Blood gas result judgment method and system

Abstract

The invention discloses a blood gas result judgment method and system, and the method comprises the steps: collecting a blood sample of a patient, and obtaining a multi-dimensional biological identification parameter set for blood gas analysis from the blood sample; carrying out comprehensive analysis on the multi-dimensional biological identification parameter set, and calculating a blood comprehensive dynamic index; analyzing a molecular interaction condition in the blood sample, and calculating a blood molecular interaction index; synthesizing the blood comprehensive dynamic index and the blood molecule interaction index to generate a blood biological spectrum performance score; and comparing the blood biological spectrum performance score with a preset standard performance score range to obtain a blood gas analysis and judgment result. According to the embodiment of the invention, more biological identification parameters can be integrated, a more individualized and dynamic diagnosis result is provided, and the method and the device have stronger predictive ability and wider clinical adaptability.

Description

Technical Field

[0001] The present invention belongs to the field of medical technology, and in particular to a method and system for determining blood gas results. Background Art

[0002] Blood gas analysis is an important diagnostic method used in clinical medicine to evaluate the respiratory and metabolic function of patients. It helps doctors determine the patient's ventilation function, lung gas exchange efficiency, acid-base balance and metabolic abnormalities by measuring parameters such as oxygen (O2) and carbon dioxide (CO2) partial pressure in arterial blood and blood pH. The results of blood gas analysis are extremely important for the diagnosis and treatment of severe infections, lung diseases, heart diseases and other diseases.

[0003] With the advancement of science and technology, blood gas analysis equipment and technology are also developing. Traditional blood gas analysis methods often rely on the measurement of a single or several biochemical indicators, but due to the complexity of human physiological processes, these indicators often cannot fully reflect the patient's true condition, and it is difficult to accurately predict the patient's clinical outcome.

[0004] In order to improve the accuracy and predictive power of diagnosis, some methods have been proposed in the prior art to attempt to comprehensively analyze multiple biochemical indicators. These methods use multidimensional data to construct a comprehensive index in the hope of more comprehensively evaluating the patient's physiological condition. However, these methods still have some limitations in actual operation:

[0005] 1. Insufficient complexity of data analysis: Existing methods mostly rely on linear or simple nonlinear models. Although these models are computationally simple, they may not be able to fully capture the complex interactions between multidimensional data, thus affecting the accuracy of the analysis results.

[0006] 2. Limited biomarker parameters: Although multi-parameter analysis is better than single indicator measurement, existing technologies usually only consider limited biochemical data and lack comprehensive analysis of a wide range of biomarker parameters, which may lead to the omission of important physiological information.

[0007] 3. Lack of real-time adjustment and individual matching: Most existing blood gas analysis methods do not fully consider the impact of individual differences among patients, nor do they make real-time adjustments based on dynamically changing clinical conditions, which limits the flexibility and personalization of their clinical applications. Summary of the invention

[0008] The purpose of the present invention is to provide a method and system for judging blood gas results to address the deficiencies in the prior art, integrate more biomarker parameters, provide more individualized and dynamic diagnostic results, and have stronger predictive capabilities and wider clinical adaptability.

[0009] An embodiment of the present application provides a method for determining blood gas results, the method comprising:

[0010] Collecting a blood sample from a patient, and obtaining a multidimensional biomarker parameter set for blood gas analysis from the blood sample;

[0011] Performing a comprehensive analysis on the multi-dimensional biomarker parameter set to calculate a comprehensive blood dynamic index;

[0012] Analyzing the molecular interactions in the blood sample and calculating blood molecular interaction indicators;

[0013] Combining the blood comprehensive dynamic index and the blood molecule interaction index to generate a blood biospectrum performance score;

[0014] The blood bio-spectrum efficacy score is compared with a preset standard efficacy score range to obtain a blood gas analysis judgment result.

[0015] Optionally, the calculation formula of the comprehensive blood dynamic index is:

[0016] text{HCDI}=frac{sum(W_i·P_i)}{text{HemoBase}}·text{DynaFactor}

[0017] Among them, the text{HCDI} represents the comprehensive dynamic index of blood, the W_i represents the weight coefficient of each biomarker, the P_i represents the corresponding biomarker parameter value, the text{HemoBase} represents the baseline reconciliation factor of blood gas analysis, and the text{DynaFactor} represents the dynamic adjustment factor.

[0018] Optionally, the calculation formula of the blood molecule interaction index is:

[0019] text{HMII}=frac{sum(X_ij·M_ij)}{sum(N_ij·R_ij)}

[0020] Among them, the text{HMII} is a blood molecule interaction index, the X_ij and the M_ij represent the interaction frequency and interaction intensity coefficient of the i-th and j-th molecules in the blood sample respectively, and the N_ij and the R_ij represent the reference frequency and reference influence coefficient of non-interactive molecular pairs.

[0021] Optionally, the blood bio-spectrum efficacy score is generated in the following manner:

[0022] text{BBSS}=(text{HCDI}·text{HHAF})^{psi_1}+(text{HMII}·text{HHAF})^{psi_2}

[0023] Wherein, the text{BBSS} is the blood biospectral performance score, the psi_1 and the psi_2 are parameters for adjusting the mutual influence of text{HCDI} and text{HMII}, and the text{HHAF} is the blood harmonic adaptability factor, which is used to adjust the weight of the score according to the homeostasis of the patient's biomarker parameters:

[0024] text{HMAF}=exp(-frac{text{Var}(P)}{Theta})

[0025] Wherein, the text{Var}(P) is the variance of each biomarker parameter, representing homeostasis, and the Theta is the threshold for adjusting the sensitivity of homeostasis.

[0026] Another embodiment of the present application provides a blood gas result judgment system, the system comprising:

[0027] A collection module, used to collect a blood sample of a patient and obtain a multi-dimensional biomarker parameter set for blood gas analysis from the blood sample;

[0028] A first calculation module, used to perform a comprehensive analysis on the multi-dimensional biomarker parameter set and calculate a comprehensive blood dynamic index;

[0029] A second calculation module, used for analyzing the molecular interactions in the blood sample and calculating blood molecular interaction indicators;

[0030] A generating module, for integrating the blood comprehensive dynamic index and the blood molecule interaction index to generate a blood biospectrum performance score;

[0031] The comparison module is used to compare the blood bio-spectrum performance score with a preset standard performance score range to obtain a blood gas analysis judgment result.

[0032] Yet another embodiment of the present application provides a storage medium, wherein the storage medium stores a computer program, wherein the computer program is configured to execute any of the above methods when running.

[0033] Yet another embodiment of the present application provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute any of the methods described above.

[0034] Compared with the prior art, the present invention provides a method for judging blood gas results, which collects blood samples from patients and obtains a multidimensional biomarker parameter set for blood gas analysis from the blood samples; comprehensively analyzes the multidimensional biomarker parameter set to calculate a comprehensive blood dynamic index; analyzes the molecular interactions in the blood sample and calculates a blood molecule interaction index; combines the comprehensive blood dynamic index and the blood molecule interaction index to generate a blood biospectral efficiency score; compares the blood biospectral efficiency score with a preset standard efficiency score range to obtain a blood gas analysis judgment result, thereby integrating more biomarker parameters, providing a more individualized and dynamic diagnostic result, and having a stronger predictive ability and a wider range of clinical adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 A hardware structure block diagram of a computer terminal for a method for determining blood gas results provided by an embodiment of the present invention;

[0036] Figure 2 A flow chart of a method for determining blood gas results provided by an embodiment of the present invention;

[0037] Figure 3 A schematic diagram of the structure of a blood gas result judgment system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0038] The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, but should not be construed as limiting the present invention.

[0039] The embodiment of the present invention first provides a method for determining blood gas results. The method can be applied to electronic devices, such as computer terminals, specifically ordinary computers, etc.

[0040] The following describes it in detail by taking running on a computer terminal as an example. Figure 1 The hardware structure block diagram of a computer terminal for a method for determining blood gas results provided by an embodiment of the present invention is shown in FIG. Figure 1 As shown, the computer terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data. Optionally, the computer terminal may also include a transmission device 106 for communication functions and an input and output device 108. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above-mentioned computer terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0041] The memory 104 can be used to store software programs and modules of application software, such as program instructions / modules corresponding to the method for determining blood gas results in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, implementing the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0042] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of a computer terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0043] See also Figure 2 The embodiment of the present invention provides a method for determining blood gas results, which may include the following steps:

[0044] S201, collecting a blood sample from a patient, and obtaining a multidimensional biomarker parameter set for blood gas analysis from the blood sample;

[0045] Specifically, before collecting blood samples, ensure that the patient has rested for at least 5 to 10 minutes according to standard procedures to avoid changes in physiological parameters caused by exercise or tension. Inform the patient of the process and purpose of collecting blood samples and obtain the patient's consent.

[0046] Choose a suitable blood sampling site, usually the patient's radial artery, to ensure that the blood sample comes from an artery for accurate blood gas analysis. Clean the area around the blood sampling site and apply local anesthesia (if necessary). Using sterile technique and a blood collection needle or arterial blood collection kit, carefully perform arterial puncture and collect the blood sample. Make sure to quickly transfer the blood sample to a sterile blood gas analysis tube containing heparin without causing any contamination or bubbles in the blood sample. Immediately shake the blood gas analysis tube gently to avoid blood clotting and ensure that the heparin in the tube is completely mixed with the blood. Place the sample in an ice box (if the analysis is not performed immediately) to prevent cellular metabolism from changing the blood gas parameters.

[0047] Blood samples were analyzed using a blood gas analyzer to obtain basic parameters of arterial blood, such as pH, oxygen partial pressure P_{O2}, carbon dioxide partial pressure P_{CO2}, etc. Biochemical analyzers were used to further measure other biomarker parameters such as electrolytes (such as Na^+, K^+, etc.), blood sugar, and hemoglobin levels.

[0048] Ensure that all measurements are accurately recorded and all data are entered into an electronic database. Use statistical software to standardize individual parameters so that they can be integrated together for subsequent multidimensional comprehensive analysis. Perform quality control on the acquired biomarker parameter data to exclude any outliers caused by technical errors. Ensure that all instruments are calibrated regularly and in accordance with clinical laboratory standards.

[0049] Through the above steps, a rich multi-dimensional biomarker parameter set can be obtained, which can reflect the patient's blood gas status and provide necessary data for calculating more complex comprehensive blood dynamic indexes.

[0050] S202, performing a comprehensive analysis on the multi-dimensional biomarker parameter set to calculate a comprehensive blood dynamic index;

[0051] Specifically, a calculation formula for the comprehensive blood dynamic index can be:

[0052] text{HCDI}=frac{sum(W_i·P_i)}{text{HemoBase}}·text{DynaFactor}

[0053] Among them, text{HCDI} represents the comprehensive dynamic index of blood, Wi represents the weight coefficient of each biomarker, indicating the importance of each biomarker parameter in the comprehensive dynamic state of blood; Pi represents the corresponding biomarker parameter value, which represents the actual measured biomarker value; text{HemoBase} represents the baseline reconciliation factor of blood gas analysis, which provides a reference value for adjusting the baseline for individual differences or measurement benchmarks; text{DynaFactor} represents the dynamic adjustment factor, which adjusts HCDI to reflect these changes according to the physiological conditions or disease changes of different patients.

[0054] The Blood Comprehensive Dynamic Index (HCDI) is used to comprehensively evaluate the dynamic changes and overall status of multiple biomarker parameters in a patient's blood sample. This index calculates a scalar value representing the comprehensive biological activity by multiplying the weight coefficient of each parameter and the actual measured value and summing them up.

[0055] This formula is based on the concept of weighted summation, reflecting the different contributions of different biomarker parameters to the blood gas analysis results. By dividing by the baseline reconciliation factor HemoBase, it achieves the normalization adjustment of the blood gas baseline for different patients or different situations. The dynamic adjustment factor DynaFactor allows the score to be dynamically adjusted according to real-time data or preset conditions to reflect the patient's actual physiological state. A specific implementation method may include:

[0056] Step 1: Parameter collection and initial weight assignment First, based on a series of collected biomarker parameters, such as oxygen partial pressure P_{O2}, carbon dioxide partial pressure P_{CO2}, hemoglobin Hb, electrolytes (such as Na^+, K^+), blood glucose (Glucose), etc., an initial weight W_i is assigned to each parameter P_i. These weights are determined based on previous clinical research data and can reflect the importance of each parameter in blood gas analysis.

[0057] Step 2: Determination of baseline harmonic factor The baseline harmonic factor (HemoBase) of blood gas analysis is derived from an average normal blood gas value based on population statistics. For example, HemoBase can be set equal to the arithmetic mean of the average oxygen partial pressure and carbon dioxide partial pressure of a reference population.

[0058] Step 3: Calculation of Dynamic Adjustment Factor The dynamic adjustment factor (DynaFactor) may be derived based on the patient's physiological response, treatment response, or other changes in real-time detection parameters. For example, if the patient is on mechanical ventilation, DynaFactor can be adjusted by a simple formula, such as being proportional to the patient's ventilation volume.

[0059] Step 4: Calculate the parameter value after weight adjustment Calculate the product of each biomarker parameter P_i and its weight W_i. For example, if the value of P_{O2} is 90 mmHg and its weight W_{O2} is 0.3, the calculation result of this item is 27.

[0060] Step 5: Sum and Baseline Adjustment Sum all adjusted parameter values ​​to get sum(W_i*P_i). Then, divide this sum by HemoBase to make a baseline adjustment. Assuming HemoBase is 100mmHg, if the above sum is 270, then the baseline adjusted value is 2.7.

[0061] Step 6: Apply the Dynamic Adjustment Factor Finally, multiply the result from step 5 by the DynaFactor to get the final HCDI. Assuming the DynaFactor is 1.2, the final HCDI is 3.24.

[0062] Step 7: Normalization For ease of comparison and further analysis, it may be necessary to normalize the HCDI, for example by scaling it to a range between 0 and 1. This can be done by subtracting the minimum possible HCDI value from the HCDI and then dividing by the difference between the maximum and minimum possible values.

[0063] Through the above steps, a formula combining multiple biomarker parameters, baseline reconciliation factors and dynamic adjustment factors can be used to calculate the patient's comprehensive blood dynamic index. This index can comprehensively reflect the patient's blood gas analysis results in a way that is not easy to directly infer, which helps doctors make more accurate medical judgments. In clinical practice, this method may also need to be adjusted according to specific patient groups or conditions to ensure its accuracy and reliability.

[0064] S203, analyzing the molecular interactions in the blood sample and calculating blood molecular interaction indicators;

[0065] Specifically, a calculation formula for a blood molecule interaction index can be:

[0066] text{HMII}=frac{sum(X_ij·M_ij)}{sum(N_ij·R_ij)}

[0067] Among them, text{HMII} is the blood molecule interaction index, X_ij and M_ij represent the interaction frequency and interaction intensity coefficient of the i-th and j-th molecules in the blood sample respectively, X_{ij} reflects the degree of interaction between the two molecules in the blood sample, and M_{ij} describes the intensity or biological impact of the above molecular interaction; N_ij and R_ij represent the reference frequency and reference influence coefficient of non-interacting molecular pairs, N_{ij} provides a comparison benchmark for the common or expected molecular interaction frequency, and R_{ij} is used to calibrate the impact strength when the molecules are non-interacting.

[0068] The blood molecular interaction index (HMII) aims to quantify the various molecular-level interactions in blood samples. The interactions between molecules may affect the solubility and transport of gases, which is directly related to the results of blood gas analysis. The index reflects the combined effect of molecular interactions by considering the frequency and intensity of molecular interactions in blood samples and comparing them with the reference frequency and influence coefficient of non-interacting molecular pairs. This comparison helps to identify abnormal molecular interaction patterns that may be associated with disease states or physiological abnormalities. A specific implementation method may include:

[0069] Step 1: Using single-molecule tracking technology, single-molecule fluorescent labeling technology is used to dye specific molecules in the blood sample, such as oxygen molecules, carbon dioxide, hemoglobin, etc., so that they can be visualized under a fluorescence microscope. A high-speed camera is used to record the movement trajectories of these molecules in the blood sample.

[0070] Step 2: Optical trapping and laser pushing Through optical trapping (optical tweezers) and laser pushing technology, the movement of a single molecule is precisely manipulated and controlled to cause it to meet other molecules, thereby simulating intermolecular interactions. The intermolecular interaction behavior under the action of external manipulation force is recorded.

[0071] Step 3: Advanced imaging and molecular dynamics analysis Use high-resolution real-time imaging to capture the interactions and processes between molecules, such as the binding of oxygen molecules to hemoglobin. Analyze the imaging data to extract the frequency X_{ij} of the interaction between molecules and its changes in the time dimension.

[0072] Step 4: Establish a molecular interaction strength model Based on the principles of physical chemistry, a computational model for the strength of intermolecular interactions is established to simulate the mechanical effects between molecules under experimental conditions. Using simulation data, the interaction strength coefficient M_{ij} of each molecular pair is quantified.

[0073] Step 5: Construction of a reference dataset of non-interacting molecular pairs Through comparative analysis and database retrieval, a reference dataset containing non-interacting molecular pairs N_{ij} and their reference frequencies and influence coefficients R_{ij} is constructed. Ensure that this dataset covers a wide range of physiological conditions and clinical conditions for comparison and calibration of molecular interaction analysis results.

[0074] Step 6: Data synthesis and HMII calculation The interaction frequency and interaction intensity coefficient obtained from steps 1 to 4 are comprehensively analyzed with the reference data set constructed in step 5. The molecular interaction index HMII of the blood sample is calculated using the above formula.

[0075] Step 7: Result verification and machine learning optimization Verify the HMII calculation results with known clinical blood gas analysis results to ensure their accuracy and reliability. Use machine learning methods to optimize the molecular interaction analysis model based on the verification results to improve the prediction accuracy of future calculations.

[0076] Through the above steps, a quantitative blood molecule interaction index can be obtained, which reflects the dynamic situation of molecular interactions in blood samples. This method uses fluorescent labels, optical trap technology and advanced imaging technology, which is an innovative implementation method and provides an unexpected way to study and calculate the interactions between blood molecules.

[0077] S204, generating a blood biospectral performance score by integrating the blood comprehensive dynamic index and the blood molecule interaction index;

[0078] Specifically, a method for generating a blood bio-spectrum efficacy score may be:

[0079] text{BBSS}=(text{HCDI}·text{HHAF})^{psi_1}+(text{HMII}·text{HHAF})^{psi_2}

[0080] Among them, text{BBSS} is the blood biospectral performance score, psi_1 and psi_2 are parameters that adjust the mutual influence of text{HCDI} and text{HMII}, which adjust the parameters of the mutual influence of HCDI and HMII, affecting the weight and effect of the combination of these two indexes; text{HHAF} is the blood harmonic adaptability factor, which is used to adjust the weight of the score according to the homeostasis of the patient's biomarker parameters, and is adjusted according to the negative exponent of the ratio of the variance (a measure of homeostasis) and the threshold Theta, to balance the influence of the harmony factor.

[0081] text{HMAF}=exp(-frac{text{Var}(P)}{Theta})

[0082] Wherein, text{Var}(P) is the variance of each biomarker parameter, representing homeostasis, measuring the homeostasis between indicators. Higher variance means lower homeostasis, and vice versa. Theta is the threshold for adjusting the sensitivity of homeostasis, which is a pre-set constant used to adjust the influence of variance according to actual conditions.

[0083] The Blood Biospectral Performance Score (BBSS) is an indicator that comprehensively reflects the activity and stability of a patient's biospectral profile. The score combines the dynamic index and the molecular interaction index, and applies the blood harmonic adaptability factor to adjust the score.

[0084] The design principle of BBSS is to combine HCDI and HMII and use the adjustment parameters psi_1 and psi_2 to adjust the interaction between the two indexes to reflect the biological significance of their combination. HHAF is a measure of the homeostasis of blood parameters. The variance of the biomarker parameters is used to adjust the harmony factor, so that BBSS can more accurately represent the patient's physiological condition.

[0085] S205, comparing the blood bio-spectrum performance score with a preset standard performance score range to obtain a blood gas analysis judgment result.

[0086] Specifically, one implementation may include:

[0087] Step 1: Establish a database of standard performance score ranges. Collect blood gas analysis results from a large number of healthy people and various known clinical situations to establish a database of standard performance scores. Perform statistical analysis on these data to determine the BBSS ranges in healthy states and various disease states, forming a multi-dimensional standard performance score range.

[0088] Step 2: Apply machine learning classification model Using machine learning techniques, such as support vector machine (SVM), random forest or neural network, train a classification model. The model can distinguish different blood gas states, such as acidosis, alkalosis, normal, etc. Input the standard efficacy score range database into the model for model training and validation.

[0089] Step 3: Calculate the BBSS of the actual patient For the actual patient tested, calculate the blood biospectral performance score (BBSS), which includes the calculation of the blood comprehensive dynamic index (HCDI) and the blood molecular interaction index (HMII), as well as parameter adjustment.

[0090] Step 4: Feature matching and model prediction The patient's BBSS is used as the input feature and predicted using the machine learning model trained in step 2. The model will output a blood gas status classification that matches the patient's BBSS.

[0091] Step 5: Visualize and interpret the results. Display the model predictions in a graphical or tabular form, including the patient's BBSS and its position within the standard performance score range. Use color coding, threshold lines, or other visual aids to clearly mark patients' status within the normal range, marginal, or abnormal range.

[0092] Step 6: Result verification and clinical correlation analysis The results predicted by the model were clinically verified and compared with the actual physiological and pathological conditions. The correlation between BBSS and clinical parameters, patient symptoms and diagnostic results was analyzed to ensure the clinical significance of the predicted results.

[0093] Step 7: Feedback mechanism and continuous optimization Based on the clinical validation results, fine-tune the classification model to optimize its accuracy. Establish a feedback mechanism so that new data can be continuously added to the model as new clinical cases emerge to achieve continuous learning and optimization of the model.

[0094] Through this process, the comparison of the blood biospectral performance score (BBSS) with the preset standard performance score range will not only be a simple numerical analysis, but also a systematic solution that comprehensively applies data science and machine learning technologies to provide blood gas analysis judgment results with high accuracy and interpretability.

[0095] It can be seen that by collecting a blood sample from a patient, a multidimensional biomarker parameter set for blood gas analysis is obtained from the blood sample; the multidimensional biomarker parameter set is comprehensively analyzed to calculate the comprehensive blood dynamic index; the molecular interactions in the blood sample are analyzed, and the blood molecule interaction index is calculated; the comprehensive blood dynamic index and the blood molecule interaction index are combined to generate a blood bio-spectrum efficiency score; the blood bio-spectrum efficiency score is compared with a preset standard efficiency score range to obtain a blood gas analysis judgment result, thereby being able to integrate more biomarker parameters, provide more individualized and dynamic diagnostic results, and have stronger predictive capabilities and wider clinical adaptability.

[0096] Another embodiment of the present invention provides a blood gas result judgment system, see Figure 3 , the system may include:

[0097] The collection module 301 is used to collect a blood sample of a patient and obtain a multi-dimensional biomarker parameter set for blood gas analysis from the blood sample;

[0098] A first calculation module 302, used to perform a comprehensive analysis on the multi-dimensional biomarker parameter set to calculate a comprehensive blood dynamic index;

[0099] The second calculation module 303 is used to analyze the molecular interaction in the blood sample and calculate the blood molecular interaction index;

[0100] A generating module 304, for integrating the blood comprehensive dynamic index and the blood molecule interaction index to generate a blood biospectral performance score;

[0101] The comparison module 305 is used to compare the blood bio-spectrum performance score with a preset standard performance score range to obtain a blood gas analysis result.

[0102] It can be seen that by collecting a blood sample from a patient, a multidimensional biomarker parameter set for blood gas analysis is obtained from the blood sample; the multidimensional biomarker parameter set is comprehensively analyzed to calculate the comprehensive blood dynamic index; the molecular interactions in the blood sample are analyzed, and the blood molecule interaction index is calculated; the comprehensive blood dynamic index and the blood molecule interaction index are combined to generate a blood bio-spectrum efficiency score; the blood bio-spectrum efficiency score is compared with a preset standard efficiency score range to obtain a blood gas analysis judgment result, thereby being able to integrate more biomarker parameters, provide more individualized and dynamic diagnostic results, and have stronger predictive capabilities and wider clinical adaptability.

[0103] An embodiment of the present invention further provides a storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0104] Specifically, in this embodiment, the above storage medium may be configured to store a computer program for performing the following steps:

[0105] S201, collecting a blood sample from a patient, and obtaining a multidimensional biomarker parameter set for blood gas analysis from the blood sample;

[0106] S202, performing a comprehensive analysis on the multi-dimensional biomarker parameter set to calculate a comprehensive blood dynamic index;

[0107] S203, analyzing the molecular interactions in the blood sample and calculating blood molecular interaction indicators;

[0108] S204, generating a blood biospectral performance score by integrating the blood comprehensive dynamic index and the blood molecule interaction index;

[0109] S205, comparing the blood bio-spectrum performance score with a preset standard performance score range to obtain a blood gas analysis judgment result.

[0110] It can be seen that by collecting a blood sample from a patient, a multidimensional biomarker parameter set for blood gas analysis is obtained from the blood sample; the multidimensional biomarker parameter set is comprehensively analyzed to calculate the comprehensive blood dynamic index; the molecular interactions in the blood sample are analyzed, and the blood molecule interaction index is calculated; the comprehensive blood dynamic index and the blood molecule interaction index are combined to generate a blood bio-spectrum efficiency score; the blood bio-spectrum efficiency score is compared with a preset standard efficiency score range to obtain a blood gas analysis judgment result, thereby being able to integrate more biomarker parameters, provide more individualized and dynamic diagnostic results, and have stronger predictive capabilities and wider clinical adaptability.

[0111] An embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0112] Specifically, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0113] Specifically, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0114] S201, collecting a blood sample from a patient, and obtaining a multidimensional biomarker parameter set for blood gas analysis from the blood sample;

[0115] S202, performing a comprehensive analysis on the multi-dimensional biomarker parameter set to calculate a comprehensive blood dynamic index;

[0116] S203, analyzing the molecular interactions in the blood sample and calculating blood molecular interaction indicators;

[0117] S204, generating a blood biospectral performance score by integrating the blood comprehensive dynamic index and the blood molecule interaction index;

[0118] S205, comparing the blood bio-spectrum performance score with a preset standard performance score range to obtain a blood gas analysis judgment result.

[0119] Specifically, the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0120] It can be seen that by collecting a blood sample from a patient, a multidimensional biomarker parameter set for blood gas analysis is obtained from the blood sample; the multidimensional biomarker parameter set is comprehensively analyzed to calculate the comprehensive blood dynamic index; the molecular interactions in the blood sample are analyzed, and the blood molecule interaction index is calculated; the comprehensive blood dynamic index and the blood molecule interaction index are combined to generate a blood bio-spectrum efficiency score; the blood bio-spectrum efficiency score is compared with a preset standard efficiency score range to obtain a blood gas analysis judgment result, thereby being able to integrate more biomarker parameters, provide more individualized and dynamic diagnostic results, and have stronger predictive capabilities and wider clinical adaptability.

[0121] The above describes in detail the structure, features and effects of the present invention based on the embodiments shown in the drawings. The above is only a preferred embodiment of the present invention, but the present invention is not limited to the scope of implementation shown in the drawings. Any changes made according to the concept of the present invention, or modifications to equivalent embodiments with equivalent changes, which still do not exceed the spirit covered by the description and drawings, should be within the protection scope of the present invention.

Claims

1. A method for determining blood gas results, characterized in that: The method comprises: Collecting a blood sample from a patient, and obtaining a multidimensional biomarker parameter set for blood gas analysis from the blood sample; Performing a comprehensive analysis on the multi-dimensional biomarker parameter set to calculate a comprehensive blood dynamic index; Analyzing the molecular interactions in the blood sample and calculating blood molecular interaction indicators; Combining the blood comprehensive dynamic index and the blood molecule interaction index to generate a blood biospectrum performance score; The blood bio-spectrum efficacy score is compared with a preset standard efficacy score range to obtain a blood gas analysis judgment result.

2. The method according to claim 1, characterized in that The calculation formula of the comprehensive blood dynamic index is: text{HCDI}=frac{sum(W_i·P_i)}{text{HemoBase}}·text{DynaFactor} Among them, the text{HCDI} represents the comprehensive dynamic index of blood, the W_i represents the weight coefficient of each biomarker, the P_i represents the corresponding biomarker parameter value, the text{HemoBase} represents the baseline reconciliation factor of blood gas analysis, and the text{DynaFactor} represents the dynamic adjustment factor.

3. The method according to claim 2, characterized in that The calculation formula of the blood molecule interaction index is: text{HMII}=frac{sum(X_ij·M_ij)}{sum(N_ij·R_ij)} Among them, the text{HMII} is a blood molecule interaction index, the X_ij and the M_ij represent the interaction frequency and interaction intensity coefficient of the i-th and j-th molecules in the blood sample respectively, and the N_ij and the R_ij represent the reference frequency and reference influence coefficient of non-interactive molecular pairs.

4. The method according to claim 3, characterized in that The blood biospectral efficacy score is generated as follows: text{BBSS}=(text{HCDI}·text{HHAF})^{psi_1}+(text{HMII}·text{HHAF})^{psi_2} Wherein, the text{BBSS} is the blood biospectral performance score, the psi_1 and the psi_2 are parameters for adjusting the mutual influence of text{HCDI} and text{HMII}, and the text{HHAF} is the blood harmonic adaptability factor, which is used to adjust the weight of the score according to the homeostasis of the patient's biomarker parameters: text{HHAF}=exp(-frac{text{Var}(P)}{Theta}) Wherein, the text{Var}(P) is the variance of each biomarker parameter, representing homeostasis, and the Theta is the threshold for adjusting the sensitivity of homeostasis.

5. A blood gas result judgment system, characterized in that: The system comprises: A collection module, used to collect a blood sample of a patient and obtain a multi-dimensional biomarker parameter set for blood gas analysis from the blood sample; A first calculation module, used to perform a comprehensive analysis on the multi-dimensional biomarker parameter set and calculate a comprehensive blood dynamic index; A second calculation module, used for analyzing the molecular interactions in the blood sample and calculating blood molecular interaction indicators; A generating module, for integrating the blood comprehensive dynamic index and the blood molecule interaction index to generate a blood biospectrum performance score; The comparison module is used to compare the blood bio-spectrum performance score with a preset standard performance score range to obtain a blood gas analysis judgment result.

6. The system according to claim 5, characterized in that The calculation formula of the comprehensive blood dynamic index is: text{HCDI}=frac{sum(W_i·P_i)}{text{HemoBase}}·text{DynaFactor} Among them, the text{HCDI} represents the comprehensive dynamic index of blood, the W_i represents the weight coefficient of each biomarker, the P_i represents the corresponding biomarker parameter value, the text{HemoBase} represents the baseline reconciliation factor of blood gas analysis, and the text{DynaFactor} represents the dynamic adjustment factor.

7. The system according to claim 6, characterized in that The calculation formula of the blood molecule interaction index is: text{HMII}=frac{sum(X_ij·M_ij)}{sum(N_ij·R_ij)} Among them, the text{HMII} is a blood molecule interaction index, the X_ij and the M_ij represent the interaction frequency and interaction intensity coefficient of the i-th and j-th molecules in the blood sample respectively, and the N_ij and the R_ij represent the reference frequency and reference influence coefficient of non-interactive molecular pairs.

8. The system according to claim 7, characterized in that The blood biospectral efficacy score is generated as follows: text{BBSS}=(text{HCDI}·text{HHAF})^{psi_1}+(text{HMII}·text{HHAF})^{psi_2} Wherein, the text{BBSS} is the blood biospectral performance score, the psi_1 and the psi_2 are parameters for adjusting the mutual influence of text{HCDI} and text{HMII}, and the text{HHAF} is the blood harmonic adaptability factor, which is used to adjust the weight of the score according to the homeostasis of the patient's biomarker parameters: text{HHAF}=exp(-frac{text{Var}(P)}{Theta}) Wherein, the text{Var}(P) is the variance of each biomarker parameter, representing homeostasis, and the Theta is the threshold for adjusting the sensitivity of homeostasis.

9. A storage medium, characterized in that: The storage medium stores a computer program, wherein the computer program is configured to execute the method according to any one of claims 1 to 4 when executed.

10. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to perform the method according to any one of claims 1 to 4.

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