Stroke-related pneumonia risk prediction method, system, equipment, medium and product for senile hemorrhagic stroke patient

Abstract

The invention discloses a stroke-related pneumonia risk prediction method, system and device for an elderly hemorrhagic stroke patient, a medium and a product, and relates to the field of disease prediction.The method comprises the steps that sample data of the elderly hemorrhagic stroke patient is obtained; the sample data comprises a plurality of variable factors; determining influence factors of occurrence of stroke-related pneumonia of the senile hemorrhagic stroke patient based on the sample data; training and verifying a plurality of machine learning models through the influence factors, and determining an optimal machine learning model; and predicting the risk of the stroke-related pneumonia of the senile hemorrhagic stroke patient through the optimal machine learning model. According to the application, the risk of the stroke-related pneumonia of the senile hemorrhagic stroke patient can be predicted, an effective tool is provided for early prediction or identification of the stroke-related pneumonia, and evidence supplement is provided for formulation and implementation of early intervention.

Description

Technical Field

[0001] The present application relates to the field of disease prediction, and in particular to a method, system, device, medium and product for predicting the risk of stroke-related pneumonia in elderly patients with hemorrhagic stroke. Background Art

[0002] Stroke-associated pneumonia (SAP) is one of the important complications of stroke, with an incidence of 6.5% to 31.3%. It not only significantly prolongs the patient's hospitalization time and increases medical expenses, but is also an important risk factor for increased disability and mortality. Since this symptom can only be further confirmed about 72 hours after onset, it leads to a serious lag in the diagnosis and treatment of the disease, poor prognosis, and even increased risk of death. At present, domestic and foreign scholars have proposed a number of SAP risk assessment tools, but all of them rely on the interpretation of clinicians and have the characteristics of subjective bias and large heterogeneity. At the same time, there is currently no research on the SAP risk prediction model for elderly hemorrhagic stroke patients. In view of this, it is necessary to construct a risk prediction model for SAP patients in elderly hemorrhagic stroke patients to provide guidance for early identification of high-risk factors and targeted prevention and treatment. Summary of the invention

[0003] Based on this, the purpose of this application is to provide a method, system, equipment, medium and product for predicting the risk of stroke-related pneumonia in elderly patients with hemorrhagic stroke.

[0004] To achieve the above objectives, this application provides the following solutions:

[0005] In a first aspect, the present application provides a method for predicting the risk of stroke-related pneumonia in elderly patients with hemorrhagic stroke, comprising:

[0006] Obtaining sample data of elderly patients with hemorrhagic stroke; the sample data includes multiple variable factors;

[0007] Determine the influencing factors of stroke-related pneumonia in elderly patients with hemorrhagic stroke based on the sample data;

[0008] Training and verifying multiple machine learning models through the influencing factors to determine the optimal machine learning model;

[0009] The optimal machine learning model is used to predict the risk of stroke-related pneumonia in elderly patients with hemorrhagic stroke.

[0010] In a second aspect, the present application provides a risk prediction system for stroke-related pneumonia in elderly hemorrhagic stroke patients, comprising:

[0011] A sample data acquisition module is used to acquire sample data of elderly hemorrhagic stroke patients; the sample data includes multiple variable factors;

[0012] An influencing factor determination module, used to determine the influencing factors of stroke-related pneumonia in elderly hemorrhagic stroke patients based on the sample data;

[0013] An optimal machine learning model determination module, used to train and verify multiple machine learning models through the influencing factors to determine the optimal machine learning model;

[0014] A risk prediction module is used to predict the risk of stroke-related pneumonia in elderly hemorrhagic stroke patients through the optimal machine learning model.

[0015] In a third aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned method for predicting the risk of stroke-related pneumonia in elderly hemorrhagic stroke patients.

[0016] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for predicting the risk of stroke-related pneumonia in elderly hemorrhagic stroke patients.

[0017] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above-mentioned method for predicting the risk of stroke-related pneumonia in elderly hemorrhagic stroke patients.

[0018] According to the specific embodiments provided in this application, this application has the following technical effects:

[0019] The present application provides a method, system, device, medium and product for predicting the risk of stroke-related pneumonia in elderly patients with hemorrhagic stroke. The influencing factors of the occurrence of stroke-related pneumonia in elderly patients with hemorrhagic stroke are determined through sample data of elderly patients with hemorrhagic stroke, and multiple machine learning models are trained and verified, so as to determine the machine learning model with better performance and stability as the optimal machine learning model, and predict the risk of stroke-related pneumonia in elderly patients with hemorrhagic stroke through the optimal machine learning model, so as to provide an effective tool for early prediction or identification of SAP, and provide evidence supplement for the formulation and implementation of early intervention. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0021] Figure 1 A flow chart of a method for predicting the risk of stroke-related pneumonia in elderly hemorrhagic stroke patients provided in one embodiment of the present application;

[0022] Figure 2 Schematic diagram of ROC curve for external validation of logistic regression model;

[0023] Figure 3 Schematic diagram of the calibration curve for internal validation of the logistic regression model;

[0024] Figure 4 Schematic diagram of the calibration curve for external validation of the logistic regression model;

[0025] Figure 5 To provide a nomogram for stroke-related pneumonia in elderly patients with hemorrhagic stroke;

[0026] Figure 6 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0028] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0029] In an exemplary embodiment, Figure 1 As shown, a method for predicting the risk of stroke-related pneumonia in elderly patients with hemorrhagic stroke is provided. The method is executed by a computer device, and can be specifically executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In an embodiment of the present application, the method is applied to a server as an example for explanation, including the following steps S1 to S4.

[0030] in:

[0031] S1: Obtain sample data of elderly patients with hemorrhagic stroke; the sample data includes multiple variable factors.

[0032] S2: Determine the influencing factors of stroke-related pneumonia in elderly hemorrhagic stroke patients based on the sample data, including: screening the variable factors according to the statistical P value of each variable factor; performing Logistic regression analysis on the screened variable factors to determine the influencing factors of stroke-related pneumonia in elderly hemorrhagic stroke patients.

[0033] In a specific embodiment, sample data of 658 elderly patients with hemorrhagic stroke were obtained and divided into a training set and an internal validation set according to the 7:3 allocation principle. At the same time, sample data of 130 elderly patients with hemorrhagic stroke were obtained as an external validation set. The training set, internal validation set, and external validation set all include multiple variable factors: gender, age, treatment cost, type of hemorrhagic stroke, length of hospital stay, education level, ethnicity, smoking history, drinking history, history of hypertension, history of diabetes, GCS score, mRS score, Braden score, SBP, DBP, BMI, WBC, Neut, Lym, Mono, RBC, PLT, HCT, APTT, Glu, nasogastric feeding therapy, and use of acid suppressants. There was no statistically significant difference in each variable factor (P < 0.05), as shown in Table 1.

[0034] Table 1

[0035]

[0036]

[0037] Individual variable factor analysis showed that there were significant differences in age, treatment cost, hospitalization days, smoking history, GCS score, mRS score, Braden score, SBP, DBP, WBC, Neut, Lym, Glu, nasogastric feeding and use of acid suppressants between SAP patients and non-SAP patients (P < 0.05). However, there were no significant differences in gender, hemorrhagic stroke type, education level, ethnicity, drinking history, hypertension history, diabetes history, BMI, Mono, RBC, PLT, HCT and APTT (P > 0.05), as shown in Table 2.

[0038] Table 2

[0039]

[0040]

[0041]

[0042] Logistic regression analysis was performed on the variables with clinical significance (P < 0.05) in the univariate factor analysis, and treatment costs and hospitalization days were eliminated. According to the results in Table 3, age, smoking history, GCS score, Braden score and nasogastric feeding therapy were determined to be the influencing factors for the occurrence of SAP in elderly patients with hemorrhagic stroke.

[0043] Table 3

[0044]

[0045]

[0046] S3: Train and verify multiple machine learning models through the influencing factors to determine the optimal machine learning model. Specifically, it includes: dividing the sample data into a training set, an internal validation set, and an external validation set; training and internally validating multiple machine learning models through the influencing factors in the training set and the internal validation set to determine the optimal machine learning model; and externally validating the optimal machine learning model through the external validation set.

[0047] After univariate and multivariate regression analysis, four machine learning models were constructed: logistic regression model (LR), XGBoost model, naive Bayes model and support vector machine model (SVM).

[0048] The AUC, sensitivity, specificity, accuracy, precision, recall and F1 value of each model in the training set and internal validation set were calculated, and the AUC values ​​of each model were compared pairwise. The results showed that the LR model performed relatively stably and well in all aspects, especially in the internal validation set. Finally, the LR model was selected as the optimal machine learning model for external validation to test the generalization ability and reliability of the model in other data sets. The results showed that the AUC and 95% CI of the LR model in the external validation set were 0.882 (0.853, 0.912). Figure 2 The calibration curve based on the HL test showed that the model had a good fit in the internal validation and external validation, with P values ​​of 0.381 and 0.067, respectively. Figure 3-Figure 4 This indicates that the model is well calibrated.

[0049] S4: The optimal machine learning model is used to predict the risk of stroke-related pneumonia in elderly patients with hemorrhagic stroke.

[0050] Finally, a nomogram for stroke-related pneumonia in elderly patients with hemorrhagic stroke was drawn, such as Figure 5 .

[0051] It should be noted that the final result of the stroke-related pneumonia risk prediction method for elderly hemorrhagic stroke patients provided in this application is not used as the doctor's final diagnosis of the patient, but is only a reference for the patient and the doctor, and does not hinder the doctor's actual diagnosis.

[0052] Based on the same inventive concept, the embodiment of the present application also provides a system for implementing the above-mentioned method for predicting the risk of stroke-related pneumonia in elderly hemorrhagic stroke patients. The implementation scheme for solving the problem provided by the system is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more embodiments of the system for predicting the risk of stroke-related pneumonia in elderly hemorrhagic stroke patients provided below can be referred to the above limitations on the method for predicting the risk of stroke-related pneumonia in elderly hemorrhagic stroke patients, and will not be repeated here.

[0053] In an exemplary embodiment, a system for predicting the risk of stroke-related pneumonia in elderly patients with hemorrhagic stroke is provided, comprising:

[0054] The sample data acquisition module is used to acquire sample data of elderly hemorrhagic stroke patients; the sample data includes multiple variable factors.

[0055] An influencing factor determination module is used to determine the influencing factors for the occurrence of stroke-related pneumonia in elderly hemorrhagic stroke patients based on the sample data.

[0056] The optimal machine learning model determination module is used to train and verify multiple machine learning models through the influencing factors to determine the optimal machine learning model.

[0057] A risk prediction module is used to predict the risk of stroke-related pneumonia in elderly hemorrhagic stroke patients through the optimal machine learning model.

[0058] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program. The computer device may be a server or a terminal, and its internal structure diagram may be as shown in FIG. Figure 6As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data to be processed. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for predicting the risk of stroke-related pneumonia in elderly patients with hemorrhagic stroke is implemented.

[0059] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0060] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0061] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0062] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0063] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0064] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0065] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0066] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for predicting the risk of stroke-related pneumonia in elderly patients with hemorrhagic stroke, characterized in that: include: Obtaining sample data of elderly patients with hemorrhagic stroke; the sample data includes multiple variable factors; Determine the influencing factors of stroke-related pneumonia in elderly patients with hemorrhagic stroke based on the sample data; Training and verifying multiple machine learning models through the influencing factors to determine the optimal machine learning model; The optimal machine learning model is used to predict the risk of stroke-related pneumonia in elderly patients with hemorrhagic stroke.

2. The method for predicting the risk of stroke-related pneumonia in elderly patients with hemorrhagic stroke according to claim 1, characterized in that: Based on the sample data, the influencing factors of stroke-related pneumonia in elderly patients with hemorrhagic stroke are determined, including: The variable factors were screened according to the statistical P value of each variable factor; Logistic regression analysis was performed on the screened variables to determine the influencing factors for stroke-related pneumonia in elderly patients with hemorrhagic stroke.

3. The method for predicting the risk of stroke-related pneumonia in elderly patients with hemorrhagic stroke according to claim 1, characterized in that: The influencing factors of stroke-related pneumonia in elderly patients with hemorrhagic stroke include: age, smoking history, GCS score, Braden score and nasogastric feeding treatment.

4. The method for predicting the risk of stroke-related pneumonia in elderly patients with hemorrhagic stroke according to claim 1, characterized in that: The multiple machine learning models are trained and verified by the influencing factors to determine the optimal machine learning model, specifically including: Dividing the sample data into a training set, an internal validation set and an external validation set; Training and internally validating multiple machine learning models respectively through the influencing factors in the training set and the internal validation set to determine the optimal machine learning model; The optimal machine learning model is externally validated using the external validation set.

5. The method for predicting the risk of stroke-related pneumonia in elderly patients with hemorrhagic stroke according to claim 1, characterized in that: The machine learning models include logistic regression model, XGBoost model, naive Bayes model and support vector machine model.

6. The method for predicting the risk of stroke-related pneumonia in elderly patients with hemorrhagic stroke according to claim 1, characterized in that: The optimal machine learning model is a logistic regression model.

7. A risk prediction system for stroke-related pneumonia in elderly patients with hemorrhagic stroke, characterized in that: include: A sample data acquisition module is used to acquire sample data of elderly hemorrhagic stroke patients; the sample data includes multiple variable factors; An influencing factor determination module, used to determine the influencing factors of stroke-related pneumonia in elderly hemorrhagic stroke patients based on the sample data; An optimal machine learning model determination module, used to train and verify multiple machine learning models through the influencing factors to determine the optimal machine learning model; A risk prediction module is used to predict the risk of stroke-related pneumonia in elderly hemorrhagic stroke patients through the optimal machine learning model.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for predicting the risk of stroke-related pneumonia in elderly hemorrhagic stroke patients as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for predicting the risk of stroke-related pneumonia in elderly hemorrhagic stroke patients described in any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for predicting the risk of stroke-related pneumonia in elderly hemorrhagic stroke patients described in any one of claims 1 to 6 is implemented.

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