Methods and devices for processing bone fascia compartment monitoring data, electronic equipment, and storage media

Abstract

The present disclosure provides a method and device for processing bone fascia compartment monitoring data, an electronic device, and a storage medium, which belongs to the field of data processing technology. The method includes: obtaining the personal information of a target person and the first disease measurement data corresponding to the target person; inputting the personal information of the target person and the first disease measurement data corresponding to the target person into a target neural network model to obtain the pathological data of the bone fascia compartment of the target person; the target neural network model is obtained by training the first neural network model based on the personal information of historical personnel and the first disease measurement data; and determining the treatment strategy of the target person based on the pathological data of the bone fascia compartment of the target person. The present disclosure can reduce the subjectivity of human judgment, improve the accuracy of diagnosis, and provide a practical treatment plan for the attending personnel.

Description

Technical Field

[0001] The present disclosure belongs to the field of data processing technology, and more specifically, relates to a method and device for processing bone fascia compartment monitoring data, an electronic device, and a storage medium. Background Art

[0002] In the medical field, especially in orthopedics and surgery, accurate monitoring and diagnosis of the state of the osteofascial compartment is crucial. The osteofascial compartment is a closed cavity containing muscle, nerve, blood vessel and other tissues. When these tissues are damaged or affected by inflammation, it may lead to increased pressure and obstructed blood circulation, which may lead to serious complications such as ischemic muscle contracture and limb necrosis. Therefore, timely and accurate monitoring of the state of the osteofascial compartment is of great significance for the prevention and treatment of related diseases.

[0003] Traditional compartment monitoring methods mainly rely on the doctor's clinical experience, the patient's symptom description, physical examination, etc. However, these methods are highly subjective and have errors, because different doctors may interpret the same symptom differently, and the patient's symptom description may also be affected by subjective factors such as pain and anxiety. Summary of the invention

[0004] The purpose of the present disclosure is to provide a method and device for processing osteofascial compartment monitoring data, an electronic device, and a storage medium to help attending personnel make accurate judgments on patients in a short period of time and gain valuable time for treatment.

[0005] A first aspect of the embodiments of the present disclosure provides a method for processing bone fascia compartment monitoring data, comprising:

[0006] Acquire personal information of a target person and first disease measurement data corresponding to the target person;

[0007] Inputting the personal information of the target person and the first disease measurement data corresponding to the target person into the target neural network model to obtain the pathological data of the target person's osteofascial compartment; the target neural network model is obtained by training the first neural network model based on the personal information of the historical person and the first disease measurement data;

[0008] A treatment strategy for the target person is determined based on the lesion data of the target person's osteofascial compartment.

[0009] A second aspect of the embodiments of the present disclosure provides a bone fascia compartment monitoring data processing device, comprising:

[0010] A data acquisition module, used to acquire personal information of a target person and first disease measurement data corresponding to the target person;

[0011] A data processing module, used for inputting the personal information of the target person and the first disease measurement data corresponding to the target person into the target neural network model to obtain the pathological data of the target person's osteofascial compartment; the target neural network model is obtained by training the first neural network model based on the personal information of the historical person and the first disease measurement data;

[0012] A strategy determination module is used to determine the treatment strategy of the target person based on the lesion data of the target person's osteofascial compartment.

[0013] According to a third aspect of an embodiment of the present disclosure, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the above-mentioned fascial compartment monitoring data processing method when executing the computer program.

[0014] According to a fourth aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned bone fascia compartment monitoring data processing method are implemented.

[0015] The beneficial effects of the bone fascia compartment monitoring data processing method and device, electronic device, and storage medium provided by the embodiments of the present disclosure are:

[0016] On the one hand, by introducing the target neural network model, the present disclosure can efficiently process and analyze the personal information of the target person and the first disease measurement data, so as to accurately obtain the pathological data of the osteofascial compartment. Compared with traditional diagnostic methods, this method greatly reduces the subjectivity and errors of human judgment and improves the accuracy of diagnosis. At the same time, the rapid processing capability of the neural network model also makes the diagnostic process more efficient, which helps doctors make accurate judgments in a short time and gain valuable time for treatment.

[0017] On the other hand, the disclosure can further determine personalized treatment strategies based on the target person's osteofascial compartment lesion data. Everyone's condition and physical condition are unique, and the traditional "one-size-fits-all" treatment method often fails to achieve the best results. The disclosure can develop a treatment plan that better meets individual needs by deeply analyzing the target person's personal information and lesion data, thereby improving treatment effects and reducing unnecessary drug use and surgical risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0019] Figure 1 A schematic diagram of a flow chart of a method for processing bone fascia compartment monitoring data provided by an embodiment of the present disclosure;

[0020] Figure 2 A system structure framework for processing bone fascia compartment monitoring data provided by an embodiment of the present disclosure;

[0021] Figure 3 A schematic diagram of constructing a neural network model provided by an embodiment of the present disclosure;

[0022] Figure 4 A schematic diagram of the overall structure of a transformer model provided in one embodiment of the present disclosure;

[0023] Figure 5 A schematic diagram of the internal structure of a transformer model provided in one embodiment of the present disclosure;

[0024] Figure 6 A schematic flow chart of a method for processing bone fascia compartment monitoring data provided by another embodiment of the present disclosure;

[0025] Figure 7 A structural block diagram of a bone fascia compartment monitoring data processing device provided in one embodiment of the present disclosure;

[0026] Figure 8 A schematic block diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0027] In the following description, specific details such as specific system structures and technologies are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present disclosure. However, it should be clear to those skilled in the art that the present disclosure may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obstructing the description of the present disclosure with unnecessary details.

[0028] In order to make the purpose, technical solutions and advantages of the present disclosure more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0029] Please refer to Figure 1 , Figure 1A flowchart of a method for processing bone fascia compartment monitoring data provided by an embodiment of the present disclosure, the method comprising:

[0030] S101: Obtaining personal information of a target person and first disease measurement data corresponding to the target person.

[0031] In this embodiment, the target person refers to a specific individual selected as a monitoring object in medical monitoring or research, and needs to monitor the osteofascial compartment pressure or other related health indicators. This individual can be a patient, an athlete, a patient in recovery, or anyone who needs such health monitoring.

[0032] refer to Figure 2 , Figure 2 A system structure framework for processing bone fascia compartment monitoring data provided in an embodiment of the present disclosure. The data acquisition terminal can send the collected health index data of the target person to the bone fascia compartment monitoring device and the comprehensive data platform respectively. The bone fascia compartment monitoring device can analyze and process the collected data to obtain the treatment strategy of the target person. At the same time, the comprehensive data platform can store the collected data for future reference.

[0033] Personal information refers to basic identity information or health background information related to the target person, which is essential for determining the target person's health status, disease history, and developing personalized monitoring strategies. Personal information includes basic information, health status, and associated medical history. Basic information includes, but is not limited to, the target person's name, gender, age, occupation, work area, contact information, identification, etc.; health status includes, but is not limited to, the target person's height, weight, allergy history, current disease, vaccination history, living habits, etc.; associated medical history includes, but is not limited to, the target person's past medical history, family medical history, medical records, long-term medication habits, etc.

[0034] The first disease measurement data refers to the initial measurement data directly related to the health status of the target person's osteofascial compartment, including osteofascial compartment pressure data, muscle tension data, blood circulation data or other physiological index data related to osteofascial compartment syndrome, etc. These data can be obtained through specific medical equipment or sensors. For example, the intracavitary pressure of the calf osteofascial compartment can be monitored by using the Whiteside acupuncture pressure measuring device. During the measurement, in order to ensure the accuracy of the measurement position, the highest pressure value is usually found within 5 cm of the injury plane. Muscle tension can be measured by muscle tension assessment, passive motion test, stretch reflex test, muscle hardness assessment, joint range of motion measurement and other methods. Blood circulation status can be evaluated by blood pressure measurement, electrocardiogram measurement, echocardiogram, Doppler ultrasound blood detector or color Doppler ultrasound. In addition to the above direct evaluation methods, the symptoms of the target person's affected limb can also be observed, and the target person can be tested for blood routine and urine routine.

[0035] S102: Input the personal information of the target person and the first disease measurement data corresponding to the target person into the target neural network model to obtain the pathological data of the target person's osteofascial compartment; the target neural network model is obtained by training the first neural network model based on the personal information and the first disease measurement data of the historical person.

[0036] In this embodiment, the target neural network model is obtained by training the first neural network model based on the personal information of the historical personnel and the first disease measurement data. The historical personnel refer to individuals who have undergone osteofascial compartment lesion assessment in the past or have relevant health records. Their personal information and the first disease measurement data are used as sample data for training the neural network model to help the model learn the mapping relationship from data to lesion assessment.

[0037] The target person's osteofascial compartment lesion data is the output result of the target neural network model, and is used to describe the lesion condition of the target person's osteofascial compartment. These lesion data may include the probability of lesions, the severity of lesions, the type of lesions, etc. The neural network model may be one of a feedforward neural network, a convolutional neural network, a recursive neural network, a generative adversarial network, etc.

[0038] The osteofascial compartment monitoring data processing method also includes: determining a neural network model according to the personal information of the target person and the type of the first disease measurement data.

[0039] If the data type contained in the target person's personal information and the first disease measurement data is a numeric type, the neural network model can be a feedforward neural network; if the data type contained in the target person's personal information and the first disease measurement data is an image type, the neural network model is determined to be a convolutional neural network; if the data type contained in the target person's personal information and the first disease measurement data is a text type, the neural network model can be a recursive neural network or a Transformer model.

[0040] refer to Figure 3 , Figure 3 A schematic diagram of constructing a neural network model provided for one embodiment of the present disclosure. First, the collected raw data is divided into a training data set, a test data set, and a validation data set. The weights and bias parameters of the neural network model are initialized, and then the training data is input into the neural network model to obtain the output result of the model, and the loss function is calculated according to the output result of the model and the true result. When the loss function is less than a preset first value, the trained neural network model is obtained. If the loss function is greater than or equal to the preset first value, the neural network model continues to be trained until the value of the loss function is less than the first value, and the training ends. The trained neural network model is verified using the test data set and the validation data set to obtain the final neural network model.

[0041] refer to Figure 4 and Figure 5 , Figure 4 A schematic diagram of the overall structure of the transformer model provided for an embodiment of the present disclosure. The encoder is composed of a plurality of identical layers stacked together, and each layer contains two main sublayers (multi-head self-attention mechanism and feedforward neural network). The decoder is also composed of a plurality of identical layers stacked together, but each layer contains three main sublayers (self-attention mechanism, encoding-decoding attention mechanism, and feedforward neural network).

[0042] Figure 5 Schematic diagram of the internal structure of the transformer model provided for one embodiment of the present disclosure. These are existing model structures and can be directly applied to data processing in this application.

[0043] S103: Determine a treatment strategy for the target person based on the lesion data of the target person's osteofascial compartment.

[0044] In this embodiment, the treatment strategy refers to a treatment plan formulated for the target person based on the analysis results of the lesion data to improve or restore the functional state of the osteofascial compartment. The treatment strategy may include drug therapy (such as the use of antibiotics, anticoagulants, etc.), physical therapy (such as massage, traction, rehabilitation training, etc.), surgical treatment (such as fasciotomy decompression), and other means.

[0045] The treatment strategies for target persons determined based on the lesion data of the target person's osteofascial compartment include:

[0046] Determine a treatment strategy for the target person based on the lesion data of the target person's osteofascial compartment and the target person's physical indicators;

[0047] Alternatively, a treatment strategy for the target person is determined based on the lesion data of the target person's osteofascial compartment and the correlation between the target person's personal information and the lesion data.

[0048] The treatment strategy of the target person is determined based on the lesion data of the target person's osteofascial compartment and the target person's physical indicators, including: determining the treatment strategy of the target person based on the number of lesions in the target person's osteofascial compartment, the target person's nerve compression data, and the target person's blood vessel compression data.

[0049] In this embodiment: the treatment strategy of the target person is related to three factors, one of which is the number of lesions in the target person's osteofascial compartment. The number of lesions refers to the number of lesions occurring in the target person's osteofascial compartment. Different numbers of lesions have different treatment priorities. A single lesion is easier to locate and treat, and the treatment strategy is relatively simple; multiple lesions require a more comprehensive assessment and treatment plan, because multiple lesions may affect each other, increasing the complexity of treatment. The second is nerve compression data. Nerve compression data includes the degree, location, and duration of nerve compression. Nerve compression is one of the serious complications of osteofascial compartment syndrome, which can cause sensory and motor dysfunction. The third is vascular compression data. Vascular compression data involves the degree and type (arterial or venous) of vascular compression, the effect on blood flow, etc. Vascular compression can cause limb ischemia or congestion, thereby affecting tissue perfusion and survival.

[0050] The treatment strategy of the target person is determined based on the lesion data of the target person's osteofascial compartment and the correlation between the target person's personal information and the lesion data: the lesion data includes key information such as the severity, location, range, and whether nerve or blood vessel compression is involved in the lesion. The lesion data is the basis for determining the treatment strategy. Personal information is essential for understanding lesion data, predicting treatment responses, and formulating personalized treatment plans. By analyzing the relationship between personal information and lesion data, key factors that affect treatment selection and effectiveness can be identified. For example, age and health status may affect the risk and recovery speed of surgery, and psychological conditions may affect patients' acceptance and compliance with treatment.

[0051] From the above, it can be concluded that, on the one hand, the present disclosure can efficiently process and analyze the personal information of the target person and the first disease measurement data by introducing the target neural network model, so as to accurately obtain the lesion data of the osteofascial compartment. Compared with traditional diagnostic methods, this method greatly reduces the subjectivity and errors of human judgment and improves the accuracy of diagnosis. At the same time, the rapid processing capability of the neural network model also makes the diagnostic process more efficient, which helps doctors make accurate judgments in a short time and gain valuable time for treatment.

[0052] On the other hand, the disclosure can further determine personalized treatment strategies based on the target person's osteofascial compartment lesion data. Everyone's condition and physical condition are unique, and the traditional "one-size-fits-all" treatment method often fails to achieve the best results. The disclosure can develop a treatment plan that better meets individual needs by deeply analyzing the target person's personal information and lesion data, thereby improving treatment effects and reducing unnecessary drug use and surgical risks.

[0053] In one embodiment of the present disclosure, the method for processing bone fascia compartment monitoring data further includes:

[0054] In response to receiving the first identification information, the first disease measurement data corresponding to the target person is optimized according to the first identification information to obtain the second disease measurement data.

[0055] In this embodiment, the first identification information may be a specific requirement, instruction or condition generated by a doctor, technician or device. The first disease measurement data is the real data of the measured target person, but these data may be affected by various factors, such as equipment accuracy, measurement environment, operator skill level, etc. These factors may cause certain deviations in the measured real data. By optimizing the first disease measurement data, these errors can be eliminated or reduced, and the accuracy of the data can be improved.

[0056] In addition, in actual medical environments, multiple devices may be used to measure the same disease indicator, and different devices will produce different results, resulting in inconsistent data. By standardizing or normalizing these data, they can be better comparable, thereby enhancing data consistency.

[0057] The optimization may include data cleaning, denoising, standardization, format conversion, or screening according to specific rules, etc. The second disease measurement data may be obtained by optimizing the first disease measurement data.

[0058] From the above, it can be concluded that this embodiment optimizes the first disease measurement data of the target person in response to the first identification information to obtain the second disease measurement data, which significantly improves the accuracy and reliability of the data, and helps doctors to more accurately judge the condition and formulate personalized treatment plans.

[0059] In one embodiment of the present disclosure, the method for processing bone fascia compartment monitoring data further includes:

[0060] The personal information and the second disease measurement data of the target person are processed to obtain the optimized lesion data of the target person's osteofascial compartment.

[0061] In this embodiment, the personal information of the target person and the second disease measurement data are processed to obtain the optimized lesion data of the target person's osteofascial compartment, including:

[0062] The target person’s personal information and the second disease measurement data are subjected to feature extraction, feature selection, and model training to obtain the optimized lesion data of the target person’s osteofascial compartment.

[0063] Alternatively, the personal information of the target person and the second disease measurement data are calculated according to the first formula to obtain the optimized lesion data of the target person's osteofascial compartment.

[0064] Feature extraction is to extract key information related to the target variable (i.e., osteofascial compartment lesions) from the original data. Feature extraction includes calculating statistical features (such as mean, standard deviation), performing frequency domain or time domain analysis, extracting text features (such as keywords, phrases), etc. After feature extraction of the target person's personal information and the second disease measurement data, a set of features that can reflect the target person's osteofascial compartment lesions can be obtained.

[0065] Feature selection is to select the most helpful features for the prediction of the target variable from the feature set to reduce the computational complexity and improve the prediction accuracy. Feature selection includes correlation analysis, principal component analysis, etc. Feature selection on the feature set obtains a streamlined feature subset, which is critical for the prediction of osteofascial compartment lesions.

[0066] Model training, using the selected feature subset to train a prediction model, the model can predict the pathological condition of the target person's osteofascial compartment based on the input data. The prediction model can be the first neural network model or other neural network models.

[0067] The first formula can be:

[0068]

[0069] in, Indicates optimized lesion data of the target person's osteofascial compartment, such as a lesion index; It represents the personal information index, which is a value obtained by comprehensively considering the target person's age, gender, physical condition and other personal information, for example, by assigning different weights to different factors and performing comprehensive calculations; The power exponent representing the personal information index is used to adjust the influence of personal information on the optimization of lesion data; Represents the main disease measurement data, that is, the main disease measurement values ​​directly related to osteofascial compartment lesions, such as specific pressure values, tissue damage indicators, etc.; is the adjustment factor; Data were measured for secondary diseases; is the power index of the secondary disease measurement data; F is the fracture factor. If the target person has a fracture, F is the first value. If the target person does not have a fracture, F is the second value. The first value is greater than the second value. The age of the target person; is the power exponent of age, reflecting the influence trend of age on the optimization of lesion data; is body mass index; , They are all constant coefficients, which are used to adjust the numerical range and influence of the formula as a whole.

[0070] In this embodiment, when the lesion index is greater than the third value, the first treatment strategy is output;

[0071] When the lesion index is less than a fourth value, outputting a second treatment strategy;

[0072] When the lesion index is greater than or equal to the fourth value and less than or equal to the third value, a third treatment strategy is output; the first treatment strategy includes surgical treatment, the second treatment strategy includes drug treatment, and the third treatment strategy includes drug treatment and limb recovery treatment. Limb recovery treatment is to use physical means to improve the degree of lesions in the patient's osteofascial compartment.

[0073] For example, suppose the personal information index is 0.8, which is derived by taking into account the target person being 40 years old, male, and in average physical condition. The power index β is 0.5. The primary disease measurement data is 12 (assuming it is a certain pressure measurement value in mmHg). The adjustment coefficient γ is 0.3, the secondary disease measurement data is 4 (assuming it is an auxiliary tissue damage indicator), and the power index is The power index for age 40 is 0.4. The fracture factor is 1.5. The target person's body mass index is 25, and the constant coefficient is 0.3. The constant coefficient α is 2. Substituting the above values ​​into the first formula, the optimized lesion data of the target person's osteofascial compartment is 97.

[0074] when When the lesion is deep, it means that the lesion in the osteofascial compartment is serious, with high pressure and extensive tissue damage, and active treatment measures need to be taken in time, such as surgical decompression as soon as possible.

[0075] when At this time, the lesions in the osteofascial compartment are relatively mild, and the symptoms can be relieved through drug treatment and other methods, and the changes in the condition are closely observed.

[0076] when When the disease is at a moderate level, it is necessary to comprehensively evaluate the patient's specific situation and carefully select a treatment plan, such as combining drug therapy and limb recovery therapy.

[0077] It can be concluded from the above that this embodiment can accurately obtain the optimized lesion data of the target person's osteofascial compartment through feature extraction, feature selection and model training, or by using a comprehensive formula calculation. This method not only improves the efficiency and accuracy of data processing, but also provides doctors with a more reliable basis for diagnosis, helps to take effective treatment measures in a timely manner, reduces the risk of complications, and improves the quality of patient rehabilitation, which has important clinical application value and practical significance.

[0078] In one embodiment of the present disclosure, reference Figure 6 , training the first neural network model based on the historical personnel's personal information and the first disease measurement data, including:

[0079] S2011: If the total amount of the historical personnel's personal information and the first disease measurement data is less than the first value, the total amount of the historical personnel's personal information and the first disease measurement data is expanded to obtain a target total amount of data;

[0080] S2012: Training the first neural network model according to the total amount of target data.

[0081] In this embodiment, first, the device determines whether the total amount of data of the historical personnel's personal information and the first disease measurement data is less than the first value. This is to ensure that there is enough data to train the neural network model, because insufficient data may lead to insufficient model training, thereby affecting the accuracy of the model.

[0082] If the total amount of data is insufficient, i.e., less than the first value, data expansion can be performed to increase the total amount of data to the target total amount of data. Data expansion can be a method such as data synthesis (such as generating new data points based on existing data). After the total amount of data reaches the target total amount of data, the first neural network model is trained using these data to obtain the target neural network model.

[0083] From the above, it can be concluded that this embodiment ensures the sufficiency of data for neural network model training by determining the total amount of data and expanding the data in a timely manner, thereby improving the generalization and accuracy of model training. The data expansion strategy effectively solves the problem of insufficient data, allowing the model to better learn the inherent laws and characteristics of the data, thereby improving the performance and reliability of the model in predicting osteofascial compartment lesions, and providing strong support for clinical decision-making.

[0084] In one embodiment of the present disclosure, the total amount of personal information of historical personnel and first disease measurement data is expanded to obtain the total amount of target data, including:

[0085] The total amount of historical personnel's personal information and the first disease measurement data is supplemented with standard data to obtain the total amount of target data.

[0086] In this embodiment, standard data refers to the disease measurement data corresponding to the pathological data of the osteofascial compartments of different persons. For example, when the first disease measurement data is the intra-compartmental pressure value and the degree of swelling, the intra-compartmental pressure value corresponding to the early compartment syndrome is 30~40mmHg; the intra-compartmental pressure value corresponding to the late compartment syndrome is greater than 40mmHg. In case of mild swelling, the circumference of the affected limb of the person increases by 1~2cm; in case of moderate swelling, the circumference of the affected limb of the person increases by 2~3cm; in case of severe swelling, the circumference of the affected limb of the person increases by more than 3cm.

[0087] From the above, it can be concluded that standard data provides additional, known information. By supplementing these standard data, the diversity and comprehensiveness of the data set can be enriched, thereby improving the neural network model's ability to identify and process different situations. Standard data are usually based on scientific research and clinical experience, and have high accuracy and reliability. Incorporating these data into the training process can help the model learn the association between lesion data and disease status more accurately, thereby improving the model's accuracy in predicting osteofascial compartment lesions. When the amount of original data is insufficient, the neural network model is prone to overfitting, that is, the model fits the training data too closely, and has poor predictive ability for new data. By supplementing standard data, the diversity of the data set can be increased, thereby reducing the risk of overfitting.

[0088] In one embodiment of the present disclosure, the personal information of the target person includes basic information, health status, and associated medical history;

[0089] The treatment strategies for target persons determined based on the lesion data of the target person's osteofascial compartment include:

[0090] Determine the relevance level of the target person's basic information, health status and associated medical history to the lesion data;

[0091] The treatment strategy of the target person is determined based on the lesion data and correlation level of the target person's osteofascial compartment.

[0092] In this embodiment, the correlation levels of the target person's basic information, health status, and associated medical history with the lesion data can be calculated by a second formula, which is:

[0093]

[0094] in, It represents the correlation level between the i-th feature and the lesion data Y, and the value is between 0 and 1. The closer to 1, the stronger the correlation. The feature vector representing the target person’s basic information, health status, and associated medical history, n is a constant. represents the i-th feature in the feature vector X, Y represents the pathological data of the target person's osteofascial compartment, which can be a value or a group of values ​​indicating the degree of the pathological change, etc.; is the weight coefficient of the i-th feature, It represents the distance metric between the i-th feature and the lesion data Y. The appropriate distance metric can be selected according to the type of feature and lesion data, such as Euclidean distance, Manhattan distance, etc. It measures how different the feature is from the lesion data, with smaller distances indicating stronger correlation.

[0095] Determine the treatment strategy for the target person based on the lesion data and correlation level of the target person's osteofascial compartment:

[0096] Each person's physical condition, medical history, and current condition are unique, so treatment strategies need to be customized. By collecting the target person's basic information, health status, and related medical history, and correlating and analyzing it with the lesion data of the osteofascial compartment, we can more accurately understand the target person's disease characteristics and physical condition, and thus develop a treatment strategy that better meets their personal needs.

[0097] Compartment syndrome is a serious disease. If it is not treated in time or treated improperly, it may lead to acute ischemic necrosis of muscles and nerves, and even serious consequences such as amputation. Therefore, the accuracy of the treatment strategy is crucial. Through analysis based on lesion data and correlation levels, the severity of the target person's condition and potential risks can be more accurately assessed, so that a more precise treatment plan can be formulated to improve treatment effectiveness and safety.

[0098] Doctors need to consider multiple factors of patients when formulating treatment strategies. By providing analysis results based on lesion data and correlation levels, doctors can be provided with more comprehensive and objective information support to help them make more informed and scientific decisions. This can not only improve treatment effects, but also reduce medical risks and enhance patient satisfaction and trust.

[0099] It can be concluded from the above that determining the treatment strategy based on the lesion data and correlation level of the target person's osteofascial compartment is a scientific, reasonable and personalized method, which helps to improve the accuracy and safety of treatment, optimize the allocation of medical resources, and support clinical decision-making.

[0100] In one embodiment of the present disclosure, the lesion data of the osteofascial compartment includes lesion type and lesion grade;

[0101] The treatment strategy of the target person is determined based on the lesion data of the target person's osteofascial compartment, including:

[0102] If the lesion type of the compartment is chronic compartment syndrome, the treatment strategy for the target person is determined based on the lesion grade;

[0103] If the lesion type of the osteofascial compartment is acute osteofascial compartment syndrome, the treatment strategy for the target person is determined based on the target person's symptoms and lesion grade.

[0104] In this embodiment, the lesion data of the osteofascial compartment include lesion types and lesion levels. The lesion types include chronic osteofascial compartment syndrome and acute osteofascial compartment syndrome. The lesion levels include primary, moderate, and severe.

[0105] For chronic compartment syndrome, since the course of the disease is relatively long and the disease progresses slowly, the lesion grade can often better reflect the patient's overall condition and severity. In this case, determining the treatment strategy based on the lesion grade can more concisely and efficiently develop a treatment plan suitable for the patient. In addition, the treatment of chronic compartment syndrome usually focuses more on long-term rehabilitation and conditioning, and the lesion grade can also be used as an important indicator to evaluate the treatment effect and rehabilitation progress.

[0106] As for acute compartment syndrome, due to its rapid progression, serious complications may occur, such as acute ischemic necrosis of muscles and nerves, so a more comprehensive and detailed assessment of the patient's condition is required. In this case, in addition to the grade of the lesion, the patient's symptoms, including symptom manifestations, physical changes, and related examination results, need to be considered in order to develop a more accurate and effective treatment plan. The assessment of symptoms helps doctors more accurately determine the severity of the patient's condition and potential risks, so as to take more timely and effective treatment measures, reduce the incidence of complications, and improve treatment outcomes and patient survival rates.

[0107] From the above, it can be concluded that there are differences in the formulation of treatment strategies for chronic compartment syndrome and acute compartment syndrome, mainly due to the different characteristics and treatment needs of the two diseases. In practical applications, doctors need to formulate personalized treatment plans based on the patient's specific situation and disease characteristics, combined with the analysis results of lesion data and correlation level, to improve the accuracy and safety of treatment.

[0108] In one embodiment of the present disclosure, the method for processing bone fascia compartment monitoring data further includes:

[0109] Recommended medical knowledge is output based on the similarity between the target person's osteofascial compartment lesion data and various types of knowledge in the target medical knowledge base.

[0110] In this embodiment, the similarity between the lesion data of the target person's osteofascial compartment and various types of knowledge in the target medical knowledge base is calculated, and the top n types of medical knowledge with similarities greater than a first similarity threshold are output.

[0111] In this embodiment, the target medical knowledge base is a database that stores a large amount of medical-related knowledge, and the database contains detailed information on diseases, treatment methods, drugs, and surgical plans related to compartment syndrome. Considering that not all medical personnel have a wealth of knowledge reserves, in order to reduce the time it takes for medical personnel to search for information and documents, this embodiment calculates the similarity between the lesion data of the target person's compartment and various types of knowledge in the target medical knowledge base, and outputs the top n types of medical knowledge with a similarity greater than a first similarity threshold, to help medical personnel quickly understand the condition and formulate treatment or recovery plans in a timely manner.

[0112] Corresponding to the bone fascia compartment monitoring data processing method of the above embodiment, Figure 7 This is a structural block diagram of a bone fascia compartment monitoring data processing device provided by an embodiment of the present disclosure. For ease of description, only the parts related to the embodiment of the present disclosure are shown. Figure 7 The bone fascia compartment monitoring data processing device 20 includes: a data acquisition module 21, a data processing module 22 and a strategy determination module 23.

[0113] The data acquisition module 21 is used to acquire the personal information of the target person and the first disease measurement data corresponding to the target person;

[0114] The data processing module 22 is used to input the personal information of the target person and the first disease measurement data corresponding to the target person into the target neural network model to obtain the pathological data of the target person's osteofascial compartment; the target neural network model is obtained by training the first neural network model based on the personal information of the historical person and the first disease measurement data;

[0115] The strategy determination module 23 is used to determine the treatment strategy of the target person based on the lesion data of the target person's osteofascial compartment.

[0116] In one embodiment of the present disclosure, the data processing module 22 is further used to:

[0117] In response to receiving the first identification information, the first disease measurement data corresponding to the target person is optimized according to the first identification information to obtain the second disease measurement data.

[0118] In one embodiment of the present disclosure, the data processing module 22 is further used to:

[0119] The personal information and the second disease measurement data of the target person are processed to obtain the optimized lesion data of the target person's osteofascial compartment.

[0120] In one embodiment of the present disclosure, the data processing module 22 is specifically used to:

[0121] If the total amount of the historical personnel's personal information and the first disease measurement data is less than the first value, expanding the total amount of the historical personnel's personal information and the first disease measurement data to obtain a target total amount of data;

[0122] The first neural network model is trained according to the total amount of target data.

[0123] In one embodiment of the present disclosure, the data processing module 22 is specifically used to:

[0124] The total amount of historical personnel's personal information and the first disease measurement data is supplemented with standard data to obtain the total amount of target data.

[0125] In one embodiment of the present disclosure, the personal information of the target person includes basic information, health status and associated medical history; the strategy determination module 23 is specifically used to:

[0126] Determine the relevance level of the target person's basic information, health status and associated medical history to the lesion data;

[0127] The treatment strategy of the target person is determined based on the lesion data and correlation level of the target person's osteofascial compartment.

[0128] In one embodiment of the present disclosure, the lesion data of the osteofascial compartment includes the lesion type and lesion grade; the strategy determination module 23 is specifically used to:

[0129] If the lesion type of the compartment is chronic compartment syndrome, the treatment strategy for the target person is determined based on the lesion grade;

[0130] If the lesion type of the osteofascial compartment is acute osteofascial compartment syndrome, the treatment strategy for the target person is determined based on the target person's symptoms and lesion grade.

[0131] See also Figure 8 , Figure 8 A schematic block diagram of an electronic device provided by an embodiment of the present disclosure. Figure 8 The electronic device 300 in the embodiment shown may include: one or more processors 301, one or more input devices 302, one or more output devices 303 and one or more memories 304. The processors 301, input devices 302, output devices 303 and memories 304 communicate with each other via a communication bus 305. The memory 304 is used to store computer programs, which include program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. The processor 301 is configured to call the program instructions to execute the functions of each module / unit in the above-mentioned device embodiments, such as Figure 7 The functions of modules 21 to 23 are shown.

[0132] It should be understood that in the embodiment of the present disclosure, the processor 301 may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0133] The input device 302 may include a touch panel, a fingerprint collection sensor (for collecting the user's fingerprint information and fingerprint direction information), a microphone, etc., and the output device 303 may include a display (LCD, etc.), a speaker, etc.

[0134] The memory 304 may include a read-only memory and a random access memory, and provide instructions and data to the processor 301. A portion of the memory 304 may also include a non-volatile random access memory. For example, the memory 304 may also store information about the device type.

[0135] In a specific implementation, the processor 301, input device 302, and output device 303 described in the embodiments of the present disclosure can execute the implementation methods described in the first and second embodiments of the bone fascia compartment monitoring data processing method provided in the embodiments of the present disclosure, and can also execute the implementation methods of the electronic device described in the embodiments of the present disclosure, which will not be repeated here.

[0136] In another embodiment of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by the processor, all or part of the processes in the above-mentioned embodiment method are implemented, and the computer program can also be completed by instructing the relevant hardware through the computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, the steps of each of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0137] The computer-readable storage medium may be an internal storage unit of the electronic device of any of the aforementioned embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium may also be an external storage device of the electronic device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Furthermore, the computer-readable storage medium may also include both an internal storage unit of the electronic device and an external storage device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium may also be used to temporarily store data that has been output or is to be output.

[0138] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this disclosure.

[0139] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the electronic devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0140] In the several embodiments provided in the present application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces or units, or it can be an electrical, mechanical or other form of connection.

[0141] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present disclosure.

[0142] In addition, each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0143] The above are only specific embodiments of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present disclosure, and these modifications or replacements should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A method for processing bone fascia compartment monitoring data, characterized in that: include: Acquire personal information of a target person and first disease measurement data corresponding to the target person; Inputting the personal information of the target person and the first disease measurement data corresponding to the target person into the target neural network model to obtain the pathological data of the target person's osteofascial compartment; the target neural network model is obtained by training the first neural network model based on the personal information of the historical person and the first disease measurement data; The personal information of the target person includes basic information, health status and related medical history; the lesion data of the osteofascial compartment includes lesion type and lesion grade; lesion types include chronic osteofascial compartment syndrome and acute osteofascial compartment syndrome, and lesion grades include primary, moderate and severe; The first disease measurement data includes compartment pressure data, muscle tension data, blood circulation data or other physiological index data related to compartment syndrome; Determining a treatment strategy for the target person based on the lesion data of the osteofascial compartment of the target person; The processing method further includes: in response to receiving the first identification information, optimizing the first disease measurement data corresponding to the target person according to the first identification information to obtain second disease measurement data; Processing the personal information of the target person and the second disease measurement data to obtain optimized lesion data of the target person's osteofascial compartment; The personal information of the target person and the second disease measurement data are processed to obtain the optimized lesion data of the target person's osteofascial compartment, including: Calculate the personal information of the target person and the second disease measurement data according to the first formula to obtain the optimized lesion data of the target person's osteofascial compartment; The first formula is: in, represents the optimized lesion data of the target person's osteofascial compartment, i.e., the lesion index; It represents the personal information index, which is a value obtained by comprehensively considering the age, gender, and physical condition of the target person, that is, by assigning different weights to different factors and performing comprehensive calculations; The power exponent representing the personal information index is used to adjust the influence of personal information on the optimization of lesion data; It represents the main disease measurement data, that is, the main disease measurement values ​​directly related to osteofascial compartment lesions, that is, specific pressure values, tissue damage indicators; is the adjustment factor; Data were measured for secondary diseases; is the power index of the secondary disease measurement data; F is the fracture factor. If the target person has a fracture, F is the first value. If the target person does not have a fracture, F is the second value. The first value is greater than the second value. The age of the target person; is the power exponent of age, reflecting the influence trend of age on the optimization of lesion data; is body mass index; , They are all constant coefficients, which are used to adjust the numerical range and influence of the formula as a whole.

2. The method for processing bone fascia compartment monitoring data according to claim 1, characterized in that: The training of the first neural network model based on the personal information of the historical personnel and the first disease measurement data includes: If the total amount of the historical personnel's personal information and the first disease measurement data is less than the first value, expanding the total amount of the historical personnel's personal information and the first disease measurement data to obtain a target total amount of data; The first neural network model is trained according to the total amount of target data.

3. The method for processing bone fascia compartment monitoring data according to claim 2, characterized in that: The total amount of the personal information of historical personnel and the first disease measurement data is expanded to obtain the total amount of target data, including: The total amount of historical personnel's personal information and the first disease measurement data is supplemented with standard data to obtain the total amount of target data.

4. The method for processing bone fascia compartment monitoring data according to claim 1, characterized in that: The step of determining the treatment strategy of the target person based on the lesion data of the target person's osteofascial compartment includes: Determine the relevance level of the target person's basic information, health status and associated medical history to the lesion data; A treatment strategy for the target person is determined based on the lesion data of the target person's osteofascial compartment and the correlation level.

5. The method for processing bone fascia compartment monitoring data according to claim 1, characterized in that: The step of determining a treatment strategy for the target person based on the lesion data of the target person's osteofascial compartment includes: If the lesion type of the osteofascial compartment is chronic osteofascial compartment syndrome, determining a treatment strategy for the target person according to the lesion grade; If the lesion type of the osteofascial compartment is acute osteofascial compartment syndrome, the treatment strategy of the target person is determined according to the symptoms and lesion grade of the target person.

6. A bone fascia compartment monitoring data processing device, characterized in that: include: A data acquisition module, used to acquire personal information of a target person and first disease measurement data corresponding to the target person; A data processing module, used for inputting the personal information of the target person and the first disease measurement data corresponding to the target person into the target neural network model to obtain the pathological data of the target person's osteofascial compartment; the target neural network model is obtained by training the first neural network model based on the personal information of the historical person and the first disease measurement data; The personal information of the target person includes basic information, health status and related medical history; the lesion data of the osteofascial compartment includes lesion type and lesion grade; lesion types include chronic osteofascial compartment syndrome and acute osteofascial compartment syndrome, and lesion grades include primary, moderate and severe; The first disease measurement data includes compartment pressure data, muscle tension data, blood circulation data or other physiological index data related to compartment syndrome; A strategy determination module, used to determine the treatment strategy of the target person based on the lesion data of the osteofascial compartment of the target person; The data processing module is also used to In response to receiving the first identification information, optimizing the first disease measurement data corresponding to the target person according to the first identification information to obtain second disease measurement data; Processing the personal information of the target person and the second disease measurement data to obtain optimized lesion data of the target person's osteofascial compartment; The personal information of the target person and the second disease measurement data are processed to obtain the optimized lesion data of the target person's osteofascial compartment, including: Calculate the personal information of the target person and the second disease measurement data according to the first formula to obtain the optimized lesion data of the target person's osteofascial compartment; The first formula is: in, represents the optimized lesion data of the target person's osteofascial compartment, i.e., the lesion index; It represents the personal information index, which is a value obtained by comprehensively considering the age, gender, and physical condition of the target person, that is, by assigning different weights to different factors and performing comprehensive calculations; The power exponent representing the personal information index is used to adjust the influence of personal information on the optimization of lesion data; It represents the main disease measurement data, that is, the main disease measurement values ​​directly related to osteofascial compartment lesions, that is, specific pressure values, tissue damage indicators; is the adjustment factor; Data were measured for secondary diseases; is the power index of the secondary disease measurement data; F is the fracture factor. If the target person has a fracture, F is the first value. If the target person does not have a fracture, F is the second value. The first value is greater than the second value. The age of the target person; is the power exponent of age, reflecting the influence trend of age on the optimization of lesion data; is body mass index; , They are all constant coefficients, which are used to adjust the numerical range and influence of the formula as a whole.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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