An intelligent assessment and decision-making system for emergency treatment of trauma patients based on artificial neural networks

Abstract

The present disclosure relates to an intelligent assessment and decision-making system for emergency treatment of trauma patients based on an artificial neural network, and relates to the field of artificial intelligence technology. Emergency treatment data of trauma patients is acquired through a data acquisition device, the difficulty of emergency treatment of trauma patients is assessed through an assessment device, and a treatment plan for emergency treatment of trauma patients is determined through a decision-making device. The assessment device utilizes a pre-trained assessment model to assess the difficulty of emergency treatment, achieving accurate and efficient assessment based on an artificial neural network, and the decision-making device utilizes a pre-trained decision-making model to determine the treatment plan for trauma patients, achieving accurate and efficient decision-making based on an artificial neural network. Thus, this technical solution can achieve intelligent assessment and decision-making for emergency treatment of trauma patients based on an artificial neural network, thereby assisting in improving the treatment effect of emergency treatment doctors on emergency trauma patients.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence technology, and in particular to an intelligent assessment and decision-making system for emergency treatment of trauma patients based on an artificial neural network. Background Art

[0002] With the development of artificial intelligence (AI) technology, the application of artificial neural networks has also grown. They are also being applied in medical settings. For example, artificial neural networks can be used to analyze medical data and obtain relevant data analysis results.

[0003] In emergency medical scenarios, trauma patients often appear. These patients can be called emergency trauma patients. Currently, some clinical databases can assist doctors in treating emergency trauma patients. Summary of the Invention

[0004] The purpose of the present disclosure is to provide an artificial neural network-based intelligent assessment and decision-making system and method for first aid of trauma patients.

[0005] In order to achieve the above-mentioned objectives, in a first aspect, the present disclosure provides an intelligent assessment and decision-making system for emergency treatment of trauma patients based on an artificial neural network, comprising: a data acquisition device, configured to obtain emergency treatment trauma patient data; an assessment device, communicatively connected to the data acquisition device, configured to determine an emergency treatment trauma patient assessment result based on the emergency treatment trauma patient data, a clinical trauma database and a pre-trained assessment model, wherein the emergency treatment trauma patient assessment result is used to characterize the emergency treatment difficulty of the emergency treatment trauma patient; a decision-making device, communicatively connected to the data acquisition device and the assessment device respectively, configured to obtain emergency treatment doctor information, and determine an emergency treatment plan for the trauma patient based on the emergency treatment trauma patient assessment result, the emergency treatment doctor information, the clinical trauma treatment database and the pre-trained decision-making model.

[0006] Optionally, the data acquisition device includes: a first acquisition device, configured to acquire physical description information of emergency trauma patients, wherein the physical description information is used to describe the physical reaction of the emergency trauma patients caused by trauma; a second acquisition device, configured to acquire trauma description information of emergency trauma patients, wherein the trauma description information is used to describe the wound condition of the emergency trauma patients; and a processing device, configured to integrate the physical description information and the trauma description information to obtain the emergency trauma patient data.

[0007] Optionally, the emergency trauma patient data includes physical description information and trauma description information of the emergency trauma patient, and the clinical trauma database includes: multiple clinical trauma data, each clinical trauma data includes clinical physical description information and clinical trauma description information, and the evaluation device is further configured to: determine a first information weight based on the physical description information of the emergency trauma patient and the multiple clinical physical description information; determine a second information weight based on the trauma description information of the emergency trauma patient and the multiple clinical trauma description information; input the physical description information of the emergency trauma patient, the first information weight, the trauma description information of the emergency trauma patient and the second information weight into the pre-trained evaluation model to obtain the emergency trauma patient evaluation result output by the pre-trained evaluation model.

[0008] Optionally, the first information weight is used to characterize the complexity or rarity of the physical description information of the emergency trauma patient, and the second information weight is used to characterize the complexity or rarity of the trauma description information of the emergency trauma patient. The evaluation device is further configured to: if target clinical physical description information that matches the physical description information of the emergency trauma patient is determined from the multiple clinical physical description information, the first information weight is determined according to the complexity feature or rarity feature of the target clinical physical description information in the multiple clinical physical description information; if target clinical physical description information that matches the physical description information of the emergency trauma patient is not determined from the multiple clinical physical description information, the first information weight is determined according to the complexity feature or rarity feature of the target clinical physical description information in the multiple clinical physical description information. The first information weight is determined based on the similarity between the body description information of the trauma patient and the multiple clinical body description information; and / or, if target clinical trauma description information matching the trauma description information of the emergency trauma patient is determined from the multiple clinical trauma description information, the second information weight is determined based on the complexity feature or rarity feature of the target clinical trauma description information in the multiple clinical trauma description information; if target clinical trauma description information matching the trauma description information of the emergency trauma patient is not determined from the multiple clinical trauma description information, the second information weight is determined based on the similarity between the trauma description information of the emergency trauma patient and the multiple clinical trauma description information.

[0009] Optionally, the evaluation device is further configured to: obtain a first training data set, the first training data set includes multiple first training samples, each of the first training samples includes: sample body description information, the weight corresponding to the sample body description information, sample trauma description information, the weight corresponding to the sample trauma description information and an evaluation label, and the evaluation label is used to characterize the difficulty of emergency treatment; determine a second training data set based on the first training data set, the second training data set includes multiple second training samples, each of the second training samples includes: sample body description information, sample trauma description information and an evaluation label; train the evaluation model to be trained based on the second training data set to obtain an initially trained evaluation model; optimize the initially trained evaluation model based on the first training data set to obtain the pre-trained evaluation model.

[0010] Optionally, the clinical trauma treatment database includes: multiple clinical trauma treatment plans, and the decision-making device is further configured to: evaluate the emergency treatment doctor based on the emergency treatment doctor information, and determine the emergency treatment doctor evaluation result, and the emergency treatment doctor evaluation result is used to characterize the emergency treatment level of the emergency treatment doctor; input the multiple clinical treatment plans into the pre-trained decision model to obtain the clinical treatment difficulty label and clinical doctor treatment level label corresponding to each of the clinical trauma treatment plans output by the pre-trained decision model; determine the emergency trauma patient treatment plan based on the clinical treatment difficulty label and clinician treatment level label corresponding to each of the clinical trauma treatment plans, the emergency trauma patient evaluation result and the emergency treatment doctor evaluation result.

[0011] Optionally, the pre-trained decision model includes a first module and a second module, and the decision device is further configured to: obtain a third training data set, the third training data set includes multiple third training samples, each of the third training samples includes: a sample trauma treatment plan, a sample trauma treatment plan feature, a treatment difficulty label, and a doctor's treatment level label; determine a fourth training data set and a fifth training data set based on the third training data set, the fourth training data set includes multiple fourth training samples, each of the fourth training samples includes a sample trauma treatment plan and a sample trauma treatment plan feature, the fifth training data set includes multiple fifth training samples, each of the fifth training samples includes a sample trauma treatment plan, a treatment difficulty label, and a doctor's treatment level label; train the first module of the decision model to be trained based on the fourth training data set to obtain an initially trained decision model; train the second module of the initially trained decision model based on the fifth training data set to obtain the pre-trained decision model.

[0012] Optionally, the decision-making device is further configured to: determine a first clinical trauma treatment plan based on the emergency trauma patient assessment result and the clinical treatment difficulty labels corresponding to each of the clinical trauma treatment plans, the clinical treatment difficulty label corresponding to the first clinical trauma treatment plan being higher than the emergency treatment difficulty represented by the emergency trauma patient assessment result; determine a second clinical trauma treatment plan based on the emergency treatment doctor assessment result and the clinical doctor treatment level labels corresponding to each of the clinical trauma treatment plans, the clinical doctor treatment level label corresponding to the second clinical trauma treatment plan being lower than the emergency treatment level represented by the emergency treatment doctor assessment result; determine the emergency trauma patient treatment plan based on the first clinical trauma treatment plan and the second clinical trauma treatment plan.

[0013] Optionally, the intelligent assessment and decision-making system for emergency treatment of trauma patients also includes: a visualization device, which is communicatively connected to the data acquisition device, the assessment device and the decision-making device respectively, and is configured to: respectively display the emergency treatment trauma patient data, the emergency treatment trauma patient assessment results and the emergency treatment trauma patient treatment plan; in response to receiving an auxiliary treatment request initiated by the emergency treatment doctor, play a locally stored auxiliary treatment video recorded by a preset doctor.

[0014] In a second aspect, the present disclosure provides an intelligent assessment and decision-making method for emergency treatment of trauma patients based on an artificial neural network, comprising: obtaining emergency treatment trauma patient data; determining an emergency treatment trauma patient assessment result based on the emergency treatment trauma patient data, a clinical trauma database, and a pre-trained assessment model, wherein the emergency treatment trauma patient assessment result is used to characterize the difficulty of emergency treatment of the emergency treatment trauma patient; obtaining emergency treatment doctor information; determining an emergency treatment plan for the trauma patient based on the emergency treatment trauma patient assessment result, the emergency treatment doctor information, a clinical trauma treatment database, and a pre-trained decision-making model.

[0015] Through the above technical solution, emergency trauma patient data is acquired through a data acquisition device, the emergency treatment difficulty of emergency trauma patients is assessed through an evaluation device, and the emergency treatment plan for emergency trauma patients is determined through a decision-making device. The evaluation device utilizes a pre-trained evaluation model to assess the difficulty of emergency treatment, achieving accurate and efficient assessments based on an artificial neural network. The decision-making device utilizes a pre-trained decision-making model to determine the emergency treatment plan for trauma patients, achieving accurate and efficient decisions based on an artificial neural network. Thus, this technical solution can achieve intelligent assessment and decision-making for emergency trauma patient care based on an artificial neural network, thereby helping to improve the treatment effectiveness of emergency physicians for emergency trauma patients.

[0016] Other features and advantages of the present disclosure will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings are used to provide a further understanding of the present disclosure and constitute a part of the specification. Together with the following detailed description, they are used to explain the present disclosure but do not constitute a limitation of the present disclosure. In the accompanying drawings:

[0018] Figure 1 The present invention is a structural block diagram of an intelligent assessment and decision-making system for emergency treatment of trauma patients based on an artificial neural network according to an exemplary embodiment.

[0019] Figure 2 It is a block diagram of a data acquisition device according to an exemplary embodiment.

[0020] Figure 3 The figure is a schematic diagram of a training process of an evaluation model according to an exemplary embodiment.

[0021] Figure 4 The figure is a flowchart of training and applying a decision model according to an exemplary embodiment.

[0022] Figure 5 It is a block diagram of another intelligent assessment and decision-making system for first aid of trauma patients based on artificial neural network according to an exemplary embodiment.

[0023] Figure 6 The present invention is a flowchart showing an intelligent assessment and decision-making method for emergency treatment of trauma patients based on an artificial neural network according to an exemplary embodiment.

[0024] Figure 7 It is a block diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION

[0025] The following describes the specific embodiments of the present disclosure in detail with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only used to illustrate and explain the present disclosure and are not intended to limit the present disclosure.

[0026] With the development of artificial intelligence (AI) technology, the application of artificial neural networks has also grown. They are also being applied in medical settings. For example, artificial neural networks can be used to analyze medical data and obtain relevant data analysis results.

[0027] In emergency medical settings, trauma patients often appear, and these patients can be called emergency trauma patients. Currently, clinical databases can assist doctors in treating emergency trauma patients. This can help ensure the effectiveness of emergency treatment for patients when doctors lack experience.

[0028] However, current emergency treatment solutions based on clinical databases do not make good use of artificial neural networks. For example, they are only modeled based on clinical databases, but the actual application effect of artificial neural network models is not very good.

[0029] Based on this, the present disclosure provides a technical solution that fully integrates artificial neural networks into the evaluation and decision-making of emergency treatment scenarios for trauma patients, realizes intelligent evaluation and decision-making of emergency treatment for trauma patients based on artificial neural networks, and thereby assists in improving the treatment effect of emergency treatment doctors on emergency trauma patients.

[0030] It is understood that the application scenario of the embodiments of the present disclosure is the emergency treatment of trauma patients. In this application scenario, on the one hand, the level of emergency doctors varies, making it difficult to guarantee treatment results; on the other hand, the condition of the emergency patients is difficult to predict, making it difficult to guarantee treatment results. Therefore, if artificial neural networks can be used to perform more accurate and reliable data analysis, and then make assessments and decisions, it can play a very important auxiliary role.

[0031] Figure 1 FIG. 1 is a structural block diagram of an intelligent assessment and decision system 10 for emergency treatment of trauma patients based on an artificial neural network according to an exemplary embodiment. Figure 1 As shown, the system includes: a data acquisition device 11 , an evaluation device 12 and a decision-making device 13 .

[0032] In some embodiments, the evaluation device 12 is communicatively connected to the data acquisition device 11, and the decision device 13 is communicatively connected to the data acquisition device 11 and the evaluation device 12 respectively, so that both the evaluation device 12 and the decision device 13 can obtain data from the data acquisition device 11, and the decision device 13 can obtain data from the evaluation device 12 and the data acquisition device 11 respectively.

[0033] Regarding the communication connection method of these devices, it can be based on the communication connection method of the Internet of Things to ensure data transmission efficiency and accuracy, etc.

[0034] In some embodiments, the data collection device 11 is configured to obtain emergency trauma patient data.

[0035] In some embodiments, the emergency trauma patient data includes physical description information and trauma description information of the emergency trauma patient.

[0036] Physical description information is used to describe the physical reactions of emergency trauma patients caused by trauma, such as headache, dizziness, numbness, and fever, and may include multiple descriptive words.

[0037] Trauma description information is used to describe the wound condition of first aid trauma patients, such as wound size, wound bleeding, and wound infection, etc. It can be expressed in different forms such as pictures and descriptive words.

[0038] Figure 2 is a block diagram of a data acquisition device 11 according to an exemplary embodiment. Figure 2 As shown, the data acquisition device 11 includes: a first acquisition device 110 , a second acquisition device 112 and a processing device 114 .

[0039] The first collection device 110 is configured to collect physical description information of trauma patients undergoing emergency treatment. For example, the first collection device 110 can be a data input device, allowing emergency trauma patients, doctors, nurses, and others to input physical description information. Information input is not limited to text, voice, or video.

[0040] The second acquisition device 112 is configured to collect wound description information from emergency trauma patients. For example, the second acquisition device 112 may include image acquisition capabilities, allowing it to capture wound images, as well as image processing capabilities, allowing it to analyze the wound condition based on the captured wound images. Of course, the second acquisition device 112 may also serve as a data input device, allowing emergency trauma patients, doctors, nurses, and others to input wound description information describing their wound conditions.

[0041] Furthermore, the processing device 114 may integrate the data collected by the two collection devices to obtain final emergency trauma patient data, for example, by removing duplicate information, normalizing or standardizing the information, etc., which are not limited here.

[0042] The emergency trauma patient data collected by the data collection device 11 can be stored locally first, and can be obtained at any time when the evaluation device 12 or the decision device 13 needs it.

[0043] The evaluation device 12 may be a device integrated with a pre-trained evaluation model, which may have functions such as data analysis and processing.

[0044] In some embodiments, the assessment device 12 is configured to determine an emergency trauma patient assessment result based on emergency trauma patient data, a clinical trauma database, and a pre-trained assessment model, where the emergency trauma patient assessment result is used to characterize the emergency treatment difficulty of the emergency trauma patient.

[0045] In some embodiments, the clinical trauma database includes: a plurality of clinical trauma data, each clinical trauma data including clinical body description information and clinical trauma description information.

[0046] It is understood that the clinical trauma database is constructed by collecting a large amount of clinical trauma data. Each piece of clinical trauma data includes both clinical physical description information and clinical trauma description information. The methods for obtaining these clinical physical description information and clinical trauma description information may be different from or the same as the methods for obtaining emergency trauma patient data.

[0047] It is understandable that the conditions of clinical patients differ from those of emergency patients, and the conditions of clinical patients can serve as a reference for emergency patients. Furthermore, the role of the pre-trained evaluation model is to perform evaluation, which can be combined with relevant reference information obtained from clinical patients for evaluation.

[0048] Therefore, the evaluation process of the evaluation device 12 may include: determining a first information weight based on the physical description information of the emergency trauma patient and multiple clinical physical description information; determining a second information weight based on the trauma description information of the emergency trauma patient and multiple clinical trauma description information; inputting the physical description information of the emergency trauma patient, the first information weight, the trauma description information of the emergency trauma patient and the second information weight into a pre-trained evaluation model to obtain the emergency trauma patient evaluation result output by the pre-trained evaluation model.

[0049] In some embodiments, the first information weight is used to characterize the complexity or rarity of the physical description information of the emergency trauma patient, and the second information weight is used to characterize the complexity or rarity of the trauma description information of the emergency trauma patient.

[0050] In this embodiment, complexity or rarity can be selected as the influence of the weight. For example, the higher the complexity or rarity of the physical description information of the emergency trauma patient, the greater the weight of the first information; the higher the complexity or rarity of the trauma description information of the emergency trauma patient, the greater the weight of the second information.

[0051] Therefore, as an example, determining the first information weight may include: if target clinical physical description information that matches the physical description information of an emergency trauma patient is determined from multiple clinical physical description information, determining the first information weight based on the complexity characteristics or rarity characteristics of the target clinical physical description information in the multiple clinical physical description information; if target clinical physical description information that matches the physical description information of an emergency trauma patient is not determined from multiple clinical physical description information, determining the first information weight based on the similarity between the physical description information of the emergency trauma patient and the multiple clinical physical description information.

[0052] In this embodiment, multiple pieces of clinical physical description information are compared with the physical description information of the emergency trauma patient to determine whether target clinical physical description information exists. The target clinical physical description information may be clinical physical description information whose similarity to the physical description information of the emergency trauma patient exceeds a preset similarity. Therefore, the similarity between the multiple pieces of clinical physical description information and the physical description information of the emergency trauma patient can be calculated to determine whether the target clinical physical description information exists.

[0053] In some embodiments, the preset similarity can be a value between 95% and 100%, and can be set specifically according to the difference between the emergency scenario and the clinical scenario in different application scenarios. The greater the difference, the lower the preset similarity can be.

[0054] Furthermore, if the target clinical physical description information exists, the first information weight is determined using the complexity or rarity of the target clinical physical description information among the multiple clinical physical description information. That is, the complexity or rarity of the physical description information of the emergency trauma patient is characterized by the complexity or rarity of the target clinical physical description information.

[0055] In some embodiments, the complexity feature or the rarity feature can be determined by frequency, similarity, etc. For example, the lower the frequency, the higher the rarity; the lower the average similarity with other clinical body description information, the higher the complexity, etc.

[0056] In some embodiments, the first information weight and complexity or rarity can have a corresponding conversion relationship. For example, the first information weight ranges from 0 to 1, and the complexity or rarity ranges from 0 to 100%. Based on the positive proportional relationship between the first information weight and complexity or rarity, a corresponding conversion function can be configured to achieve conversion. For example, the conversion function is: y = 0.01x, where x represents complexity or rarity, and y represents the first information weight.

[0057] In some embodiments, if the target clinical physical description information does not exist, a first information weight can be determined based on the average similarity between the physical description information of the emergency trauma patient and multiple clinical physical description information. The lower the average similarity, the lower the first information weight. The conversion relationship between the first information weight and the average similarity can be referenced to the implementation of the conversion between the first information weight and complexity or rarity, and will not be repeated here.

[0058] As an example, determining the second information weight may include: if target clinical trauma description information that matches the trauma description information of an emergency trauma patient is determined from multiple clinical trauma description information, determining the second information weight based on the complexity characteristics or rarity characteristics of the target clinical trauma description information in the multiple clinical trauma description information; if target clinical trauma description information that matches the trauma description information of the emergency trauma patient is not determined from the multiple clinical trauma description information, determining the second information weight based on the similarity between the trauma description information of the emergency trauma patient and the multiple clinical trauma description information.

[0059] The determination of the second information weight follows a similar approach to the first information weight, with both cases involving the presence and absence of target clinical trauma description information. Therefore, the detailed implementation of each step involved can be found in the detailed implementation of the first information weight, and will not be repeated here.

[0060] Furthermore, the body description information, the first information weight, the trauma description information and the second information weight of the emergency trauma patient are input into the pre-trained evaluation model to obtain the emergency trauma patient evaluation result output by the pre-trained evaluation model.

[0061] In some embodiments, the pre-trained evaluation model is an artificial neural network model, and its corresponding artificial neural network algorithm can be a natural language processing algorithm, etc.

[0062] In some embodiments, the body description information and the trauma description information of the emergency trauma patient can be converted into corresponding vectors respectively to facilitate model processing. For details, reference can be made to mature artificial neural network technology in the field.

[0063] In some embodiments, the training process of the pre-trained evaluation model may include:

[0064] Obtain a first training data set, the first training data set includes multiple first training samples, each first training sample includes: sample body description information, a weight corresponding to the sample body description information, sample trauma description information, a weight corresponding to the sample trauma description information and an evaluation label, the evaluation label is used to characterize the difficulty of emergency treatment; based on the first training data set, determine a second training data set, the second training data set includes multiple second training samples, each second training sample includes: sample body description information, sample trauma description information and an evaluation label; train the evaluation model to be trained based on the second training data set to obtain an initially trained evaluation model; optimize the initially trained evaluation model based on the first training data set to obtain a pre-trained evaluation model.

[0065] In some embodiments, the sample body description information and sample trauma description information can be obtained from existing relevant databases, regardless of clinical or emergency scenarios. Accordingly, the weights and evaluation labels corresponding to the sample body description information and sample trauma description information can be manually configured, or the evaluation labels can be determined based on the actual difficulty of the treatment.

[0066] In some embodiments, the difficulty of emergency treatment can be represented by a difficulty level, for example, 1 to 5, where the higher the level, the greater the difficulty.

[0067] In some embodiments, part of the data may be separated from the first training data set as a second training data set, and the second training data set does not involve information weight.

[0068] Furthermore, the evaluation model to be trained is first trained using the second training data set to obtain an initially trained evaluation model, which can also be evaluated without the need for weight information.

[0069] Next, the initially trained evaluation model is optimized using the first training dataset to improve its generalization capabilities. For example, the first training dataset can be used as a test dataset and fed into the evaluation model to obtain test results. The test results are used to determine the accuracy of the evaluation model with weight information. Based on this accuracy, training data including weight information is then selected. For example, the higher the accuracy, the less training data is selected, leading to optimized training.

[0070] It can be understood that if the model training is performed directly based on the first training data set, the evaluation model cannot be evaluated based on the unweighted situation. However, if the training method disclosed in the present invention is adopted, the evaluation model can be evaluated based on the unweighted situation, and the unweighted situation can correspond to the situation where the accuracy of the first information weight and the second information weight is poor, thereby ensuring the accuracy of the evaluation model in various situations.

[0071] Figure 3 FIG. 1 is a schematic diagram of a training process of an evaluation model according to an exemplary embodiment. Figure 3 As shown, first configure the original training dataset (i.e., the first training dataset), then select part of the original training dataset as a sub-training dataset (i.e., the second training dataset), first use the sub-training dataset for training, then use the original training dataset to test the accuracy, and then combine the test results to select a new sub-training dataset for optimization training.

[0072] Furthermore, the pre-trained evaluation model can ultimately evaluate the difficulty of emergency treatment for trauma patients and obtain emergency treatment evaluation results for trauma patients.

[0073] In some embodiments, the decision-making device 13 may be a device integrated with a pre-trained decision-making model, which may have functions such as data analysis and processing.

[0074] In some embodiments, the evaluation device 12 and the decision device 13 may be integrated into the same device, but their functions are different.

[0075] In some embodiments, the decision-making device 13 is configured to obtain emergency treatment physician information, and determine an emergency trauma patient treatment plan based on emergency trauma patient assessment results, emergency treatment physician information, a clinical trauma treatment database, and a pre-trained decision-making model.

[0076] In some embodiments, the clinical trauma treatment database includes multiple clinical trauma treatment protocols. These protocols are actual, effective, and effective clinical trauma treatment protocols used in clinical trauma scenarios. The clinical trauma treatment database can be used to determine treatment options.

[0077] In some embodiments, a clinical wound treatment plan may include a treatment approach, and more specifically, various treatment steps. For example, if the treatment approach is conservative treatment, the corresponding treatment steps may include: first, wound cleansing, then wound dressing, then observation of the patient's condition, and finally, whether to hospitalize the patient based on the patient's condition. Another example is if the treatment approach is a single-stage treatment, the corresponding treatment steps may include: wound cleansing, wound dressing, and prescribing wound recovery medication. It is understood that many more treatment options may be involved, and these examples are not listed here.

[0078] Furthermore, different treatment plans may require matching different levels of doctors. For example, conservative treatment requires higher levels of doctors, and it is necessary to judge whether the patient needs further treatment; one-time treatment requires lower levels of doctors, and only requires bandaging skills.

[0079] Therefore, as an optional implementation method, the process of determining a decision plan includes: evaluating the emergency treatment physician based on the emergency treatment physician information, determining the emergency treatment physician evaluation result, and the emergency treatment physician evaluation result is used to characterize the emergency treatment level of the emergency treatment physician; inputting multiple clinical treatment plans into the pre-trained decision model to obtain the clinical treatment difficulty label and clinical physician treatment level label corresponding to each clinical trauma treatment plan output by the pre-trained decision model; determining the emergency trauma patient treatment plan based on the clinical treatment difficulty label and clinical physician treatment level label corresponding to each clinical trauma treatment plan, the emergency trauma patient evaluation result and the emergency treatment physician evaluation result.

[0080] In this embodiment, the emergency treatment physician is first evaluated, and then a decision model is used to determine the clinical treatment difficulty label and the clinician treatment level label that match the clinical treatment plan, and then a plan is selected.

[0081] It is understood that multiple clinical treatment plans can also be implemented by pre-configuring clinical treatment difficulty labels and clinician treatment level labels. However, since multiple clinical treatment plans may be updated at any time, the labels configured in this way are less accurate. Using models for real-time decision-making is more accurate.

[0082] In some embodiments, emergency physician information may include the physician's age, years of practice, physician rank, number of emergency treatments performed by the physician, and number of clinical treatments performed by the physician. Based on this information, the emergency physician may be evaluated to determine an emergency physician evaluation result. For example, the longer the physician's years of practice, the higher the physician rank, the more emergency treatments performed by the physician, and the more clinical treatments performed by the physician, the higher the emergency physician's level of expertise.

[0083] In some embodiments, the pre-trained decision model includes a first module and a second module, wherein the first module can be used for feature extraction and the second module can be used for label determination.

[0084] The training process of the decision model may include: obtaining a third training data set, the third training data set includes multiple third training samples, each third training sample includes: a sample trauma treatment plan, a sample trauma treatment plan feature, a treatment difficulty label, and a doctor's treatment level label; based on the third training data set, determining a fourth training data set and a fifth training data set, the fourth training data set includes multiple fourth training samples, each fourth training sample includes a sample trauma treatment plan and a sample trauma treatment plan feature, and the fifth training data set includes multiple fifth training samples, each fifth training sample includes a sample trauma treatment plan, a treatment difficulty label, and a doctor's treatment level label; training the first module of the decision model to be trained based on the fourth training data set to obtain an initially trained decision model; training the second module of the initially trained decision model based on the fifth training data set to obtain a pre-trained decision model.

[0085] In this embodiment, a third training dataset is first obtained. The third training dataset includes sample trauma treatment plans, sample trauma treatment plan features, treatment difficulty labels, and physician treatment level labels. The sample trauma treatment plans can be actual treatment plans recorded in a database. Sample trauma treatment plan features can be obtained by extracting features from the plans. These features may include plan type features (e.g., the aforementioned single-use, conservative, etc.) and plan keyword features (e.g., the aforementioned cleaning, bandaging, and hospitalization, etc.).

[0086] In some embodiments, the treatment difficulty label and the doctor's treatment level label can be labels determined based on actual recorded situations, or can be manually configured labels.

[0087] In some embodiments, the third training data is split to obtain a fourth training data set for feature extraction training and a fifth training data set for label determination training.

[0088] Furthermore, the first module of the decision model to be trained is trained according to the fourth training data set to obtain an initially trained decision model; and the second module of the initially trained decision model is trained according to the fifth training data set to obtain a pre-trained decision model.

[0089] It can be understood that through this separate training method, the accuracy of different functional modules can be guaranteed, thereby improving the accuracy of the entire model.

[0090] Figure 4 FIG. 1 is a flow chart showing the training and application of a decision model according to an exemplary embodiment. Figure 4 As shown in the figure, during model training, the first module and the second module are trained separately. When the model is applied, the input of the first module is the input of the entire model, the output of the first model is the input of the second module, and the output of the second model is the output of the entire model.

[0091] Furthermore, determining the emergency trauma patient treatment plan based on the clinical treatment difficulty labels and clinical doctor treatment level labels corresponding to each clinical trauma treatment plan, the emergency trauma patient assessment results and the emergency treatment doctor assessment results may include: determining a first clinical trauma treatment plan based on the emergency trauma patient assessment results and the clinical treatment difficulty labels corresponding to each clinical trauma treatment plan, the clinical treatment difficulty label corresponding to the first clinical trauma treatment plan being higher than the emergency treatment difficulty represented by the emergency trauma patient assessment results; determining a second clinical trauma treatment plan based on the emergency treatment doctor assessment results and the clinical doctor treatment level labels corresponding to each clinical trauma treatment plan, the clinical doctor treatment level label corresponding to the second clinical trauma treatment plan being lower than the emergency treatment level represented by the emergency treatment doctor assessment results; and determining the emergency trauma patient treatment plan based on the first clinical trauma treatment plan and the second clinical trauma treatment plan.

[0092] In some embodiments, the first clinical trauma treatment plan and the second clinical trauma treatment plan are treatment plans determined based on different label conditions.

[0093] In some embodiments, if the first clinical wound treatment plan and the second clinical wound treatment plan have the same treatment plan, a treatment plan is randomly selected from the same treatment plan. If the first clinical wound treatment plan and the second clinical wound treatment plan do not overlap, a treatment plan is selected from the clinical wound treatment plan involving a larger number of patients.

[0094] In some embodiments, if neither the first clinical wound treatment plan nor the second clinical wound treatment plan exists, the treatment plan with a label closer to the current situation is determined as the final treatment plan.

[0095] Furthermore, after the decision-making device 13 determines a plan, feedback can be provided, and the doctor can make a choice according to needs.

[0096] Figure 5 FIG. 1 is a block diagram of another artificial neural network-based intelligent assessment and decision-making system 10 for emergency treatment of trauma patients according to an exemplary embodiment. Figure 5 As shown, the system further includes a visualization device 14 .

[0097] The visualization device 14 is in communication with the data acquisition device 11, the evaluation device 12, and the decision-making device 13. The visualization device 14 has visualization and interaction functions, and thus may include an input and output module, a display module, and the like.

[0098] In some embodiments, the visualization device 14 is configured to display emergency trauma patient data, emergency trauma patient assessment results, and emergency trauma patient treatment plans, respectively. It is understood that it can display various data involved in the system.

[0099] In some embodiments, the visualization device 14 is configured to, in response to receiving an auxiliary treatment request initiated by an emergency treatment physician, play a locally stored auxiliary treatment video recorded by a preset physician.

[0100] In this embodiment, the emergency treatment doctor can initiate an auxiliary treatment request through the interactive function of the visualization device 14. For example, the interface displayed by the visualization device 14 includes an auxiliary treatment control. When the emergency treatment doctor triggers the control, the visualization device 14 is deemed to have received the auxiliary treatment request.

[0101] In some embodiments, the visualization device 14 stores an auxiliary treatment video recorded by a preset doctor. If auxiliary treatment is needed, the auxiliary treatment video can be played directly.

[0102] In some embodiments, the preset doctor can be a doctor with strong expertise in emergency treatment. The auxiliary treatment video recorded by the doctor may include: first aid precautions, key first aid treatment steps and other first aid reference information to assist first aid doctors, nurses, etc. in providing more professional treatment.

[0103] Through this implementation, it can play a comprehensive auxiliary role in emergency treatment and further improve the effect of emergency treatment.

[0104] Figure 6 FIG. 1 is a flow chart showing an intelligent assessment and decision-making method for emergency treatment of trauma patients based on an artificial neural network according to an exemplary embodiment. Figure 6 As shown, the method includes:

[0105] Step S61, obtaining emergency trauma patient data.

[0106] Step S62: determining an emergency trauma patient assessment result based on the emergency trauma patient data, the clinical trauma database, and the pre-trained assessment model. The emergency trauma patient assessment result is used to characterize the emergency treatment difficulty of the emergency trauma patient.

[0107] Step S63: Obtain emergency treatment doctor information.

[0108] Step S64: Determine a treatment plan for the emergency trauma patient based on the emergency trauma patient assessment results, emergency treatment physician information, a clinical trauma treatment database, and a pre-trained decision model.

[0109] Regarding the method in the above embodiment, the specific manner of each step has been described in detail in the embodiment of the system and will not be elaborated here.

[0110] Figure 7 FIG. 7 is a block diagram of an electronic device 700 according to an exemplary embodiment. Figure 7 As shown, the electronic device 700 may include: a processor 701 , a memory 702 , and may further include one or more of a multimedia component 703 , an input / output (I / O) interface 704 , and a communication component 705 .

[0111] It can be understood that the electronic device 700 can serve as part or all of the aforementioned data acquisition device, decision-making device, evaluation device, and visualization device.

[0112] The processor 701 is used to control the overall operation of the electronic device 700 to complete all or part of the steps in the aforementioned artificial neural network-based intelligent emergency assessment and decision-making method for trauma patients. The memory 702 is used to store various types of data to support the operation of the electronic device 700. This data may include, for example, instructions for any application or method operating on the electronic device 700, as well as application-related data such as contact information, sent and received messages, images, audio, video, etc. The memory 702 can be implemented by any type of volatile or non-volatile storage device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The multimedia component 703 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the memory 702 or transmitted via the communication component 705. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 704 provides an interface between the processor 701 and other interface modules. The above-mentioned other interface modules may be a keyboard, a mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 705 is used for wired or wireless communication between the electronic device 700 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G or 4G, or a combination of one or more thereof, so the corresponding communication component 705 may include: a Wi-Fi module, a Bluetooth module, an NFC module.

[0113] In an exemplary embodiment, the electronic device 700 can be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to execute some or all of the steps of the above-mentioned artificial neural network-based intelligent assessment and decision-making method for first aid of trauma patients.

[0114] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided. When executed by a processor, the program instructions implement some or all of the steps of the aforementioned method for intelligent assessment and decision-making for emergency treatment of trauma patients based on an artificial neural network. For example, the computer-readable storage medium may be the aforementioned memory 702 including the program instructions. The program instructions may be executed by the processor 701 of the electronic device 700 to implement some or all of the steps of the aforementioned method for intelligent assessment and decision-making for emergency treatment of trauma patients based on an artificial neural network.

[0115] In another exemplary embodiment, a computer program product is also provided, which includes a computer program that can be executed by a processor, and when the computer program is executed by the processor, it implements part or all of the steps of the above-mentioned artificial neural network-based intelligent assessment and decision-making method for first aid of trauma patients.

[0116] The preferred embodiments of the present disclosure are described in detail above in conjunction with the accompanying drawings. However, the present disclosure is not limited to the specific details of the above embodiments. Within the technical concept of the present disclosure, various simple modifications can be made to the technical solutions of the present disclosure, and these simple modifications all fall within the scope of protection of the present disclosure.

[0117] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any appropriate manner without contradiction. In order to avoid unnecessary repetition, the present disclosure will not further describe various possible combinations.

[0118] In addition, the various embodiments of the present disclosure may be arbitrarily combined, and as long as they do not violate the concept of the present disclosure, they should also be regarded as the contents disclosed by the present disclosure.

Claims

1. An intelligent assessment and decision-making system for emergency treatment of trauma patients based on artificial neural networks, characterized in that: include: a data acquisition device configured to acquire emergency trauma patient data; an assessment device, communicatively connected to the data acquisition device, and configured to determine an assessment result of the emergency trauma patient based on the emergency trauma patient data, the clinical trauma database, and the pre-trained assessment model, wherein the assessment result of the emergency trauma patient is used to characterize the difficulty of emergency treatment of the emergency trauma patient; a decision-making device, communicatively connected to the data acquisition device and the evaluation device, and configured to obtain information about the emergency treatment physician and determine a treatment plan for the emergency trauma patient based on the emergency trauma patient evaluation result, the emergency treatment physician information, a clinical trauma treatment database, and a pre-trained decision-making model; The emergency trauma patient data includes body description information and trauma description information of the emergency trauma patient, the clinical trauma database includes: multiple clinical trauma data, each clinical trauma data includes clinical body description information and clinical trauma description information, and the evaluation device is further configured to: determining a first information weight based on the physical description information of the emergency trauma patient and the plurality of clinical physical description information; determining a second information weight based on the trauma description information of the emergency trauma patient and the plurality of clinical trauma description information; Inputting the body description information of the emergency trauma patient, the first information weight, the trauma description information of the emergency trauma patient, and the second information weight into the pre-trained evaluation model to obtain an evaluation result of the emergency trauma patient output by the pre-trained evaluation model; The first information weight is used to characterize the complexity or rarity of the physical description information of the emergency trauma patient, and the second information weight is used to characterize the complexity or rarity of the trauma description information of the emergency trauma patient. The evaluation device is further configured to: If target clinical physical description information that matches the physical description information of the emergency trauma patient is not determined from the multiple clinical physical description information, determining the first information weight according to the similarity between the physical description information of the emergency trauma patient and the multiple clinical physical description information; If target clinical trauma description information that matches the trauma description information of the emergency trauma patient is not determined from the multiple clinical trauma description information, the second information weight is determined based on the similarity between the trauma description information of the emergency trauma patient and the multiple clinical trauma description information.

2. The intelligent assessment and decision-making system for emergency treatment of trauma patients according to claim 1, characterized in that: The data acquisition device comprises: A first collecting device is configured to collect physical description information of an emergency trauma patient, wherein the physical description information is used to describe a physical reaction of the emergency trauma patient caused by trauma; A second collecting device is configured to collect trauma description information of an emergency trauma patient, wherein the trauma description information is used to describe the wound condition of the emergency trauma patient; The processing device is configured to integrate the body description information and the trauma description information to obtain the emergency trauma patient data.

3. The intelligent assessment and decision-making system for emergency treatment of trauma patients according to claim 1, characterized in that: The evaluation device is further configured to: If target clinical physical description information that matches the physical description information of the emergency trauma patient is determined from the multiple clinical physical description information, the first information weight is determined according to the complexity feature or rarity feature of the target clinical physical description information in the multiple clinical physical description information; and / or, If target clinical trauma description information that matches the trauma description information of the emergency trauma patient is determined from the multiple clinical trauma description information, the second information weight is determined based on the complexity characteristics or rarity characteristics of the target clinical trauma description information in the multiple clinical trauma description information.

4. The intelligent assessment and decision-making system for emergency treatment of trauma patients according to claim 1 or 3, characterized in that: The evaluation device is further configured to: Obtaining a first training data set, the first training data set including a plurality of first training samples, each of the first training samples including: sample body description information, a weight corresponding to the sample body description information, sample trauma description information, a weight corresponding to the sample trauma description information, and an evaluation label, the evaluation label being used to characterize the difficulty of emergency treatment; Determine a second training data set based on the first training data set, where the second training data set includes a plurality of second training samples, each of the second training samples including: sample body description information, sample trauma description information, and an evaluation label; Training the evaluation model to be trained based on the second training data set to obtain an initially trained evaluation model; The initially trained evaluation model is optimized and trained according to the first training data set to obtain the pre-trained evaluation model.

5. The intelligent assessment and decision-making system for emergency treatment of trauma patients according to claim 1, characterized in that: The clinical trauma treatment database includes: a plurality of clinical trauma treatment plans, and the decision-making device is further configured to: Evaluate the emergency treatment physician according to the emergency treatment physician information to determine an emergency treatment physician evaluation result, wherein the emergency treatment physician evaluation result is used to represent the emergency treatment level of the emergency treatment physician; Inputting multiple clinical treatment plans into the pre-trained decision model to obtain clinical treatment difficulty labels and clinician treatment level labels corresponding to each of the clinical trauma treatment plans output by the pre-trained decision model; The emergency trauma patient treatment plan is determined according to the clinical treatment difficulty label and the clinical doctor treatment level label corresponding to each clinical trauma treatment plan, the emergency trauma patient assessment result and the emergency treatment doctor assessment result.

6. The intelligent assessment and decision-making system for emergency treatment of trauma patients according to claim 5, characterized in that: The pre-trained decision model includes a first module and a second module, and the decision device is further configured to: Acquire a third training data set, the third training data set including a plurality of third training samples, each of the third training samples including: a sample trauma treatment plan, a sample trauma treatment plan feature, a treatment difficulty label, and a doctor treatment level label; Determining a fourth training data set and a fifth training data set based on the third training data set, wherein the fourth training data set includes a plurality of fourth training samples, each of the fourth training samples includes a sample trauma treatment plan and a sample trauma treatment plan feature, and the fifth training data set includes a plurality of fifth training samples, each of the fifth training samples includes a sample trauma treatment plan, a treatment difficulty label, and a doctor's treatment level label; Training the first module of the decision model to be trained according to the fourth training data set to obtain an initially trained decision model; The second module of the initially trained decision model is trained according to the fifth training data set to obtain the pre-trained decision model.

7. The intelligent assessment and decision-making system for emergency treatment of trauma patients according to claim 5 or 6, characterized in that: The decision-making device is further configured to: determining a first clinical trauma treatment plan based on the emergency trauma patient assessment result and the clinical treatment difficulty labels corresponding to the respective clinical trauma treatment plans, wherein the clinical treatment difficulty label corresponding to the first clinical trauma treatment plan is higher than the emergency treatment difficulty represented by the emergency trauma patient assessment result; determining a second clinical trauma treatment plan based on the emergency treatment physician's evaluation result and the clinician treatment level labels corresponding to each of the clinical trauma treatment plans, wherein the clinician treatment level label corresponding to the second clinical trauma treatment plan is lower than the emergency treatment level represented by the emergency treatment physician's evaluation result; The emergency trauma patient treatment plan is determined based on the first clinical trauma treatment plan and the second clinical trauma treatment plan.

8. The intelligent assessment and decision-making system for emergency treatment of trauma patients according to claim 1, characterized in that: The intelligent assessment and decision-making system for first aid of trauma patients also includes: A visualization device is communicatively connected to the data acquisition device, the evaluation device, and the decision-making device, and is configured to: The emergency trauma patient data, the emergency trauma patient assessment results and the emergency trauma patient treatment plan are displayed respectively; In response to receiving the auxiliary treatment request initiated by the emergency treatment doctor, playing the locally stored auxiliary treatment video recorded by the preset doctor.

9. An intelligent assessment and decision-making method for emergency treatment of trauma patients based on artificial neural networks, characterized in that: include: Obtain emergency trauma patient data; Determining an emergency trauma patient assessment result based on the emergency trauma patient data, the clinical trauma database, and the pre-trained assessment model, wherein the emergency trauma patient assessment result is used to characterize the emergency treatment difficulty of the emergency trauma patient; Obtain information about emergency medical treatment physicians; Determining a treatment plan for the emergency trauma patient based on the emergency trauma patient assessment results, the emergency treatment physician information, a clinical trauma treatment database, and a pre-trained decision model; The emergency trauma patient data includes physical description information and trauma description information of the emergency trauma patient, the clinical trauma database includes: multiple clinical trauma data, each clinical trauma data includes clinical physical description information and clinical trauma description information, and determining the emergency trauma patient assessment result based on the emergency trauma patient data, the clinical trauma database and the pre-trained assessment model includes: determining a first information weight based on the physical description information of the emergency trauma patient and the plurality of clinical physical description information; determining a second information weight based on the trauma description information of the emergency trauma patient and the plurality of clinical trauma description information; Inputting the body description information of the emergency trauma patient, the first information weight, the trauma description information of the emergency trauma patient, and the second information weight into the pre-trained evaluation model to obtain an evaluation result of the emergency trauma patient output by the pre-trained evaluation model; The first information weight is used to characterize the complexity or rarity of the physical description information of the emergency trauma patient, and the second information weight is used to characterize the complexity or rarity of the trauma description information of the emergency trauma patient. Determining the first information weight based on the physical description information of the emergency trauma patient and the plurality of clinical physical description information includes: If target clinical physical description information that matches the physical description information of the emergency trauma patient is not determined from the multiple clinical physical description information, determining the first information weight according to the similarity between the physical description information of the emergency trauma patient and the multiple clinical physical description information; Determining the second information weight based on the trauma description information of the emergency trauma patient and the plurality of clinical trauma description information includes: If target clinical trauma description information that matches the trauma description information of the emergency trauma patient is not determined from the multiple clinical trauma description information, the second information weight is determined based on the similarity between the trauma description information of the emergency trauma patient and the multiple clinical trauma description information.

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