Clinical-data analysis method and apparatus, and computer device and storage medium

Abstract

A clinical-data analysis method and apparatus, and a computer device and a storage medium. The clinical-data analysis method comprises: acquiring clinical data that comprises biochemical markers and / or an electrocardiogram of a target user; pre-processing the clinical data to obtain target data; performing data analysis on the target data by means of a trained deep learning model, so as to obtain an analysis vector, wherein the deep learning model is a model trained on the basis of an activation model, a pooling model and a fully connected model, which are connected in sequence; and finally, performing vector analysis on the basis of the analysis vector, so as to obtain an analysis result. By means of a deep learning model trained on the basis of an activation model, a pooling model and a fully connected model, efficient and accurate data analysis of clinical data is realized.

Description

Clinical data analysis method, device, computer equipment and storage medium Technical Field

[0001] The present invention relates to the technical field of clinical data analysis, and in particular to a clinical data analysis method, apparatus, computer equipment and storage medium. Background Art

[0002] With the rapid development of computer microelectronics and signal processing technologies, ECG signal acquisition equipment is also moving towards intelligence and portability. Furthermore, in clinical practice, according to diagnostic and treatment guidelines from the American College of Cardiology, the European Society of Cardiology, and the Chinese Medical Association, the diagnostic criteria for acute myocardial infarction (AMI) must include at least two of the following three criteria: first, a clinical history of ischemic chest pain; second, dynamic changes in the electrocardiogram (ECG); and third, dynamic changes in the concentration of myocardial markers in the bloodstream indicating myocardial necrosis. These guidelines emphasize the need to reduce the effectiveness of using only the electrocardiogram (ECG) to assist with physical examinations.

[0003] The electrocardiogram (ECG) is a routine adjunctive diagnostic tool for acute myocardial infarction (AMI), offering advantages such as low cost, high safety, and simultaneous reporting. However, it cannot be used alone as a comprehensive marker for ischemic heart disease. Typical ECG findings in patients with MI include ST-segment elevation with arched or T-wave inversion (TWI), as well as pathological Q waves (QW) in patients with prior MI. Conditions in which these ECG changes are present are called ST-segment elevation myocardial infarction (STEMI). However, STEMI with ST-segment elevation only accounts for approximately 20% of MI events. Over 70% of MI patients lack these conventional ECG findings, typically presenting with ST-segment depression, T-wave changes, or no distinct QRS complex changes. These unremarkable conditions are termed NSTEMI (non-ST-segment elevation myocardial infarction). However, there is currently no simple method for detecting NSTEMI, and conventional ECG results are negative in most NSTEMI patients, leading to missed diagnosis.

[0004] With the rise and development of artificial intelligence (AI), ECG data analysis and processing using AI can support disease-assisted diagnosis, health management, remote consultation, and other functions. However, AI currently performs poorly on ECG data, resulting in poor diagnostic results for doctors. Technical issues

[0005] Based on this, it is necessary to propose a clinical data analysis method, device, computer equipment and storage medium to address the technical problem that the existing technology is not accurate enough in analyzing clinical data. Technical Solutions

[0006] In a first aspect, a clinical data analysis method is provided, comprising:

[0007] Acquiring clinical data, wherein the clinical data includes biochemical indicators and / or electrocardiogram data of the target user;

[0008] Preprocessing the clinical data to obtain target data;

[0009] Performing data analysis on the target data using a trained deep learning model to obtain an analysis vector, wherein the deep learning model is a model trained based on an activation model, a pooling model, and a fully connected model connected in sequence;

[0010] Vector analysis is performed according to the analysis vector to obtain an analysis result.

[0011] In a second aspect, a clinical data analysis device is provided, comprising:

[0012] an acquisition module, configured to acquire clinical data, wherein the clinical data includes biochemical indicators and / or electrocardiogram data of the target user;

[0013] A preprocessing module, used for preprocessing the clinical data to obtain target data;

[0014] A vector module is used to perform data analysis on the target data using a trained deep learning model to obtain an analysis vector, wherein the deep learning model is a model trained based on an activation model, a pooling model, and a fully connected model connected in sequence;

[0015] The analysis module is used to perform vector analysis based on the analysis vector to obtain an analysis result.

[0016] In a third aspect, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned clinical data analysis method when executing the computer program.

[0017] In a fourth aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned clinical data analysis method are implemented. Beneficial effects

[0018] The clinical data analysis method proposed in the present invention obtains clinical data, wherein the clinical data includes biochemical indicators and / or electrocardiogram data of the target user, and then preprocesses the clinical data to obtain target data, and performs data analysis on the target data through a trained deep learning model to obtain an analysis vector, wherein the deep learning model is a model trained based on an activation model, a pooling model and a fully connected model connected in sequence. Finally, vector analysis is performed based on the analysis vector to obtain an analysis result. The deep learning model trained based on the activation model, the pooling model and the fully connected model can realize efficient and accurate data analysis of clinical data, thereby obtaining an analysis result with improved analysis accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only 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.

[0020] in:

[0021] FIG1 is a diagram illustrating an application environment of a clinical data analysis method according to an embodiment;

[0022] FIG2 is a flow chart of a clinical data analysis method according to one embodiment;

[0023] FIG3 is a block diagram of a clinical data analysis device according to an embodiment;

[0024] FIG4 is a block diagram of a computer device according to an embodiment;

[0025] FIG5 is a structural block diagram of a computer device in another embodiment. Modes for Carrying Out the Invention

[0026] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0027] The clinical data analysis method provided by the embodiment of the present invention can be applied in an application environment as shown in Figure 1, wherein the client 110 communicates with the server 120 via a network. The server 120 can receive clinical data through the client 110, wherein the clinical data includes biochemical indicators and / or electrocardiogram data of the target user, and the server 120 preprocesses the clinical data to obtain target data, and performs data analysis on the target data through a trained deep learning model to obtain an analysis vector, wherein the deep learning model is a model trained based on an activation model, a pooling model, and a fully connected model connected in sequence. Finally, the server 120 performs vector analysis based on the analysis vector to obtain an analysis result. The deep learning model trained based on the activation model, the pooling model, and the fully connected model can achieve efficient and accurate data analysis of clinical data, thereby obtaining an analysis result with improved analysis accuracy. Among them, the client 110 can be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, and portable wearable devices. The server 120 can be implemented using an independent server or a server cluster consisting of multiple servers. The present invention is described in detail below through specific embodiments.

[0028] Please refer to FIG2 , which is a flow chart of a clinical data analysis method provided by an embodiment of the present invention, including the following steps:

[0029] Step S101: Acquire clinical data, wherein the clinical data includes biochemical indicators and / or electrocardiogram data of the target user;

[0030] The biochemical indexes may be serum myocardial enzymes, troponin T, brain natriuretic peptide (BNP), etc., and the electrocardiogram data may be electrocardiogram signals. As an example, the clinical data may be a physical examination report provided by the target user.

[0031] Step S102: pre-processing the clinical data to obtain target data;

[0032] For example, standardizing the names of biochemical indicators to obtain standardized biochemical indicators can help improve the accuracy of subsequent data analysis. Denoising electrocardiogram data can also be performed to obtain de-noised data. The standardized biochemical indicators and de-noised data are then used as target data.

[0033] Furthermore, in one embodiment, the step of preprocessing the clinical data to obtain target data includes:

[0034] Step S1021: using discrete wavelet packet transform and sliding average method to perform noise reduction processing on the electrocardiogram data to obtain noise-reduced data;

[0035] Step S1022: Using the Pan-Tompkins algorithm and wavelet packet transform, the characteristic wave groups of the denoised data are located to obtain the target data.

[0036] In this embodiment, discrete wavelet packet transform and sliding average method are used to reduce the power frequency interference, baseline drift and myoelectric interference of the electrocardiogram data, and the PT algorithm and wavelet packet transform are further used to locate the characteristic wave group of the noise-reduced data obtained after noise reduction, where the characteristic wave group can be QRS wave group, P wave and T wave.

[0037] Step S103: performing data analysis on the target data using a trained deep learning model to obtain an analysis vector, wherein the deep learning model is a model trained based on an activation model, a pooling model, and a fully connected model connected in sequence;

[0038] It should be noted that the deep learning model is a model trained based on the activation model, pooling model and fully connected model connected in sequence. The activation model is used to activate the target data, the pooling model is used to enhance the features, and the fully connected model is used to connect the features.

[0039] Optionally, the deep learning model may be a medical data analysis model, wherein the medical data analysis model may be used to analyze medical data to obtain analysis text, and the medical data may be clinical data.

[0040] Optionally, the deep learning model may be a medical image generation model, wherein the medical image generation model is used to analyze clinical data and generate medical images corresponding to the clinical data.

[0041] Optionally, the deep learning model can be a medical data classification model, wherein the medical data classification model can be used to classify clinical data and generate one or more medical classification labels corresponding to the clinical data.

[0042] Furthermore, in one embodiment, the activation model includes a first convolutional layer, an activation layer, and a first pooling layer connected in sequence. The step of performing data analysis on the target data using the trained deep learning model to obtain an analysis vector includes:

[0043] Step A: inputting the target data into a first convolutional layer for convolution processing to obtain a first processing result output by the first convolutional layer;

[0044] The first convolutional layer is a convolutional layer. In one implementation, the size of the first convolutional layer is 196*128, the stride is 6, and the number of filters in the first convolutional layer is 96.

[0045] Step B: inputting the first convolution result into the activation layer for activation processing, and inputting the activation processing result output by the activation layer into the first pooling layer for pooling processing to obtain a second processing result;

[0046] The activation layer uses the ReLU function for activation processing. The first pooling layer is a type of pooling layer. In one implementation, the size of the first pooling layer is 3×3, with a stride of 2. The first pooling layer can reduce the dimension of the activation processing results while preserving a high degree of effective information.

[0047] Step C: obtaining an analysis vector according to the second processing result, the pooling model, and the fully connected model.

[0048] In this embodiment, the second processing result is processed by the pooling model and the fully connected model to obtain an analysis vector.

[0049] Furthermore, in one embodiment, the pooling model includes a first pooling module and a second pooling module connected in sequence, the first pooling module includes a second convolutional layer and a second pooling layer connected in sequence, and the step of obtaining the analysis vector according to the second processing result, the pooling model, and the fully connected model includes:

[0050] Step C1: inputting the second processing result into the second convolutional layer for convolution processing to obtain a third processing result output by the second convolutional layer;

[0051] The second convolutional layer is a convolutional layer. In one implementation, the size of the second convolutional layer is 5×5, and the number of filters in the second convolutional layer is 256.

[0052] Step C2: inputting the third processing result into the second pooling layer for pooling processing to obtain a fourth processing result output by the second pooling layer;

[0053] The second pooling layer is a pooling layer. In one implementation, the size of the second pooling layer is 3×3 and the stride is 2.

[0054] Step C3: Obtain the analysis vector according to the fourth processing result, the second pooling module, and the fully connected model.

[0055] Furthermore, in one embodiment, the second pooling module includes a third convolutional layer, a fourth convolutional layer, a fifth convolutional layer, and a third pooling layer connected in sequence, and the step of obtaining the analysis vector according to the fourth processing result, the second pooling module, and the fully connected model includes:

[0056] Step C31: inputting the fourth processing result into the third convolutional layer for convolution processing to obtain a fifth processing result output by the third convolutional layer;

[0057] Step C32: inputting the fifth processing result into the fourth convolutional layer for convolution processing to obtain a sixth processing result output by the fourth convolutional layer;

[0058] Step C33: inputting the sixth processing result into the fifth convolutional layer for convolution processing to obtain a seventh processing result output by the fifth convolutional layer;

[0059] Step C34: inputting the seventh processing result into the third pooling layer for pooling processing to obtain an eighth processing result output by the third pooling layer;

[0060] Step C35: Obtain the analysis vector according to the eighth processing result and the fully connected model.

[0061] The third, fourth, and fifth convolutional layers are all types of convolutional layers. In one implementation, the third, fourth, and fifth convolutional layers all have a channel size of 3*3 and the number of filters is 384, 384, and 256, respectively. The third pooling layer is a type of pooling layer.

[0062] Furthermore, in one embodiment, the fully connected model includes a first fully connected layer, a first dropout layer, a second fully connected layer, and a second dropout layer connected in sequence, and the step of obtaining the analysis vector according to the eighth processing result and the fully connected model includes:

[0063] Step C351: input the eighth processing result into the first fully connected layer for connection, to obtain a first connection result output by the first fully connected layer;

[0064] Step C352: inputting the first connection result into the first Dropout layer for anti-overfitting processing to obtain a target result;

[0065] Step C353: input the target result into the second fully connected layer for connection, to obtain a second connection result output by the second fully connected layer;

[0066] Step C354: input the second connection result into the second Dropout layer for anti-overfitting processing to obtain the analysis vector.

[0067] The first fully connected layer and the second fully connected layer both refer to a type of fully connected layer. For example, the number of channels of the first fully connected layer and the second fully connected layer is 4096.

[0068] The first Dropout layer and the second Dropout layer are both Dropout layers used to prevent overfitting of deep learning models.

[0069] Step S104: performing vector analysis based on the analysis vector to obtain an analysis result.

[0070] In one implementation, vector analysis can be performed on the analysis vector to generate a symptom profile corresponding to the clinical data; or, vector analysis can be performed on the analysis vector to generate a symptom analysis text corresponding to the clinical data. In another implementation, vector analysis can be performed on the analysis vector to generate a probability value, where the probability value refers to the probability value of the target user suffering from a myocardial infarction.

[0071] As an example, a camera module captures the target user's biochemical indicators and electrocardiogram data. A posture sensing module performs motion processing to reduce the impact of settings and sensor drift, minimizing data collection and transmission when the patient is in a supine position. The target user's biochemical indicators and electrocardiogram data are then transmitted to a Bluetooth module.

[0072] Using Bluetooth communication, the target user's ECG data is transferred to a pre-set database. The ECG data in the database is amplified, denoised, and filtered. Then, using physiological signal analysis tools, the amplified, denoised, and filtered ECG data is converted to a file format and imported in batches. This imported ECG data is then analyzed in the cloud using a trained deep learning model.

[0073] The clinical data analysis method proposed in this embodiment obtains clinical data, wherein the clinical data includes biochemical indicators and / or electrocardiogram data of the target user, and then preprocesses the clinical data to obtain target data, and performs data analysis on the target data through a trained deep learning model to obtain an analysis vector, wherein the deep learning model is a model trained based on an activation model, a pooling model, and a fully connected model connected in sequence. Finally, vector analysis is performed based on the analysis vector to obtain an analysis result. The deep learning model trained based on the activation model, the pooling model, and the fully connected model can realize efficient and accurate data analysis of clinical data, thereby obtaining an analysis result with improved analysis accuracy.

[0074] Referring to FIG. 3 , in one embodiment, a clinical data analysis device is provided, the device comprising: an acquisition module 10 for acquiring clinical data, wherein the clinical data comprises biochemical indicators and / or electrocardiogram data of a target user;

[0075] A preprocessing module 20 is used to preprocess the clinical data to obtain target data;

[0076] A vector module 30 is configured to perform data analysis on the target data using a trained deep learning model to obtain an analysis vector, wherein the deep learning model is a model trained based on an activation model, a pooling model, and a fully connected model connected in sequence;

[0077] The analysis module 40 is configured to perform vector analysis based on the analysis vector to obtain an analysis result.

[0078] The clinical data analysis method proposed in this embodiment obtains clinical data, wherein the clinical data includes biochemical indicators and / or electrocardiogram data of the target user, and then preprocesses the clinical data to obtain target data, and performs data analysis on the target data through a trained deep learning model to obtain an analysis vector, wherein the deep learning model is a model trained based on an activation model, a pooling model, and a fully connected model connected in sequence. Finally, vector analysis is performed based on the analysis vector to obtain an analysis result. The deep learning model trained based on the activation model, the pooling model, and the fully connected model can realize efficient and accurate data analysis of clinical data, thereby obtaining an analysis result with improved analysis accuracy.

[0079] Furthermore, in one embodiment, the vector module 30 is further configured to:

[0080] Inputting the target data into a first convolutional layer for convolution processing to obtain a first processing result output by the first convolutional layer;

[0081] Inputting the first convolution result into the activation layer for activation processing, and inputting the activation processing result output by the activation layer into the first pooling layer for pooling processing to obtain a second processing result;

[0082] An analysis vector is obtained according to the second processing result, the pooling model, and the fully connected model.

[0083] Furthermore, in one embodiment, the vector module 30 is further configured to: input the second processing result into the second convolutional layer for convolution processing to obtain a third processing result output by the second convolutional layer;

[0084] Inputting the third processing result into the second pooling layer for pooling processing to obtain a fourth processing result output by the second pooling layer;

[0085] The analysis vector is obtained according to the fourth processing result, the second pooling module and the fully connected model.

[0086] Furthermore, in one embodiment, the vector module 30 is further configured to: input the fourth processing result into the third convolution layer for convolution processing to obtain a fifth processing result output by the third convolution layer;

[0087] Inputting the fifth processing result into the fourth convolutional layer for convolution processing to obtain a sixth processing result output by the fourth convolutional layer;

[0088] Inputting the sixth processing result into the fifth convolutional layer for convolution processing to obtain a seventh processing result output by the fifth convolutional layer;

[0089] Inputting the seventh processing result into the third pooling layer for pooling processing to obtain an eighth processing result output by the third pooling layer;

[0090] The analysis vector is obtained according to the eighth processing result and the fully connected model.

[0091] Furthermore, in one embodiment, the vector module 30 is further configured to: input the eighth processing result into the first fully connected layer for connection, to obtain a first connection result output by the first fully connected layer;

[0092] Inputting the first connection result into the first Dropout layer for anti-overfitting processing to obtain the target result;

[0093] Inputting the target result into the second fully connected layer for connection, to obtain a second connection result output by the second fully connected layer;

[0094] The second connection result is input into the second Dropout layer for anti-overfitting processing to obtain the analysis vector.

[0095] Furthermore, in one embodiment, the pre-processing module 20 is further configured to: perform noise reduction processing on the electrocardiogram data using discrete wavelet packet transform and sliding average method to obtain noise-reduced data;

[0096] The Pan-Tompkins algorithm and wavelet packet transform are used to locate the characteristic wave groups of the denoised data and obtain the target data.

[0097] Furthermore, in one embodiment, the analysis module 40 is further used to: generate a symptom portrait corresponding to the clinical data by performing vector analysis on the analysis vector; or generate a symptom analysis text corresponding to the clinical data by performing vector analysis on the analysis vector.

[0098] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in FIG4 . The computer device includes a processor, a memory, a network interface, and a database connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile and / or 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 network interface of the computer device is used to communicate with an external client via a network connection. When the computer program is executed by the processor, it implements the functions or steps of the server side of a clinical data analysis method.

[0099] In one embodiment, a computer device is provided, which can be a client, and its internal structure diagram can be shown in Figure 5. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected via a system bus. 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 and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external server via a network connection. When the computer program is executed by the processor, it implements the functions or steps of the client side of a clinical data analysis method.

[0100] In one embodiment, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the following steps are implemented:

[0101] Acquiring clinical data, wherein the clinical data includes biochemical indicators and / or electrocardiogram data of the target user;

[0102] Preprocessing the clinical data to obtain target data;

[0103] Performing data analysis on the target data using a trained deep learning model to obtain an analysis vector, wherein the deep learning model is a model trained based on an activation model, a pooling model, and a fully connected model connected in sequence;

[0104] Vector analysis is performed according to the analysis vector to obtain an analysis result.

[0105] The clinical data analysis method proposed in this embodiment obtains clinical data, wherein the clinical data includes biochemical indicators and / or electrocardiogram data of the target user, and then preprocesses the clinical data to obtain target data, and performs data analysis on the target data through a trained deep learning model to obtain an analysis vector, wherein the deep learning model is a model trained based on an activation model, a pooling model, and a fully connected model connected in sequence. Finally, vector analysis is performed based on the analysis vector to obtain an analysis result. The deep learning model trained based on the activation model, the pooling model, and the fully connected model can realize efficient and accurate data analysis of clinical data, thereby obtaining an analysis result with improved analysis accuracy.

[0106] In one embodiment, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0107] Acquiring clinical data, wherein the clinical data includes biochemical indicators and / or electrocardiogram data of the target user;

[0108] Preprocessing the clinical data to obtain target data;

[0109] Performing data analysis on the target data using a trained deep learning model to obtain an analysis vector, wherein the deep learning model is a model trained based on an activation model, a pooling model, and a fully connected model connected in sequence;

[0110] Vector analysis is performed according to the analysis vector to obtain an analysis result.

[0111] The clinical data analysis method proposed in this embodiment obtains clinical data, wherein the clinical data includes biochemical indicators and / or electrocardiogram data of the target user, and then preprocesses the clinical data to obtain target data, and performs data analysis on the target data through a trained deep learning model to obtain an analysis vector, wherein the deep learning model is a model trained based on an activation model, a pooling model, and a fully connected model connected in sequence. Finally, vector analysis is performed based on the analysis vector to obtain an analysis result. The deep learning model trained based on the activation model, the pooling model, and the fully connected model can realize efficient and accurate data analysis of clinical data, thereby obtaining an analysis result with improved analysis accuracy.

[0112] It should be noted that the above functions or steps that can be implemented by the computer-readable storage medium or computer device can be found in the relevant descriptions of the server side and the client side in the aforementioned method embodiment. To avoid repetition, they will not be described one by one here.

[0113] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. 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 memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0114] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0115] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. 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. 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, and should all be included in the scope of protection of the present invention.

Claims

1. A clinical data analysis method, characterized in that: The clinical data analysis method comprises: Acquiring clinical data, wherein the clinical data includes biochemical indicators and / or electrocardiogram data of the target user; Preprocessing the clinical data to obtain target data; Performing data analysis on the target data through a trained deep learning model to obtain an analysis vector, wherein the deep learning model is a model trained based on an activation model, a pooling model, and a fully connected model connected in sequence; Vector analysis is performed according to the analysis vector to obtain an analysis result.

2. The clinical data analysis method according to claim 1, characterized in that: The activation model includes a first convolutional layer, an activation layer, and a first pooling layer connected in sequence. The step of performing data analysis on the target data using the trained deep learning model to obtain an analysis vector includes: Inputting the target data into a first convolutional layer for convolution processing, and obtaining a first processing result output by the first convolutional layer; Inputting the first convolution result into the activation layer for activation processing, and inputting the activation processing result output by the activation layer into the first pooling layer for pooling processing to obtain a second processing result; An analysis vector is obtained according to the second processing result, the pooling model and the fully connected model.

3. The clinical data analysis method according to claim 2, characterized in that: The pooling model includes a first pooling module and a second pooling module connected in sequence, the first pooling module includes a second convolutional layer and a second pooling layer connected in sequence, and the step of obtaining an analysis vector according to the second processing result, the pooling model and the fully connected model includes: Inputting the second processing result into the second convolutional layer for convolution processing to obtain a third processing result output by the second convolutional layer; Inputting the third processing result into the second pooling layer for pooling processing to obtain a fourth processing result output by the second pooling layer; The analysis vector is obtained according to the fourth processing result, the second pooling module and the fully connected model.

4. The clinical data analysis method according to claim 3, characterized in that: The second pooling module includes a third convolutional layer, a fourth convolutional layer, a fifth convolutional layer and a third pooling layer connected in sequence, and the step of obtaining the analysis vector according to the fourth processing result, the second pooling module and the fully connected model includes: Inputting the fourth processing result into the third convolution layer for convolution processing to obtain a fifth processing result output by the third convolution layer; Inputting the fifth processing result into the fourth convolutional layer for convolution processing to obtain a sixth processing result output by the fourth convolutional layer; Inputting the sixth processing result into the fifth convolutional layer for convolution processing to obtain a seventh processing result output by the fifth convolutional layer; Inputting the seventh processing result into the third pooling layer for pooling processing to obtain an eighth processing result output by the third pooling layer; The analysis vector is obtained according to the eighth processing result and the fully connected model.

5. The clinical data analysis method according to claim 4, characterized in that: The fully connected model includes a first fully connected layer, a first Dropout layer, a second fully connected layer, and a second Dropout layer connected in sequence, and the step of obtaining the analysis vector according to the eighth processing result and the fully connected model includes: Inputting the eighth processing result into the first fully connected layer for connection, to obtain a first connection result output by the first fully connected layer; Inputting the first connection result into the first Dropout layer for anti-overfitting processing to obtain a target result; Inputting the target result into the second fully connected layer for connection, and obtaining a second connection result output by the second fully connected layer; The second connection result is input into the second Dropout layer for anti-overfitting processing to obtain the analysis vector.

6. The clinical data analysis method according to claim 1, characterized in that: The step of preprocessing the clinical data to obtain target data includes: The electrocardiogram data is denoised using discrete wavelet packet transform and sliding average method to obtain denoised data. The Pan-Tompkins algorithm and wavelet packet transform are used to locate the characteristic wave groups of the denoised data and obtain the target data.

7. The clinical data analysis method according to any one of claims 1 to 6, characterized in that: The step of performing vector analysis according to the analysis vector to obtain an analysis result comprises: By performing vector analysis on the analysis vector, a symptom portrait corresponding to the clinical data is generated; or, by performing vector analysis on the analysis vector, a symptom analysis text corresponding to the clinical data is generated.

8. A clinical data analysis device, characterized in that: The clinical data analysis device comprises: An acquisition module, used for acquiring clinical data, wherein the clinical data includes biochemical indicators and / or electrocardiogram data of the target user; A preprocessing module, used for preprocessing the clinical data to obtain target data; A vector module, used to perform data analysis on the target data through a trained deep learning model to obtain an analysis vector, wherein the deep learning model is a model trained based on an activation model, a pooling model, and a fully connected model connected in sequence; The analysis module is used to perform vector analysis according to the analysis vector to obtain an analysis result.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the clinical data analysis method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the clinical data analysis method according to any one of claims 1 to 7 are implemented.

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