Method, device and medium for predicting hypotension during hemodialysis

Abstract

The present invention relates to the field of hemodialysis monitoring technology, and in particular to a method, device, and medium for predicting hypotension during hemodialysis. The method comprises: obtaining medical data, determining a data set after preprocessing; inputting the data set into a statistical model, obtaining correlation index factors based on the definition of hemodialysis hypotension and data analysis strategy in combination with expert data; inputting the correlation index factors into an AI prediction model to obtain hemodialysis record results and AUC values; performing a comprehensive evaluation based on the AUC value; if it is less than or equal to a preset value, adjusting the definition value of hemodialysis hypotension; if it is greater than the preset value, updating the definition of hemodialysis hypotension and optimizing the AI ​​prediction model; if it is greater than or equal to an expected value, comparing the hemodialysis record results with the prediction results, and if the conditions are met, the current AI prediction model is the optimal prediction model; inputting the hemodialysis data of the current detection target into the optimal prediction model to obtain the probability that the current detection target will experience hypotension during the hemodialysis process, with high prediction accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of hemodialysis monitoring, and in particular to a method, device and medium for predicting hypotension during hemodialysis. Background Art

[0002] Hypotension is a common complication in maintenance hemodialysis (HD) patients during dialysis, significantly impacting their survival and, in severe cases, inducing arrhythmias. It is therefore one of the main causes of death. Despite the rapid advancements in hemodialysis technology, the incidence of intradialytic hypotension has not decreased significantly. With the aging of today's dialysis patients and the increasing proportion of diabetic nephropathy in the dialysis population, the problem of intradialytic hypotension may become more serious in the future. While there are currently a variety of clinical measures to reduce or improve the incidence of intradialytic hypotension, early intervention based on the pre-dialysis prediction of the probability of hypotension occurring during dialysis remains an important research direction and solution.

[0003] Predicting the probability of intradialytic hypotension in patients requires a definition of intradialytic hypotension. However, the existing technology "Prediction Model of Blood Pressure during Hemodialysis Base on DeepLearning" points out that there is currently no international consensus on the definition of intradialytic hypotension. This means that different definitions of intradialytic hypotension have a critical impact on the establishment of prediction models for the probability of intradialytic hypotension, clinical analysis during dialysis, and clinical evaluation of prediction results (such as accuracy and AUC). For medical staff, different definitions of intradialytic hypotension focus on different indicators or the content and process of indicator changes. However, the experience of medical staff in analyzing large-scale patient population indicator data and clinical interventions in discovering and coping with intradialytic hypotension is also based on the definition of intradialytic hypotension and related analysis methods, and must be consistent with the results of relevant prediction models. This means that current prediction models for intradialytic hypotension have corresponding limitations.

[0004] For example, the definition of a 30 mmHg drop in systolic blood pressure during dialysis (based on the literature "Analysis of Risk Factors Related to Maintenance Hemodialysis Hypotension"). In actual clinical experience, when the pre-dialysis systolic blood pressure is less than 110 mmHg and the systolic blood pressure drops by more than 22 mmHg during dialysis, the probability of dialysis hypotension is relatively high. In the corresponding clinical cases of dialysis hypotension, when the pre-dialysis systolic blood pressure is less than 120 mmHg, the blood pressure value △SBP dropped during dialysis is almost always less than 30 mmHg. Obviously, the current definition is too strict for the pre-dialysis systolic blood pressure range of less than 120 mmHg. However, for clinical cases of dialysis hypotension in the pre-dialysis systolic blood pressure range of greater than 150 mmHg, the blood pressure value △SBP dropped during dialysis is almost always greater than 30 mmHg. Obviously, the current definition is too loose.

[0005] Based on the general definition in the literature "Prediction Model of Blood Pressure during Hemodialysis Base on DeepLearning," a 20 mmHg drop in systolic blood pressure during dialysis, accompanied by clinical symptoms, is considered a complication. In clinical dialysis records with a pre-dialysis systolic blood pressure greater than 120 mmHg, there is a 45% or greater probability of a ΔSBP drop greater than 20 mmHg during dialysis. In these records with a ΔSBP greater than 20 mmHg, complications with documented clinical symptoms can be identified as cases of hemodialytic hypotension. However, in actual hemodialysis management, records may be incomplete or erroneous, and the authenticity of clinical complications (whether they are caused by hemodialytic hypotension) may be affected by other diseases or medications. Due to differences in management levels and medical resources across campuses and hospitals, the current definition has limitations that may lead to discrepancies in discriminative results during widespread application.

[0006] In summary, different definitions may have different case discriminations for different pre-dialysis systolic blood pressure intervals, which may be too loose, too strict, or poorly controllable. Therefore, the standards represented by the above-mentioned definition of hypotension cannot well discriminate cases of intradialytic hypotension. This will have a decisive impact on the subsequent intradialytic hypotension prediction model, that is, the intradialytic hypotension prediction model cannot discriminate actual cases of intradialytic hypotension, and the prediction results are inaccurate. Summary of the Invention

[0007] The present invention aims to solve at least one of the technical problems existing in the prior art.

[0008] To this end, the present invention provides a method for predicting hypotension during hemodialysis, which can update the definition of hemodialysis hypotension and predict hemodialysis hypotension based on the updated definition of hemodialysis hypotension, thereby improving the accuracy of the prediction.

[0009] According to an embodiment of the present invention, the method for predicting hypotension during hemodialysis comprises the following steps:

[0010] S1, obtaining medical data of a target subject and preprocessing the medical data to determine a data set of the target subject; wherein the medical data includes HIS data, LIS data, and hemodialysis clinical data;

[0011] S2, inputting the target subject's data set into a statistical model, and obtaining associated indicator factors of the definition of hypotension based on the definition of dialysis hypotension and data analysis strategy in combination with expert data;

[0012] S3, inputting the associated indicator factors of the hypotension definition into the AI ​​prediction model to obtain the dialysis record results and AUC value of the target subject;

[0013] S4, performing a comprehensive evaluation based on the AUC value;

[0014] If the AUC value is less than or equal to the preset value, the dialysis hypotension definition value is adjusted and the process returns to step S2;

[0015] If the AUC value is greater than the preset value, the definition of dialysis hypotension is updated, the AI ​​prediction model is optimized, and the process returns to step S2;

[0016] If the AUC value is greater than or equal to the expected value, the dialysis record result of the target subject is compared with the predicted result. If the comparison result meets the preset conditions, the current AI prediction model is the optimal prediction model and proceeds to the next step;

[0017] S5, inputting the dialysis data of the current detection target into the optimal prediction model to obtain the probability that the current detection target will suffer from hypotension during the dialysis process.

[0018] The beneficial effect of the present invention is that the method for predicting hypotension during hemodialysis of the present invention uses an AI prediction model to obtain and update the definition of optimal dialysis hypotension. The AI ​​prediction model predicts hypotension during hemodialysis for patients based on the updated definition of dialysis hypotension, and the obtained results are highly accurate.

[0019] According to one embodiment of the present invention, in step S1, HIS, LIS data and hemodialysis clinical data are obtained from the hospital information system, laboratory information system and dialysis system respectively;

[0020] Using a time span of one day, all the medical data are cleaned and processed to obtain the medical data of the target object every day to form a data set of the target object.

[0021] According to one embodiment of the present invention, step S2 specifically includes the following steps:

[0022] Obtaining a distribution pattern of dialysis hypotension case data in a data set of the target object;

[0023] Combined with expert data, by statistically analyzing the distribution of dialysis hypotension case data in the target object's data set, the characteristic indicator factor variables related to whether dialysis hypotension occurs are obtained.

[0024] The characteristic indicator factor variables are screened to obtain associated indicator factors for the definition of hypotension.

[0025] According to one embodiment of the present invention, in step S3, the algorithms used by the AI ​​model to process the associated indicator factors defined for hypotension include: logistic regression, linear regression, ridge regression, XGBoost, and random forest.

[0026] According to one embodiment of the present invention, updating the definition of dialysis hypotension specifically includes:

[0027] S41, determining the pre-diastolic systolic blood pressure interval based on the target object's data set, and calculating the initial value a of the blood pressure reduction ratio imin And the maximum blood pressure drop ratio a imax , the calculation formula is:

[0028]

[0029] a imin =(ξ imax -90) / ξ imax ;

[0030] where ξ ia is the average value or boundary value of the pre-diastolic systolic pressure interval, ξ imax The maximum value of the dialysis systolic blood pressure interval of the current target subject;

[0031] S42, classifying and labeling the dataset of the target subject as positive and negative examples of hypotension;

[0032] S43, inputting the classified data set of the target subject into the AI ​​model to obtain the dialysis record results and AUC value of the target subject;

[0033] S44, repeating steps S52 and S53 until the blood pressure reduction ratio value α reaches the most stringent standard definition value, obtaining the dialysis record results of the target subject whose AUC value is greater than the preset value, and collecting the corresponding blood pressure reduction ratio value set and calculating the corresponding pressure difference value;

[0034] S45, calculating the difference between the minimum systolic blood pressure during dialysis and the systolic blood pressure before dialysis in the actual report, and finding a maximum value ΔR in the set of blood pressure reduction difference values ​​Δ, so that all values ​​in the blood pressure reduction value set of hypotension records in the actual report are greater than ΔR. If the ΔR value is less than the minimum value Δmin in the set of blood pressure reduction difference values ​​Δ, then ΔR is the minimum value Δmin, and the reduction ratio value corresponding to ΔR is the obtained optimal value α ir , that is, in [α imin ,α imax ] to find an optimal value α ir , and obtain the latest definition standard of dialysis hypotension.

[0035] According to one embodiment of the present invention, optimizing the AI ​​prediction model specifically includes the following steps:

[0036] updating the hypotension label value in the target object's data set according to the latest definition standard of dialysis hypotension;

[0037] Then, a multi-class machine learning classification algorithm model is used to select the corresponding data of each factor indicator combination in the factor combination set for fitting, and the particle swarm optimization algorithm is used to tune the parameters of each algorithm of the AI ​​model;

[0038] The greedy algorithm is used to filter out the local optimal solutions in multiple dimensions from the fitting results obtained by different algorithms and different hypotension record division strategies;

[0039] Use simulated annealing algorithm to find the local approximate optimal solution of AI model;

[0040] The prediction results of multiple locally optimal algorithm models are used as new features, and the predicted dependent variable, that is, the qualitative variable (True or False) indicating whether the dialysis patient has hypotension during the dialysis process, is combined into a new training data set. A new AI model is then constructed through machine learning and neural network regression model fitting.

[0041] According to one embodiment of the present invention, the distribution pattern of dialysis hypotension case data in the target object's data set is obtained:

[0042] Using principal component analysis (PCA) to repeatedly reduce and increase the dimensionality of the target object dataset;

[0043] A clustering strategy for surface splicing is introduced to extract nonlinear manifold features, and the vectors of the target object data set are quantified using unsupervised learning and k-means based on the Euclidean distance metric;

[0044] The dataset of the target object is smoothed by using an Edited Nearest Neighbours undersampling method algorithm and a Borderline SMO oversampling method algorithm.

[0045] A computer device according to an embodiment of the present invention includes:

[0046] processor;

[0047] a memory for storing executable instructions;

[0048] The processor is configured to read the executable instructions from the memory and execute the executable instructions to implement the method for predicting hypotension during hemodialysis as described above.

[0049] According to an embodiment of the present invention, a computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the processor implements the method for predicting hypotension during hemodialysis as described above.

[0050] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or understood by practicing the present invention. The purposes and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0051] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The present invention will be further described below with reference to the accompanying drawings and examples.

[0053] Figure 1 It is a schematic diagram of the method flow of embodiment 1 of the present invention.

[0054] Figure 2 Schematic diagram of the overall histogram of the data set in Example 1 of the present invention.

[0055] Figure 3 This is a schematic diagram of the overall analysis of data distribution in Example 1 of the present invention.

[0056] Figure 4 Schematic diagram of data density analysis of all indicator factors in the data set of embodiment 1 of the present invention.

[0057] Figure 5 It is a heat map diagram of the relationship between various characteristic index data of the initial screening in Example 1 of the present invention.

[0058] Figure 6This is a schematic diagram of the definition of hypotension when ΔSBP is 20 mmHg according to the first embodiment of the present invention.

[0059] Figure 7 This is a schematic diagram of the definition of hypotension when ΔSBP is 90 mmHg according to the first embodiment of the present invention.

[0060] Figure 8 This is a schematic diagram of hypotension definition, in which different pre-dialysis pressure intervals should correspond to an unknown dialysis hypotension definition value ΔSBP value according to the first embodiment of the present invention.

[0061] Figure 9 Schematic diagram of the dialysis hypotensive pressure difference value according to the first embodiment of the present invention.

[0062] Figure 10 This is a schematic diagram of verifying the definition of dialysis hypotension according to the first embodiment of the present invention.

[0063] Figure 11 It is a schematic diagram of the computer device structure of the second embodiment of the present invention.

[0064] In the figure, 10 is a computer device; 1002 is a processor; 1004 is a memory; and 1006 is a transmission device. DETAILED DESCRIPTION

[0065] The present invention will now be described in further detail with reference to the accompanying drawings, which are simplified schematic diagrams that illustrate the basic structure of the present invention in a schematic manner.

[0066] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, features defined as "first" or "second" may explicitly or implicitly include one or more of such features. In the description of the present invention, unless otherwise specified, "multiple" means two or more.

[0067] In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed, detachable, or integral connections; mechanical or electrical connections; direct or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.

[0068] Example 1

[0069] The present application embodiment provides a method for predicting hypotension during hemodialysis, such as Figure 1 As shown, the method includes the following steps:

[0070] S1, obtaining the medical data of the target object, and preprocessing the medical data to determine the target object's data set; wherein the medical data includes HIS data, LIS data and hemodialysis clinical data.

[0071] S2, the target subject’s data set is input into the statistical model, and based on the definition of dialysis hypotension and the data analysis strategy, the associated indicator factors of the definition of hypotension are obtained in combination with expert data.

[0072] S3, input the associated indicator factors defined by hypotension into the AI ​​prediction model to obtain the dialysis record results and AUC values ​​of the target subjects.

[0073] S4, perform a comprehensive assessment based on the AUC value; if the AUC value is less than or equal to the preset value, adjust the definition of dialysis hypotension △ SBP, return to step S2; if the AUC value is greater than the preset value, the definition of dialysis hypotension is updated, the AI ​​prediction model is optimized, and the process returns to step S2; if the AUC value is greater than or equal to the expected value, the dialysis record result of the target object is compared with the prediction result. If the comparison result meets the preset conditions, the current AI prediction model is the optimal prediction model, and the process proceeds to the next step; in this embodiment, the preset value is set to 75% and the expected value is set to 80%, and the definition of dialysis hypotension can meet the requirements.

[0074] S5, inputting the dialysis data of the current detection target into the optimal prediction model to obtain the probability that the current detection target will suffer from hypotension during the dialysis process.

[0075] As in step S1, HIS, LIS data, and hemodialysis clinical data can be obtained from the hospital information system, laboratory information system, and dialysis system, respectively. HIS data includes, but is not limited to, diagnostic data and medication data; LIS data includes, but is not limited to, laboratory data (such as blood routine) and other indicator data. Hemodialysis clinical data includes, but is not limited to, patient basic dialysis data such as age, weight, systolic blood pressure records before and during dialysis, diastolic blood pressure records before and during dialysis, ultrafiltration, heart rate, diagnosis, and complication records.

[0076] Using a daily time span, all medical data was cleaned and processed to obtain daily medical data for the target patient, forming a dataset for the target patient. To maximize the role of LIS data in supporting hemodialysis clinical data, LIS data from the previous month of dialysis was obtained. Furthermore, the target patient's medical data was the latest historical data retrieved from the system based on update time; the target patient dataset packaged each patient's medical data by patient ID and dialysis time.

[0077] Step S2 specifically includes the following steps:

[0078] Obtain the distribution pattern of dialysis hypotension case data in the target object's dataset; specifically including:

[0079] A, using principal component analysis (PCA) to repeatedly reduce and increase the dimension of the target object's data set, and perform filtering and noise reduction on the target object's data set.

[0080] B. Introducing a clustering strategy for surface splicing to extract nonlinear manifold features, using unsupervised learning and k-means based on Euclidean distance measurement to quantize the vectors of the target object's dataset, thereby enhancing the characterization capability of the target object's dataset's nonlinear data.

[0081] C. Use the Edited Nearest Neighbours undersampling algorithm and the Borderline SMO oversampling algorithm to smooth the target object dataset and solve the problem of data skew in the dataset.

[0082] Combined with expert opinions, the distribution pattern of dialysis hypotension case data in the target object's data set was statistically analyzed to obtain the characteristic indicator factor variables related to the occurrence of dialysis hypotension, that is, based on the data distribution of the target factor classification variable (the distribution of cases with hypotension), the distribution pattern of dialysis hypotension case data in the target object's data set was analyzed, and the characteristic indicator factor variables related to the occurrence of dialysis hypotension were mined.

[0083] Characteristic indicator factor variables were screened to identify relevant indicator factors for the definition of hypotension. Statistical analysis was performed using SPSS 22.0 statistical software and the statsmodels library in Python. Continuous data were expressed as mean ± standard deviation, and intergroup comparisons were performed using t-tests and analysis of variance. Continuous data were expressed as percentages, and intergroup comparisons were performed using the chi-square test. Significance tests were performed between characteristic items and the quantitative target (ultrafiltration volume) for categorical data using the squared t-test, and between characteristic items and the quantitative target (ultrafiltration volume) using the variance test for quantitative data. The optimal characteristic parameter set was determined through multivariate statistical analysis (considering the simultaneous influence of multiple factors on hemodialysis hypotension) and multivariate interaction analysis.

[0084] After preliminary research on relevant literature and consultation with relevant experts, expert data was formed. In the early stage, multivariate analysis based on random forest and XGBoost was used to screen the factors, including age, pre-dialysis weight, dry weight, pre-dialysis systolic blood pressure, pre-dialysis diastolic blood pressure, pre-dialysis pulse, target dehydration, monocytes, eosinophils, pre-dialysis calcium, hematocrit, platelet count, pre-dialysis blood creatinine, pre-dialysis urea nitrogen, fasting blood glucose, and pre-dialysis uric acid, as preliminary screening factors related to dialysis hypotension. The data set corresponding to each factor consisted of 7098 dialysis records (dialysis reports plus laboratory report data). According to the methods and requirements in 4, 6353 cases were identified as non-hypotensive records and 745 cases were identified as hypotensive records. The overall histogram of the data can be found in [1]. Figure 2 For an overall analysis of data distribution, see Figure 3 , data density analysis of all indicator factors in the data set can be found in Figure 4 , see the heat map of the relationship between the initial screening characteristic index data Figure 5 .

[0085] To verify whether the associated indicator factors defined by hypotension are statistically significant, the data of patients in the data set were retrospectively analyzed. 7098 cases of data were included in the model training set and the model validation set through random balanced sampling. In the model training set, multiple factor indicators affecting dialysis hypotension were first screened out through univariate statistical analysis, and the indicator items with statistical significance of P < 0.05 were used as variable factors of the model; at the same time, a multivariate statistical analysis was performed on the key definition value related to hypotension, namely the blood pressure drop value (or the lowest blood pressure value available during dialysis), and the indicator factors with clinical significance of P < 0.2 were used as model influencing factors. The factor indicators and analysis of the two types of statistics are shown in Table 1:

[0086] Table 1

[0087]

[0088]

[0089] For univariate analysis, P < 0.05 indicated statistically significant differences, and for multivariate analysis, P < 0.2 was considered clinically significant by the expert panel. Eosinophils failed to meet statistical requirements in both univariate and multivariate analyses and may be removed from the set of associated indicator factors once data stabilize.

[0090] In step S3, the AI ​​model processes the associated indicator factors defined for hypotension using algorithms including logistic regression, linear regression, ridge regression, XGBoost, and random forest. The target patient's dialysis record indicates whether the patient experienced hypotension during dialysis. The ACU value, or the area under the receiver operating characteristic (ROC) curve, is used to evaluate the AI ​​model's performance.

[0091] In the existing definition of dialysis hypotension, when the △SBP definition value of dialysis hypotension is 20 mmHg, the △SBP definition value of dialysis hypotension is the minimum value, which can be called the most relaxed definition of hypotension. Figure 6 shown.

[0092] When the dialysis hypotension definition value △SBP is 90mmHg, the dialysis hypotension definition value △SBP is the minimum value, which can be called the most relaxed definition of hypotension. Figure 7 shown.

[0093] If the strictest definition standard, i.e., the definition value of △SBP for dialysis hypotension is 90mmHg, the vast majority (more than 98%) of dialysis records will be classified as non-low normal pressure. On the contrary, if the most relaxed definition standard, i.e., the definition value of △SBP for dialysis hypotension is 20mmHg, nearly half of the dialysis records will be classified as low normal pressure. Using any value at either end of the △SBP value range for dialysis hypotension definition as the standard value for low blood pressure does not conform to the medical common sense that the incidence of dialysis hypotension is 10% to 30%. Therefore, it can be seen that different pre-dialysis pressure ranges should correspond to an unknown △SBP value for dialysis hypotension definition to define low blood pressure. Figure 8 shown.

[0094] Therefore, it can be proposed that there is a certain pre-diastolic systolic blood pressure interval ξ a -ξ b A corresponding definition value of dialysis hypotension △SBP can be found in i , so that the interval hypotension recording data conforms to the current definition, which can be understood as the difference between the pre-diastolic systolic pressure and the minimum diastolic pressure in the interval hypotension recording is greater than or equal to the definition-related value △SBP i .

[0095] In addition, in the dialysis records (hypotension complication reports) recorded by medical staff as dialysis hypotension, as the pre-dialysis systolic blood pressure value increases, the corresponding dialysis hypotension definition value △SBP and the pre-dialysis systolic blood pressure value ratio, i.e., the pre-dialysis systolic blood pressure decrease ratio α, will also increase, which is consistent with the above-mentioned reasoning hypothesis, see Figure 9 shown.

[0096] Therefore, according to the above principle, the relationship between the pre-dialysis systolic blood pressure value interval and the corresponding pre-dialysis systolic blood pressure reduction ratio α is shown in the following table:

[0097] Table 2

[0098]

[0099] It should be noted that the analysis interval of pre-dialysis systolic blood pressure less than 90 mmHg is not within the analysis range of this embodiment.

[0100] The method for assigning the initial value of the blood pressure drop ratio α is based on the most relaxed definition of hypotension (a drop pressure difference of 20 mmHg) with an average value of 135 mmHg for the average overall interval (90-180 mmHg, data above 180 mmHg are close to 0 and can be ignored). The initial drop ratio corresponding to the average value is 20 mmHg / 135 mmHg. Since the blood pressure drop ratio α increases with the increase of the pre-dialysis systolic blood pressure value, the proportional coefficient can be added to correct the initial α value. That is, the definition of dialysis hypotension is updated specifically as follows:

[0101] S41, based on the target object's data set, determine the pre-dialysis systolic blood pressure interval and calculate the initial value a of the blood pressure reduction ratio imin And the maximum blood pressure drop ratio a imax , the calculation formula is:

[0102]

[0103] a imin =(ξ imax -90) / ξ imax ;

[0104] where ξ ia is the average or boundary value of the pre-diastolic systolic pressure interval, ξ imax The maximum value of the dialysis systolic blood pressure interval of the current target object.

[0105] If the AUC value is less than or equal to the preset value, the definition value of dialysis hypotension is adjusted. The specific implementation process is: according to the current definition of dialysis hypotension, the initial value of the blood pressure drop ratio during dialysis is the theoretical minimum value a imin , gradually increase, the step value is 0.01, increase to the theoretical maximum value a imax stop

[0106] S42, classifying and labeling the target object's data set as positive and negative examples of hypotension; the positive and negative examples of hypotension are classified into hypotensive dialysis records and non-hypotensive dialysis records.

[0107] S43, inputting the classified target object data set into the AI ​​model to obtain the target object's dialysis record results and AUC value;

[0108] S44, repeating steps S52 and S53 until the blood pressure reduction ratio value α reaches the most stringent standard definition value, obtaining the dialysis record results of the target subject whose AUC value is greater than the preset value, and collecting the corresponding blood pressure reduction ratio value set and calculating the corresponding pressure difference value;

[0109] S45, calculating the difference between the minimum systolic blood pressure value during dialysis and the systolic blood pressure value before dialysis in the actual report, and finding a maximum value ΔR in the set of blood pressure reduction difference values ​​Δ, so that all values ​​in the blood pressure reduction value set of hypotension records in the actual report are greater than ΔR. If the ΔR value is less than the minimum value Δmin in the set of blood pressure reduction difference values ​​Δ, then ΔR is the minimum value Δmin, and the reduction ratio value corresponding to ΔR at this time is the obtained optimal value α ir , that is, in [α imin ,α imax ] to find an optimal value α ir , for the latest definition of dialysis hypotension, see Figure 10 shown.

[0110] Optimizing the AI ​​prediction model involves the following steps:

[0111] Update the hypotension label value in the target object's dataset according to the latest definition standard of dialysis hypotension;

[0112] Then, a multi-class machine learning classification algorithm model is used to select the corresponding data of each factor indicator combination in the factor combination set for fitting, and the particle swarm optimization algorithm is used to tune the parameters of each algorithm of the AI ​​model;

[0113] The greedy algorithm is used to filter out the local optimal solutions in multiple dimensions from the fitting results obtained by different algorithms and different hypotension record division strategies;

[0114] Use simulated annealing algorithm to find the local approximate optimal solution of AI model;

[0115] The prediction results of multiple locally optimal algorithm models are used as new features, and the predicted dependent variable, that is, the categorical variable of whether dialysis patients experience hypotension during dialysis, is combined into a new training data set. A new AI model is then constructed by fitting the machine learning and neural network regression model.

[0116] Test and Validation: 8522 dialysis records were used as fitting analysis data, of which 15% were test data points and 2359 were recent validation data points. Different blood pressure drop ratio values ​​α were used to generate data record distributions of different types. After fitting the algorithm model and combining the corresponding complication report verification and comprehensive analysis, the blood pressure drop ratio values ​​α, accuracy, and AUC corresponding to each pre-dialysis systolic blood pressure interval are shown in Table 3:

[0117] Pre-diastolic systolic blood pressure range Optimal blood pressure reduction ratio Validation set accuracy Validation set AUC 90-100 mmHg 0.13 96.20％ 0.82 100-110 mmHg 0.14 95.29％ 0.81 110-140 mmHg 0.20 98.54％ 0.91 140-160 mmHg 0.26 96.19％ 0.85 160-180 mmHg 0.32 99.11％ 0.95 >180 mmHg 0.34 90.91％ 0.82

[0118] As can be seen from the above table, the updated definition of dialysis hypotension meets the definition of dialysis hypotension in the current patient population. In the pre-dialysis systolic blood pressure range of 90-180 mmHg, where the data are more densely distributed, the AUC value is greater than 0.80, which is in line with the expected value.

[0119] As described in the review, a method for predicting hypotension during hemodialysis of the present invention uses an AI prediction model to obtain and update the definition of optimal hemodialysis hypotension. The AI ​​prediction model predicts hypotension during hemodialysis for patients based on the updated definition of hemodialysis hypotension, and the obtained results are highly accurate. The present invention analyzes and mines the definition of hemodialysis hypotension applicable to the patient group of a medical unit that meets the current medical management baseline, and models the unit based on the definition to analyze the characteristics related to hemodialysis hypotension in the patient group data (including correlation and distribution, etc.), which has good generalizability and easy operability. At the same time, the data set of the present invention is based on real clinical data for research, which is different from the retrospective analysis (or limited to theoretical research level) after interpolation of complete data obtained by data analysis methods. Therefore, the analysis and research method of the present invention can be directly applied to various medical units, thereby applying and productizing the constructed model, and has high medical application value. 。

[0120] Example 2

[0121] An embodiment of the present application provides a computer device, which includes a processor and a memory, wherein the memory stores at least one instruction or at least one program, and the at least one instruction or the at least one program is loaded and executed by the processor to implement a method for predicting hypotension during hemodialysis as provided in the above-mentioned method embodiment.

[0122] Figure 11 The hardware structure diagram of a device for implementing a method for predicting low blood pressure in hemodialysis provided by an embodiment of the present application is shown. The device may participate in or include the apparatus or system provided by an embodiment of the present application. Figure 11As shown, the computer device 10 may include one or more processors 1002 (the processor may include but is not limited to a microprocessor MCU or a programmable logic device FPGA, etc.), a memory 1004 for storing data, and a transmission device 1006 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the I / O interface), a network interface, a power supply and / or a camera. It will be understood by those skilled in the art that Figure 11 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 11 More or fewer components than shown, or with Figure 11 Different configurations shown.

[0123] It should be noted that the one or more processors and / or other data processing circuits described above may generally be referred to herein as "data processing circuitry". The data processing circuitry may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuitry may be a single independent processing module, or may be incorporated in whole or in part into any of the other components of the computer device 10 (or mobile device). As described in the embodiments of the present application, the data processing circuitry serves as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0124] The memory 1004 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to a method for predicting hypotension during hemodialysis in an embodiment of the present application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 1004, that is, implementing one of the above methods. The memory 1004 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 1004 may further include a memory remotely located relative to the processor, and these remote memories may be connected to the computer device 10 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.

[0125] Transmission device 1006 is configured to receive or transmit data via a network. A specific example of the aforementioned network may include a wireless network provided by the communications provider of computer device 10. In one embodiment, transmission device 1006 includes a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In another embodiment, transmission device 1006 may be a radio frequency (RF) module configured to communicate with the Internet wirelessly.

[0126] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the computer device 10 (or mobile device).

[0127] Example 3

[0128] An embodiment of the present application also provides a computer-readable storage medium, which can be set in a server to store at least one instruction or at least one program related to a method for predicting hypotension during hemodialysis in a method embodiment. The at least one instruction or the at least one program is loaded and executed by the processor to implement a method for predicting hypotension during hemodialysis provided in the above method embodiment.

[0129] Optionally, in this embodiment, the storage medium may be located in at least one of a plurality of network servers in a computer network. Optionally, in this embodiment, the storage medium may include, but is not limited to, various media capable of storing program code, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk, or an optical disk.

[0130] Example 4

[0131] An embodiment of the present invention further provides a computer program product or computer program, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform a method for predicting hypotension during hemodialysis provided in any of the aforementioned optional embodiments.

[0132] It should be noted that the order of the embodiments of the present application described above is for descriptive purposes only and does not represent the superiority or inferiority of the embodiments. The above description is of specific embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0133] The various embodiments in this application are described in a progressive manner. Similar portions between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the device, equipment, and storage medium embodiments are generally similar to the method embodiments, so their descriptions are relatively simple. For relevant portions, refer to the descriptions of the method embodiments.

[0134] Those skilled in the art will understand that all or part of the steps to implement the above embodiments may be accomplished by hardware, or by a program to instruct the relevant hardware, and the program may be stored in a computer-readable storage medium, which may be a read-only memory, a disk, or an optical disk, etc.

[0135] With the above-described preferred embodiments of the present invention as a guide, and with reference to the above description, relevant personnel are fully capable of making various changes and modifications without departing from the technical scope of this invention. The technical scope of this invention is not limited to the contents of the specification and must be determined according to the scope of the claims.

Claims

1. A method for predicting hypotension during hemodialysis, characterized in that: The method comprises the following steps: S1, obtaining medical data of a target subject and preprocessing the medical data to determine a data set of the target subject; wherein the medical data includes HIS data, LIS data, and hemodialysis clinical data; S2, inputting the target subject's data set into a statistical model, and obtaining associated indicator factors of the definition of hypotension based on the definition of dialysis hypotension and data analysis strategy in combination with expert data; S3, inputting the associated indicator factors of the hypotension definition into the AI ​​prediction model to obtain the prediction result and AUC value of the target object; S4, performing a comprehensive evaluation based on the AUC value; If the AUC value is less than or equal to the preset value, the dialysis hypotension definition value is adjusted and the process returns to step S2; If the AUC value is greater than the preset value, the definition of dialysis hypotension is updated, the AI ​​prediction model is optimized, and the process returns to step S2; Updates to the definition of intradialytic hypotension include: S41, determining the pre-diastolic systolic blood pressure interval based on the target object's data set, and calculating the initial value a of the blood pressure reduction ratio imin And the maximum blood pressure drop ratio a imax , the calculation formula is: a imax =(ξ imax -90)x imax ; where ξ ia is the average value or boundary value of the pre-diastolic systolic pressure interval, ξ imax The maximum value of the dialysis systolic blood pressure interval of the current target object; S42, classifying and labeling the dataset of the target subject as positive and negative examples of hypotension; S43, inputting the classified target object data set into the AI ​​model to obtain the prediction result and AUC value of the target object; S44, repeating steps S42 and S43 until the blood pressure reduction ratio value α reaches the most stringent standard definition value, obtaining the prediction result of the target subject whose AUC value is greater than the preset value, and correspondingly collecting the relevant blood pressure reduction ratio value set and calculating the corresponding blood pressure difference value; S45, calculating the difference between the minimum systolic blood pressure during dialysis and the systolic blood pressure before dialysis in the actual report, and finding a maximum value ΔR in the set of blood pressure reduction difference values ​​Δ, so that all values ​​in the blood pressure reduction value set of hypotension records in the actual report are greater than ΔR. If the ΔR value is less than the minimum value Δmin in the set of blood pressure reduction difference values ​​Δ, then ΔR is the minimum value Δmin, and the reduction ratio value corresponding to ΔR is the obtained optimal value α ir， That is, in [α imin ,α imax ] to find an optimal value α ir , obtain the latest definition standard of current dialysis hypotension; If the AUC value is greater than or equal to the expected value, the dialysis record result of the target subject is compared with the predicted result. If the comparison result meets the preset conditions, the current AI prediction model is the optimal prediction model and proceeds to the next step; S5, inputting the dialysis data of the current detection target into the optimal prediction model to obtain the probability that the current detection target will suffer from hypotension during the dialysis process.

2. The method for predicting hypotension during hemodialysis according to claim 1, wherein: In step S1, HIS, LIS data and hemodialysis clinical data are obtained from the hospital information system, laboratory information system and dialysis system respectively; Using a time span of one day, all the medical data are cleaned and processed to obtain the medical data of the target object every day to form a data set of the target object.

3. The method for predicting hypotension during hemodialysis according to claim 1, wherein: The step S2 specifically includes the following steps: Obtaining a distribution pattern of dialysis hypotension case data in a data set of the target object; Combined with expert data, by statistically analyzing the distribution pattern of dialysis hypotension case data in the target subject's data set, characteristic indicator factor variables related to whether dialysis hypotension occurs are obtained; The characteristic indicator factor variables are screened to obtain associated indicator factors for the definition of hypotension.

4. The method for predicting hypotension during hemodialysis according to claim 1, wherein: In step S3, the algorithms used by the AI ​​model to process the associated indicator factors defined for hypotension include: logistic regression, linear regression, ridge regression, XGBoost, and random forest.

5. The method for predicting hypotension during hemodialysis according to claim 1, wherein: Optimizing the AI ​​prediction model specifically includes the following steps: updating the hypotension label value in the target object's data set according to the latest definition standard of dialysis hypotension; Then, a multi-class machine learning classification algorithm model is used to select the corresponding data of each factor indicator combination in the factor combination set for fitting, and the particle swarm optimization algorithm is used to tune the parameters of each algorithm of the AI ​​model; The greedy algorithm is used to filter out the local optimal solutions in multiple dimensions from the fitting results obtained by different algorithms and different hypotension record division strategies; Use simulated annealing algorithm to find the local approximate optimal solution of AI model; The prediction results of multiple locally optimal algorithm models are used as new features, and combined with the predicted dependent variable, the ultrafiltration volume of MHD patients, to form a new training dataset. A new AI model is then constructed through machine learning and neural network regression model fitting.

6. The method for predicting hypotension during hemodialysis according to claim 2, wherein: Obtain the distribution pattern of dialysis hypotension case data in the target object's data set: Using principal component analysis (PCA) to repeatedly reduce and increase the dimensionality of the target object dataset; A clustering strategy for surface splicing is introduced to extract nonlinear manifold features, and the vectors of the target object data set are quantified using unsupervised learning and k-means based on the Euclidean distance metric; The dataset of the target object is smoothed by using an Edited Nearest Neighbours undersampling method algorithm and a Borderline SMO oversampling method algorithm.

7. A computer device, characterized in that: include: processor; a memory for storing executable instructions; The processor is configured to read the executable instructions from the memory and execute the executable instructions to implement the method for predicting hypotension during hemodialysis according to any one of claims 1 to 6.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor implements the method for predicting hypotension during hemodialysis according to any one of claims 1 to 6.

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