A paliperidone therapeutic effect prediction method and system based on plasma metabolomics

Abstract

The present application relates to the field of medical informatics, and provides a paliperidone efficacy prediction method and system based on plasma metabolomics. The method comprises the following steps: collecting basic data of a target patient; performing feature selection on metabolites through the basic data to obtain relevant metabolites corresponding to paliperidone efficacy; training a neural network based on the relevant metabolites and baseline data in the basic data to obtain a prediction model; and performing paliperidone efficacy prediction on a patient to be predicted through the prediction model to obtain a prediction result. The present application can predict the probability of patient response to paliperidone, thereby more accurately evaluating the efficacy of paliperidone before medication, and providing an auxiliary decision for individualized medication.

Description

Technical Field

[0001] This invention relates to the field of medical informatics technology, and in particular to a method and system for predicting the efficacy of paliperidone based on plasma metabolomics. Background Technology

[0002] Schizophrenia (SCZ) is a severe mental disorder with complex causes and a high rate of disability, imposing a heavy economic and health burden on patients. Antipsychotics are the main treatment for schizophrenia, but their efficacy varies significantly among individuals. Approximately 30% of patients do not respond well to existing antipsychotics, necessitating repeated changes in medication in clinical practice to select the most suitable antipsychotic. Paliperidone, as a relatively new antipsychotic, still exhibits significant individual variability in efficacy, with approximately 40% of patients showing an unsatisfactory response.

[0003] Although there are currently gene testing kits for antipsychotic drugs on the market, these products mostly focus on genes related to drug metabolism, and their prediction of drug efficacy is limited and the accuracy is poor. In addition, there is still a lack of biomarkers for the efficacy of paliperidone, making it difficult to provide patients with personalized auxiliary decision-making before medication. Summary of the Invention

[0004] This invention provides a method and system for predicting the efficacy of paliperidone based on plasma metabolomics, in order to overcome the shortcomings of the prior art.

[0005] This invention provides a method for predicting the efficacy of paliperidone based on plasma metabolomics, comprising: S1: Collect basic data of the target patient; S2: Feature selection of metabolites is performed using the aforementioned basic data to obtain relevant metabolites corresponding to the therapeutic effects of paliperidone; S3: Based on the relevant metabolites and baseline data in the basic data, train the neural network to obtain a prediction model; S4: The predictive model is used to predict the efficacy of paliperidone for the patients to be predicted, and the prediction results are obtained.

[0006] According to the present invention, a method for predicting the efficacy of paliperidone based on plasma metabolomics is provided. The target patient in step S1 is a patient with schizophrenia, and the basic data includes: baseline information, paliperidone treatment records, and plasma metabolite data.

[0007] According to the method for predicting the efficacy of paliperidone based on plasma metabolomics provided by the present invention, step S2 further includes: S21: Extract the target variable from the aforementioned basic data; S22: Preprocess the target variable to obtain preprocessed features; S23: The preprocessed features are input into the L1 regularized LASSO regression model to perform feature selection and obtain the relevant metabolites corresponding to the efficacy of paliperidone.

[0008] According to the method for predicting the efficacy of paliperidone based on plasma metabolomics provided by the present invention, in step S23, the expression of the objective function of the LASSO regression model is:

[0009] in, These are the parameters of the LASSO regression model. This represents the feature vector index value corresponding to the input preprocessed features. This represents the total number of feature vectors corresponding to the input preprocessed features. For the first For the input preprocessed features, For the first The labels of the preprocessed features are the input. Let be the hypothesis function for Logistic regression. For L1 regularization hyperparameters, It is the sum of the absolute values ​​of the L1 norms of all parameter terms.

[0010] According to the method for predicting the efficacy of paliperidone based on plasma metabolomics provided by the present invention, the relevant metabolites in step S23 include: Various phosphatidylethanolamines, 2-hydroxyhippuric acid, 3β-hydroxy-Δ5-cholenic acid, 3-isodeoxycholic acid, glycoursodeoxycholic acid, hexanocarnitine, 1-methylinosine, 3-hydroxy-2-aminobenzoic acid, phosphatidylcholine, arachidonic acid, linoleic acid, L-histidine, Omega-mouse cholic acid, creatinine, sphingomyelin, pentadecanoic acid, and carnosine.

[0011] According to the method for predicting the efficacy of paliperidone based on plasma metabolomics provided by the present invention, step S3 further includes: S31: Input the relevant metabolites and the baseline data in the basic data, and perform a linear transformation combination on the relevant metabolites and the baseline data in the basic data to obtain a linear output; S32: Perform a nonlinear transformation on the linear output to obtain a probability output; S33: Construct a loss function based on the probability output; S34: Optimize the model parameters of the neural network according to the loss function to obtain the prediction model.

[0012] According to the method for predicting the efficacy of paliperidone based on plasma metabolomics provided by the present invention, in step S34, when optimizing the model parameters of the neural network, the expression for updating the parameters is:

[0013] in, For the first The model parameters before the update, For the first The updated model parameters, For learning rate, This is the loss function.

[0014] According to the method for predicting the efficacy of paliperidone based on plasma metabolomics provided by the present invention, step S4 further includes: S41: Collect the prediction data of the patients to be predicted; S42: Standardize the data to be predicted to obtain standardized data to be predicted; S43: Input the standardized data to be predicted into the prediction model to obtain the prediction result; the prediction result is the probability of the patient responding to paliperidone treatment.

[0015] According to the present invention, a method for predicting the efficacy of paliperidone based on plasma metabolomics is provided, wherein the data to be predicted in step S41 includes: the age of the patient to be predicted, the baseline PANSS score of the patient to be predicted, and the absolute concentration of plasma metabolites of the patient to be predicted.

[0016] This invention also provides a paliperidone efficacy prediction system based on plasma metabolomics, comprising: Data Acquisition Module: Used to collect basic data from the target patient; Selection module: used to select metabolites based on the basic data to obtain relevant metabolites corresponding to the efficacy of paliperidone; Training module: used to train the neural network based on the relevant metabolites and baseline data in the basic data to obtain a prediction model; The prediction module is configured to use the prediction model trained by the training module to predict the efficacy of paliperidone for patients to be predicted and obtain prediction results.

[0017] This invention provides a method and system for predicting the efficacy of paliperidone based on plasma metabolomics. By deeply integrating high-throughput mass spectrometry metabolomics technology with machine learning algorithms, it has achieved significant technological breakthroughs and clinical application value in the field of predicting the efficacy of paliperidone in patients with schizophrenia.

[0018] First, the LASSO feature selection algorithm used in this invention demonstrates superior performance in processing high-dimensional metabolite data. It can accurately screen biomarkers closely related to paliperidone efficacy from hundreds of plasma metabolites, effectively solving the dimensionality problem in high-dimensional data. Furthermore, it automatically selects features through the sparsity characteristics of L1 regularization, avoiding the negative impact of multicollinearity on model stability in traditional statistical methods, while significantly improving the model's interpretability and clinical applicability. Second, the application of the Logistic Regression algorithm in this invention fully demonstrates its advantages in binary classification prediction tasks. By mapping linear combinations to the probability space through the Sigmoid function, it can not only output accurate treatment response probability values ​​but also provide clinicians with intuitive risk assessment indicators. The probabilistic output method is more accurate and objective than traditional qualitative judgments. Simultaneously, cross-validation ensures the model's generalization ability across different datasets, and the excellent predictive performance directly translates into significant diagnostic and treatment benefits in clinical practice.

[0019] From a clinical application perspective, this invention can provide doctors with objective efficacy prediction data before paliperidone treatment, effectively reducing the risk of treatment delays and adverse drug reactions caused by traditional experimental drug use. In particular, for approximately 40% of patients with poor paliperidone response, it can identify high-risk individuals before medication, thereby avoiding ineffective treatment cycles and significantly shortening the time window for patients to achieve effective treatment.

[0020] Furthermore, this invention integrates a multi-dimensional prediction model that combines metabolomics data, clinical scale scores, and demographic information. Compared to single-dimensional prediction methods, it has higher accuracy and stability. The multi-modal data fusion approach not only improves prediction efficacy but also provides a new technical path for individualized treatment of schizophrenia.

[0021] From the perspective of medical resource allocation, this invention helps optimize the clinical decision-making process, reduce unnecessary drug trials and frequent medication changes, lower medical costs, and improve treatment efficiency. It provides strong technical support for individualized treatment and prognosis improvement for patients with schizophrenia, and has significant clinical application value and socio-economic benefits. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0023] Figure 1This is a schematic diagram of a method for predicting the efficacy of paliperidone based on plasma metabolomics, provided in an embodiment of the present invention. Figure 2 A schematic diagram of a paliperidone efficacy prediction system based on plasma metabolomics provided in an embodiment of the present invention; Figure 3 A schematic diagram of the ROC curve of the paliperidone efficacy prediction model provided in this embodiment of the invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, embodiments of this invention, and should not be construed as limiting the invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention. In the description of this invention, it should be understood that the terminology used is for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0025] The embodiments of the present invention are described below with reference to the figures.

[0026] like Figure 1 As shown, this invention provides a method for predicting the efficacy of paliperidone based on plasma metabolomics, comprising: S1: Collect basic data of the target patient.

[0027] Furthermore, the target patients were those with schizophrenia, and the screening criteria strictly followed the DSM-IV diagnostic criteria for diagnosis. Specific screening criteria included: age range 18-45 years, first-episode, medication-naïve patients or patients with a chronic disease course currently experiencing an acute exacerbation of schizophrenia, and a baseline PANSS (Positive and Negative Syndrome Scale) total score greater than or equal to 60. Exclusion criteria included: pregnant or lactating women or women planning pregnancy, patients with contraindications to paliperidone treatment, patients with unstable physical illness, prolonged QTc interval, decompensated congestive heart failure, or complete left bundle branch block. Based on these screening criteria, a total of 208 patients were ultimately included, comprising 188 identified schizophrenia patients and 20 independently validated schizophrenia patients.

[0028] In step S1, the target patient is a patient with schizophrenia, and the basic data includes: baseline information, paliperidone treatment records, and plasma metabolite data.

[0029] Baseline data collection encompassed the patient's demographic characteristics and clinical assessment indicators. Demographic characteristics primarily included patient age, recorded in years as a continuous variable. Clinical assessment indicators included the baseline PANSS score, which comprises three parts: a positive symptom scale, a negative symptom scale, and a general psychopathology scale. The total score ranges from 30 to 210 points, with higher scores indicating more severe symptoms. During data collection, trained physicians from the relevant department conducted assessments before the patient began paliperidone treatment to ensure standardization and accuracy. Baseline data was stored numerically; age data was recorded directly as integers or decimals, and the baseline PANSS score was recorded as an integer.

[0030] Data collection for paliperidone treatment included pre- and post-treatment clinical assessments and efficacy determination. Patients underwent a repeat PANSS assessment at the end of the sixth week after receiving paliperidone treatment, obtaining their six-week PANSS score. Efficacy determination was based on the PANSS score reduction rate at the end of the sixth week, calculated as: PANSS reduction rate = (baseline PANSS score - six-week PANSS score) / (baseline PANSS score - 30) × 100%. Based on the PANSS reduction rate, patients were divided into two treatment response groups: patients with a PANSS reduction rate greater than or equal to 50% were defined as having a good response to paliperidone treatment and were labeled 1; patients with a PANSS reduction rate less than 50% were defined as having a poor response to paliperidone treatment and were labeled 0. This binary labeling method provided clear labeled data for subsequent supervised learning of the machine learning model.

[0031] Plasma metabolite data acquisition employed high-throughput mass spectrometry-based non-targeted metabolomics. Fasting venous blood samples were collected from patients before starting paliperidone treatment. After plasma separation, sample preprocessing, and mass spectrometry analysis, absolute concentration data for 778 plasma metabolites were obtained, including 433 lipid metabolites. Concentration data for each metabolite were recorded in μmol / L as a continuous variable. It should be noted that some values ​​were missing in the original metabolite data. This invention employed median imputation: for each metabolite, the median value of all non-missing samples was calculated, and the missing values ​​were replaced with the median value of the corresponding metabolite. Median imputation is more robust to outliers than mean imputation and better preserves the characteristics of the data distribution.

[0032] S2: Using the aforementioned basic data, metabolites are characterized to obtain the relevant metabolites corresponding to the therapeutic effects of paliperidone.

[0033] Step S2 further includes: S21: Extract the target variable from the aforementioned basic data.

[0034] The basic dataset contains 780 variables, including one variable of age, one variable of baseline PANSS score, and 778 standardized plasma metabolite concentrations. During the target variable extraction process, this invention ensured a one-to-one correspondence between the feature variables and label variables for each sample, maintaining data integrity and consistency. Ultimately, data from 188 patients were extracted into a 188x778 feature matrix and a 188x1 label vector, while data from 20 independent validation patients were extracted into a 20x778 feature matrix and a 20x1 label vector.

[0035] S22: Preprocess the target variable to obtain preprocessed features.

[0036] Furthermore, firstly, missing value checks and processing were performed on the 778 metabolite data. Although median imputation was performed in step S1, data integrity needed to be verified again before feature selection to ensure the absence of NaN values ​​or outliers. Secondly, the standardized metabolite data underwent data type conversion, converting all values ​​to floating-point format to ensure the accuracy and stability of numerical calculations. Thirdly, the feature matrix was checked for dimensionality to verify that the feature matrix's dimension was correctly formatted as sample number × feature number. Fourthly, the label vectors were encoded and verified to ensure that all label values ​​were integers of 0 or 1.

[0037] S23: The preprocessed features are input into the L1 regularized LASSO regression model to perform feature selection and obtain the relevant metabolites corresponding to the efficacy of paliperidone.

[0038] In step S23, the expression for the objective function of the LASSO regression model is:

[0039] in, These are the parameters of the LASSO regression model. This represents the feature vector index value corresponding to the input preprocessed features. This represents the total number of feature vectors corresponding to the input preprocessed features. For the first For the input preprocessed features, For the first The labels of the preprocessed features are the input. Let be the hypothesis function for Logistic regression. For L1 regularization hyperparameters, It is the sum of the absolute values ​​of the L1 norms of all parameter terms.

[0040] Furthermore, the feature selection process of this invention is based on an optimized LASSO regression model with L1 regularization constraints. In the objective function of the LASSO regression model, the initial parameters are zero vectors, the search range of the L1 regularization hyperparameter λ is set, and the optimization problem is solved using the coordinate descent method. The values ​​of the parameters are updated one by one, and the parameters are iteratively updated until convergence or the maximum number of iterations of 100 is reached. The L1 regularization constraint causes some parameters to shrink to zero, and the corresponding metabolite features are automatically eliminated. The metabolite features corresponding to non-zero parameters are retained. After optimizing the parameters through grid search and 3-fold cross-validation, the following 18 related metabolites are obtained.

[0041] The relevant metabolites in step S23 include: Various phosphatidylethanolamines, 2-hydroxyhippuric acid, 3β-hydroxy-Δ5-cholenic acid, 3-isodeoxycholic acid, glycoursodeoxycholic acid, hexanocarnitine, 1-methylinosine, 3-hydroxy-2-aminobenzoic acid, phosphatidylcholine, arachidonic acid, linoleic acid, L-histidine, Omega-mouse cholic acid, creatinine, sphingomyelin, pentadecanoic acid, and carnosine.

[0042] Specifically, the metabolites identified as being associated with the efficacy of paliperidone include: phosphatidylethanolamine (16:0 / 18:2) (PE(16:0 / 18:2), μmol / L), 2-hydroxyhippuric acid (μmol / L), 3β-hydroxy-Δ5-cholenoic acid (μmol / L), 3-epideoxycholic acid (μmol / L), gycoursodeoxycholic acid (μmol / L), hexanoylcarnitine (μmol / L), 1-methylinosine (μmol / L), and 3-hydroxy-2-aminobenzoic acid (3-Hydroxyanthranilic acid). The following 18 indicators were tested: acid (μmol / L), phosphatidylcholine (34:2) (PC(34:2), μmol / L), arachidonic acid (μmol / L), linoleic acid (μmol / L), L-histidine (μmol / L), Omega-muricholic acid (μmol / L), creatinine (μmol / L), sphingomyelin (30:2) (SM(30:2), μmol / L), pentadecanoic acid (μmol / L), carnosine (μmol / L), and phosphatidylethanolamine (38:2) (PE(38:2), μmol / L).

[0043] S3: Based on the relevant metabolites and baseline data in the basic data, train the neural network to obtain a prediction model.

[0044] Preferably, the neural network is a Logistic regression model.

[0045] Step S3 further includes: S31: Input the relevant metabolites and the baseline data in the basic data, and perform a linear transformation combination on the relevant metabolites and the baseline data in the basic data to obtain a linear output.

[0046] Furthermore, during model training, the input data consists of two main parts: relevant metabolite data and baseline data from the basic data. The relevant metabolite data comprises 18 key metabolites selected from 778 plasma metabolites using the LASSO feature selection method in the previous step. The baseline data from the basic data includes patient age (age, years) and baseline PANSS score (PANSS baseline, unitless).

[0047] After input, the features are combined through the linear transformation layer of the neural network, specifically by calculating a weighted sum, that is, for each sample, a linear combination is calculated. The expression is: ,in, As a bias term, during initialization, the weight parameters are usually set to small random values, and the bias term is set to 0.

[0048] S32: Perform a nonlinear transformation on the linear output to obtain a probability output.

[0049] In step S32, the present invention performs a nonlinear transformation on the linear output obtained in step S31, using the Sigmoid function to map it to the (0,1) interval to achieve a probability output. The output value obtained after the Sigmoid function transformation represents the probability that the patient sample belongs to the category of good response to paliperidone treatment. When the probability value is greater than 0.5, it predicts that the patient has a good response to paliperidone treatment; when the probability value is less than or equal to 0.5, it predicts that the patient has a poor response to paliperidone treatment.

[0050] S33: Construct a loss function based on the probability output.

[0051] Furthermore, in step S33, based on the probability output obtained in step S32, this invention constructs a log-likelihood loss function (cross-entropy loss function) to measure the error between the model's predicted probability and the true label. Specifically, first, the home function is defined, and then, combined with the aforementioned nonlinear mapping, the hypothesis function of the model is defined. This is used to describe the mapping relationship between input features and output class probabilities. Then, a loss function is defined based on the hypothesis function. The specific expression of the loss function is as follows:

[0052] in, For the defined loss function, For model parameters, The input sample index value, The total number of samples input. For the input of the first One sample, For the first The true label of each sample.

[0053] S34: Optimize the model parameters of the neural network according to the loss function to obtain the prediction model.

[0054] In step S34, the model parameters are optimized using gradient descent to minimize the loss function. In each iteration, the partial derivative of the loss function with respect to each parameter is calculated, and the parameters are updated accordingly. The specific expression is shown below.

[0055] In step S34, when optimizing the model parameters of the neural network, the expression for updating the parameters is:

[0056] in, For the first The model parameters before the update, For the first The updated model parameters, For learning rate, This is the loss function.

[0057] Furthermore, the learning rate is used to control the magnitude of each parameter update. The above iterative process continues until the loss function converges to a predetermined threshold or reaches the maximum number of iterations. After the loss function converges, the final trained model parameters are output. and bias At this point, the obtained model is able to perform probability prediction and binary classification on new input samples.

[0058] S4: The predictive model is used to predict the efficacy of paliperidone for the patients to be predicted, and the prediction results are obtained.

[0059] Step S4 further includes: S41: Collect the prediction data for the patient to be predicted. The prediction data in step S41 includes: the patient's age, baseline PANSS score, and absolute concentration of plasma metabolites. S42: Standardize the prediction data to obtain standardized prediction data. S43: Input the standardized prediction data into the prediction model to obtain the prediction result; the prediction result is the probability that the patient will respond to paliperidone treatment.

[0060] In step S4, during the prediction process, for schizophrenia patients with complete paliperidone treatment records and baseline plasma metabolite data, the patient's age, baseline PANSS score, and plasma metabolite concentration are first extracted. Subsequent data preprocessing includes imputing missing baseline plasma metabolite data using median imputation and standardizing the plasma metabolite data. After preprocessing, the data is input into the model, which outputs the patient's response probability and efficacy grouping, including good response and poor response.

[0061] In addition, the process of this invention can be adjusted to suit different practical application scenarios, supporting the predictive analysis of the efficacy of paliperidone acute phase treatment for different cohorts of schizophrenia patients. The relevant results of model construction and validation can provide a scientific basis for subsequent individualized treatment decisions and clinical auxiliary diagnosis.

[0062] like Figure 2 As shown, the present invention also provides a paliperidone efficacy prediction system based on plasma metabolomics, comprising: Data Acquisition Module 100: Used to collect basic data of the target patient; Selection module 200: used to perform feature selection on metabolites based on the basic data to obtain relevant metabolites corresponding to the efficacy of paliperidone; Training module 300: used to train the neural network based on the relevant metabolites and baseline data in the basic data to obtain a prediction model; The prediction module 400 is configured to use the prediction model trained by the training module 300 to predict the efficacy of paliperidone for the patient to be predicted and obtain the prediction result.

[0063] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0064] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0065] In this invention, the absolute quantitative plasma metabolomics data obtained by high-throughput mass spectrometry provides rich biological status information of schizophrenia patients and is organically integrated with the patients' demographic and clinical indicators, which significantly improves the objectivity and information content of the data.

[0066] This invention, based on the LASSO feature selection method, can efficiently screen biomarkers closely related to drug efficacy in a high-dimensional metabolite feature space, improving the efficiency of variable selection and model interpretability, and reducing the impact of multicollinearity on model results. The logistic regression prediction model can more efficiently and flexibly predict the probability of paliperidone treatment response. During model construction, multidimensional clinical indicators such as age and baseline PANSS score are combined, and logistic regression is used, with cross-validation and grid search optimization parameters introduced to ensure the model's generalization ability and stability on both the training and independent validation sets.

[0067] The probability of response to paliperidone treatment can be predicted using this invention. For example... Figure 3 As shown, the prediction model constructed by the method of the present invention can achieve an average AUC (area under the curve, which is between 0 and 1, and the closer the value is to 1, the better the prediction effect) of 0.815 on the discovery samples and an AUC of 0.750 on the independent validation samples, showing good prediction effect and generalization.

[0068] This invention is applicable to the rapid identification of high-risk individuals with poor paliperidone response in the early stages of treatment. After the data is entered into the doctor's computer to predict the paliperidone treatment response of schizophrenia patients, it assists in the decision-making of whether to give the patient a subsequent paliperidone treatment plan, providing a scientific basis for subsequent treatment adjustment and individualized management, and helping to improve patient prognosis and overall medical benefits.

[0069] This invention is applicable to the rapid identification of high-risk individuals with poor paliperidone response in the early stages of treatment. By inputting relevant data into a physician's computer, it predicts the treatment response of schizophrenia patients to paliperidone, assists in the decision-making process regarding paliperidone treatment, and provides a scientific basis for subsequent treatment adjustments and individualized management, ultimately contributing to improved patient prognosis and overall clinical benefits.

[0070] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting the efficacy of paliperidone based on plasma metabolomics, characterized in that, include: S1: Collect basic data of the target patient; The target patients are patients with schizophrenia, and the basic data includes: baseline information, paliperidone treatment records, and plasma metabolite data; S2: Based on the aforementioned basic data, metabolites are characterized to obtain relevant metabolites corresponding to the therapeutic effects of paliperidone; the relevant metabolites include: various phosphatidylethanolamines, 2-hydroxyhippuric acid, 3β-hydroxy-Δ5-cholenic acid, 3-isodeoxycholic acid, glycoursodeoxycholic acid, hexanocarnitine, 1-methylinosine, 3-hydroxy-2-aminobenzoic acid, phosphatidylcholine, arachidonic acid, linoleic acid, L-histidine, Omega-mouse cholic acid, creatinine, sphingomyelin, pentadecanoic acid, and carnosine; S3: Based on the relevant metabolites and baseline data in the basic data, train the neural network to obtain a prediction model; S4: The prediction model is used to predict the efficacy of paliperidone in the patients to be predicted, and the prediction results are obtained. Step S4 further includes: S41: collecting the data to be predicted from the patient to be predicted; S42: standardizing the data to be predicted to obtain standardized data to be predicted; S43: inputting the standardized data to be predicted into the prediction model to obtain the prediction result; the prediction result is the probability of the patient to be predicted responding to paliperidone treatment.

2. The method for predicting paliperidone efficacy based on plasma metabolomics according to claim 1, characterized in that, Step S2 further includes: S21: Extract the target variable from the aforementioned basic data; S22: Preprocess the target variable to obtain preprocessed features; S23: The preprocessed features are input into the L1 regularized LASSO regression model to perform feature selection and obtain the relevant metabolites corresponding to the efficacy of paliperidone.

3. The method for predicting paliperidone efficacy based on plasma metabolomics according to claim 2, characterized in that, In step S23, the expression for the objective function of the LASSO regression model is: in, These are the parameters of the LASSO regression model. This represents the feature vector index value corresponding to the input preprocessed features. This represents the total number of feature vectors corresponding to the input preprocessed features. For the first For the input preprocessed features, For the first The labels of the preprocessed features are the input. Let be the hypothesis function for Logistic regression. For L1 regularization hyperparameters, It is the sum of the absolute values ​​of the L1 norms of all parameter terms.

4. The method for predicting paliperidone efficacy based on plasma metabolomics according to claim 1, characterized in that, Step S3 further includes: S31: Input the relevant metabolites and the baseline data in the basic data, and perform a linear transformation combination on the relevant metabolites and the baseline data in the basic data to obtain a linear output; S32: Perform a nonlinear transformation on the linear output to obtain a probability output; S33: Construct a loss function based on the probability output; S34: Optimize the model parameters of the neural network according to the loss function to obtain the prediction model.

5. The method for predicting paliperidone efficacy based on plasma metabolomics according to claim 4, characterized in that, In step S34, when optimizing the model parameters of the neural network, the expression for updating the parameters is: in, For the first The model parameters before the update, For the first The updated model parameters, For learning rate, This is the loss function.

6. The method for predicting the efficacy of paliperidone based on plasma metabolomics according to claim 1, characterized in that, The data to be predicted in step S41 includes: the age of the patient to be predicted, the baseline PANSS score of the patient to be predicted, and the absolute concentration of plasma metabolites of the patient to be predicted.

7. A paliperidone efficacy prediction system based on plasma metabolomics, characterized in that, include: Data Acquisition Module: Used to collect basic data from the target patient; The target patients are patients with schizophrenia, and the basic data includes: baseline information, paliperidone treatment records, and plasma metabolite data; Selection module: used to perform feature selection on metabolites based on the basic data to obtain relevant metabolites corresponding to the therapeutic effects of paliperidone; the relevant metabolites include: various phosphatidylethanolamines, 2-hydroxyhippuric acid, 3β-hydroxy-Δ5-cholenic acid, 3-isodeoxycholic acid, glycoursodeoxycholic acid, hexanocarnitine, 1-methylinosine, 3-hydroxy-2-aminobenzoic acid, phosphatidylcholine, arachidonic acid, linoleic acid, L-histidine, Omega-mouse cholic acid, creatinine, sphingomyelin, pentadecanoic acid, and carnosine; Training module: used to train the neural network based on the relevant metabolites and baseline data in the basic data to obtain a prediction model; A prediction module is configured to use the prediction model trained by the training module to predict the efficacy of paliperidone for the patient to be predicted and obtain the prediction result. The prediction module is further used for: collecting the prediction data of the patient to be predicted; standardizing the prediction data to obtain standardized prediction data; inputting the standardized prediction data into the prediction model to obtain the prediction result; the prediction result is the probability of the patient responding to paliperidone treatment.

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