An explainable analysis method and system for burn prediction results

Abstract

The application provides an explainable analysis method and system for burn prediction results, and belongs to the technical field of intelligent burn analysis. The method is to set a target result, continuously adjust biomarkers corresponding to the burn prediction result, obtain improved biomarkers, use a random forest model to predict the improved biomarkers to obtain improved results, continuously adjust the biomarkers corresponding to the burn prediction result until the improvement target is reached, and then explain and analyze the burn prediction result according to the improvement target. The technical problem that the existing technology cannot perform explainable analysis on the grading and typing prediction results by using the machine learning method is solved, a clinically understandable decision basis is formed, and systematic computing support is provided for burn diagnosis and treatment.

Description

Technical Field

[0001] This invention belongs to the field of intelligent burn analysis technology, and relates to an analysis method for burn prediction results using counterfactual analysis technology, specifically an interpretability analysis method and system for burn prediction results. Background Technology

[0002] In medicine, burns refer to tissue damage caused by heat (including flames, hot liquids, steam, or high-temperature objects), electric current, chemical substances (strong acids, strong alkalis), radioactive materials, and certain light waves (such as intense ultraviolet radiation). These burns primarily occur when these causative agents act on the skin and deeper tissues, leading to protein denaturation, cell dysfunction, and even necrosis. Current techniques mainly employ a "three-degree four-classification" or "four-degree five-classification" system to classify the severity of burns. However, these classification methods suffer from drawbacks such as high subjectivity, vague quantitative standards, and difficulties in dynamic assessment.

[0003] See patent application CN2025114554222, which discloses a method for predicting burn grading and classification using machine learning (random forest model). The predicted burn grading and classification results, as shown in the attached figures, are determined by biomarkers BG1, IL-1β, EGF, and BG2. These biomarkers reflect the severity of burns, providing an objective and quantitative predictive reference. However, this machine learning-based grading and classification prediction is a black-box approach, lacking interpretability analysis of the results and thus failing to translate them into clinically understandable decision-making criteria. Summary of the Invention

[0004] In view of the above-described background technology, the existing technology of using machine learning for graded classification prediction is a black box prediction, which has the technical problem that the graded classification prediction results cannot be interpreted. In order to address this technical problem, the present invention proposes an interpretable analysis method and system for burn prediction results.

[0005] This invention sets a target result. By continuously adjusting the biomarkers corresponding to the burn prediction results, improved biomarkers can be obtained. This will improve biomarkers Improved results predicted using the random forest model By continuously adjusting the biomarkers corresponding to the burn prediction results, until... This approach aims to improve burn prediction results and interpret them accordingly, addressing the technical challenge of interpreting graded and classified burn predictions using existing machine learning methods. It provides a clinically understandable basis for decision-making and offers systematic computational support for burn diagnosis and treatment.

[0006] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0007] A method for interpretability analysis of burn prediction results includes the following steps:

[0008] S1: Obtain the target result of the burn. The set of biomarkers corresponding to burn prediction results Where i is the biomarker index and n is the number of biomarkers. For the i-th biomarker;

[0009] S2: Determine the adjustment increment Using adjustment increment For the i-th biomarker Adjustments were made to obtain improved biomarkers. This will improve biomarkers As a clinical burn diagnostic factor, prediction was performed using a random forest model, resulting in improved outcomes. ;

[0010] S3: Improve the results With the target result Compare;

[0011] like Then the improvement goal will be achieved;

[0012] like Then modify and adjust the increment. Repeat step S2 until the improvement target is achieved;

[0013] S4: Use the improvement targets to interpret and analyze the burn prediction results.

[0014] Further specifying, S1 specifically includes:

[0015] S1.1: Obtain the target result of the burn. The set of biomarkers corresponding to burn prediction results ;

[0016] S1.2: Determine the overall weight of each biomarker, and determine the biomarker set based on the overall weight of each biomarker. Adjustment priority for each biomarker;

[0017] S1.3: Execute S2 according to the adjustment priority of each biomarker.

[0018] Further specifying, the formula for calculating the overall weight of each biomarker is as follows:

[0019]

[0020] In the formula, Let be the overall weight of the i-th biomarker, which is dimensionless; SHAP contribution value of the i-th biomarker after standardization, dimensionless; The importance weight of the contribution value to SHAP, dimensionless; The degree centrality of the i-th biomarker after standardization is dimensionless; Weights for degree centrality; The betweenness centrality of the i-th biomarker after standardization is dimensionless; The betweenness centrality weight is dimensionless. To represent the compact centrality of the i-th biomarker after standardization, dimensionless; The centrality weights are dimensionless.

[0021] Further specifying, the SHAP contribution value of the i-th biomarker The calculation formula is:

[0022]

[0023] In the formula, The SHAP contribution value for the i-th biomarker is dimensionless. This is a dimensionless set of biomarkers corresponding to burn prediction results. The prediction result obtained by using the random forest model after adding the i-th biomarker is dimensionless. A collection of biomarkers The prediction results obtained using the random forest model are dimensionless; n is the number of biomarkers, in units of 1, and i is the serial number of the biomarker, in units of 1.

[0024] The SHAP contribution value of the i-th biomarker After standardization, the SHAP contribution value of the i-th biomarker is obtained. .

[0025] Further limiting the degree centrality of the i-th biomarker after standardization Betweenness centrality of the i-th biomarker after standardization and the tight centrality of the i-th biomarker after standardization It is obtained by processing the i-th biomarker through graph and network analysis and then standardizing the processing results.

[0026] Further specifying the degree centrality of the i-th biomarker The calculation formula is:

[0027]

[0028] In the formula, In the graph network model, the degree centrality of the i-th biomarker is dimensionless; The adjustment increment for the i-th biomarker is dimensionless; n is the number of biomarkers.

[0029] Betweenness centrality of the i-th biomarker The calculation formula is:

[0030]

[0031] In the formula, In the graph network model, the betweenness centrality of the i-th biomarker is dimensionless; Both j and j are path points in the graph network model; For the i-th biomarker Number of shortest pathological pathways between path point j and path point j, in units of: number; In the graph network model, Number of shortest pathological pathways between path point j and path point j, in units of: number;

[0032] tight centrality of the i-th biomarker The calculation formula is:

[0033]

[0034] In the formula, In the graph network model, represents the compact centrality of the i-th biomarker, dimensionless; j represents the path point in the graph network model, dimensionless; and n represents the number of biomarkers, in units of 1; The distance correlation between the i-th biomarker and the j-th path point is dimensionless;

[0035] Degree centrality of the i-th biomarker After standardization, the degree centrality of the i-th biomarker is obtained. ; the betweenness centrality of the i-th biomarker After standardization, the betweenness centrality of the i-th biomarker is obtained. ; the tight centrality of the i-th biomarker After standardization, the compact centrality of the i-th biomarker was obtained. .

[0036] Further specifying, the adjustment increment Includes a set of reduction coefficients and increase the set of coefficients If the SHAP contribution value of the i-th biomarker If the value is greater than τ, then the coefficient set is reduced. For the i-th biomarker Make adjustments; if If <τ, then use the increased coefficient set. For the i-th biomarker Adjustments will be made; if the requirements are not met... >τ and If the condition is <τ, then iterate through the set of reduction coefficients. and increase the set of coefficients For the i-th biomarker Adjustments are made to determine the i-th biomarker. The minimum adjustment amount.

[0037] Further specifying, the use of adjustment increment For the i-th biomarker Adjustments were made to obtain improved biomarkers. Specifically:

[0038]

[0039] In the formula, Let i be the i-th biomarker, which is dimensionless; This is the adjustment increment for the i-th biomarker, dimensionless; Let be the i-th improved biomarker, which is dimensionless.

[0040] Further specifying, S4 specifically includes: using the improvement target to determine the expected burn grade after adjustment, the adjusted biomarkers, and the adjustment increment. Based on burn prediction results, adjusted expected burn severity, adjusted biomarkers, and adjustment increments. The results of burn prediction were interpreted and analyzed.

[0041] An interpretability analysis system for burn prediction results, used to implement the aforementioned method for interpretable analysis of burn prediction results, includes:

[0042] Acquisition module: Used to acquire the target results of burns. The set of biomarkers corresponding to burn prediction results Where i is the biomarker index and n is the number of biomarkers. For the i-th biomarker;

[0043] Improvement module: Used to determine the adjustment increment. Using adjustment increment For the i-th biomarker Adjustments were made to obtain improved biomarkers. This will improve biomarkers As a clinical burn diagnostic factor, prediction was performed using a random forest model, resulting in improved outcomes. ;

[0044] Judgment module: used to evaluate the improvement results With the target result Compare; if If so, the improvement goal is achieved; if Then modify and adjust the increment. Repeat the process of improving the module until the improvement goal is achieved;

[0045] And the interpretation and analysis module: used to interpret and analyze the burn prediction results using the improvement targets.

[0046] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0047] 1. This invention provides an interpretability analysis method for burn prediction results, which involves setting target results. By continuously adjusting the biomarkers corresponding to the burn prediction results, improved biomarkers can be obtained. This will improve biomarkers Improved results predicted using the random forest model By continuously adjusting the biomarkers corresponding to the burn prediction results, until... This approach aims to improve burn prediction results and interpret them accordingly, addressing the technical challenge of interpreting graded and classified burn predictions using existing machine learning methods. It provides a clinically understandable basis for decision-making and offers systematic computational support for burn diagnosis and treatment.

[0048] 2. This invention utilizes counterfactual analysis to reduce the coefficient set. and / or increase the set of coefficients Identify the i-th biomarker The adjustment amount is used to determine the i-th biomarker. The minimum adjustment amount, such that the i-th biomarker The adjustment range is minimized, and then the optimal improvement target is determined, so that the i-th improved biomarker is obtained after adjustment. The predicted improvement target is to improve the current burn severity to a more ideal level.

[0049] 3. This invention determines the biomarker set based on the comprehensive weight of each biomarker. The adjustment priority of each biomarker is determined, and the biomarkers are adjusted according to the determined adjustment priority. This provides important guidance for the search strategy in counterfactual analysis and realizes a multi-dimensional comprehensive evaluation from local predictive importance to global structural importance. Attached Figure Description

[0050] Figure 1 This is a schematic diagram of the method for interpretability analysis of burn prediction results in this invention;

[0051] Figure 2 This is a schematic diagram of the burn prediction result interpretability analysis system of the present invention;

[0052] Figure 3 A diagram showing the importance ranking of the SHAP contribution values ​​of the four biomarkers BG1, IL-1β, EGF, and BG2;

[0053] Figure 4 This is a diagram of the network topology formed by four biomarkers: BG1, IL-1β, EGF, and BG2.

[0054] Figure 5 A radar chart showing the centrality analysis of four biomarkers: BG1, IL-1β, EGF, and BG2.

[0055] Figure 6 A heatmap showing the characteristic associations formed by four biomarkers: BG1, IL-1β, EGF, and BG2.

[0056] Figure 7 This is a logical block diagram of a hierarchical decision computing framework. Detailed Implementation

[0057] The technical solution of the present invention will be further explained and described below with reference to the accompanying drawings and embodiments, but the present invention is not limited to the embodiments described below.

[0058] See Figure 1 This invention proposes a method for interpretability analysis of burn prediction results, comprising the following steps:

[0059] S1: Obtain the target result of the burn. The set of biomarkers corresponding to burn prediction results Where i is the biomarker index and n is the number of biomarkers. Let be the i-th biomarker; in this invention, BG1, IL-1β, EGF, and BG2 are used as the biomarkers corresponding to the burn prediction results, that is, in this invention, =BG1、 =IL-1β、 =EGF、 =BG2.

[0060] Specifically, S1 includes:

[0061] S1.1: Obtain the target result of the burn. The set of biomarkers corresponding to burn prediction results ;

[0062] S1.2: Determine the overall weight of each biomarker, and determine the biomarker set based on the overall weight of each biomarker. Adjustment priority for each biomarker;

[0063] The formula for calculating the overall weight of each biomarker is as follows:

[0064] In the formula, Let be the overall weight of the i-th biomarker, which is dimensionless; SHAP contribution value of the i-th biomarker after standardization, dimensionless; The importance weight of the contribution value to SHAP, dimensionless; The degree centrality of the i-th biomarker after standardization is dimensionless; Weights for degree centrality; The betweenness centrality of the i-th biomarker after standardization is dimensionless; The betweenness centrality weight is dimensionless. To represent the compact centrality of the i-th biomarker after standardization, dimensionless; The centrality weights are dimensionless.

[0065] S1.3: Execute S2 according to the adjustment priority of each biomarker.

[0066] S2: Determine the adjustment increment Using adjustment increment For the i-th biomarker Adjustments were made to obtain improved biomarkers. This will improve biomarkers As a clinical burn diagnostic factor, prediction was performed using a random forest model, resulting in improved outcomes. Among them, adjusting the increment Includes a set of reduction coefficients and increase the set of coefficients If the SHAP contribution value of the i-th biomarker If the value is greater than τ, then the coefficient set is reduced. For the i-th biomarker Make adjustments; if If <τ, then use the increased coefficient set. For the i-th biomarker Adjustments will be made; if the requirements are not met... >τ and If the condition is <τ, then iterate through the set of reduction coefficients. and increase the set of coefficients For the i-th biomarker Make adjustments. Utilize adjustment increments. For the i-th biomarker Adjustments were made to obtain improved biomarkers. Specifically:

[0067]

[0068] In the formula, Let i be the i-th biomarker, which is dimensionless; This is the adjustment increment for the i-th biomarker, dimensionless; Let be the i-th improved biomarker, which is dimensionless.

[0069] S3: Improve the results With the target result Compare;

[0070] like Then the improvement goal will be achieved;

[0071] like Then modify and adjust the increment. Repeat step S2 until the improvement target is achieved;

[0072] S4: Interpretation and analysis of burn prediction results using improvement targets; S4 specifically includes: using improvement targets to determine the adjusted expected burn grade, adjusted biomarkers, and adjustment increments. Based on burn prediction results, adjusted expected burn severity, adjusted biomarkers, and adjustment increments. The results of burn prediction were interpreted and analyzed.

[0073] See Figure 3The SHAP contribution value of the i-th biomarker mentioned above The calculation formula is:

[0074]

[0075] In the formula, The SHAP contribution value for the i-th biomarker is dimensionless. This is a dimensionless set of biomarkers corresponding to burn prediction results. The prediction result obtained by using the random forest model after adding the i-th biomarker is dimensionless. A collection of biomarkers The prediction results obtained using the random forest model are dimensionless; n is the number of biomarkers (unit: number), and i is the biomarker index (unit: number); where... Figure 3 The SHAP value in this context refers to the SHAP contribution value.

[0076] The SHAP contribution value of the i-th biomarker After standardization, the SHAP contribution value of the i-th biomarker is obtained. Among them, the SHAP contribution value of the i-th biomarker after standardization. The calculation formula is:

[0077]

[0078] In the formula, The SHAP contribution value for the i-th biomarker is dimensionless. The SHAP contribution value of the m-th biomarker, dimensionless; The highest SHAP contribution value among all biomarkers. Here is the numerical stability constant, and its value is... The purpose of setting this value is to prevent division by zero.

[0079] In this invention, the degree centrality of the i-th biomarker after standardization is... Betweenness centrality of the i-th biomarker after standardization and the tight centrality of the i-th biomarker after standardization It is obtained by processing the i-th biomarker through graph and network analysis and then standardizing the processing results.

[0080] Degree centrality of the i-th biomarker mentioned above The calculation formula is:

[0081]

[0082] In the formula, In the graph network model, the degree centrality of the i-th biomarker is dimensionless; The adjustment increment for the i-th biomarker is dimensionless; n is the number of biomarkers.

[0083] Betweenness centrality of the i-th biomarker mentioned above The calculation formula is:

[0084]

[0085] In the formula, In the graph network model, the betweenness centrality of the i-th biomarker is dimensionless; Both j and j are path points in the graph network model; For the i-th biomarker Number of shortest pathological pathways between path point j and path point j, in units of: number; In the graph network model, Number of shortest pathological pathways between path point j and path point j, in units of: number;

[0086] The tight centrality of the i-th biomarker mentioned above The calculation formula is:

[0087]

[0088] In the formula, In the graph network model, represents the compact centrality of the i-th biomarker, dimensionless; j represents the path point in the graph network model, dimensionless; and n represents the number of biomarkers, in units of 1; The distance correlation between the i-th biomarker and the j-th path point is dimensionless;

[0089] See Figure 4 , Figure 5 and Figure 6 The graph network analysis is built upon a network of biomarker interactions. The network topology uses nodes to represent biomarkers, with node size reflecting the importance of features derived from SHAP analysis. Edge weights are calculated based on eigenvalue correlation (threshold > 0.3). Four nodes are color-coded for easy identification of different pathological factors. The centrality analysis radar chart comprehensively displays three indicators: degree centrality, betweenness centrality, and tight centrality. Degree centrality reflects the number of direct connections between features, betweenness centrality identifies bridge nodes in the network, and tight centrality measures information transmission efficiency. The feature association heatmap precisely displays the correlation strength between biomarkers in matrix form, with numerical labels providing specific quantitative information on the association. Figure 5In this paper, polar coordinates are used to visually compare three network centrality measures of nodes (degree centrality, betweenness centrality, and compact centrality). Degree centrality (red line) reflects the number of direct connections between nodes; betweenness centrality (blue line) reflects the control a node has over the information flow path; and compact centrality (green line) reflects the average "distance" efficiency from a node to other points in the network. Figure 6 The original correlation coefficients between biomarkers are presented in the form of a numerical matrix, with the color intensity indicating the strength of the correlation. The darker the color, the stronger the correlation.

[0090] Degree centrality of the i-th biomarker After standardization, the degree centrality of the i-th biomarker is obtained. ; the betweenness centrality of the i-th biomarker After standardization, the betweenness centrality of the i-th biomarker is obtained. ; the tight centrality of the i-th biomarker After standardization, the compact centrality of the i-th biomarker was obtained. .

[0091] The degree centrality of the i-th biomarker after standardization is obtained. The calculation formula is:

[0092]

[0093] In the formula, To obtain the degree centrality of the i-th biomarker after standardization, which is dimensionless; Let be the degree centrality of the i-th biomarker, which is dimensionless; Let m be the degree centrality of the m-th biomarker, dimensionless. It represents the maximum degree centrality among all biomarkers. Here is the numerical stability constant, and its value is... The purpose of setting this value is to prevent division by zero.

[0094] The betweenness centrality of the i-th biomarker after standardization is obtained. The calculation formula is:

[0095]

[0096] In the formula, The betweenness centrality of the i-th biomarker after standardization is dimensionless. represents the betweenness centrality of the i-th biomarker, which is dimensionless; is the betweenness centrality of the m-th biomarker, dimensionless; is the maximum value among the betweenness centralities of all biomarkers; is the numerical stability constant, and its value is , and the purpose of setting this value is to prevent division by zero.

[0097] After standardization, the closeness centrality of the i-th biomarker after standardization is obtained The calculation formula is:

[0098]

[0099] In the formula, is the closeness centrality of the i-th biomarker after standardization, dimensionless; is the closeness centrality of the i-th biomarker, dimensionless; is the closeness centrality of the m-th biomarker, dimensionless; is the maximum value among the closeness centralities of all biomarkers; is the numerical stability constant, and its value is , and the purpose of setting this value is to prevent division by zero.

[0100] In the present invention, the counterfactual analysis method is combined with the target priority decision-making mechanism, and the target priority decision-making mechanism provides a search direction guidance that conforms to clinical logic for counterfactual analysis. Based on the clinical knowledge of burn severity, a burn severity function s: {0, 1,..., 5} → is defined, satisfying s(c) = , where 0 represents normal tissue, the larger the value, the more severe the burn, c is the serial number of burn severity, and s(c) is the burn severity. For the prediction result All possible target categories are sorted according to four levels of priority: The first priority P1 = {s(c) = s(c0) - 1} includes direct improvement targets that are one level lower than the current severity, and c0 is the serial number of the initially predicted burn severity; The second priority P2 = {s(c) < s(c0) - 1} includes multi-level improvement targets that are more than two levels lower than the current severity; The third priority P3 = {0} is the ideal target for restoring normal tissue; The fourth priority P4 = C \ ({c0} ∪ P1 ∪ P2 ∪ P3) includes all other categories. This hierarchical structure reflects the progressive principle of clinical treatment: first try the most likely direct improvement, then consider greater improvement, and finally consider the ultimate goal of restoring normal tissue. Based on the combination of this counterfactual analysis method and the target priority decision-making mechanism, the above-mentioned burn prediction results, adjusted expected burn grades, adjusted biomarkers, and adjustment increments can be obtained Interpretation and analysis of burn prediction results, for example: the adjusted expected burn grade is grade III burn. t =3, which is the same as the burn prediction result. The adjusted biomarker is IL-1β, thus forming a clinically understandable basis for decision-making.

[0101] See Figure 7 The aforementioned counterfactual analysis method and target priority decision-making mechanism are based on a hierarchical decision-making computation framework. This framework comprises four logical layers: an input parsing layer, an analysis and weighting layer, a feature weight fusion module, and a target-driven decision-making module. The input parsing layer receives the set of biomarkers corresponding to the burn prediction results. The process involves receiving [BG1, EGF, IL-1β, BG2] and using a random forest model to predict the burn severity, yielding a burn prediction result. Next, analysis and weighting layers are used to perform multi-dimensional analysis on [BG1, EGF, IL-1β, BG2], calculating the SHAP contribution value of each biomarker to quantify the direction and intensity of each biomarker's contribution to the burn prediction result. A network topology graph is constructed by calculating the degree centrality, betweenness centrality, and tight centrality of each biomarker. Finally, a feature weight fusion module is used to perform a comprehensive weighting based on the biomarkers. The corresponding comprehensive weights are calculated. Finally, using the goal-driven decision-making module, based on the burn prediction results, strategies for transitioning to adjacent milder grades (e.g., from grade 5 to grade 4) are prioritized. If no feasible path exists, the possibility of transitioning to multiple milder grades or even normal tissue (grade 0) is explored. Finally, other grade changes are considered. This layer systematically traverses each biomarker according to priority, combining the direction indicated by the biomarker's SHAP contribution value to generate counterfactual samples scaled on the original values ​​with a series of adjustment increments (e.g., 0.1, 0.3, 1.1, 1.3, etc.). The modified samples are then re-input into the random forest model to evaluate whether the improvement results reach the target grade and to calculate the prediction confidence. Finally, it ranks all feasible intervention options according to the formula "efficiency = confidence × comprehensive weights" and outputs a structured list of intervention recommendations. Therefore, the "structure" of this system is a non-parametric intelligent decision-making process that tightly couples interpretability analysis, network analysis, clinical priority rules, and iterative search strategies.

[0102] See Figure 2 The present invention also proposes an interpretability analysis system for burn prediction results, which is used to implement the above-mentioned interpretability analysis method for burn prediction results, including an acquisition module, an improvement module, a judgment module and an interpretation analysis module;

[0103] Acquisition module: Used to acquire the target results of burns. The set of biomarkers corresponding to burn prediction results Where i is the biomarker index and n is the number of biomarkers. For the i-th biomarker;

[0104] Improvement module: Used to determine the adjustment increment. Using adjustment increment For the i-th biomarker Adjustments were made to obtain improved biomarkers. This will improve biomarkers As a clinical burn diagnostic factor, prediction was performed using a random forest model, resulting in improved outcomes. ;

[0105] Judgment module: used to evaluate the improvement results With the target result Compare; if If so, the improvement goal is achieved; if Then modify and adjust the increment. Repeat the process of improving the module until the improvement goal is achieved;

[0106] Interpretation and Analysis Module: Used to interpret and analyze burn prediction results using improvement targets.

[0107] The present invention provides an interpretability analysis system for burn prediction results that is completely consistent with the aforementioned method for interpretability analysis of burn prediction results. For details regarding the acquisition module, improvement module, judgment module, and interpretation analysis module that are not fully disclosed in the burn prediction result interpretability analysis system, please refer to the description in the section on interpretability analysis method for burn prediction results above. The present invention will not repeat these details here.

[0108] The above description is only used to illustrate the technical solutions of the present invention, and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing, those skilled in the art should understand that modifications can still be made to the technical solutions described above, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the present invention.

Claims

1. An explainability analysis method of burn prediction results, characterized by, Includes the following steps: S1: obtaining a target result of a burn a biomarker set corresponding to the burn prediction result , i is the serial number of the biomarker, n is the number of biomarkers, is the i-th biomarker; S2: Determine the adjustment increment Using adjustment increment For the i-th biomarker Adjustments were made to obtain improved biomarkers. This will improve biomarkers As a clinical burn diagnostic factor, prediction was performed using a random forest model, resulting in improved outcomes. The adjustment increment Includes a set of reduction coefficients and increase the set of coefficients If the SHAP contribution value of the i-th biomarker If the value is greater than τ, then the coefficient set is reduced. For the i-th biomarker Make adjustments; if If <τ, then use the increased coefficient set. For the i-th biomarker Adjustments will be made; if the requirements are not met... >τ and If the condition is <τ, then iterate through the set of reduction coefficients. and increase the set of coefficients For the i-th biomarker Adjustments are made to determine the i-th biomarker. The minimum adjustment amount; S3: Improve the results With the target result Compare; If then the improvement goal is reached; If then modify the adjustment increment repeat S2 until the improvement goal is reached; S4: Interpretation and analysis of burn prediction results using improvement targets; S1 specifically includes: S1.1 : obtaining target outcomes for burns a set of biomarkers corresponding to the burn prediction outcomes ; S1.2: determining a comprehensive weight of each biomarker, determining the biomarker set according to the comprehensive weight of each biomarker the adjustment priority of each biomarker; the comprehensive weight of each biomarker corresponds to a calculation formula: In the formula, Let be the overall weight of the i-th biomarker, which is dimensionless; SHAP contribution value of the i-th biomarker after standardization, dimensionless; The importance weight of the contribution value to SHAP, dimensionless; The degree centrality of the i-th biomarker after standardization is dimensionless; Weights for degree centrality; The betweenness centrality of the i-th biomarker after standardization is dimensionless; The betweenness centrality weight is dimensionless. To represent the compact centrality of the i-th biomarker after standardization, dimensionless; The centrality weights are dimensionless. S1.3: Execute S2 according to the adjustment priority of each biomarker. 2.The burn prediction result explainability analysis method of claim 1, wherein, The SHAP contribution value of the ith biomarker The calculation formula is: In the formula, ) represents the SHAP contribution value of the i-th biomarker, which is dimensionless; This is a dimensionless set of biomarkers corresponding to burn prediction results. The prediction result obtained by using the random forest model after adding the i-th biomarker is dimensionless. A collection of biomarkers The prediction results obtained using the random forest model are dimensionless; n is the number of biomarkers, in units of 1, and i is the serial number of the biomarker, in units of 1. SHAP contribution value of the i-th biomarker after normalization SHAP contribution value of the i-th biomarker after normalization .

3. The method for interpretability analysis of burn prediction results according to claim 2, characterized in that, Degree centrality of the i-th biomarker after standardization Betweenness centrality of the i-th biomarker after standardization and the tight centrality of the i-th biomarker after standardization It is obtained by processing the i-th biomarker through graph and network analysis and then standardizing the processing results. 4.The burn prediction result explainability analysis method of claim 3, wherein, Degree centrality of the ith biomarker The formula for calculating the degree centrality of the ith biomarker is: In the formula, In the graph network model, the degree centrality of the i-th biomarker is dimensionless; The adjustment increment for the i-th biomarker is dimensionless; n is the number of biomarkers. Betweenness centrality of the ith biomarker The formula for calculating the betweenness centrality of the ith biomarker is: In the formula, In the graph network model, the betweenness centrality of the i-th biomarker is dimensionless; Both j and j are path points in the graph network model; For the i-th biomarker Number of shortest pathological pathways between path point j and path point j, in units of: number; In the graph network model, Number of shortest pathological pathways between path point j and path point j, in units of: number; Close centrality of the ith biomarker The formula for calculating the close centrality of the ith biomarker is: In the formula, In the graph network model, represents the compact centrality of the i-th biomarker, dimensionless; j represents the path point in the graph network model, dimensionless; and n represents the number of biomarkers, in units of 1; The distance correlation between the i-th biomarker and the j-th path point is dimensionless; Degree centrality of the i-th biomarker After standardization, the degree centrality of the i-th biomarker is obtained. ; the betweenness centrality of the i-th biomarker After standardization, the betweenness centrality of the i-th biomarker is obtained. ; the tight centrality of the i-th biomarker After standardization, the compact centrality of the i-th biomarker was obtained. . 5.The burn prediction result explainability analysis method of claim 1, wherein, The utilization adjustment increment For the i-th biomarker Adjustment is made to obtain improved biomarkers Specifically: wherein is the i-th biomarker, dimensionless; is the adjusted increment of the i-th biomarker, dimensionless; is the i-th improvement biomarker, dimensionless. 6.The burn prediction result explainability analysis method of claim 1, wherein, Specifically, S4 includes: using improvement targets to determine the expected burn grade after adjustment, the adjusted biomarkers, and the adjustment increment. Based on burn prediction results, adjusted expected burn severity, adjusted biomarkers, and adjustment increments. The results of burn prediction were interpreted and analyzed.

7. An interpretable analysis system for burn prediction results for implementing the interpretable analysis method for burn prediction results according to any one of claims 1 to 6, characterized in that include: Acquisition module: Used to acquire the target results of burns. The set of biomarkers corresponding to burn prediction results Where i is the biomarker index and n is the number of biomarkers. For the i-th biomarker; Improvement module: Used to determine the adjustment increment. Using adjustment increment For the i-th biomarker Adjustments were made to obtain improved biomarkers. This will improve biomarkers As a clinical burn diagnostic factor, prediction was performed using a random forest model, resulting in improved outcomes. ; judgment module: for comparing the improvement result with the target result ; if , the improvement target is reached; if , the adjustment increment is modified , the improvement module is repeated until the improvement target is reached; And the interpretation and analysis module: used to interpret and analyze the burn prediction results using the improvement targets.

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