Emergency treatment level determination method and system based on deep learning

Abstract

The invention provides an emergency treatment level determination method and system based on deep learning, and the method comprises the steps: carrying out the effective feature extraction of historical diagnosis data, weak electrophysiological parameters and medical image data of a patient, and fusing the historical diagnosis data, the weak electrophysiological parameters and the medical image data to obtain emergency treatment level features; and constructing an emergency treatment level determination model according to the emergency treatment grading features and the corresponding emergency treatment condition severity levels, inputting related data of the target to be subjected to emergency treatment into the emergency treatment level determination model to obtain the current emergency treatment condition severity level of the target, and assisting doctors in performing emergency treatment judgment. The emergency treatment level of a target patient is determined by combining diagnosis data, weak electrophysiological parameters and medical image data, doctors are assisted to diagnose the patient, the confirmation cost and time of the emergency treatment level are reduced, the accuracy of emergency treatment level judgment is improved, personalized emergency treatment judgment is achieved for frequent admission patients, and the emergency treatment efficiency is improved. Therefore, humanized emergency service is realized.

Description

Technical Field

[0001] The present invention belongs to the field of medical emergency technology, and in particular, relates to a method and system for determining an emergency level based on deep learning. Background Art

[0002] The emergency department is a place where various critically ill patients are relatively concentrated. Due to the wide variety of diseases and the urgent rescue time, it has become the department with the heaviest management tasks and is the only way for all emergency patients to be admitted to the hospital for treatment. The emergency department diagnosis has three significant characteristics: the first characteristic is that time is urgent. If it is a serious burn, sudden heart attack, cerebral hemorrhage, or brain surgery injury, if targeted rescue cannot be carried out in time, the best rescue time will be missed, resulting in the patient's death or lifelong disability. The second characteristic is that the diagnostic task is heavy. When there are many patients, due to the limited detection resources of the emergency department: mainly manifested in limited equipment and limited personnel, each patient needs different detection equipment. For example, the symptom of dyspnea may be caused by the new coronavirus, it may be caused by advanced lung cancer, or it may be caused by surgical injury to the lungs. If the cause and lesion of the patient cannot be distinguished in time, it will lead to misdiagnosis and the patient will miss the best rescue time. Therefore, how to provide timely and accurate diagnostic and treatment data for emergency doctors is an important part of the diagnosis and nursing work of the emergency department and has become the key to the success of saving lives.

[0003] In addition, in the prior art, when witnesses or parties discover that a patient has fallen into shock or has lost blood and is unconscious, they do not know the cause of the illness. At this time, the emergency department receives a call and only prepares a few relevant medicines. As a result, it is not uncommon for patients to suddenly die due to lack of a specific medicine while getting the medicine.

[0004] It can be seen that the existing emergency department patient monitoring and emergency rescue system is still very backward. The emergency department has not fully coordinated to retrieve and utilize the patient's previous diagnostic data. Due to various reasons, the rescue time is prolonged, and the rescue time must be every second. Therefore, the existing technology still has the problems of being unable to conduct comprehensive inspections, too long rescue time, low rescue efficiency, and unable to assist doctors in quickly classifying patients' emergency levels and conducting targeted treatments for corresponding symptoms. At the same time, the processing methods for filming patient examinations are relatively simple. For the black background areas and target areas that appear in medical images, and even the target areas and serious lesion areas, the lesion areas cannot be well separated from the target areas and the black background areas. In addition, the use of machine learning and deep learning models to process medical data is already very mature, but when using neural network models, the characteristics of medical data cannot be taken into account and the model parameters are not adaptively adjusted, resulting in large fluctuations in the model's processing results for medical data. Such problems have always plagued the emergency department's use of machine learning models. Summary of the invention

[0005] In order to solve the above technical problems, the present invention proposes a method and system for determining emergency level based on deep learning.

[0006] In a first aspect of the present invention, a method for determining emergency level based on deep learning is provided, characterized in that the method comprises:

[0007] S1, obtaining the patient's historical diagnostic data, first weak electrophysiological parameters and first medical imaging data, and obtaining the first emergency condition severity level set by the corresponding doctor at this time;

[0008] S2, fusing the historical diagnosis data, the first weak electrophysiological parameter and the first medical imaging data to obtain a first emergency classification feature;

[0009] S3. Constructing an emergency level determination model based on the first emergency classification characteristics and the first emergency condition severity level;

[0010] S4. Receive the real-time diagnostic data, the second weak electrophysiological parameter and the second medical imaging data of the target to be treated for emergency, fuse the real-time diagnostic data, the second weak electrophysiological parameter and the second medical imaging data to obtain a second emergency classification feature, use the emergency level determination model to process the second emergency classification feature to generate a second emergency condition severity level, and assist doctors in emergency treatment according to the second emergency condition severity level.

[0011] Furthermore, the historical diagnostic data is the patient's historical hospitalization diagnostic data, including blood pressure, heart rate, respiratory rate and special diagnostic parameters of each examination during hospitalization; the special diagnostic parameters are determined by the patient's characteristic symptoms, and the historical diagnostic data constitutes parameter N;

[0012] The first weak electrophysiological parameter or the second weak electrophysiological parameter is a cardiac electrophysiological parameter or a brain electrophysiological parameter or a myoelectrical physiological parameter.

[0013] Furthermore, the first medical image data or the second medical image data is acquired by using an X-ray, a CT scan or a magnetic resonance imaging.

[0014] Furthermore, the emergency level determination model adopts a deep learning model, and the deep learning model is a CNN convolutional neural network model or a RNN convolutional neural network model.

[0015] Furthermore, before fusing to obtain the first emergency grading feature or the second emergency grading feature, the separated first lesion image data or the second lesion image data is compressed by calculation to obtain the first lesion compression vector or the second lesion compression vector P(M) i .

[0016] Further, the real vector P(M) i and blood pressure, heart rate, respiratory rate and special diagnostic parameter characteristics N, the EEG parameter B f The first emergency classification feature or the second emergency classification feature is obtained by fusion.

[0017] A deep learning-based emergency level determination system is also provided, comprising an emergency data acquisition terminal and an emergency level determination terminal, characterized in that:

[0018] The emergency data acquisition terminal includes an emergency data collection module, an electrophysiological parameter detection module and a medical image acquisition module, and the emergency level determination terminal includes a hospital main control module, an emergency level determination model construction module and an emergency level determination module, wherein:

[0019] The emergency data collection module: connected to the hospital main control module to obtain the patient's historical diagnosis data stored therein or the real-time diagnosis data of the emergency target, and connected to the electrophysiological parameter detection module to obtain the first weak electrophysiological parameter or the second weak electrophysiological parameter, and connected to the medical image acquisition module to obtain the first medical image data or the second medical image data;

[0020] The electrophysiological parameter detection module is used to collect the first weak electrophysiological parameter or the second weak electrophysiological parameter of the patient and is connected to the emergency data collection module;

[0021] The medical image acquisition module is used to acquire the first medical image data or the second medical image data by using an X-ray film, a CT film or a nuclear magnetic resonance imaging;

[0022] The hospital main control module is used to store the first emergency condition severity level corresponding to the patient's historical diagnosis data;

[0023] The emergency level determination model construction module: fuses the historical diagnosis data, the first weak electrophysiological parameter and the first medical imaging data to obtain a first emergency classification feature, and jointly constructs an emergency level determination model according to the first emergency classification feature and the first emergency condition severity level;

[0024] The emergency level determination module is connected to the hospital main control module and assists doctors in emergency treatment according to the severity level of the second emergency condition.

[0025] The present invention reduces rescue time and improves rescue efficiency by utilizing comprehensive inspection data. The emergency level classification obtained by the model assists doctors in quickly classifying patients into emergency levels and conducting targeted treatments for corresponding symptoms. At the same time, for the films of patient examinations, for the black background areas and target areas, and even for the target areas and serious lesion areas that appear in the medical images, the image separation technology is used to separate the lesion areas from the target areas and the black background areas. In addition, when utilizing the neural network model, the model parameters are adaptively adjusted in consideration of the characteristics of medical data of different orders of magnitude, so that the fluctuation of the model's processing results on the medical data is greatly reduced, thereby improving the utilization level of the machine learning model by the emergency department.

[0026] More embodiments and improved effects of the present invention will be further introduced in conjunction with the drawings and specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 is a flow chart of the emergency level determination method based on deep learning of the present invention;

[0028] Figure 2 It is a schematic diagram of an emergency level determination terminal in the emergency level determination system based on deep learning of the present invention;

[0029] Figure 3 It is a schematic diagram of an emergency data acquisition terminal in the emergency level determination system based on deep learning of the present invention;

[0030] Figure 4 It is a schematic diagram of image separation required for obtaining the features of the lesion area in the present invention;

[0031] Figure 5 It is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0032] The invention is further described below in conjunction with the accompanying drawings and specific implementation methods.

[0033] In order to solve the above technical problems, the present invention proposes a method and system for determining emergency level based on deep learning.

[0034] In a first aspect of the present invention, a method for determining emergency level based on deep learning is provided, characterized in that the method comprises:

[0035] S1, obtaining the patient's historical diagnostic data, first weak electrophysiological parameters and first medical imaging data, and obtaining the first emergency condition severity level set by the corresponding doctor at this time;

[0036] S2, fusing the historical diagnosis data, the first weak electrophysiological parameter and the first medical imaging data to obtain a first emergency classification feature;

[0037] S3. Constructing an emergency level determination model based on the first emergency classification characteristics and the first emergency condition severity level;

[0038] S4. Receive the real-time diagnostic data, the second weak electrophysiological parameter and the second medical imaging data of the target to be treated for emergency, fuse the real-time diagnostic data, the second weak electrophysiological parameter and the second medical imaging data to obtain a second emergency classification feature, use the emergency level determination model to process the second emergency classification feature to generate a second emergency condition severity level, and assist doctors in emergency treatment according to the second emergency condition severity level.

[0039] Furthermore, the historical diagnostic data is the patient's historical hospitalization diagnostic data, including blood pressure, heart rate, respiratory rate and special diagnostic parameters of each examination during hospitalization; the special diagnostic parameters are determined by the patient's characteristic symptoms, and the historical diagnostic data constitutes parameter N;

[0040] In this embodiment, if the patient has chronic lung problems, the special diagnostic parameters are physiological parameters related to the lungs, including total white blood cell count, white blood cell classification, total red blood cell count, hemoglobin, C-reactive protein, platelets, etc. Any one of them can be selected as a special diagnostic parameter in this embodiment.

[0041] The first weak electrophysiological parameter or the second weak electrophysiological parameter is a cardiac electrophysiological parameter or a brain electrophysiological parameter or a myoelectrical physiological parameter.

[0042] In this embodiment, the electroencephalographic physiological parameters are collected using a wireless electroencephalographic acquisition device, g-tec.

[0043] Furthermore, the first medical image data or the second medical image data is acquired by using an X-ray, a CT scan or a magnetic resonance imaging.

[0044] Furthermore, the emergency level determination model adopts a deep learning model, and the deep learning model is a CNN convolutional neural network model or a RNN convolutional neural network model.

[0045] Furthermore, the calculation formula of the electroencephalographic physiological parameters is as follows:

[0046]

[0047] In the formula, B f is the electroencephalographic physiological parameter, k is the electroencephalographic characteristic coefficient, the value range is 0.05-0.10, T is the time period for collecting the patient's electroencephalogram, n is the number of electroencephalographic points collected, S1 , S 2 ,...,S n It is the amplitude of the EEG signal measured at the corresponding EEG point within a period of time, in μV.

[0048] In this embodiment, the value of n is the number of points set for collection on the wireless EEG collection device, and the upper limit is 64.

[0049] Furthermore, before using the emergency level determination model, the collected images need to be processed. The image processing includes image separation. The lesion samples are separated from the background by binarization. The improved Os algorithm is used. The inter-class variance S between the lesion and the background is defined as:

[0050]

[0051] In the formula, a is the total average grayscale of the image, n is the image sampling rate, T n is the gray value of the sampled lesion image, M n To sample the grayscale value of the lesion-related image, the grayscale value is obtained based on the sampling rate, p 0 is the ratio of the number of pixels belonging to the target to the entire image, p 1 is the ratio of background pixels to the entire image, a 0 is the average grayscale of the target, a 0 The average grayscale of the background is processed to obtain first lesion image data or second lesion image data.

[0052] The features obtained after image separation in this embodiment are: Figure 4 Characteristics of some areas of the disease are shown in .

[0053] Furthermore, before fusing to obtain the first emergency grading feature or the second emergency grading feature, the separated first lesion image data or the second lesion image data is compressed by calculation to obtain the first lesion compression vector or the second lesion compression vector P(M) i , and its calculation formula is:

[0054]

[0055] Wherein, the first lesion image data or the second lesion image data M after separation is i Compress to another real vector P(M) i In the example, the eigenvalue range is between -1 and 1, and the sum of all eigenvalues ​​is 0. K represents the Kth dimension, and T i Represents the image feature of the i-th lesion.

[0056] Further, the real vector P(M)i and blood pressure, heart rate, respiratory rate and special diagnostic parameter characteristics N, the EEG parameter B f The first emergency classification feature or the second emergency classification feature is obtained by fusion.

[0057] In this embodiment, the first emergency classification feature or the second emergency classification feature obtained by fusion is (P(M) i , N, B f ).

[0058] Furthermore, the activation function calculation formula of the improved CNN convolutional neural network model is as follows:

[0059]

[0060] X is the first emergency classification feature or the second emergency classification feature, H(S) is the output activation function value, f , S, P(M) i > indicates taking EEG parameter B f , parameter characteristics N and real vector P(M) i The minimum order of magnitude in the fusion feature, |B f , N, P(M) i | indicates the EEG parameter B f , parameter characteristics N and real vector P(M) i The maximum magnitude among fused features.

[0061] The CNN convolutional neural network model with improved activation function concluded that the emergency level confirmation can be considered to be divided into four levels, namely, level A endangered, level B critical, level C emergency, and level D non-emergency. The method of CNN convolutional neural network model to classify four emergency levels is similar to the CNN neural network model in the field, and will not be repeated here.

[0062] A deep learning-based emergency level determination system is also provided, comprising an emergency data acquisition terminal and an emergency level determination terminal, characterized in that:

[0063] The emergency data acquisition terminal includes an emergency data collection module, an electrophysiological parameter detection module and a medical image acquisition module, and the emergency level determination terminal includes a hospital main control module, an emergency level determination model construction module and an emergency level determination module, wherein:

[0064] The emergency data collection module: connected to the hospital main control module to obtain the patient's historical diagnosis data stored therein or the real-time diagnosis data of the emergency target, and connected to the electrophysiological parameter detection module to obtain the first weak electrophysiological parameter or the second weak electrophysiological parameter, and connected to the medical image acquisition module to obtain the first medical image data or the second medical image data;​

[0065] The electrophysiological parameter detection module is used to collect the first weak electrophysiological parameter or the second weak electrophysiological parameter of the patient and is connected to the emergency data collection module;

[0066] The medical image acquisition module is used to acquire the first medical image data or the second medical image data by using an X-ray film, a CT film or a nuclear magnetic resonance imaging;

[0067] The hospital main control module is used to store the first emergency condition severity level corresponding to the patient's historical diagnosis data;

[0068] The emergency level determination model construction module: fuses the historical diagnosis data, the first weak electrophysiological parameter and the first medical imaging data to obtain a first emergency classification feature, and jointly constructs an emergency level determination model according to the first emergency classification feature and the first emergency condition severity level;

[0069] The emergency level determination module is connected to the hospital main control module and assists doctors in emergency treatment according to the severity level of the second emergency condition.

[0070] The present invention reduces rescue time and improves rescue efficiency by utilizing comprehensive inspection data. The emergency level classification obtained by the model assists doctors in quickly classifying patients into emergency levels and conducting targeted treatments for corresponding symptoms. At the same time, for the films of patient examinations, for the black background areas and target areas, and even for the target areas and serious lesion areas that appear in the medical images, the image separation technology is used to separate the lesion areas from the target areas and the black background areas. In addition, when utilizing the neural network model, the model parameters are adaptively adjusted in consideration of the characteristics of medical data of different orders of magnitude, so that the fluctuation of the model's processing results on the medical data is greatly reduced, thereby improving the utilization level of the machine learning model by the emergency department.

[0071] The control device may include a processor 401 and a memory 402 storing computer program instructions. Specifically, the processor 401 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiment of the present invention.

[0072] Memory 402 may include a large capacity memory for data or instructions. For example, but not limitation, memory 402 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. In appropriate cases, memory 402 may include a removable or non-removable (or fixed) medium. In appropriate cases, memory 402 may be inside or outside a data processing device. In a specific embodiment, memory 402 is a non-volatile solid-state memory. In a specific embodiment, memory 402 includes a read-only memory (ROM). In appropriate cases, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM) or a flash memory or a combination of two or more of these.

[0073] The processor 401 reads and executes the computer program instructions stored in the memory 402 to implement any one of the methods for analyzing the storage tank detection data in the above embodiments.

[0074] In one example, the electronic device may further include a communication interface 403 and a bus 410. Figure 3 As shown, the processor 401, the memory 402, and the communication interface 403 are connected and communicate with each other via a bus 410. The communication interface 403 is mainly used to implement the communication between the modules, devices, units and / or equipment in the embodiment of the present invention.

[0075] Bus 410 includes hardware, software or both, and the parts of electronic equipment are coupled to each other. For example, but not limitation, bus may include accelerated graphics port (AGP) or other graphics bus, enhanced industrial standard architecture (EISA) bus, front side bus (FSB), hypertransport (HT) interconnection, industrial standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations. In appropriate cases, bus 410 may include one or more buses. Although the embodiment of the present invention describes and shows a specific bus, the present invention considers any suitable bus or interconnection.

[0076] Of course, it can be understood that each embodiment of the present invention can achieve one of the effects alone, and a combination of multiple embodiments of the present invention can achieve all of the above effects, but it is not required that every embodiment of the present invention achieve all of the above advantages and effects, because each embodiment of the present invention can constitute a separate technical solution and make one or more contributions to the prior art.

[0077] The present invention does not particularly specify the structure of some modules, which shall be subject to the contents recorded in the prior art. The prior art mentioned in the above background technology section and the specific embodiment section of the present invention can be used as part of the present invention to understand the meaning of some technical features or parameters. The scope of protection of the present invention shall be subject to the contents actually recorded in the claims.

Claims

1. A method for determining emergency level based on deep learning, characterized in that: The method comprises: S1, obtaining the patient's historical diagnostic data, first weak electrophysiological parameters and first medical imaging data, and obtaining the first emergency condition severity level set by the corresponding doctor at this time; S2, fusing the historical diagnosis data, the first weak electrophysiological parameter and the first medical imaging data to obtain a first emergency classification feature; S3. Constructing an emergency level determination model based on the first emergency classification characteristics and the first emergency condition severity level; S4. Receive the real-time diagnostic data, the second weak electrophysiological parameter and the second medical imaging data of the target to be treated for emergency, fuse the real-time diagnostic data, the second weak electrophysiological parameter and the second medical imaging data to obtain a second emergency classification feature, use the emergency level determination model to process the second emergency classification feature to generate a second emergency condition severity level, and assist doctors in emergency treatment according to the second emergency condition severity level.

2. The method for determining emergency level based on deep learning according to claim 1, characterized in that: The historical diagnostic data is the patient's historical hospitalization diagnostic data, including blood pressure, heart rate, respiratory rate and special diagnostic parameters of each examination during hospitalization; the special diagnostic parameters are determined by the patient's characteristic symptoms, and the historical diagnostic data constitutes parameter N; The first weak electrophysiological parameter or the second weak electrophysiological parameter is a cardiac electrophysiological parameter or a brain electrophysiological parameter or a myoelectrical physiological parameter.

3. The method for determining emergency level based on deep learning according to claim 1, characterized in that: The first medical image data or the second medical image data is acquired by using an X-ray film, a CT film or a magnetic resonance imaging film.

4. The method for determining emergency level based on deep learning according to claim 1, characterized in that: The emergency level determination model adopts a deep learning model, and the deep learning model is a CNN convolutional neural network model or a RNN convolutional neural network model.

5. The method for determining emergency level based on deep learning according to claim 4, characterized in that: Before using the emergency level determination model, the collected images need to be processed, and the image processing includes image separation. The lesion samples are separated from the background by binarization. The improved Os algorithm is used, and the inter-class variance S between the lesion and the background is defined. The first medical image data or the second medical image data is processed to obtain the first lesion image data or the second lesion image data.

6. The method for determining emergency level based on deep learning according to claim 5, characterized in that: Before fusing to obtain the first emergency grading feature or the second emergency grading feature, the separated first lesion image data or the second lesion image data is compressed by calculation to obtain the first lesion compression vector or the second lesion compression vector P(M) i .

7. The method for determining emergency level based on deep learning according to claim 6, characterized in that: The real vector P(M) i and blood pressure, heart rate, respiratory rate and special diagnostic parameter characteristics N, the EEG parameter B f The first emergency classification feature or the second emergency classification feature is obtained by fusion.

8. A system for determining emergency level based on deep learning, comprising an emergency data acquisition terminal and an emergency level determination terminal, characterized in that: The emergency data acquisition terminal includes an emergency data collection module, an electrophysiological parameter detection module and a medical image acquisition module, and the emergency level determination terminal includes a hospital main control module, an emergency level determination model construction module and an emergency level determination module, wherein: The emergency data collection module: connected to the hospital main control module to obtain the patient's historical diagnosis data stored therein or the real-time diagnosis data of the emergency target, and connected to the electrophysiological parameter detection module to obtain the first weak electrophysiological parameter or the second weak electrophysiological parameter, and connected to the medical image acquisition module to obtain the first medical image data or the second medical image data; The electrophysiological parameter detection module is used to collect the first weak electrophysiological parameter or the second weak electrophysiological parameter of the patient and is connected to the emergency data collection module; The medical image acquisition module is used to acquire the first medical image data or the second medical image data by using an X-ray film, a CT film or a nuclear magnetic resonance imaging; The hospital main control module is used to store the first emergency condition severity level corresponding to the patient's historical diagnosis data; The emergency level determination model construction module: fuses the historical diagnosis data, the first weak electrophysiological parameter and the first medical imaging data to obtain a first emergency classification feature, and jointly constructs an emergency level determination model according to the first emergency classification feature and the first emergency condition severity level; The emergency level determination module is connected to the hospital main control module and assists doctors in emergency treatment according to the severity level of the second emergency condition.

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