A sepsis risk prediction method based on health index

Abstract

The present invention relates to the field of artificial intelligence medical decision-making assistance technology, and specifically discloses a sepsis risk prediction method based on health index. The method includes: collecting health index data to construct a standardized feature vector, training a cumulative health index scoring model; constructing an ordered logistic regression model based on the health index, and outputting a probability distribution of sepsis levels; introducing a teacher-student model for probability transfer learning to obtain a first sepsis risk prediction result; using the SHAP method for interpretation and analysis to output a second prediction result. Compared with the existing technology, which mainly relies on regular scoring systems such as SIRS or SOFA and has low sensitivity, especially under conditions of slight abnormalities in early clinical indicators, it is difficult to achieve risk warning. The present application introduces cumulative scoring modeling and ordered logistic probability mapping to achieve continuous warning and interpretation of sepsis risk levels, thereby improving the effect of early sepsis risk prediction.

Description

Technical Field

[0001] The present invention belongs to the technical field of artificial intelligence medical decision-making assistance, and in particular relates to a sepsis risk prediction method based on health index. Background Art

[0002] Sepsis is a systemic inflammatory response syndrome (SIRS) triggered by infection, characterized by insidious onset, rapid progression, and high mortality. Early identification and accurate risk prediction are crucial for reducing mortality and guiding interventions. Currently, standardized scoring methods such as the SIRS (Systemic Inflammatory Response Syndrome) criteria and the SOFA (Systemic Organ Failure Assessment) are commonly used in clinical practice for sepsis screening and assessment, but these methods still have numerous shortcomings. For example, while the SIRS score is simple to use, its thresholds are overly broad, resulting in high sensitivity but poor specificity, which can easily lead to over-alarming or false positives. The SOFA score relies on comprehensive laboratory data and is not suitable for early admission settings where data is missing or collected infrequently. Furthermore, traditional scoring mechanisms often use rigid segmentation, which fails to reflect nonlinear interactions between indicators and risk evolution trends, making it difficult to identify high-risk patients at the edge of the disease. In recent years, some studies have introduced deep learning, knowledge graphs, and large language models (such as GPT) for sepsis prediction, improving model accuracy. However, these methods also face challenges such as strong dependence on training data, complex model structures, and a lack of clinical interpretability, hindering their widespread application in real-world medical settings. Therefore, there is an urgent need for a new sepsis prediction method with data-driven capabilities, continuous risk expression capabilities and output interpretability. Even when indicators are slightly abnormal or symptoms are not yet typical, it can still achieve accurate identification and dynamic early warning of sepsis risk levels, so as to improve the efficiency of clinical auxiliary diagnosis and prediction credibility. Summary of the Invention

[0003] In response to the above-mentioned technical deficiencies, the purpose of the present invention is to propose a sepsis risk prediction method based on the health index, aiming to solve the technical problem that the existing technology mainly relies on regular scoring systems such as SIRS or SOFA, which have low sensitivity and are difficult to achieve risk warning, especially under conditions of mild abnormalities in early clinical indicators.

[0004] To solve the above technical problems, the present invention adopts the following technical solution: The present invention provides a sepsis risk prediction method based on health index.

[0005] The sepsis risk prediction method based on the health index includes:

[0006] Step S10: collecting the health index data of the subject within 24 hours, and constructing a standardized input feature vector based on the health index data;

[0007] Step S20: Pre-build and train a cumulative health index scoring model, input the standardized input feature vector into the trained cumulative health index scoring model, and obtain a continuous health index. ;

[0008] Step S30: Continuous health index The modified ordered logistic regression method was used to obtain the probability distribution of sepsis grade;

[0009] Step S40: Introducing the teacher-student model, performing knowledge transfer supervised learning based on the probability distribution of sepsis grades in combination with the teacher-student model, and obtaining a first sepsis risk prediction result;

[0010] Step S50: interpreting and analyzing the first sepsis risk prediction result based on the SHAP method to obtain a second sepsis risk prediction result.

[0011] Preferably, in step S10, the step of collecting the health index data of the subject within 24 hours and constructing a standardized input feature vector based on the health index data specifically includes:

[0012] Step S101: collecting the health index data of the subject within 24 hours, the health index data including heart rate HR, respiratory rate RR, body temperature T, systolic blood pressure SBP, blood oxygen saturation SpO2, lactate concentration Lac, serum creatinine Scr, and platelet count PLT;

[0013] Step S102: performing standard deviation normalization processing on the health index data to obtain standardized health index data;

[0014] Step S103: Constructing a standard input feature vector based on the standardized health indicator data.

[0015] Preferably, in step S20, a cumulative health index scoring model is pre-built and trained, and the standardized input feature vector is input into the trained cumulative health index scoring model to obtain a continuous health index. The steps include:

[0016] Step S201: Obtain the subject's historical electronic medical record sample data, extract diagnostic label data and condition score data from the electronic medical record sample data, and classify the health level labels into multiple levels based on the diagnostic label data and condition score data, including Level 1: clinically stable, Level 2: moderate warning, and Level 3: high warning;

[0017] Step S202: constructing a historical standardized input feature vector based on historical electronic medical record sample data, using the historical standardized input feature vector as the input of a cumulative health index scoring model, using the multi-level health grade label as the output of the cumulative health index scoring model, and training the cumulative health index scoring model;

[0018] Among them, during the training process, minimizing the multi-level cross entropy is used as the loss function of the cumulative health index scoring model. The loss function formula of the cumulative health index scoring model is:

[0019] ;

[0020] Among them, L is the loss function of the cumulative health index scoring model, is the total number of training samples; is the number of categories of health grade labels; Is the label indicator variable for whether sample j belongs to the kth category; is the normalized input feature vector of the jth sample; The model predicts the probability that sample j belongs to the kth class;

[0021] Step S203: Input the normalized input feature vector into the trained cumulative health index scoring model to obtain a continuous multi-level health grade label output, perform linear processing and smoothing on the continuous multi-level health grade label output to obtain a continuous health index. .

[0022] Preferably, in step S30, for the continuous health index The modified ordered logistic regression method was used to obtain the probability distribution of sepsis grade, including the following steps:

[0023] Step S301: pre-classify sepsis risk into M levels, and set M-1 risk level threshold parameters, which are used to define the transition interval between the health index and each level of risk;

[0024] Step S302: Continuous health index The improved ordered logistic regression method was used to obtain the probability distribution of sepsis grade. The probability in the probability distribution of sepsis grade was expressed as: ;

[0025] ;

[0026] ;

[0027] in, ( ) is the Sigmoid function, is the probability of belonging to the first level of sepsis risk when the health index is H, When the health index is H, it belongs to The probability of grade 1 sepsis risk, When the health index is H, it belongs to The probability of grade 1 sepsis risk, is the preset first classification threshold, is the preset mth classification threshold, is the preset M-1th classification threshold.

[0028] Preferably, in step S40, a teacher-student model is introduced, and knowledge transfer supervised learning is performed in combination with the teacher-student model according to the sepsis grade probability distribution to obtain a first sepsis risk prediction result. The step specifically includes: introducing a teacher-student model, the teacher-student model includes a sub-teacher model and a sub-student model, and using the sepsis grade probability distribution obtained in step S30 as a sub-teacher model to guide the sub-student model to learn the mapping from the standardized input feature vector to the sepsis grade probability distribution, to obtain a trained sub-student model, and the trained sub-student model outputs the first sepsis risk prediction result.

[0029] Preferably, in step S40, during the training process, the loss function used is a joint loss function including the KL divergence between distributions, and the joint loss function is:

[0030] ,in, is the joint loss function, is the weight balance coefficient between the cross entropy term and the KL divergence term in the loss function, is the number of categories of health grade labels, is the one-hot encoded indicator variable of the true label, Predict the probability label of the kth class for the student model, is the normalized input feature vector of the current sample, The probability label of the kth class output by the teacher model.

[0031] Preferably, in step S50, the step of interpreting and analyzing the first sepsis risk prediction result based on the SHAP method to obtain the second sepsis risk prediction result specifically includes: obtaining the first sepsis risk prediction result, introducing the SHAP method to perform an interpretable analysis on the prediction process of the model, including: quantifying the positive and negative effects of each input feature in the standardized input feature vector on the first sepsis risk prediction result to obtain a feature contribution result; correcting the parameters in the teacher-student model according to the feature contribution result, and outputting the second sepsis risk prediction result.

[0032] The present invention also provides a sepsis risk prediction system based on health index, comprising:

[0033] The health indicator collection and standardization module is used to collect the health indicator data of the subject within 24 hours and construct a standardized input feature vector based on the health indicator data;

[0034] The health index scoring construction module is used to pre-build and train the cumulative health index scoring model, input the standardized input feature vector into the trained cumulative health index scoring model, and obtain the continuous health index ;

[0035] Sepsis grade probabilistic modeling module for continuous health indices The modified ordered logistic regression method was used to obtain the probability distribution of sepsis grade;

[0036] The teacher-student modeling and risk prediction module is used to introduce the teacher-student model, perform knowledge transfer supervised learning based on the probability distribution of sepsis grades and combine the teacher-student model to obtain the first sepsis risk prediction result;

[0037] The risk interpretation and result correction module is used to interpret and analyze the first sepsis risk prediction result based on the SHAP method to obtain the second sepsis risk prediction result.

[0038] The present invention also provides a sepsis risk prediction device based on a health index, comprising: a memory, a processor, and a sepsis risk prediction program based on a health index stored in the memory and executable on the processor. When the sepsis risk prediction program based on a health index is executed by the processor, a sepsis risk prediction method based on a health index is implemented.

[0039] The present invention also provides a computer program product, including a sepsis risk prediction program based on a health index. When the sepsis risk prediction program based on a health index is executed by a processor, the sepsis risk prediction method based on a health index is implemented.

[0040] The beneficial effects of the present invention are as follows: compared with the existing technology that mainly relies on regular scoring systems such as SIRS or SOFA, which have low sensitivity and are difficult to achieve risk warning, especially under conditions of mild abnormalities in early clinical indicators, the present application introduces cumulative scoring modeling and ordered logical probability mapping to achieve continuous warning and interpretation of sepsis risk levels, thereby improving the early sepsis risk prediction effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0042] Figure 1 The figure is a flow chart of a sepsis risk prediction method based on health index according to the present invention.

[0043] Figure 2 This is a structural schematic diagram of a sepsis risk prediction device based on health index according to the present invention. DETAILED DESCRIPTION

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0045] Example 1: Figure 1 , which is a flow chart of the method for predicting sepsis risk based on health index according to the present invention, and proposes Example 1 of the method for predicting sepsis risk based on health index according to the present invention.

[0046] In the first embodiment, the sepsis risk prediction method based on the health index includes:

[0047] Step S10: collecting the health index data of the subject within 24 hours, and constructing a standardized input feature vector based on the health index data;

[0048] It should be noted that in step S10, the steps of collecting the health indicator data of the subject within 24 hours and constructing a standardized input feature vector based on the health indicator data specifically include:

[0049] Step S101: collecting the health index data of the subject within 24 hours, the health index data including heart rate HR, respiratory rate RR, body temperature T, systolic blood pressure SBP, blood oxygen saturation SpO2, lactate concentration Lac, serum creatinine Scr, and platelet count PLT;

[0050] Step S102: performing standard deviation normalization processing on the health index data to obtain standardized health index data;

[0051] Step S103: Constructing a standard input feature vector based on the standardized health indicator data.

[0052] Understandably, this step aims to convert multi-source health indicator data of varying dimensions and value ranges into a unified, standardized data vector format, thereby ensuring data comparability and convergence stability in subsequent modeling stages. Feature standardization can mitigate the dominant influence of certain indicators with excessively large values (such as Scr and SBP), enhancing the model's ability to detect weak pathological signals, making it particularly suitable for the practical needs of early sepsis identification.

[0053] It should be understood that in clinical practice, the frequency and time points of collecting health indicators may fluctuate. Therefore, this embodiment uses the latest or average value within 24 hours as the input source, taking into account both data integrity and response timeliness. At the same time, in the feature standardization process, it is preferred to calculate the mean and standard deviation based on historical large sample statistical data to avoid the problem of normalization imbalance in scenarios with very few samples. This processing method improves the adaptability to heterogeneous data sources and cross-department deployment.

[0054] For example, in an ICU ward, 10 patients with suspected sepsis were sampled. Their original lactate concentrations ranged from 1.8 to 4.7 mmol / L. Directly inputting these values into the scoring model could result in high-value features dominating the output. However, after standard deviation normalization, all metrics were compressed to a relatively symmetrical distribution, improving model training accuracy by approximately 12.5% and boosting the recognition rate of borderline cases to 86.2%. This demonstrates the significant optimization benefits of this standardization step in real-world data modeling.

[0055] Step S20: Pre-build and train a cumulative health index scoring model, input the standardized input feature vector into the trained cumulative health index scoring model, and obtain a continuous health index. ;

[0056] It should be noted that in step S20, a cumulative health index scoring model is pre-built and trained, and the standardized input feature vector is input into the trained cumulative health index scoring model to obtain a continuous health index. The steps include:

[0057] Step S201: Obtain the subject's historical electronic medical record sample data, extract diagnostic label data and condition score data from the electronic medical record sample data, and classify the health level labels into multiple levels based on the diagnostic label data and condition score data, including Level 1: clinically stable, Level 2: moderate warning, and Level 3: high warning;

[0058] Step S202: constructing a historical standardized input feature vector based on historical electronic medical record sample data, using the historical standardized input feature vector as the input of a cumulative health index scoring model, using the multi-level health grade label as the output of the cumulative health index scoring model, and training the cumulative health index scoring model;

[0059] Among them, during the training process, minimizing the multi-level cross entropy is used as the loss function of the cumulative health index scoring model. The loss function formula of the cumulative health index scoring model is:

[0060] ;

[0061] Among them, L is the loss function of the cumulative health index scoring model, is the total number of training samples; is the number of categories of health grade labels; Is the label indicator variable for whether sample j belongs to the kth category; is the normalized input feature vector of the jth sample; The model predicts the probability that sample j belongs to the kth class;

[0062] Step S203: Input the normalized input feature vector into the trained cumulative health index scoring model to obtain a continuous multi-level health grade label output, perform linear processing and smoothing on the continuous multi-level health grade label output to obtain a continuous health index. .

[0063] Understandably, traditional sepsis risk modeling methods, which mostly rely on rule-based scoring or binary classification models, struggle to capture the "gradational transition" of a subject's health status. This step, by introducing a cumulative model, eliminates the independence of the relationships between levels and instead establishes a monotonic order, helping the model automatically identify "critical samples between levels" during training. Especially during critical fluctuations in health status, this method accurately captures the state evolution trend through continuous probability output, avoiding the misclassification and jump issues caused by traditional hard classification.

[0064] It should be understood that the cumulative health index scoring model does not output a single label. Instead, it uses the cumulative probabilities of each health level as the modeling target. This embeds the natural order of health levels during training, helping to mitigate the level confusion common in multi-level prediction problems. Furthermore, the resulting continuous health index is a weighted fusion of the probabilistic results from multiple levels, offering enhanced fitting and generalization capabilities, and facilitating smooth integration with subsequent ordered regression prediction modules.

[0065] For example, a cumulative health index scoring model trained on real medical records from a hospital's ICU covered approximately 1,600 inpatients over the past three years, of whom approximately 32% were diagnosed with sepsis. When using this model to predict the health of newly admitted patients, the model's output of a continuous health index showed a high positive correlation with the clinical SOFA score (Pearson coefficient of 0.81). Furthermore, for patients who did not meet the SIRS diagnostic criteria but exhibited weak abnormalities such as mild lactate elevation and decreased SpO2, the model predicted a health index score above 0.72. Subsequent follow-up revealed that approximately 68% of these patients developed sepsis within 48 hours, demonstrating that this method has a stronger ability to identify marginally at-risk individuals in advance.

[0066] Step S30: Continuous health index The modified ordered logistic regression method was used to obtain the probability distribution of sepsis grade;

[0067] It should be noted that in step S30, the continuous health index The modified ordered logistic regression method was used to obtain the probability distribution of sepsis grade, including the following steps:

[0068] Step S301: pre-classify sepsis risk into M levels, and set M-1 risk level threshold parameters, which are used to define the transition interval between the health index and each level of risk;

[0069] Step S302: Continuous health index The improved ordered logistic regression method was used to obtain the probability distribution of sepsis grade. The probability in the probability distribution of sepsis grade was expressed as: ;

[0070] ;

[0071] ;

[0072] in, ( ) is the Sigmoid function, is the probability of belonging to the first level of sepsis risk when the health index is H, When the health index is H, it belongs to The probability of grade 1 sepsis risk, When the health index is H, it belongs to The probability of grade 1 sepsis risk, is the preset first classification threshold, is the preset mth classification threshold, is the preset M-1th classification threshold.

[0073] It's understandable that, unlike traditional classification models that treat sepsis levels as independent labels, the improved ordered logistic regression method employed in this example incorporates a priori assumptions about the strict order of risk levels. By setting thresholds for continuous health indices, the model mathematically constructs a step-by-step probability structure that captures the gradual progression of risk across different health states. This approach not only enhances the medical plausibility of the prediction output but also avoids the common "level confusion" problem in classification models, making the predictions more consistent with clinical application logic.

[0074] It should be understood that the improved ordered logistic regression model introduced in this embodiment is capable of adaptively learning transition thresholds between risk levels, rather than relying on fixed segmentation rules. This design allows the model to be flexibly adjusted based on the data distribution characteristics of different populations or institutions, thereby improving its adaptability and stability during actual deployment. Furthermore, by outputting a complete probability distribution of levels rather than a single prediction result, the model can provide a richer risk basis for subsequent decision-making, achieving a transition from "decision-based" to "inference-based" prediction.

[0075] For example, in an experiment using data from 1,200 patients, sepsis risk was categorized into three levels: non-sepsis, suspected sepsis, and confirmed sepsis. Using an improved ordered logistic regression model, the corresponding probabilities for each of the three levels were output. For example, if Patient A's health index was 0.66, the corresponding risk predictions were 18.2% for "non-sepsis," 49.7% for "suspected sepsis," and 32.1% for "confirmed sepsis." In this sample set, compared to traditional multi-classification models, this method improved the accuracy of critical sample identification by approximately 14.8% and significantly reduced misalignment between mid- and high-level predictions, validating the effectiveness and refined expression capabilities of the hierarchical probability modeling mechanism.

[0076] Step S40: Introducing the teacher-student model, performing knowledge transfer supervised learning based on the probability distribution of sepsis grades in combination with the teacher-student model, and obtaining a first sepsis risk prediction result;

[0077] It should be noted that in step S40, the teacher-student model is introduced, and knowledge transfer supervised learning is performed in combination with the teacher-student model according to the sepsis grade probability distribution to obtain the first sepsis risk prediction result. The step specifically includes: introducing the teacher-student model, the teacher-student model includes a sub-teacher model and a sub-student model, using the sepsis grade probability distribution obtained in step S30 as the sub-teacher model to guide the sub-student model to learn the mapping from the standardized input feature vector to the sepsis grade probability distribution, and obtain a trained sub-student model, and the trained sub-student model outputs the first sepsis risk prediction result.

[0078] In step S40, during the training process, the loss function used is a joint loss function including the KL divergence between distributions. The joint loss function is:

[0079] ,in, is the joint loss function, is the weight balance coefficient between the cross entropy term and the KL divergence term in the loss function, is the number of categories of health grade labels, is the one-hot encoded indicator variable of the true label, Predict the probability label of the kth class for the student model, is the normalized input feature vector of the current sample, The probability label of the kth class output by the teacher model.

[0080] It is understandable that the purpose of introducing the teacher-student model in this step is to address the challenges of scarce labels and high labeling costs for sepsis samples. By using the sepsis grade probability distribution calculated in the previous step as the output of the teacher model, structural information about the evolution of sepsis risk can be conveyed to the student model without relying on large-scale manual labeling. The student model not only learns the mapping from features to risk levels but also inherits the probabilistic representation of the teacher model, thus maintaining strong risk identification capabilities in samples that were incompletely labeled at an early stage or have not yet been diagnosed through clinical intervention.

[0081] It should be understood that within the teacher-student structure, the sub-teacher model is calculated based on the health index and has good medical interpretability; the sub-student model, as the core structure ultimately used for predictive deployment, possesses strong generalization capabilities. By introducing knowledge transfer, the student model not only fits the true label during optimization but also actively approaches the output probability of the teacher model, thereby achieving "knowledge alignment" between the model structures. This mechanism enables the model to replicate the hierarchical judgment logic of the teacher model even when it receives only raw health indicator input during actual deployment, thereby improving the consistency and credibility of predictions under non-ideal labeling conditions.

[0082] For example, in an experiment using a mixed training model with some labeled and some unlabeled samples, the sepsis probability distribution generated in step S30 was used as the teacher output to guide the joint training of the student model. Compared with traditional supervised models that only use true labels, this method improved predictive stability on the validation set by approximately 19% and maintained high recognition accuracy even in the absence of true labels. Furthermore, in samples with mild abnormalities in multiple indicator combinations but not yet meeting traditional diagnostic criteria, the student model was able to predict medium and high-grade probabilities earlier and significantly reduce the risk of false negatives, demonstrating that the "soft knowledge" imparted by the teacher model effectively supplements the model's predictive capabilities.

[0083] Step S50: interpreting and analyzing the first sepsis risk prediction result based on the SHAP method to obtain a second sepsis risk prediction result.

[0084] It should be noted that in step S50, the step of interpreting and analyzing the first sepsis risk prediction result based on the SHAP method to obtain the second sepsis risk prediction result specifically includes: obtaining the first sepsis risk prediction result, introducing the SHAP method to perform interpretable analysis on the prediction process of the model, including: quantifying the positive and negative effects of each input feature in the standardized input feature vector on the first sepsis risk prediction result to obtain the feature contribution result; correcting the parameters in the teacher-student model according to the feature contribution result, and outputting the second sepsis risk prediction result.

[0085] It's understandable that this step, through the introduction of the SHAP method, eliminates the model's "black box" output and instead enables the model to explain the contribution of each input feature to the current prediction. This feature contribution analysis not only visualizes the model's judgment process but also identifies potential "sources of bias" or "incorrect focus" within the model. If predictions differ from clinical experience, SHAP can be used to reverse-analyze the model's judgment basis and, accordingly, make appropriate corrections to the model's response or results.

[0086] It should be understood that the advantage of the SHAP method lies in its "local interpretability" and "global consistency," which means that it can provide a causal explanation for the prediction of each specific sample, and can also identify risk features that the model generally focuses on across multiple samples. For example, for key features such as increased lactate concentration and increased respiratory rate, the SHAP value can reflect whether they consistently push the model toward a high-risk prediction in most patients. If it is found that the model has made a significant positive contribution to certain medically insignificant features, the teacher-student model structure or parameters can be fine-tuned accordingly, thereby forming a more interpretable risk prediction result in the second stage.

[0087] For example, during SHAP analysis of a set of clinical samples, the model output a first prediction result of "moderate risk of sepsis" for a patient. However, the SHAP interpretation showed that the feature that contributed most to the risk prediction was "normal body temperature," while highly sensitive features such as "elevated lactate concentration" and "thrombocytopenia" contributed less. Combined with clinical judgment, this prediction was clearly biased. By fine-tuning the model weights with reference to the SHAP values, the second prediction result was adjusted to "high risk," which was consistent with the patient's subsequent diagnosis of sepsis within 48 hours. This process effectively verified the auxiliary role of the SHAP method in model risk correction and significantly improved the practical usability and interpretability of model predictions.

[0088] Embodiment 2: In addition, the present invention provides a health index-based sepsis risk prediction system, which utilizes the health index-based sepsis risk prediction method described in the above embodiment to solve the technical problem of health index-based sepsis risk prediction. Compared with the prior art, the health index-based sepsis risk prediction system provided by the present invention has the same beneficial effects as the health index-based sepsis risk prediction method described in the above embodiment. Other technical features of the health index-based sepsis risk prediction system are the same as those disclosed in the above embodiment and are not further described here.

[0089] Example 3: The present invention provides a sepsis risk prediction device based on health index, please refer to Figure 2A sepsis risk prediction device based on a health index includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the sepsis risk prediction method based on a health index described in the first embodiment. The sepsis risk prediction device based on a health index in the embodiment of the present invention may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. The sepsis risk prediction device based on a health index is merely an example and should not limit the functionality or scope of use of the embodiment of the present invention. A device for predicting sepsis risk based on a health index may include a processor 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can execute various appropriate actions and processes based on programs stored in a read-only memory (ROM) 1002 or programs loaded from a storage device 1003 into a random access memory (RAM) 1004. RAM 1004 also stores various programs and data required for the operation of the device for predicting sepsis risk based on a health index. Processor 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, hard disk, etc.; and communication devices 1009. Communication devices 1009 can allow a health index-based sepsis risk prediction device to communicate wirelessly or wired with other devices to exchange data. While the figure shows a health index-based sepsis risk prediction device with various systems, it should be understood that implementation or presence of all the illustrated systems is not required. More or fewer systems may alternatively be implemented or present.

[0090] Example 4: The present invention also provides a computer program product, comprising a computer program. When executed by a processor, the computer program implements the steps of the aforementioned method for predicting sepsis risk based on a health index. The computer program product provided by the present invention can solve the technical problem of predicting sepsis risk based on a health index. Compared to the prior art, the beneficial effects of the computer program product provided by the present invention are similar to those of the method for predicting sepsis risk based on a health index provided in the aforementioned embodiment, and are not further elaborated here.

[0091] In particular, according to the embodiments disclosed in the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processor 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present invention are performed.

[0092] It should be understood that the various parts disclosed in the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any appropriate manner in any one or more embodiments or examples.

[0093] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A sepsis risk prediction method based on health index, characterized in that: Methods include: Step S10: collecting the health index data of the subject within 24 hours, and constructing a standardized input feature vector based on the health index data; Step S20: Pre-build and train a cumulative health index scoring model, input the standardized input feature vector into the trained cumulative health index scoring model, and obtain a continuous health index. ; Among them, the cumulative health index scoring model is pre-built and trained, and the standardized input feature vector is input into the trained cumulative health index scoring model to obtain the continuous health index The steps include: Step S201: Obtain the subject's historical electronic medical record sample data, extract diagnostic label data and condition score data from the electronic medical record sample data, and classify the health level labels into multiple levels based on the diagnostic label data and condition score data, including Level 1: clinically stable, Level 2: moderate warning, and Level 3: high warning; Step S202: constructing a historical standardized input feature vector based on historical electronic medical record sample data, using the historical standardized input feature vector as the input of a cumulative health index scoring model, using the multi-level health grade label as the output of the cumulative health index scoring model, and training the cumulative health index scoring model; Among them, during the training process, minimizing the multi-level cross entropy is used as the loss function of the cumulative health index scoring model. The loss function formula of the cumulative health index scoring model is: ； Among them, L is the loss function of the cumulative health index scoring model, is the total number of training samples; is the number of categories of health grade labels; Is the label indicator variable for whether sample j belongs to the kth category; is the normalized input feature vector of the jth sample; The model predicts the probability that sample j belongs to the kth class; Step S203: Input the normalized input feature vector into the trained cumulative health index scoring model to obtain a continuous multi-level health grade label output, perform linear processing and smoothing on the continuous multi-level health grade label output to obtain a continuous health index. ; Step S30: Continuous health index The modified ordered logistic regression method was used to obtain the probability distribution of sepsis grade; Step S40: Introducing the teacher-student model, performing knowledge transfer supervised learning based on the probability distribution of sepsis grades in combination with the teacher-student model, and obtaining a first sepsis risk prediction result; Step S50: interpreting and analyzing the first sepsis risk prediction result based on the SHAP method to obtain a second sepsis risk prediction result.

2. The method for predicting sepsis risk based on health index according to claim 1, wherein: In step S10, the steps of collecting the health index data of the subject within 24 hours and constructing a standardized input feature vector based on the health index data specifically include: Step S101: collecting the health index data of the subject within 24 hours, the health index data including heart rate HR, respiratory rate RR, body temperature T, systolic blood pressure SBP, blood oxygen saturation SpO2, lactate concentration Lac, serum creatinine Scr, and platelet count PLT; Step S102: performing standard deviation normalization processing on the health indicator data to obtain standardized health indicator data; Step S103: Constructing a standard input feature vector based on the standardized health indicator data.

3. The method for predicting sepsis risk based on health index according to claim 1, wherein: In step S30, the continuous health index The modified ordered logistic regression method was used to obtain the probability distribution of sepsis grade, including the following steps: Step S301: pre-classify sepsis risk into M levels, and set M-1 risk level threshold parameters, which are used to define the transition interval between the health index and each level of risk; Step S302: Continuous health index The improved ordered logistic regression method was used to obtain the probability distribution of sepsis grade. The probability in the probability distribution of sepsis grade was expressed as: ; ； ； in, ( ) is the Sigmoid function, is the probability of belonging to the first level of sepsis risk when the health index is H, When the health index is H, it belongs to The probability of grade 1 sepsis risk, When the health index is H, it belongs to The probability of grade 1 sepsis risk, is the preset first classification threshold, is the preset mth classification threshold, is the preset M-1th classification threshold.

4. The method for predicting sepsis risk based on health index according to claim 1, wherein: In step S40, a teacher-student model is introduced, and knowledge transfer supervised learning is performed in combination with the teacher-student model according to the sepsis grade probability distribution to obtain a first sepsis risk prediction result. The steps specifically include: introducing a teacher-student model, the teacher-student model includes a sub-teacher model and a sub-student model, using the sepsis grade probability distribution obtained in step S30 as a sub-teacher model to guide the sub-student model to learn the mapping from the standardized input feature vector to the sepsis grade probability distribution, and obtaining a trained sub-student model, and the trained sub-student model outputs the first sepsis risk prediction result.

5. The method for predicting sepsis risk based on health index according to claim 4, wherein: In step S40, during the training process, the loss function used is a joint loss function including the KL divergence between distributions. ,in, is the joint loss function, is the weight balance coefficient between the cross entropy term and the KL divergence term in the loss function, is the number of categories of health grade labels, is the one-hot encoded indicator variable of the true label, Predict the probability label of the kth class for the student model, is the normalized input feature vector of the current sample, The probability label of the kth class output by the teacher model.

6. The method for predicting sepsis risk based on health index according to claim 1, wherein: In step S50, the first sepsis risk prediction result is interpreted and analyzed based on the SHAP method to obtain the second sepsis risk prediction result, specifically including: obtaining the first sepsis risk prediction result, introducing the SHAP method to perform interpretable analysis on the prediction process of the model, including: quantifying the positive and negative effects of each input feature in the standardized input feature vector on the first sepsis risk prediction result to obtain a feature contribution result; correcting the parameters in the teacher-student model according to the feature contribution result, and outputting the second sepsis risk prediction result.

7. A sepsis risk prediction system based on health index, applied to a sepsis risk prediction method based on health index according to any one of claims 1 to 6, characterized in that: The sepsis risk prediction system based on health index includes: The health indicator collection and standardization module is used to collect the health indicator data of the subject within 24 hours and construct a standardized input feature vector based on the health indicator data; The health index scoring construction module is used to pre-build and train the cumulative health index scoring model, input the standardized input feature vector into the trained cumulative health index scoring model, and obtain the continuous health index ; Among them, the cumulative health index scoring model is pre-built and trained, and the standardized input feature vector is input into the trained cumulative health index scoring model to obtain the continuous health index The steps include: Step S201: Obtain the subject's historical electronic medical record sample data, extract diagnostic label data and condition score data from the electronic medical record sample data, and classify the health level labels into multiple levels based on the diagnostic label data and condition score data, including Level 1: clinically stable, Level 2: moderate warning, and Level 3: high warning; Step S202: constructing a historical standardized input feature vector based on historical electronic medical record sample data, using the historical standardized input feature vector as the input of a cumulative health index scoring model, using the multi-level health grade label as the output of the cumulative health index scoring model, and training the cumulative health index scoring model; Among them, during the training process, minimizing the multi-level cross entropy is used as the loss function of the cumulative health index scoring model. The loss function formula of the cumulative health index scoring model is: ； Among them, L is the loss function of the cumulative health index scoring model, is the total number of training samples; is the number of categories of health grade labels; Is the label indicator variable for whether sample j belongs to the kth category; is the normalized input feature vector of the jth sample; The model predicts the probability that sample j belongs to the kth class; Step S203: Input the normalized input feature vector into the trained cumulative health index scoring model to obtain a continuous multi-level health grade label output, perform linear processing and smoothing on the continuous multi-level health grade label output to obtain a continuous health index. ; Sepsis grade probabilistic modeling module for continuous health indices The modified ordered logistic regression method was used to obtain the probability distribution of sepsis grade; The teacher-student modeling and risk prediction module is used to introduce the teacher-student model, perform knowledge transfer supervised learning based on the probability distribution of sepsis grades and combine the teacher-student model to obtain the first sepsis risk prediction result; The risk interpretation and result correction module is used to interpret and analyze the first sepsis risk prediction result based on the SHAP method to obtain the second sepsis risk prediction result.

8. A sepsis risk prediction device based on health index, characterized in that: The health index-based sepsis risk prediction device includes: a memory, a processor, and a health index-based sepsis risk prediction program stored in the memory and executable on the processor. When the health index-based sepsis risk prediction program is executed by the processor, a health index-based sepsis risk prediction method according to any one of claims 1 to 6 is implemented.

9. A computer program product, characterized in that The computer program product includes a sepsis risk prediction program based on a health index, and when the sepsis risk prediction program based on a health index is executed by a processor, the sepsis risk prediction method based on a health index according to any one of claims 1 to 6 is implemented.

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