regression model is the diagnostic feature, and the dependent variable of the logistic regression model is the diagnostic result of the sample corresponding to the diagnostic feature;

[0142] A diagnostic calculator is constructed based on the logistic regression model, and the diagnostic calculator inputs the received feature values ​​into the logistic regression model to diagnose sudden cardiac death.

[0143] In addition, the logic instructions in the above-mentioned memory 530 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0144] On the other hand, the present invention further provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the method for constructing a diagnostic model for sudden cardiac death provided by the above methods, which includes:

[0145] Collecting data samples and constructing a training set based on the data samples, wherein the training set includes diagnostic features, and the diagnostic features indicate that the data samples in the training set are sudden cardiac death;

[0146] performing data interpolation and variable screening on the diagnostic features in the training set;

[0147] Constructing a logistic regression model based on the training set, wherein the independent variable of the logistic regression model is the diagnostic feature, and the dependent variable of the logistic regression model is the diagnostic result of the sample corresponding to the diagnostic feature;

[0148] A diagnostic calculator is constructed based on the logistic regression model, and the diagnostic calculator inputs the received feature values ​​into the logistic regression model to diagnose sudden cardiac death.

[0149] In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for constructing a diagnostic model for sudden cardiac death provided by the above methods, the method comprising:

[0150] Collecting data samples and constructing a training set based on the data samples, wherein the training set includes diagnostic features, and the diagnostic features indicate that the data samples in the training set are sudden cardiac death;

[0151] performing data interpolation and variable screening on the diagnostic features in the training set;

[0152] Constructing a logistic regression model based on the training set, wherein the independent variable of the logistic regression model is the diagnostic feature, and the dependent variable of the logistic regression model is the diagnostic result of the sample corresponding to the diagnostic feature;

[0153] A diagnostic calculator is constructed based on the logistic regression model, and the diagnostic calculator inputs the received feature values ​​into the logistic regression model to diagnose sudden cardiac death.

[0154] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units. That is, they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0155] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus the necessary general hardware platform, or of course, by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the existing technology can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of each embodiment or certain parts of the embodiment.

[0156] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for constructing a diagnostic model for sudden cardiac death, characterized in that: include: Collecting data samples and constructing a training set based on the data samples, wherein the training set includes diagnostic features, and the diagnostic features indicate that the data samples in the training set are sudden cardiac death; performing data interpolation and variable screening on the diagnostic features in the training set; Constructing a logistic regression model based on the training set, wherein the independent variable of the logistic regression model is the diagnostic feature, and the dependent variable of the logistic regression model is the diagnostic result of the sample corresponding to the diagnostic feature; Building a diagnostic calculator based on the logistic regression model, wherein the diagnostic calculator inputs the received feature values ​​into the logistic regression model to diagnose sudden cardiac death; The diagnostic features include anatomical data and pathological analysis results of the data sample; The anatomical data includes the stenosis degree of the left anterior descending artery, the stenosis degree of the left circumflex artery, the stenosis degree of the right coronary artery, the left ventricular wall thickness and the pulmonary valve circumference of the data sample; The pathological analysis results include myocardial infarction, cardiomyopathy, valvulitis, pericarditis, myocarditis, and conduction system lesions; The diagnostic features also include height, abdominal wall subcutaneous fat thickness, gender and age of the data sample; The data interpolation and variable screening of the diagnostic features in the training set include: Using the Lost Forest interpolation method to interpolate data for the diagnostic features in the training set; The diagnostic features in the training set were screened using the LASSO regression method with ten-fold cross validation and the recursive feature elimination method based on random forests; The LASSO regression method with ten-fold cross validation and the recursive feature elimination method based on random forest are used to screen the diagnostic features in the training set, including: The diagnostic features in the training set are screened using the LASSO regression method with ten-fold cross validation to obtain a first variable set; Using a recursive feature elimination method based on random forest to screen the diagnostic features in the training set to obtain a second variable set; Taking the intersection of the first variable set and the second variable set to obtain a variable screening result; The diagnostic calculator inputs the received characteristic values ​​into the logistic regression model to diagnose sudden cardiac death, including: Obtain the diagnostic characteristics of the body to be tested through the input box; inputting the diagnostic characteristics of the corpse to be tested into the logistic regression model; Calculating the diagnostic probability corresponding to the diagnostic feature using a Norman plot derived from the logistic regression model, wherein the diagnostic probability reflects the probability that the corpse to be tested is diagnosed with sudden cardiac death; If the diagnosis probability corresponding to the diagnostic feature is higher than the pre-calculated critical probability value, it is determined that the corpse to be tested is suffering from sudden cardiac death.

2. The method for constructing a diagnostic model for sudden cardiac death according to claim 1, wherein: The critical probability value is calculated as follows: Calculating the Youden index of the training set; The critical probability value is determined based on the maximum value of the Youden Index.

3. A system for constructing a diagnostic model for sudden cardiac death, applied to the method for constructing a diagnostic model for sudden cardiac death according to claim 1 or 2, characterized in that: include: A data acquisition module, configured to collect data samples and construct a training set based on the data samples, wherein the training set includes diagnostic features, and the diagnostic features indicate that the data samples in the training set are sudden cardiac death; A data processing module, configured to perform data interpolation and variable screening on the diagnostic features in the training set; A model building module, configured to build a logistic regression model based on the training set, wherein the independent variable of the logistic regression model is the diagnostic feature, and the dependent variable of the logistic regression model is the diagnostic result of the sample corresponding to the diagnostic feature; The model diagnosis module is used to construct a diagnosis calculator based on the logistic regression model, and the diagnosis calculator inputs the received characteristic values ​​into the logistic regression model to diagnose sudden cardiac death.

4. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method for constructing a diagnostic model for sudden cardiac death as claimed in claim 1 or 2 is implemented.

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