Method for determining medication effect of anlotinib and related device

Abstract

The invention provides a medication effect determination method for anlotinib and a related device, and relates to the technical field of artificial intelligence such as curative effect prediction, machine learning, deep learning and pharmacokinetics models. The method comprises the following steps: acquiring the dose, medication cycle, weight and plasma concentration observation time point of anlotinib taken by a patient; taking the dose, the medication cycle, the weight and the blood concentration observation time point as input data of a preset blood concentration prediction model to obtain an output blood concentration parameter; in response to a received curative effect prediction function selection instruction transmitted by the patient, acquiring the actual age of the patient; and jointly using the blood concentration parameter and the actual age as input data of a preset curative effect prediction model to obtain an output cancer progress probability. By applying the method, the accuracy of determining the actual medication effect of the taken anlotinib by an individual patient based on individual differences can be improved, so that accurate medication is facilitated.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing technologies, specifically to artificial intelligence technology fields such as efficacy prediction, machine learning, deep learning, and pharmacokinetic models, and particularly to a method, device, electronic device, computer-readable storage medium, and computer program product for determining the medication effect of anlotinib. Background Art

[0002] As a multi-target tyrosine kinase inhibitor, anlotinib has shown good efficacy in the treatment of various tumors. However, due to significant differences in the pharmacokinetics and pharmacodynamics of drugs among individuals, the responses and tolerances of patients to treatment show a high degree of individualization. Such individual differences make the standard dosage regimen unable to meet the needs of all patients, resulting in insufficient drug exposure and poor efficacy for some patients, while other patients may face excessive adverse reactions.

[0003] In clinical practice, the efficacy and adverse reactions of drugs are not only affected by a single factor but are often the result of the combined action of multiple factors. Traditional statistical methods usually have difficulty capturing these complex non-linear relationships.

[0004] Therefore, how to better combine the individual differences of different patients and accurately determine the medication effect of anlotinib for different patients is an urgent problem to be solved by those skilled in the art. Summary of the Invention

[0005] Embodiments of the present disclosure propose a method, device, electronic device, computer-readable storage medium, and computer program product for determining the medication effect of anlotinib.

[0006] In a first aspect, an embodiment of the present disclosure provides a method for determining the medication effect of anlotinib, including: obtaining the dose, medication cycle, body weight, and blood drug concentration observation time point of a patient taking anlotinib; using the dose, medication cycle, body weight, and blood drug concentration observation time point together as input data of a preset blood drug concentration prediction model to obtain the output blood drug concentration parameter; wherein, the blood drug concentration prediction model is trained using a training sample composed of medication-related parameters and actual blood drug concentration parameters of sample patients matched with anlotinib based on a population pharmacokinetic model, and the medication-related parameters at least include the dose, medication cycle, body weight, and blood drug concentration observation time point; in response to receiving a curative effect prediction function selection instruction transmitted by the patient, obtaining the actual age of the patient; using the blood drug concentration parameter and the actual age together as input data of a preset curative effect prediction model to obtain the output cancer progression probability; wherein, the curative effect prediction model is trained using a training sample composed of the actual blood drug concentration parameter, actual age, and actual cancer progression within a preset duration of sample patients based on a gradient boosting machine model; determining the actual medication effect of the patient taking anlotinib based on the blood drug concentration parameter and the cancer progression probability.

[0007] In a second aspect, an embodiment of the present disclosure provides a device for determining the medication effect of anlotinib, including: a medication parameter acquisition unit configured to obtain the dose, medication cycle, body weight, and blood drug concentration observation time point of a patient taking anlotinib; a blood drug concentration prediction unit configured to use the dose, medication cycle, body weight, and blood drug concentration observation time point together as input data of a preset blood drug concentration prediction model to obtain the output blood drug concentration parameter; wherein, the blood drug concentration prediction model is trained using a training sample composed of medication-related parameters and actual blood drug concentration parameters of sample patients matched with anlotinib based on a population pharmacokinetic model, and the medication-related parameters at least include the dose, medication cycle, body weight, and blood drug concentration observation time point; an actual age acquisition unit configured to obtain the actual age of the patient in response to receiving a curative effect prediction function selection instruction transmitted by the patient; a cancer progression probability prediction unit configured to use the blood drug concentration parameter and the actual age together as input data of a preset curative effect prediction model to obtain the output cancer progression probability; wherein, the curative effect prediction model is trained using a training sample composed of the actual blood drug concentration parameter, actual age, and actual cancer progression within a preset duration of sample patients based on a gradient boosting machine model; an actual medication effect determination unit configured to determine the actual medication effect of the patient taking anlotinib based on the blood drug concentration parameter and the cancer progression probability.

[0008] In a third aspect, embodiments of the present disclosure provide an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and when the instructions are executed by the at least one processor, the at least one processor is enabled to implement the method for determining the medication effect of anlotinib as described in the first aspect.

[0009] In a fourth aspect, embodiments of the present disclosure provide a non-transitory computer-readable storage medium storing computer instructions, which are used to enable a computer to implement the method for determining the medication effect of anlotinib as described in the first aspect when executed.

[0010] In a fifth aspect, embodiments of the present disclosure provide a computer program product including a computer program, and when the computer program is executed by a processor, it can implement the steps of the method for determining the medication effect of anlotinib as described in the first aspect.

[0011] The solution for determining the medication effect of anlotinib provided by the present disclosure first obtains the dose of anlotinib taken by a patient, the medication cycle, body weight, and the time point for observing blood drug concentration, and then uses them together as input for predicting blood drug concentration in a preset blood drug concentration prediction model to obtain the output blood drug concentration parameter. The blood drug concentration prediction model is trained using a training sample composed of medication-related parameters matching the patient and actual blood drug concentration parameters based on a population pharmacokinetic model. By controlling the medication-related parameters to at least include the above four specific parameters used as input data together, it provides the potential to improve the accuracy of the predicted blood drug concentration results. Then, after further receiving a selection instruction for the efficacy prediction function from the patient, the actual age of the patient is further obtained and used together with the blood drug concentration parameter output by the blood drug concentration prediction model as input data for a preset efficacy prediction model, and then the output cancer progression probability is obtained. The efficacy prediction model is trained using a training sample composed of actual blood drug concentration parameters, actual age, and actual cancer progression within a preset duration based on a gradient boosting machine model, so that the efficacy prediction model learns the corresponding relationship between input and output through training. Finally, based on the obtained blood drug concentration parameter and cancer progression probability, the actual medication effect of the patient taking anlotinib is determined to facilitate subsequent medication adjustment considering individual differences.

[0012] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it used to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Other features, objects, and advantages of the present disclosure will become more apparent from the following detailed description of non - limiting embodiments read in conjunction with the accompanying drawings:

[0014] Figure 1 is an exemplary system architecture to which the present disclosure can be applied;

[0015] Figure 2 is a flowchart of a method for determining the medication effect of anlotinib provided by an embodiment of the present disclosure;

[0016] Figure 3-1 is a schematic diagram of the interface of a blood drug concentration prediction function provided by an embodiment of the present disclosure;

[0017] Figure 3-2 is a schematic diagram of the interface of a curative effect prediction function provided by an embodiment of the present disclosure;

[0018] Figure 4-1 is a flowchart of another method for determining the medication effect of anlotinib provided by an embodiment of the present disclosure;

[0019] Figure 4-2 is a schematic diagram of the interface of a prognosis prediction function provided by an embodiment of the present disclosure;

[0020] Figure 5-1 is a flowchart of yet another method for determining the medication effect of anlotinib provided by an embodiment of the present disclosure;

[0021] Figure 5-2 is a schematic diagram of the interface of an adverse reaction prediction function provided by an embodiment of the present disclosure;

[0022] Figure 6 is a flowchart of still another method for determining the medication effect of anlotinib provided by an embodiment of the present disclosure;

[0023] Figure 7 is a schematic diagram of the initial interface of a medication effect evaluation tool for anlotinib provided by an embodiment of the present disclosure;

[0024] Figure 8 is a block diagram of the structure of a device for determining the medication effect of anlotinib provided by an embodiment of the present disclosure;

[0025] Figure 9 is a schematic diagram of the structure of an electronic device suitable for executing the method for determining the medication effect of anlotinib provided by an embodiment of the present disclosure. Detailed Description of the Invention

[0026] Existing population pharmacokinetic studies have found that body weight is an important factor affecting drug exposure, but dose adjustment based on body weight is not required clinically. Similarly, some studies have used longitudinal metabolomics methods to establish prediction models for efficacy and safety, and the results are relatively good. However, there are still difficulties in the practical clinical application of this method. Currently, the dose adjustment of anlotinib mainly depends on the clinical manifestations and laboratory indicators of patients. This method often lags behind the changes in patients' drug responses and it is difficult to make the optimal dose adjustment in a timely manner.

[0027] In clinical practice, the efficacy and adverse reactions of drugs are not only affected by a single factor, but are often the result of the combined action of multiple factors. Traditional statistical methods usually have difficulty capturing these complex non-linear relationships. Machine learning, through adaptive algorithms, can efficiently identify the complex interaction effects between variables and help better predict individualized drug responses. Clinical data usually involves multiple patient characteristics (such as age, body weight, genes, medical history, etc.) and multiple drug exposure amounts (such as Cmax, AUC, etc.). These high-dimensional data may face overfitting problems in traditional models, but machine learning models are good at dealing with high-dimensional heterogeneous data and can make full use of the useful information in the data, thereby improving the accuracy of prediction. Therefore, the present disclosure provides various models trained by means of machine learning algorithms and deep learning algorithms to better predict the above non-linear relationships, improve the prediction accuracy and reflect individual differences.

[0028] The following describes exemplary embodiments of the present disclosure with reference to the accompanying drawings. Various details of the embodiments of the present disclosure are included to assist in understanding, and they should be considered merely exemplary. Therefore, those of ordinary skill in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted below. It should be noted that, without conflict, the embodiments in the present disclosure and the features in the embodiments can be combined with each other.

[0029] In the technical solution of the present disclosure, the collection, storage, use, processing, transmission, provision, and disclosure of the user's personal information and other processes all comply with the provisions of relevant laws and regulations and do not violate public order and good customs.

[0030] Figure 1 An exemplary system architecture 100 is shown, which can apply the embodiments of the method, device, electronic device, and computer-readable storage medium for determining the drug effect of anlotinib according to the present disclosure.

[0031] As Figure 1As shown, the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, etc.

[0032] Users can use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages, etc. Various applications for implementing information communication between the two can be installed on the terminal devices 101, 102, 103 and the server 105, such as drug efficacy evaluation applications, medication advice applications, instant messaging applications, etc.

[0033] The terminal devices 101, 102, 103 and the server 105 can be hardware or software. When the terminal devices 101, 102, 103 are hardware, they can be various electronic devices with a display screen, including but not limited to smartphones, tablets, laptop computers, and desktop computers, etc.; when the terminal devices 101, 102, 103 are software, they can be installed in the above-listed electronic devices, and can be implemented as multiple software or software modules, or can be implemented as a single software or software module, and no specific limitation is made here. When the server 105 is hardware, it can be implemented as a distributed server cluster composed of multiple servers, or can be implemented as a single server; when the server is software, it can be implemented as multiple software or software modules, or can be implemented as a single software or software module, and no specific limitation is made here.

[0034] Server 105 can provide various services through various built-in applications. Taking the drug efficacy evaluation application that can provide drug efficacy evaluation services as an example, when Server 105 runs this drug efficacy evaluation application, the following effects can be achieved: First, through Network 104, it receives the dose of anlotinib taken by the patient, the medication cycle, the weight, and the time points for observing blood drug concentration transmitted by the user through terminal devices 101, 102, and 103; then, it uses this dose, this medication cycle, this weight, and this time point for observing blood drug concentration as the input data of a preset blood drug concentration prediction model, and obtains the output blood drug concentration parameters. This blood drug concentration prediction model is trained using a training sample composed of the medication-related parameters and actual blood drug concentration parameters of sample patients matched with the anlotinib on the basis of a population pharmacokinetic model; then, in response to receiving the efficacy prediction function selection instruction transmitted by this user, it obtains the actual age of the patient transmitted; next, it uses this blood drug concentration parameter and this actual age as the input data of a preset efficacy prediction model, and obtains the output cancer progression probability. This efficacy prediction model is trained using a training sample composed of the actual blood drug concentration parameters, actual age, and actual cancer progression within a preset time period of sample patients on the basis of a gradient boosting machine model; finally, based on this blood drug concentration parameter and this cancer progression probability, it determines the actual medication effect of the patient taking anlotinib.

[0035] Furthermore, Server 105 can also transmit the evaluated actual medication effect to a professional doctor or medical institution for processing, and transmit the processed result back to terminal devices 101, 102, and 103 through Network 104.

[0036] It should be noted that the input parameters required for each function such as dose, medication cycle, weight, and time points for observing blood drug concentration can be obtained from terminal devices 101, 102, and 103 through Network 104, and can also be pre-stored locally in Server 105 in various ways. Therefore, when Server 105 detects that these data have been stored locally (such as a pending task retained before starting processing), it can choose to directly obtain these data from the local. In this case, the exemplary system architecture 100 may not include terminal devices 101, 102, and 103 and Network 104.

[0037] The method for determining the medication effect of anlotinib provided in the subsequent embodiments of the present disclosure is generally executed by the server 105 with strong computing power and a large amount of computing resources. Correspondingly, the device for determining the medication effect of anlotinib is generally also set in the server 105. However, it should also be noted that when the terminal devices 101, 102, and 103 also have the required computing power and computing resources, the terminal devices 101, 102, and 103 can also complete the above operations originally performed by the server 105 through the medication effect evaluation applications installed thereon, and then output the same results as the server 105. Especially in the case where there are multiple terminal devices with different computing capabilities at the same time, when the medication effect evaluation application determines that the terminal device where it is located has strong computing power and a large amount of remaining computing resources, the terminal device can be allowed to execute the above operations, thereby appropriately reducing the computing pressure on the server 105. Correspondingly, the device for determining the medication effect of anlotinib can also be set in the terminal devices 101, 102, and 103. In this case, the exemplary system architecture 100 may not include the server 105 and the network 104.

[0038] It should be understood that Figure 1 the numbers of the terminal devices, the network, and the server in

[0039] Please refer to Figure 2 , Figure 2 which is a flowchart of a method for determining the medication effect of anlotinib provided in an embodiment of the present disclosure. The process 200 includes the following steps:

[0040] Step 201: Obtain the dose, medication cycle, weight, and blood drug concentration observation time point of the patient taking anlotinib;

[0041] This step aims to obtain the medication-related parameters of the patient for the drug anlotinib by the execution entity of the method for determining the medication effect of anlotinib (such as Figure 1 the server 105 shown).

[0042] Specifically, the medication-related parameters can directly come from the input of the user (or patient), or can be extracted from an image recording relevant information or analyzed from relevant reports or documents generated by some professional medical treatment structures. There is no specific limitation here, and it can be flexibly selected according to the actual situation.

[0043] Step 202: Use the dose, medication cycle, weight, and blood drug concentration observation time point as the input data of a preset blood drug concentration prediction model to obtain the output blood drug concentration parameter;

[0044] Based on step 201, this step aims to use the dose, treatment cycle, body weight, and blood drug concentration observation time points as input data for a preset blood drug concentration prediction model by the above-mentioned execution entity. This blood drug concentration prediction model is trained using training samples composed of drug use-related parameters and actual blood drug concentration parameters of sample patients matched with anlotinib on the basis of a population pharmacokinetic model. The drug use-related parameters at least include the dose, treatment cycle, body weight, and blood drug concentration observation time points to obtain the output blood drug concentration parameters. That is, in this way, the blood drug concentration prediction model learns the corresponding relationship between the input data and the output data through training, and can then output the matched blood drug concentration parameters for the actual input data. It should be noted that by controlling that the drug use-related parameters at least include the above four specific parameters that are jointly used as input data, potential can be provided for improving the accuracy of the prediction result of the blood drug concentration in the future. That is, when the user can continue to provide more drug use-related parameters included in the training samples, the accuracy of the output blood drug concentration parameters should be further improved.

[0045] Specifically, the blood drug concentration prediction model can be specifically a one-compartment pharmacokinetic model of first-order absorption and elimination obtained from population pharmacokinetic analysis. The blood drug concentration parameters output by the model can specifically include: AUC (area under the curve, which refers to the area enclosed by the curve of the drug concentration in the body changing with time and the time axis, and can reflect the exposure degree of the drug in the body, that is, the total amount of drug absorption. The larger the AUC, the higher the exposure degree of the drug in the body), Cmax (peak concentration, which is the highest concentration reached by the drug in the body and is an important indicator for evaluating drug bioavailability and efficacy. Too high a peak concentration may cause drug poisoning, while too low a peak concentration may affect the efficacy), C1min (the trough concentration in the first cycle, which refers to the lowest concentration of the drug at the end of the first dosing cycle. This indicator helps to evaluate the elimination rate and accumulation of the drug in the body), C2min (the trough concentration in the second cycle, which refers to the lowest concentration of the drug at the end of the second dosing cycle. By comparing the trough concentrations in the first and second cycles, it can be judged whether the drug accumulates in the body), the blood drug concentration at any point, etc.

[0046] The steps provided in steps 201 - 202 can be referred to Figure 3-1 The interface schematic diagram of the provided blood drug concentration prediction function, that is Figure 3-1 clarifies the forms and positions of each input data and output data.

[0047] Step 203: In response to receiving a patient's incoming instruction to select the efficacy prediction function, obtain the patient's actual age;

[0048] On the basis of step 202, this step aims to further obtain the actual age of the patient when the above-mentioned execution entity further receives a therapeutic effect prediction function selection instruction transmitted by the patient, so as to meet the input items required for the therapeutic effect prediction function to perform therapeutic effect prediction.

[0049] It should be noted that there was no similar blood drug concentration prediction function selection instruction before step 201 because the prediction result of the blood drug concentration prediction function exists as basic information. Therefore, generally, it is required to perform blood drug concentration prediction first, and whether to use the therapeutic effect prediction function subsequently can be based on the actual needs of the user or patient. Of course, it is not impossible to add a trigger instruction before step 201, that is, a blood drug concentration prediction function selection instruction transmitted by the user received can also be added as a trigger instruction for step 201.

[0050] Step 204: Use the blood drug concentration parameter and the actual age together as the input data of a preset therapeutic effect prediction model to obtain the output cancer progression probability;

[0051] On the basis of step 203, this step aims to have the above-mentioned execution entity use the blood drug concentration parameter and the actual age together as the input data of a preset therapeutic effect prediction model. The therapeutic effect prediction model is trained using the actual blood drug concentration parameter, actual age, and actual cancer progression within a preset duration of the sample patient on the basis of the gradient boosting machine model, that is, in this way, the therapeutic effect prediction model learns the corresponding relationship between the input data and the output data through training, and then can output the cancer progression probability corresponding to the actually input blood drug concentration parameter and actual age.

[0052] The steps provided in step 203 - step 204 can be referred to Figure 3-2 to the schematic diagram of the interface of the provided therapeutic effect prediction function, that is Figure 3-2 which clarifies the forms and positions of each input data and output data.

[0053] Specifically, only the AUC and Cmax in the blood drug concentration parameter may be required for the therapeutic effect prediction model.

[0054] Step 205: Determine the actual medication effect of the patient taking anlotinib based on the blood drug concentration parameter and the cancer progression probability.

[0055] On the basis of step 202 and step 204, this step aims to have the above-mentioned execution entity comprehensively determine the actual medication effect of the patient taking anlotinib based on the blood drug concentration parameter and the cancer progression probability.

[0056] The method for determining the drug effect of anlotinib provided by the embodiments of the present disclosure first obtains the dose, medication cycle, body weight, and blood drug concentration observation time point of a patient taking anlotinib, and then uses them together as inputs for a preset blood drug concentration prediction model to predict the blood drug concentration, obtaining the output blood drug concentration parameter. The blood drug concentration prediction model is trained using a training sample composed of the medication-related parameters of the matching patient and the actual blood drug concentration parameters based on a population pharmacokinetic model. By controlling the medication-related parameters to at least include the above four specific parameters that are jointly used as input data, potential is provided for improving the accuracy of the blood drug concentration prediction result in the future. Then, after further receiving a selection instruction from the patient for the efficacy prediction function, the actual age of the patient is further obtained, and it is used together with the blood drug concentration parameter output by the blood drug concentration prediction model as the input data of a preset efficacy prediction model, thereby obtaining the output cancer progression probability. The efficacy prediction model is trained using a training sample composed of the actual blood drug concentration parameters, actual age, and actual cancer progression within a preset duration of the matching patient based on a gradient boosting machine model, so that the efficacy prediction model learns the corresponding relationship between the input and output through training. Finally, based on the obtained blood drug concentration parameter and cancer progression probability, the actual drug effect of the patient taking anlotinib is determined, facilitating subsequent medication adjustment in combination with individual differences.

[0057] Please refer to Figure 4-1 , Figure 4-1 FIG. is a flowchart of another method for determining the drug effect of anlotinib provided by the embodiments of the present disclosure. The process 400 includes the following steps:

[0058] Step 401: Obtain the dose, medication cycle, body weight, and blood drug concentration observation time point of a patient taking anlotinib;

[0059] Step 402: Use the dose, medication cycle, body weight, and blood drug concentration observation time point together as the input data of a preset blood drug concentration prediction model to obtain the output blood drug concentration parameter;

[0060] Step 403: In response to receiving a selection instruction for the efficacy prediction function passed in by the patient, obtain the actual age of the patient;

[0061] Step 404: Use the blood drug concentration parameter and the actual age together as the input data of a preset efficacy prediction model to obtain the output cancer progression probability;

[0062] The above steps 401-404 are the same as steps 201-204 shown in Figure 2 . For the same parts, please refer to the corresponding parts of the previous embodiment, and details will not be repeated here.

[0063] Step 405: In response to receiving the prognosis prediction function selection instruction sent by the patient, obtain the actual age;

[0064] Based on step 404, this step aims to further obtain the actual age of the patient when the above-mentioned execution entity further receives the prognosis prediction function selection instruction sent by the patient, so as to meet the input items required for prognosis prediction by the prognosis prediction function.

[0065] Among them, prognosis prediction refers to predicting the possible outcomes of disease development and the possible survival status of the patient based on various factors such as the patient's condition, physiological indicators, gene expression, etc. Prognosis prediction is of great significance for formulating treatment plans, evaluating treatment effects, and improving the quality of life of patients.

[0066] Step 406: Use the blood drug concentration parameter and the actual age together as the input data of a preset prognosis prediction model to obtain the output cancer progression risk score;

[0067] Based on step 405, this step aims to use the blood drug concentration parameter and the actual age together as the input data of a preset prognosis prediction model by the above-mentioned execution entity. The prognosis prediction model is trained using a training sample composed of the actual blood drug concentration parameter, actual age, and actual cancer progression risk score of sample patients based on the proportional hazards model. That is, in this way, the prognosis prediction model learns the correspondence between the input data and the output data through training, and then is able to output the cancer progression risk score corresponding to the actually input blood drug concentration parameter and actual age.

[0068] The steps provided in steps 405 - 406 can be referred to Figure 4-2 to the interface schematic diagram of the provided prognosis prediction function, that is Figure 4-2 which clarifies the forms and positions of each input data and output data.

[0069] Specifically, what the efficacy prediction model needs may only be the AUC and Cmax in the blood drug concentration parameter.

[0070] And in special cases, if the user does not use the blood drug concentration prediction model first before using the prognosis prediction model for prognosis prediction, then the user can also be required to directly input the globulin concentration (GLB, the globulin concentration can reflect the function status of the liver. Abnormal globulin concentration may indicate immune system diseases, liver diseases, etc.) to replace the blood drug concentration parameter.

[0071] Step 407: Determine the actual medication effect of the patient taking anlotinib based on the blood drug concentration parameter, cancer progression probability, and cancer progression risk score.

[0072] Adaptive. Due to the additional prognosis prediction function introduced in steps 405 - 406, in this step, the actual medication effect of the patient taking anlotinib will be comprehensively determined based on the three parameters of blood drug concentration parameter, cancer progression probability, and cancer progression risk score.

[0073] Compared with Figure 2 the embodiment shown, this embodiment additionally provides a prognosis prediction function to provide a score for the cancer progression risk of the patient, so as to more comprehensively determine the actual medication effect by combining the blood drug concentration parameter and the cancer progression probability.

[0074] Please refer to Figure 5-1 , Figure 5-1 which is a flowchart of another method for determining the medication effect of anlotinib provided by an embodiment of the present disclosure. The process 500 includes the following steps:

[0075] Step 501: Obtain the dose, medication cycle, weight, and blood drug concentration observation time point of the patient taking anlotinib;

[0076] Step 502: Use the dose, medication cycle, weight, and blood drug concentration observation time point as the input data of a preset blood drug concentration prediction model to obtain the output blood drug concentration parameter;

[0077] Step 503: In response to receiving the efficacy prediction function selection instruction transmitted by the patient, obtain the actual age of the patient;

[0078] Step 504: Use the blood drug concentration parameter and the actual age as the input data of a preset efficacy prediction model to obtain the output cancer progression probability;

[0079] The above steps 501 - 504 are the same as steps 201 - 204 shown in Figure 2 . For the same parts, please refer to the corresponding parts of the previous embodiment, and details will not be repeated here.

[0080] Step 505: In response to receiving the adverse reaction prediction function selection instruction transmitted by the patient, obtain the height, white - to - globulin ratio, white blood cell count, and neutrophil count of the patient;

[0081] Based on step 504, this step aims to further obtain the height, white - to - globulin ratio, white blood cell count, and neutrophil count of the patient when the above - mentioned execution entity further receives the adverse reaction prediction function selection instruction transmitted by the patient, so as to meet the input items required for the adverse reaction prediction function to perform adverse reaction prediction.

[0082] Among them, the white blood cell ratio, white blood cell count, and neutrophil count can be directly from the input of the user (or patient), or extracted from an image recording relevant information or analyzed from relevant reports or documents generated by certain professional diagnosis and treatment structures (such as laboratory test documents). There is no specific limitation here, and it can be flexibly selected according to the actual situation.

[0083] Step 506: Use the trough concentration in the second cycle, height, white blood cell ratio, white blood cell count, and neutrophil count in the blood drug concentration parameters as the input data of a preset adverse reaction prediction model to obtain the probability of the occurrence of an adverse reaction as the output.

[0084] Based on step 505, this step aims to have the above-mentioned execution entity use the trough concentration in the second cycle, height, white blood cell ratio, white blood cell count, and neutrophil count in the blood drug concentration parameters as the input data of a preset adverse reaction prediction model. The adverse reaction prediction model is trained using a training sample composed of adverse reaction-related parameters and actual adverse reactions of sample patients based on a random forest model. The adverse reaction-related parameters at least include: trough concentration in the second cycle (C2min), height, white blood cell ratio (the ratio of albumin to globulin, abbreviated as A / G. Albumin and globulin are two main proteins in plasma, and normally it is 1.5 - 2.5:1. An abnormal A / G ratio may indicate liver diseases, malnutrition, etc. For example, a decreased A / G ratio may be seen in chronic liver diseases, cirrhosis, etc.), white blood cell count (abbreviated in English as: WBC), and neutrophil count (abbreviated in English as: NEUT). That is, in this way, the adverse reaction prediction model can learn the corresponding relationship between the input data and the output data through training, and then be able to output the probability of the occurrence of an adverse reaction corresponding to the actually input trough concentration in the second cycle, height, white blood cell ratio, white blood cell count, and neutrophil count.

[0085] By controlling that the adverse reaction-related parameters at least include the above four specific parameters that are jointly used as input data, it provides the potential to improve the accuracy of the prediction result of the probability of the occurrence of an adverse reaction in the future. That is, when the user can continue to provide more adverse reaction-related parameters included in the training sample, it should be possible to further improve the accuracy of the probability of the occurrence of an adverse reaction output.

[0086] The steps provided in steps 505 - 506 can be referred to Figure 5-2 to the schematic diagram of the interface of the provided adverse reaction prediction function, that is Figure 5-2 which clarifies the forms and positions of each input data and output data.

[0087] It should be noted that including Figure 3-1 , 3-2Each interface including 4-2 and 5-2 should be able to provide a user-friendly data input environment, where users can input or upload basic information, clinical data, and treatment data of patients. The application or plug-in corresponding to the interface will perform format and logic verification on the data input by the user to ensure the accuracy and completeness of the data. In addition, the results of the blood drug concentration prediction model will be automatically input into the efficacy, prognosis, and adverse reaction models for prediction, that is, the parameters can be transferred between the models as required.

[0088] Step 507: Determine the actual medication effect of the patient taking anlotinib based on the blood drug concentration parameter, the probability of cancer progression and the probability of adverse reaction occurrence.

[0089] Adaptively, due to the additional adverse reaction prediction function introduced in step 505-step 506, this step will comprehensively determine the actual medication effect of the patient taking anlotinib based on three parameters: blood drug concentration parameter, cancer progression probability and adverse reaction occurrence probability.

[0090] Compared to Figure 2 The embodiment shown in this embodiment additionally provides an adverse reaction prediction function to provide patients with a predicted probability of adverse reactions after medication, thereby combining blood drug concentration parameters and cancer progression probability to more comprehensively determine the actual medication effect.

[0091] Please refer to Figure 6 , Figure 6 A flowchart of another method for determining the medication effect of anlotinib provided in an embodiment of the present disclosure, wherein process 600 includes the following steps:

[0092] Step 601: Obtain the patient's dose of anlotinib, medication cycle, body weight, and blood drug concentration observation time point;

[0093] Step 602: taking the dosage, medication cycle, body weight and blood drug concentration observation time point as input data of a preset blood drug concentration prediction model to obtain output blood drug concentration parameters;

[0094] Step 603: in response to receiving the therapeutic effect prediction function selection instruction transmitted by the patient, obtaining the actual age of the patient;

[0095] Step 604: using the blood drug concentration parameter and the actual age as input data of a preset efficacy prediction model to obtain an output cancer progression probability;

[0096] The above steps 601-604 are similar to Figure 2 Steps 201-204 shown are consistent, and for the same contents, please refer to the corresponding parts of the previous embodiment, which will not be repeated here.

[0097] Step 605: In response to receiving a prognosis prediction function selection instruction input by the patient, obtain the actual age;

[0098] Step 606: Use the blood drug concentration parameter and the actual age together as the input data of a preset prognosis prediction model to obtain the output cancer progression risk score;

[0099] The above steps 605-606 are the same as steps 405-406 as shown in Figure 4-1 For the same parts, please refer to the corresponding parts of the previous embodiment, and details will not be repeated here.

[0100] Step 607: In response to receiving an adverse reaction prediction function selection instruction input by the patient, obtain the patient's height, white-to-globulin ratio, white blood cell count, and neutrophil count;

[0101] Step 608: Use the second-cycle trough concentration in the blood drug concentration parameter, height, white-to-globulin ratio, white blood cell count, and neutrophil count together as the input data of a preset adverse reaction prediction model to obtain the output probability of adverse reactions occurring;

[0102] The above steps 607-608 are the same as steps 505-506 as shown in Figure 5-1 For the same parts, please refer to the corresponding parts of the previous embodiment, and details will not be repeated here.

[0103] It should be noted that the blood drug concentration parameter, cancer progression probability, cancer progression risk score, and probability of adverse reactions occurring can all be presented in at least one of the following visualization forms:

[0104] Text, table, graph. For example, the blood drug concentration parameter represented by a concentration change curve, the blood drug concentration-time curve and the corresponding table, the cancer progression probability represented by a line graph, the cancer progression risk score and the probability of adverse reactions occurring represented by numbers, etc.

[0105] In addition, each model can also generate a detailed text description for each prediction result, including the basis for the prediction, possible clinical significance, and suggestions. For cases where the prediction result shows potential adverse reactions, a warning signal is generated to alert the user.

[0106] Step 609: Determine the actual medication effect of the patient taking anlotinib based on the blood drug concentration parameter, cancer progression probability, cancer progression risk score, and probability of adverse reactions occurring.

[0107] Adaptive. Since steps 605 - 608 additionally introduce a prognosis prediction function and an adverse reaction prediction function, this step will comprehensively determine the actual medication effect of the patient taking anlotinib based on the four parameters of blood drug concentration parameter, cancer progression probability, cancer progression risk score, and adverse reaction occurrence probability.

[0108] Compared with Figure 4-1 and Figure 5-1 the embodiments respectively shown, this embodiment combines the functions independently provided by the two embodiments to simultaneously provide the predicted probability of adverse reactions occurring after medication and the cancer progression risk score of the prognosis for the patient, so as to more comprehensively determine the actual medication effect by combining the blood drug concentration parameter and the cancer progression probability.

[0109] It should be noted that the efficacy prediction models mentioned in the above embodiments are trained based on the Gradient Boosting Machines (GBM) model of the machine learning algorithm, the prognosis prediction model is trained based on the proportional hazards model, and the adverse reaction prediction model is trained based on the Random Forest (RF) model of the machine learning algorithm. The above three different model bases are not randomly selected or can be randomly replaced. They are selected through horizontal comparison after experimenting with various types of base models on the basis of determining the training samples corresponding to each prediction function. That is, the reason for choosing the above three models is that the above three models have all been well verified in the test set and have better prediction result accuracy compared with other types of base models.

[0110] For better understanding, the present disclosure also gives a specific implementation solution in combination with a specific application scenario. Please refer to Figure 7 :

[0111] A certain patient X suffering from a certain type of cancer that can be treated by reusing anlotinib, after reusing anlotinib for a period of time according to the doctor's advice after the first visit, by running the anlotinib medication effect evaluation tool as shown in Figure 7 on a mobile terminal or a web page, as shown in Figure 7 , there are various prediction functions on the first interface, which are blood drug concentration prediction, efficacy prediction, prognosis prediction, and adverse reaction prediction from top to bottom in order, and there is an instruction message of "It is recommended to use them in order in sequence".

[0112] Assume that after patient X clicks or selects the blood drug concentration prediction button, it can enter the Figure 3-1 shown user operation interface, and can input the required medication-related parameters according to the guidance, and obtain the blood drug concentration prediction result presented below through operation;

[0113] Then, patient X returnsFigure 7 After the initial interface shown, continue to click or select the efficacy prediction button, and then you can enter Figure 3-2 the user operation interface shown. You can input new parameter items according to the guidance (the blood drug concentration parameter can be inherited from the blood drug concentration prediction result and does not need to be input manually). By running the operation, you can obtain the cancer development probability presented below;

[0114] Next, patient X returns to Figure 7 the initial interface shown. After continuing to click or select the prognosis prediction button, you can enter Figure 4-2 the user operation interface shown. You can input new parameter items according to the guidance (the blood drug concentration parameter can be inherited from the blood drug concentration prediction result and does not need to be input manually). By running the operation, you can obtain the cancer development risk score presented below;

[0115] Finally, patient X returns to Figure 7 the initial interface shown. After continuing to click or select the adverse reaction prediction button, you can enter Figure 5-2 the user operation interface shown. You can input new parameter items according to the guidance (the blood drug concentration parameter can be inherited from the blood drug concentration prediction result and does not need to be input manually). By running the operation, you can obtain the probability of adverse reactions presented below.

[0116] In this embodiment, a multi-modal clinical prediction model is constructed for advanced cancer patients taking anlotinib based on population pharmacokinetics and machine learning methods. It fully considers the factors affecting the safety and efficacy of patients, and develops a web tool that can be applied to clinical guidance for personalized medication, with the following technical effects:

[0117] 1) By accurately predicting blood drug concentration, efficacy, prognosis, and adverse reactions, it can help doctors customize more personalized treatment plans for each patient, improving the effectiveness and safety of treatment;

[0118] 2) The provided prediction results can serve as a powerful aid for doctors' clinical decisions, helping doctors adjust treatment plans according to the prediction results, thereby improving treatment effects and patients' quality of life;

[0119] 3) Accurate prediction can avoid unnecessary drug use and over-treatment, reduce waste of medical resources, and at the same time reduce the economic burden on patients;

[0120] 4) Patients can better understand their condition and treatment process through intuitive prediction results, improving patients' satisfaction and trust in treatment;

[0121] 5) Using advanced machine learning algorithms and big data analysis, it can provide more accurate prediction results than traditional methods;

[0122] 6) By predicting adverse reactions, especially severe side effects such as myelosuppression, it can help doctors take measures in advance and reduce the risk of patients suffering from adverse reactions.

[0123] Further reference Figure 8 , as an implementation of the methods shown in the above figures, the present disclosure provides an embodiment of a device for determining the medication effect of anlotinib. This device embodiment corresponds to Figure 2 the method embodiment shown, and this device can be specifically applied to various electronic devices.

[0124] As Figure 8 shown, the device 800 for determining the medication effect of anlotinib in this embodiment may include: a medication parameter acquisition unit 801, a blood drug concentration prediction unit 802, an actual age acquisition unit 803, a cancer progression probability prediction unit 804, and an actual medication effect determination unit 805. Among them, the medication parameter acquisition unit 801 is configured to acquire the dose, medication cycle, weight, and blood drug concentration observation time points of the patient taking anlotinib; the blood drug concentration prediction unit 802 is configured to use the dose, medication cycle, weight, and blood drug concentration observation time points together as input data of a preset blood drug concentration prediction model to obtain the output blood drug concentration parameters; among them, the blood drug concentration prediction model is trained using a training sample composed of the medication-related parameters and actual blood drug concentration parameters of sample patients matching anlotinib on the basis of a population pharmacokinetic model, and the medication-related parameters at least include the dose, medication cycle, weight, and blood drug concentration observation time points; the actual age acquisition unit 803 is configured to acquire the actual age of the patient in response to receiving a therapeutic effect prediction function selection instruction transmitted by the patient; the cancer progression probability prediction unit 804 is configured to use the blood drug concentration parameters and the actual age together as input data of a preset therapeutic effect prediction model to obtain the output cancer progression probability; among them, the therapeutic effect prediction model is trained using a training sample composed of the actual blood drug concentration parameters, actual age, and actual cancer progression within a preset time period of sample patients on the basis of a gradient boosting machine model; the actual medication effect determination unit 805 is configured to determine the actual medication effect of the patient taking anlotinib based on the blood drug concentration parameters and the cancer progression probability.

[0125] In this embodiment, in the device 800 for determining the medication effect of anlotinib: the specific processing of the medication parameter acquisition unit 801, the blood drug concentration prediction unit 802, the actual age acquisition unit 803, the cancer progression probability prediction unit 804, and the actual medication effect determination unit 805 and the technical effects brought by them can respectively refer to Figure 2 the relevant descriptions of steps 201 - 205 in the corresponding embodiments, which will not be elaborated here.

[0126] In some alternative implementation manners of this embodiment, the apparatus 800 for determining the drug effect of anlotinib may further include:

[0127] A prognostic prediction function selection processing unit, configured to obtain the actual age in response to receiving a prognostic prediction function selection instruction transmitted by a patient;

[0128] A prognostic prediction unit, configured to use both the blood drug concentration parameter and the actual age as input data of a preset prognostic prediction model to obtain an output cancer progression risk score; wherein, the prognostic prediction model is trained based on a proportional hazards model using a training sample composed of the actual blood drug concentration parameter, actual age, and actual cancer progression risk score of sample patients;

[0129] Correspondingly, the actual drug effect determination unit 805 is further configured to:

[0130] Determine the actual drug effect of the patient taking anlotinib according to the blood drug concentration parameter, cancer progression probability, and cancer progression risk score.

[0131] In some alternative implementation manners of this embodiment, the apparatus 800 for determining the drug effect of anlotinib may further include:

[0132] An adverse reaction prediction function selection processing unit, configured to obtain the patient's height, albumin-globulin ratio, white blood cell count, and neutrophil count in response to receiving an adverse reaction prediction function selection instruction transmitted by the patient;

[0133] An adverse reaction prediction unit, configured to use the trough concentration in the second cycle in the blood drug concentration parameter, height, albumin-globulin ratio, white blood cell count, and neutrophil count as input data of a preset adverse reaction prediction model to obtain an output probability of adverse reaction occurrence; wherein, the adverse reaction prediction model is trained based on a random forest model using a training sample composed of adverse reaction-related parameters and actual adverse reactions of sample patients, and the adverse reaction-related parameters at least include: trough concentration in the second cycle, height, albumin-globulin ratio, white blood cell count, and neutrophil count;

[0134] Correspondingly, the actual drug effect determination unit 805 is further configured to:

[0135] Determine the actual drug effect of the patient taking anlotinib according to the blood drug concentration parameter, cancer progression probability, and probability of adverse reaction occurrence.

[0136] In some alternative implementation manners of this embodiment, the apparatus 800 for determining the drug effect of anlotinib may further include:

[0137] The adverse reaction prediction function selection processing unit is configured to obtain the patient's height, albumin / globulin ratio, white blood cell count, and neutrophil count in response to receiving an adverse reaction prediction function selection instruction transmitted by the patient;

[0138] The adverse reaction prediction unit is configured to use the trough concentration in the second cycle, height, albumin / globulin ratio, white blood cell count, and neutrophil count in the blood drug concentration parameters as the input data of a preset adverse reaction prediction model to obtain the probability of the occurrence of an adverse reaction as the output; wherein, the adverse reaction prediction model is trained using a training sample composed of adverse reaction-related parameters and actual adverse reactions of sample patients based on a random forest model, and the adverse reaction-related parameters at least include: the trough concentration in the second cycle, height, albumin / globulin ratio, white blood cell count, and neutrophil count;

[0139] Correspondingly, the actual medication effect determination unit 805 is further configured to:

[0140] Determine the actual medication effect of the patient taking anlotinib based on the blood drug concentration parameters, cancer progression probability, cancer progression risk score, and probability of the occurrence of an adverse reaction.

[0141] In some other implementation manners of this embodiment, the blood drug concentration parameters, cancer progression probability, cancer progression risk score, and probability of the occurrence of an adverse reaction are presented in at least one of the following visualization forms:

[0142] Text, table, graph.

[0143] In some other implementation manners of this embodiment, the anlotinib medication effect determination device 800 may further include:

[0144] The alternative parameter import unit is configured to extract the globulin concentration from the test report imported by the patient as an alternative parameter to replace the blood drug concentration parameter in the input data of the efficacy prediction model;

[0145] The parameter import unit is configured to extract the albumin / globulin ratio, white blood cell count, and neutrophil count from the test report imported by the patient.

[0146] In some other implementation manners of this embodiment, the anlotinib medication effect determination device 800 may further include:

[0147] The adjustment unit is configured to adjust the medication dose and / or administration frequency for the patient according to the actual medication effect.

[0148] This embodiment exists as a device embodiment corresponding to the above method embodiment. The device for determining the medication effect of anlotinib provided in this embodiment first obtains the dose of anlotinib taken by a patient, the medication cycle, the body weight, and the time points for observing blood drug concentration, and then uses them jointly to predict the blood drug concentration in a preset blood drug concentration prediction model, obtaining the output blood drug concentration parameters. The blood drug concentration prediction model is trained using training samples composed of the medication-related parameters and actual blood drug concentration parameters of patients matching the population pharmacokinetic model. By controlling the medication-related parameters to at least include the above four specific parameters jointly used as input data, potential is provided for improving the accuracy of the blood drug concentration prediction result in the future. Then, after further receiving a selection instruction for the efficacy prediction function from the patient, the actual age of the patient is further obtained and used together with the blood drug concentration parameters output by the blood drug concentration prediction model as the input data of a preset efficacy prediction model, thereby obtaining the output probability of cancer progression. The efficacy prediction model is trained using training samples composed of the actual blood drug concentration parameters, the actual age, and the actual cancer progression within a preset duration of patients matching the gradient boosting machine model, so that the efficacy prediction model learns the corresponding relationship between the input and the output through training. Finally, based on the obtained blood drug concentration parameters and the probability of cancer progression, the actual medication effect of the patient taking anlotinib is determined to facilitate subsequent medication adjustment in combination with individual differences.

[0149] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and when the instructions are executed by the at least one processor, the at least one processor is enabled to implement the method for determining the medication effect of anlotinib described in any of the above embodiments.

[0150] According to an embodiment of the present disclosure, the present disclosure also provides a readable storage medium storing computer instructions for enabling a computer to implement the method for determining the medication effect of anlotinib described in any of the above embodiments when executed.

[0151] According to an embodiment of the present disclosure, the present disclosure also provides a computer program product, which can implement the method for determining the medication effect of anlotinib described in any of the above embodiments when executed by a processor.

[0152] Figure 9FIG. shows a schematic block diagram of an exemplary electronic device 900 that may be used to implement embodiments of the present disclosure. The electronic device is intended to represent various forms of digital computers, such as, for example, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as, personal digital processors, cellular telephones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are only exemplary and are not intended to limit the implementations of the present disclosure described and / or claimed herein.

[0153] As Figure 9 shown, the device 900 includes a computing unit 901 that can perform various appropriate actions and processes in accordance with a computer program stored in a read-only memory (ROM) 902 or a computer program loaded from a storage unit 908 into a random access memory (RAM) 903. In the RAM 903, various programs and data required for the operation of the device 900 can also be stored. The computing unit 901, the ROM 902, and the RAM 903 are connected to each other via a bus 904. An input / output (I / O) interface 905 is also connected to the bus 904.

[0154] A plurality of components in the device 900 are connected to the I / O interface 905, including: an input unit 906, such as a keyboard, a mouse, etc.; an output unit 907, such as various types of displays, speakers, etc.; a storage unit 908, such as a magnetic disk, an optical disk, etc.; and a communication unit 909, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 909 allows the device 900 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0155] The computing unit 901 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 901 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 901 executes the various methods and processes described above, such as the method for determining the medication effect of anlotinib. For example, in some embodiments, the method for determining the medication effect of anlotinib can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 908. In some embodiments, part or all of the computer program can be loaded and / or installed onto the device 900 via the ROM 902 and / or the communication unit 909. When the computer program is loaded into the RAM 903 and executed by the computing unit 901, one or more steps of the method for determining the medication effect of anlotinib described above can be executed. Alternatively, in other embodiments, the computing unit 901 can be configured to execute the method for determining the medication effect of anlotinib in any other suitable manner (e.g., by means of firmware).

[0156] Various embodiments of the systems and techniques described above in this document can be implemented in digital electronic circuitry, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special-purpose or general-purpose programmable processor that receives data and instructions from a storage system, at least one input device, and at least one output device, and transmits the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0157] The program code for implementing the methods of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the program code is executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0158] In the context of this disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0159] In order to provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0160] The systems and techniques described herein can be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0161] A computer system may include a client and a server. The client and the server are generally far from each other and usually interact via a communication network. The relationship between the client and the server is generated by computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system to address the defects of difficult management and weak business scalability existing in traditional physical hosts and virtual private server (VPS) services.

[0162] According to the technical solution of the embodiment of the present disclosure, the beneficial effects are repeated.

[0163] It should be understood that various forms of the processes shown above can be used, steps can be reordered, added, or deleted. For example, the steps recited in the present disclosure can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution disclosed in the present disclosure can be achieved, and no limitations are imposed herein.

[0164] The above specific embodiments do not constitute a limitation on the protection scope of the present disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present disclosure shall be included within the protection scope of the present disclosure.

Claims

1. A method for determining the efficacy of anlotinib, characterized in that including: Obtaining the dose of anlotinib taken by the patient, the medication cycle, the weight, and the time points for observing the blood drug concentration; Using the dose, the medication cycle, the weight, and the time points for observing the blood drug concentration as input data for a preset blood drug concentration prediction model to obtain the output blood drug concentration parameter; In response to receiving the efficacy prediction function selection instruction transmitted by the patient, obtaining the actual age of the patient; Using the blood drug concentration parameter and the actual age as input data for a preset efficacy prediction model to obtain the output probability of cancer progression; Determining the actual medication effect of the patient taking the anlotinib based on the blood drug concentration parameter and the probability of cancer progression; 2. The method according to claim 1, wherein It also includes: In response to receiving the prognosis prediction function selection instruction transmitted by the patient, obtaining the actual age; Using the blood drug concentration parameter and the actual age as input data for a preset prognosis prediction model to obtain the output cancer progression risk score; Correspondingly, the determining the actual medication effect of the patient taking the anlotinib based on the blood drug concentration parameter and the probability of cancer progression includes: Determining the actual medication effect of the patient taking the anlotinib according to the blood drug concentration parameter, the probability of cancer progression, and the cancer progression risk score; 3. The method according to claim 1, wherein It also includes: In response to receiving the adverse reaction prediction function selection instruction transmitted by the patient, obtaining the height, albumin-globulin ratio, white blood cell count, and neutrophil count of the patient; Using the trough concentration in the second cycle in the blood drug concentration parameter, the height, the albumin-globulin ratio, the white blood cell count, and the neutrophil count as input data for a preset adverse reaction prediction model to obtain the output probability of adverse reactions occurring; Correspondingly, the determining the actual medication effect of the patient taking the anlotinib based on the blood drug concentration parameter and the probability of cancer progression includes: Determining the actual medication effect of the patient taking the anlotinib according to the blood drug concentration parameter, the probability of cancer progression, and the probability of adverse reactions occurring; 4. The method according to claim 2, characterized in that It also includes: In response to receiving the adverse reaction prediction function selection instruction transmitted by the patient, obtaining the height, albumin-globulin ratio, white blood cell count, and neutrophil count of the patient; Using the trough concentration in the second cycle in the blood drug concentration parameter, the height, the albumin-globulin ratio, the white blood cell count, and the neutrophil count as input data for a preset adverse reaction prediction model to obtain the output probability of adverse reactions occurring; Correspondingly, the determining the actual medication effect of the patient taking the anlotinib based on the blood drug concentration parameter and the probability of cancer progression includes: Determining the actual medication effect of the patient taking the anlotinib according to the blood drug concentration parameter, the probability of cancer progression, the cancer progression risk score, and the probability of adverse reactions occurring; 5. The method according to claim 4, characterized in that, The blood drug concentration parameter, the probability of cancer progression, the cancer progression risk score, and the probability of adverse reactions occurring are presented in at least one of the following visualization forms: Text, table, graph.

6. The method according to claim 4, characterized in that It also includes: Extract the globulin concentration from the test bill imported from the patient as an alternative parameter to replace the blood drug concentration parameter in the input data of the efficacy prediction model; Extract the white-to-globulin ratio, the white blood cell count, and the neutrophil count from the test bill imported from the patient.

7. An apparatus for determining the efficacy of anlotinib, characterized in that, Comprising: A drug usage parameter acquisition unit configured to acquire the dosage, the medication cycle, the body weight, and the blood drug concentration observation time point of the patient taking anlotinib; A blood drug concentration prediction unit configured to use the dosage, the medication cycle, the body weight, and the blood drug concentration observation time point together as the input data of a preset blood drug concentration prediction model to obtain the output blood drug concentration parameter; An actual age acquisition unit configured to acquire the actual age of the patient in response to receiving the efficacy prediction function selection instruction transmitted by the patient; A cancer progression probability prediction unit configured to use the blood drug concentration parameter and the actual age together as the input data of a preset efficacy prediction model to obtain the output cancer progression probability; An actual medication effect determination unit configured to determine the actual medication effect of the patient taking the anlotinib based on the blood drug concentration parameter and the cancer progression probability.

8. An electronic device, comprising: At least one processor; And A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method for determining the medication effect of anlotinib according to any one of claims 1-6.

9. A non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the method for determining the medication effect of anlotinib according to any one of claims 1-6.

10. A computer program product comprising a computer program, and when the computer program is executed by a processor, the steps of the method for determining the medication effect of anlotinib according to any one of claims 1-6 are implemented.

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