Knowledge graph curative effect auxiliary supervision system and method based on multi-modal driving

Abstract

The invention discloses a knowledge graph curative effect auxiliary supervision system and method based on multi-modal driving, and belongs to the technical field of curative effect auxiliary supervision. Constructing a first entity set and a first relation set between the patient and the electronic case, constructing a second entity set and a second relation set between the patient and the B ultrasonic image, and forming a patient personalized knowledge graph; adding a time sequence period label to the focus, and recording the time sequence period label as a period focus; calculating a curative effect predicted value of the patient; calculating the focus change rate of the periodic focus; calculating a comprehensive curative effect evaluation value of the patient; calculating optimized curative effect prediction values among different patients; calculating an optimal treatment scheme; according to the invention, focus information is extracted by using an image analysis technology, a dynamic association between an illness state and a treatment scheme is constructed through a knowledge graph, and focus change and curative effect prediction are quantified; and finally, the treatment scheme is optimized through the similarity analysis of the curative effects of the patients, and the optimal treatment scheme is recommended for the patients, so that the accuracy of medical decision and the scientificity of the treatment effect are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of therapeutic effect auxiliary supervision, and specifically to a therapeutic effect auxiliary supervision system and method based on a multimodal driven knowledge graph. Background Art

[0002] In recent years, with the rapid development of medical information technology, the integrated application of electronic medical record (EMR) systems and medical imaging data has provided new opportunities for patient health management and treatment effect evaluation. As an important tool in the field of artificial intelligence, knowledge graph technology has been widely used in the medical field by constructing a semantic network of multidimensional data to effectively represent the complex relationships between entities. Especially in the treatment process, the patient's disease progression and efficacy evaluation data often come from multiple modes of data sources, including electronic medical record texts, B-ultrasound and other imaging data. Through the fusion of these multimodal data, doctors can more comprehensively grasp the patient's treatment process and make more accurate treatment decisions. However, most of the current efficacy evaluation methods still rely on single-modality data, lack dynamic tracking and comprehensive analysis of disease changes, and it is difficult to comprehensively and timely evaluate the patient's treatment effect.

[0003] The deficiencies in existing technologies are mainly manifested in several aspects. First, most efficacy evaluation systems are limited to analysis based on text data, and it is difficult to effectively integrate medical image data, so that the spatial changes of patient lesions are not fully reflected in the evaluation results. Second, existing technologies are mostly based on static historical data and lack dynamic tracking and analysis of changes in the patient's condition over time, making it difficult to effectively predict future disease progression and treatment effects. In addition, individual differences between patients are not fully considered, resulting in insufficient personalization of treatment plans, affecting the accuracy and scientificity of clinical treatment. Summary of the invention

[0004] The purpose of the present invention is to provide a multi-modal driven knowledge graph therapeutic efficacy auxiliary supervision system and method to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] A method for auxiliary supervision of therapeutic effect based on a multimodal driven knowledge graph, the method comprising the following steps: establishing a patient database, constructing an electronic medical record data set and a B-ultrasound image data set for the patient; extracting entities and relationships between the patient and the electronic medical record; constructing a first entity set and a first relationship set between the patient and the electronic medical record, and constructing an initial knowledge graph; using image analysis technology to extract the lesion area in the B-ultrasound image, and constructing a second entity set and a second relationship set between the patient and the B-ultrasound image; adding the second entity set and the second relationship set to the initial knowledge graph to form a patient personalized knowledge graph; attaching a time series period label to the lesion, recording it as a periodic lesion; calculating the patient's efficacy prediction value; calculating the lesion change rate of the periodic lesion; calculating the patient's comprehensive efficacy evaluation value based on the efficacy prediction value and the lesion change rate; constructing an efficacy evaluation set based on the comprehensive efficacy evaluation value, and updating it to the patient's personalized knowledge graph; calculating the optimized efficacy prediction value between different patients; and calculating the optimal treatment plan based on the optimized efficacy prediction value.

[0007] As a preferred solution of the multi-modal driven knowledge graph therapeutic effect auxiliary supervision method described in the present invention, a patient database is established, denoted as HZ = {HZ a |a∈[1,A]}, where HZ a represents the ath patient, A represents the total number of patients; obtain the patient HZ a The electronic medical record data and B-ultrasound image data are used to construct the electronic medical record data set DB = {DB a |a∈[1,A]} and B-ultrasound image dataset BC={BC a |a∈[1,A]}, where DB a represents the electronic medical record data of the ath patient, BC a represents the B-ultrasound image data of the ath patient; the electronic medical record data includes patient HZ a symptoms, treatment options and medications.

[0008] Based on the electronic medical record dataset and the patient database, entities and relationships between patients and electronic medical records are extracted, and the entities include patient HZ a 、Symptoms i Treatment plan b and drug D c , where S i represents the ith symptom, T b represents the bth treatment plan, D c represents the cth drug; the relationship includes suffering from, receiving and using; the suffering from represents the patient HZ a Symptoms i The relationship between the patient and the patient, the acceptance indicates that the patient HZ a Treatment plan Tb The acceptance relationship between the patient HZ a With drug D c The usage relationship between them.

[0009] As a preferred solution of the multimodal driven knowledge graph therapeutic effect auxiliary supervision method described in the present invention, a first entity set and a first relationship set between the patient and the electronic medical record are constructed, which are respectively recorded as the first entity set FE = {patient HZ a 、Symptoms i Treatment plan b , Drug D c} and the first relationship set FR = {suffer from, accept, use}, based on the first entity set E and the first relationship set R, construct an initial knowledge graph, denoted as G = {FE, FR}.

[0010] The image analysis technology is used to extract the lesion area L in the B-ultrasound image, and the position and size of the lesion are recorded. The lesion area refers to the abnormal area of ​​the tissue and organ displayed on the B-ultrasound image; the second entity set and the second relationship set between the patient and the B-ultrasound image are constructed, which are respectively recorded as the second entity set SE = {patient HZ a , lesion location, lesion size} and a second relationship set SR = {belongs to, is located at, measurement value}, where the belonging indicates that the lesion belongs to the patient HZ a , the located indicates that the lesion is located at the lesion position in the B-ultrasound image, and the measured value indicates the lesion size measured by the lesion.

[0011] As a preferred solution of the method for auxiliary supervision of therapeutic efficacy based on a multimodal driven knowledge graph described in the present invention, the second entity set and the second relationship set are added to the initial knowledge graph G = {FE, FR} to form a final patient personalized knowledge graph, denoted as HG = {FE∪SE, FR∪SR}; a time series is constructed, denoted as SJ = {t|t∈[1,T]}, where t represents the tth time point and T represents the total number of time points, and a time series period label is attached to the lesion L, denoted as periodic lesion L t .

[0012] Calculate patient HZ a The predictive value of the efficacy P(HZ a ,S i ,T b ,D c ), the calculation formula is as follows:

[0013] P(HZ a ,S i ,T b ,D c )=α1·f(S i )+α2·f(Tb )+α3·f(D c );

[0014]

[0015] f(D c ) = d c ·p c ·e c ;

[0016] Among them, f(S i ) represents the characteristic function of the disease symptoms, f(T b ) represents the characteristic function of the treatment plan, f(D c ) represents the drug characteristic function, α1, α2 and α3 represent the weights of the preset symptoms, treatment plans and drugs respectively, and s i Indicates symptoms of illness i The severity of the disease is determined by the doctor's rating criteria. i Indicates the duration of symptoms, f i Indicates the frequency of symptoms, I b Indicates the intensity of the treatment plan, E b Indicates the suitability of the treatment method, which is scored according to the matching degree between the treatment plan and the patient's indication. b Indicates the duration of the treatment course, d c Indicates drug D c The dose, p c represents the frequency of drug use, e c Represents the efficacy score of a drug based on clinical data.

[0017] In the present invention, the formula is based on the weighted sum of the patient's symptoms, treatment plans and drugs; by quantifying the severity, duration, frequency of the symptoms, the intensity, adaptability and course of the treatment plan, the dosage, frequency and clinical effect of the drug, a comprehensive efficacy prediction value is obtained; the core function of the formula is to quantify the impact of the patient's current symptoms, treatment plan and drugs on the treatment effect; by converting the text information in the electronic medical record (such as symptoms, treatment plan, drug information) into quantifiable features, and then combining the corresponding weights, the efficacy prediction value of each patient is calculated; this value can be used as the basis for evaluating the effectiveness of the treatment plan and provide support for subsequent efficacy evaluation and optimization.

[0018] Calculate the periodic lesion L at time point t t The lesion change rate ΔL t , the calculation formula is as follows:

[0019]

[0020] Among them, ΔLt Indicates periodic lesion L t The lesion change rate, L1 represents the initial lesion size before treatment, and t≠1.

[0021] In the present invention, the formula is used to measure the degree of shrinkage of the lesion during the treatment process, reflecting the effect of the treatment. The ratio of the difference between the initial lesion size and the current lesion size to the initial lesion size represents the rate of change of the lesion; its main function is to dynamically track the changes in the patient's lesions and quantify the degree of shrinkage or deterioration of the lesions during the treatment process; by combining the lesion size in the time series data, the doctor can intuitively see the changes in the lesions before and after the treatment; this indicator helps to evaluate the actual performance of the treatment effect, especially when combined with imaging data analysis, it plays a role in dynamically monitoring the progression of the disease.

[0022] Based on the efficacy prediction value P(HZ a ,S i ,T b ,D c ) and the lesion change rate ΔL t , calculate the patient's HZ a The comprehensive efficacy evaluation value R(HZ a ,S i ,T b ,D c ,t), the calculation formula is as follows:

[0023] R(HZ a ,S i ,T b ,D c ,t)=β·P(HZ a ,S i ,T b ,D c )+γ·ΔL t ;

[0024] Among them, β and γ represent preset parameters, which are adjusted according to different conditions, symptoms and treatment stages.

[0025] In the present invention, the comprehensive efficacy evaluation value combines the efficacy prediction value with the lesion change rate to form an evaluation index for the overall efficacy; it integrates data from different sources (electronic medical record data and imaging data) to generate a more comprehensive efficacy evaluation; this index can reflect the patient's overall health status and provide doctors with efficacy analysis results based on multiple dimensions to support medical decision-making.

[0026] As a preferred solution of the multi-modal driven knowledge graph therapeutic effect auxiliary supervision method described in the present invention, based on the comprehensive therapeutic effect evaluation value R(HZ a ,S i ,T b,D c ,t), construct the efficacy evaluation set PG={[S i ,T b ,D c ,R(HZ a ,S i ,T b ,D c ,t)]}, update the efficacy evaluation set PG to the patient personalized knowledge graph HG, the updating formula is: TG=HG∪PG, where TG represents the updated patient personalized knowledge graph.

[0027] Based on the updated patient personalized knowledge graph TG, the patient HZ is calculated by default a With patient HZ o The similarity between them is denoted as sim(HZ a ,HZ o ), where HZ o represents the oth patient, and o≠a; based on patient HZ a With patient HZ o The similarity between sim(HZ a ,HZ o ), calculate the patient's HZ a With patient HZ o The optimized efficacy prediction value is calculated as follows:

[0028]

[0029] Among them, R(HZ o ,S i ,T b ,D c ,t) represents the patient HZ o The comprehensive efficacy evaluation value, R opt (HZ a ,S i ) indicates patient HZ a With patient HZ o The optimized efficacy prediction value.

[0030] In the present invention, the formula is calculated through the similarity of therapeutic effects between different patients and the comprehensive therapeutic effect evaluation value to provide patients with more personalized therapeutic effect predictions; through similarity analysis of cross-patient data, the accuracy of therapeutic effect predictions is improved to provide patients with more personalized treatment evaluations; by drawing on the treatment effects of other similar patients, this formula can provide a more valuable reference analysis for the efficacy prediction of current patients.

[0031] Based on the patient's HZ a With patient HZ o The optimized efficacy prediction value R opt(HZ a ,S i ), calculate the optimal treatment plan, the calculation formula is as follows:

[0032] B(T b ,D c ) = argmax Tb,Dc [R opt (HZ a ,S i )];

[0033] Among them, B(T b ,D c ) represents the optimal treatment plan.

[0034] If the patient HZ a With patient HZ o The optimized efficacy prediction value R opt (HZ a ,S i ) is greater than the patient's HZ a The comprehensive efficacy evaluation value R(HZ a ,S i ,T b ,D c ,t), then it is the patient HZ a Recommend the best treatment plan B(T b ,D c ).

[0035] In the present invention, the formula compares the optimized efficacy prediction values ​​of multiple treatment plans and selects the plan that can maximize the efficacy; based on the optimized efficacy prediction value, the optimal treatment plan is automatically recommended, which can help doctors choose the best treatment plan from multiple treatment plans and improve the scientificity and accuracy of medical decision-making; with the help of this formula, doctors can develop personalized treatment plans for patients, thereby improving the treatment effect.

[0036] A multimodal driven knowledge graph therapeutic effect auxiliary supervision system, the system includes: a patient data management module, a knowledge graph construction module, a therapeutic effect evaluation module and a personalized therapeutic effect optimization module.

[0037] The patient data management module: establishes a patient database, constructs a patient's electronic medical record data set and a B-ultrasound image data set; and extracts entities and relationships between patients and electronic medical records.

[0038] The knowledge graph construction module: constructs a first entity set and a first relationship set between the patient and the electronic medical record to construct an initial knowledge graph; uses image analysis technology to extract the lesion area in the B-ultrasound image to construct a second entity set and a second relationship set between the patient and the B-ultrasound image.

[0039] The efficacy evaluation module: adds the second entity set and the second relationship set to the initial knowledge graph to form a patient personalized knowledge graph; attaches a time series period label to the lesion, recording it as a periodic lesion; calculates the patient's efficacy prediction value; calculates the lesion change rate of the periodic lesion; and calculates the patient's comprehensive efficacy evaluation value based on the efficacy prediction value and the lesion change rate.

[0040] The personalized efficacy optimization module: constructs an efficacy evaluation set based on the comprehensive efficacy evaluation value, and updates it to the patient's personalized knowledge graph; calculates the optimized efficacy prediction value between different patients; and calculates the optimal treatment plan based on the optimized efficacy prediction value.

[0041] Furthermore, the patient data management module includes a database construction unit and an entity and relationship extraction unit.

[0042] The database construction unit: establishes a patient database, denoted as HZ = {HZ a |a∈[1,A]}, where HZ a represents the ath patient, A represents the total number of patients; obtain the patient HZ a The electronic medical record data and B-ultrasound image data are used to construct the electronic medical record data set DB = {DB a |a∈[1,A]} and B-ultrasound image dataset BC={BC a |a∈[1,A]}, where DB a represents the electronic medical record data of the ath patient, BC a represents the B-ultrasound image data of the ath patient; the electronic medical record data includes patient HZ a symptoms, treatment options and medications.

[0043] The entity and relationship extraction unit: based on the electronic medical record data set and the patient database, extracts the entities and relationships between the patient and the electronic medical records, the entities include the patient HZ a 、Symptoms i Treatment plan b and drug D c , where S i represents the ith symptom, T b represents the bth treatment plan, D c represents the cth drug; the relationship includes suffering from, receiving and using; the suffering from represents the patient HZ a Symptoms i The relationship between the patient and the patient, the acceptance indicates that the patient HZ a Treatment plan T b The acceptance relationship between the patient HZ a With drug D c The usage relationship between them.

[0044] Furthermore, the knowledge graph construction module includes a first entity set and a first relationship set construction unit and a second entity set and a second relationship set construction unit.

[0045] The first entity set and the first relationship set construction unit: constructs the first entity set and the first relationship set between the patient and the electronic medical record, respectively denoted as the first entity set FE = {patient HZ a 、Symptoms i Treatment plan b , Drug D c} and the first relationship set FR = {suffer from, accept, use}, based on the first entity set E and the first relationship set R, construct an initial knowledge graph, denoted as G = {FE, FR}.

[0046] The second entity set and the second relationship set construction unit: use image analysis technology to extract the lesion area L in the B-ultrasound image, and record the position and size of the lesion, wherein the lesion area refers to the abnormal area of ​​the tissue and organ displayed on the B-ultrasound image; construct the second entity set and the second relationship set between the patient and the B-ultrasound image, respectively recorded as the second entity set SE = {patient HZ a , lesion location, lesion size} and a second relationship set SR = {belongs to, is located at, measurement value}, where the belonging indicates that the lesion belongs to the patient HZ a , the located indicates that the lesion is located at the lesion position in the B-ultrasound image, and the measured value indicates the lesion size measured by the lesion.

[0047] Furthermore, the efficacy evaluation module includes an efficacy prediction and lesion change rate calculation unit and a comprehensive efficacy evaluation value calculation unit.

[0048] The efficacy prediction and lesion change rate calculation unit: adds the second entity set and the second relationship set to the initial knowledge graph G = {FE, FR} to form the final patient personalized knowledge graph, denoted as HG = {FE∪SE, FR∪SR}; constructs a time series, denoted as SJ = {t|t∈[1,T]}, where t represents the tth time point and T represents the total number of time points, and adds a time series period label to the lesion L, denoted as periodic lesion L t ; Calculate the patient's HZ a The predictive value of the efficacy P(HZ a ,S i ,T b ,D c ).

[0049] The comprehensive efficacy evaluation value calculation unit calculates the periodic lesion L at time point t. t The lesion change rate ΔL t Based on the predicted value of efficacy P(HZa ,S i ,T b ,D c ) and the lesion change rate ΔL t , calculate the patient's HZ a The comprehensive efficacy evaluation value R(HZ a ,S i ,T b ,D c ,t).

[0050] Furthermore, the personalized efficacy optimization module includes a knowledge graph updating unit and an optimal treatment plan recommendation unit.

[0051] The knowledge graph updating unit: based on the comprehensive efficacy evaluation value R(HZ a ,S i ,T b ,D c ,t), construct the efficacy evaluation set PG={[S i ,T b ,D c ,R(HZ a ,S i ,T b ,D c ,t)]}, update the efficacy evaluation set PG to the patient personalized knowledge graph HG, the update formula is: TG = HG ∪ PG, where TG represents the updated patient personalized knowledge graph; based on the updated patient personalized knowledge graph TG, preset calculation of patient HZ a With patient HZ o The similarity between them is denoted as sim(HZ a ,HZ o ), where HZ o represents the oth patient, and o≠a; based on patient HZ a With patient HZ o The similarity between sim(HZ a ,HZ o ), calculate the patient's HZ a With patient HZ o The optimized efficacy prediction value R opt (HZ a ,S i ).

[0052] The optimal treatment plan recommendation unit: based on the patient's HZ a With patient HZ o The optimized efficacy prediction value R opt (HZ a ,S i ), calculate the optimal treatment plan B(T b,D c ); if the patient HZ a With patient HZ o The optimized efficacy prediction value R opt (HZ a ,S i ) is greater than the patient's HZ a The comprehensive efficacy evaluation value R(HZ a ,S i ,T b ,D c ,t), then it is the patient HZ a Recommend the best treatment plan B(T b ,D c ).

[0053] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: in a multimodally driven knowledge graph-based therapeutic efficacy auxiliary supervision system and method provided by the present invention, a personalized knowledge graph is constructed by integrating electronic medical records, B-ultrasound images and time series data, thereby achieving a comprehensive evaluation of patient efficacy; first, the patient's multimodal data is collected and structured to lay the foundation for subsequent analysis; secondly, image analysis technology is used to extract lesion information, and a dynamic association between the disease condition and the treatment plan is constructed through the knowledge graph, and lesion changes and efficacy predictions are quantified; finally, the treatment plan is optimized through the similarity analysis of efficacy between patients, and the optimal treatment plan is recommended to the patient, thereby improving the accuracy of medical decision-making and the scientific nature of the treatment effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0055] Figure 1 It is a schematic diagram of the steps of a method for auxiliary supervision of therapeutic effect based on a knowledge graph driven by multimodality in the present invention;

[0056] Figure 2 It is a structural schematic diagram of a multi-modal driven knowledge graph therapeutic effect auxiliary supervision system of the present invention. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] See also Figure 1In the first embodiment of the present invention, a method for auxiliary supervision of therapeutic effect based on a knowledge graph driven by multimodality is provided, and the method comprises the following steps:

[0059] Step S1: Establish a patient database, construct the patient's electronic medical record dataset and B-ultrasound image dataset; extract the entities and relationships between the patient and the electronic medical record.

[0060] Specifically, a patient database is established, denoted as HZ = {HZ a |a∈[1,A]}, where HZ a represents the ath patient, A represents the total number of patients; obtain the patient HZ a The electronic medical record data and B-ultrasound image data are used to construct the electronic medical record data set DB = {DB a |a∈[1,A]} and B-ultrasound image dataset BC={BC a |a∈[1,A]}, where DB a represents the electronic medical record data of the ath patient, BC a represents the B-ultrasound image data of the ath patient; the electronic medical record data includes patient HZ a symptoms, treatment options and medications.

[0061] Furthermore, based on the electronic medical record data set and the patient database, entities and relationships between patients and electronic medical records are extracted, wherein the entities include patient HZ a 、Symptoms i Treatment plan b and drug D c , where S i represents the ith symptom, T b represents the bth treatment plan, D c represents the cth drug; the relationship includes suffering from, receiving and using; the suffering from represents the patient HZ a Symptoms i The relationship between the patient and the patient, the acceptance indicates that the patient HZ a Treatment plan T b The acceptance relationship between the patient HZ a With drug D c The usage relationship between

[0062] Step S2: construct a first entity set and a first relationship set between the patient and the electronic medical record, and construct an initial knowledge graph; use image analysis technology to extract the lesion area in the B-ultrasound image, and construct a second entity set and a second relationship set between the patient and the B-ultrasound image.

[0063] Specifically, the first entity set and the first relationship set between the patient and the electronic medical record are constructed, which are respectively recorded as the first entity set FE = {patient HZa 、Symptoms i Treatment plan b , Drug D c} and the first relationship set FR = {suffer from, accept, use}, based on the first entity set E and the first relationship set R, construct an initial knowledge graph, denoted as G = {FE, FR}.

[0064] Furthermore, the image analysis technology is used to extract the lesion area L in the B-ultrasound image, and the position and size of the lesion are recorded. The lesion area refers to the abnormal area of ​​the tissue and organ displayed on the B-ultrasound image; a second entity set and a second relationship set between the patient and the B-ultrasound image are constructed, which are respectively recorded as the second entity set SE = {patient HZ a , lesion location, lesion size} and a second relationship set SR = {belongs to, is located at, measurement value}, where the belonging indicates that the lesion belongs to the patient HZ a , the located indicates that the lesion is located at the lesion position in the B-ultrasound image, and the measured value indicates the lesion size measured by the lesion.

[0065] Step S3: Add the second entity set and the second relationship set to the initial knowledge graph to form a personalized knowledge graph for the patient; attach a time series period label to the lesion and record it as a periodic lesion; calculate the patient's efficacy prediction value; calculate the lesion change rate of the periodic lesion; and calculate the patient's comprehensive efficacy evaluation value based on the efficacy prediction value and the lesion change rate.

[0066] Specifically, the second entity set and the second relationship set are added to the initial knowledge graph G = {FE, FR} to form the final patient personalized knowledge graph, denoted as HG = {FE∪SE, FR∪SR}; a time series is constructed, denoted as SJ = {t|t∈[1,T]}, where t represents the tth time point and T represents the total number of time points, and a time series period label is added to the lesion L, denoted as periodic lesion L t .

[0067] Furthermore, the patient's HZ is calculated a The predictive value of the efficacy P(HZ a ,S i ,T b ,D c ), the calculation formula is as follows:

[0068] P(HZ a ,S i ,T b ,D c )=α1·f(S i )+α2·f(T b )+α3·f(D c );

[0069]

[0070] f(D c ) = d c ·p c ·e c ;

[0071] Among them, f(S i ) represents the characteristic function of the disease symptoms, f(T b ) represents the characteristic function of the treatment plan, f(D c ) represents the drug characteristic function, α1, α2 and α3 represent the weights of the preset symptoms, treatment plans and drugs respectively, and s i Indicates symptoms of illness i The severity of the disease is determined by the doctor's rating criteria. i Indicates the duration of symptoms, f i Indicates the frequency of symptoms, I b Indicates the intensity of the treatment plan, E b Indicates the suitability of the treatment method, which is scored according to the matching degree between the treatment plan and the patient's indication. b Indicates the duration of the treatment course, d c Indicates drug D c The dose, p c represents the frequency of drug use, e c Represents the efficacy score of a drug based on clinical data.

[0072] For example, assuming that the severity s1 is 3, the duration of symptoms d1 is 2, the frequency f1 is 4, the intensity of the treatment plan I1 is 1.5, the adaptability E1 is 0.9, the duration of the treatment course D1 is 10, the dosage d1 is 2, the frequency of drug use p1 is 3, the efficacy score e1 is 0.8, α1, α2 and α3 are 0.4, 0.3 and 0.3 respectively, substitute them into the formula to calculate f(D1)=2*3*0.8=4.8, the predicted value of the efficacy of the patient HZ1 is P(HZ1,S i ,T b ,D c )=0.4*1.2+0.3*0.14+0.3*4.8=0.48+0.042+1.44=1.962.

[0073] Calculate the periodic lesion L at time point t t The lesion change rate ΔL t , the calculation formula is as follows:

[0074]

[0075] Among them, ΔLt Indicates periodic lesion L t The lesion change rate, L1 represents the initial lesion size before treatment, and t≠1.

[0076] For example, suppose the initial lesion size L1 before treatment is 10, and the period lesion L t is 6, and the periodic lesion L is calculated by substituting it into the formula t The lesion change rate ΔL t is 0.4.

[0077] Furthermore, based on the efficacy prediction value P(HZ a ,S i ,T b ,D c ) and the lesion change rate ΔL t , calculate the patient's HZ a The comprehensive efficacy evaluation value R(HZ a ,S i ,T b ,D c ,t), the calculation formula is as follows:

[0078] R(HZ a ,S i ,T b ,D c ,t)=β·P(HZ a ,S i ,T b ,D c )+γ·ΔL t ;

[0079] Among them, β and γ represent preset parameters, which are adjusted according to different conditions, symptoms and treatment stages.

[0080] For example, assuming that β and γ are 0.7 and 0.3 respectively, the comprehensive efficacy evaluation value R(HZ1,S i ,T b ,D c ,t)=0.7*1.962+0.3*0.4=1.37+0.12=1.49.

[0081] Step S4: Based on the comprehensive efficacy evaluation value, construct an efficacy evaluation set and update it to the patient's personalized knowledge graph; calculate the optimized efficacy prediction value between different patients; based on the optimized efficacy prediction value, calculate the optimal treatment plan.

[0082] Specifically, based on the comprehensive efficacy evaluation value R(HZ a ,S i ,T b ,D c,t), construct the efficacy evaluation set PG={[S i ,T b ,D c ,R(HZ a ,S i ,T b ,D c ,t)]}, update the efficacy evaluation set PG to the patient personalized knowledge graph HG, the updating formula is: TG=HG∪PG, where TG represents the updated patient personalized knowledge graph.

[0083] Furthermore, based on the updated patient personalized knowledge graph TG, by presetting the patient HZ a With patient HZ o The similarity between them is denoted as sim(HZ a ,HZ o ), where HZ o represents the oth patient, and o≠a; based on patient HZ a With patient HZ o The similarity between sim(HZ a ,HZ o ), calculate the patient's HZ a With patient HZ o The optimized efficacy prediction value is calculated as follows:

[0084] R opt (HZ a ,S i )=sim(HZ a ,HZ o )·R(HZ o ,S i ,T b ,D c ,t);

[0085] Among them, R(HZ o ,S i ,T b ,D c ,t) represents the patient HZ o The comprehensive efficacy evaluation value, R opt (HZ a ,S i ) indicates patient HZ a With patient HZ o The optimized efficacy prediction value.

[0086] For example, suppose the similarity sim(HZ1,HZ2) between patients HZ1 and HZ2 is 1.5, R(HZ2,S i ,T b ,D c,t) is 1.6, and the optimized efficacy prediction value R is calculated by substituting it into the formula opt (HZ a ,S i )=1.5*1.6=2.4.

[0087] Furthermore, based on the patient's HZ a With patient HZ o The optimized efficacy prediction value R opt (HZ a ,S i ), calculate the optimal treatment plan, the calculation formula is as follows:

[0088]

[0089] Among them, B(T b ,D c ) represents the optimal treatment plan.

[0090] If the patient HZ a With patient HZ o The optimized efficacy prediction value R opt (HZ a ,S i ) is greater than the patient's HZ a The comprehensive efficacy evaluation value R(HZ a ,S i ,T b ,D c ,t), then it is the patient HZ a Recommend the best treatment plan B(T b ,D c ).

[0091] See also Figure 2 In the second embodiment of the present invention, a knowledge graph-based multimodal driven therapeutic efficacy auxiliary supervision system is provided, the system comprising: a patient data management module, a knowledge graph construction module, a therapeutic efficacy evaluation module and a personalized therapeutic efficacy optimization module.

[0092] The patient data management module: establishes a patient database, constructs a patient's electronic medical record data set and a B-ultrasound image data set; and extracts entities and relationships between patients and electronic medical records.

[0093] The knowledge graph construction module: constructs a first entity set and a first relationship set between the patient and the electronic medical record to construct an initial knowledge graph; uses image analysis technology to extract the lesion area in the B-ultrasound image to construct a second entity set and a second relationship set between the patient and the B-ultrasound image.

[0094] The efficacy evaluation module: adds the second entity set and the second relationship set to the initial knowledge graph to form a patient personalized knowledge graph; attaches a time series period label to the lesion, recording it as a periodic lesion; calculates the patient's efficacy prediction value; calculates the lesion change rate of the periodic lesion; and calculates the patient's comprehensive efficacy evaluation value based on the efficacy prediction value and the lesion change rate.

[0095] The personalized efficacy optimization module: constructs an efficacy evaluation set based on the comprehensive efficacy evaluation value, and updates it to the patient's personalized knowledge graph; calculates the optimized efficacy prediction value between different patients; and calculates the optimal treatment plan based on the optimized efficacy prediction value.

[0096] Furthermore, the patient data management module includes a database construction unit and an entity and relationship extraction unit.

[0097] The database construction unit: establishes a patient database, denoted as HZ = {HZ a |a∈[1,A]}, where HZ a represents the ath patient, A represents the total number of patients; obtain the patient HZ a The electronic medical record data and B-ultrasound image data are used to construct the electronic medical record data set DB = {DB a |a∈[1,A]} and B-ultrasound image dataset BC={BC a |a∈[1,A]}, where DB a represents the electronic medical record data of the ath patient, BC a represents the B-ultrasound image data of the ath patient; the electronic medical record data includes patient HZ a symptoms, treatment options and medications.

[0098] The entity and relationship extraction unit: based on the electronic medical record data set and the patient database, extracts the entities and relationships between the patient and the electronic medical records, the entities include the patient HZ a 、Symptoms i Treatment plan b and drug D c , where S i represents the ith symptom, T b represents the bth treatment plan, D c represents the cth drug; the relationship includes suffering from, receiving and using; the suffering from represents the patient HZ a Symptoms i The relationship between the patient and the patient, the acceptance indicates that the patient HZ a Treatment plan T b The acceptance relationship between the patient HZ a With drug D c The usage relationship between them.

[0099] Furthermore, the knowledge graph construction module includes a first entity set and a first relationship set construction unit and a second entity set and a second relationship set construction unit.

[0100] The first entity set and the first relationship set construction unit: constructs the first entity set and the first relationship set between the patient and the electronic medical record, respectively denoted as the first entity set FE = {patient HZ a 、Symptoms i Treatment plan b , Drug D c} and the first relationship set FR = {suffer from, accept, use}, based on the first entity set E and the first relationship set R, construct an initial knowledge graph, denoted as G = {FE, FR}.

[0101] The second entity set and the second relationship set construction unit: use image analysis technology to extract the lesion area L in the B-ultrasound image, and record the position and size of the lesion, wherein the lesion area refers to the abnormal area of ​​the tissue and organ displayed on the B-ultrasound image; construct the second entity set and the second relationship set between the patient and the B-ultrasound image, respectively recorded as the second entity set SE = {patient HZ a , lesion location, lesion size} and a second relationship set SR = {belongs to, is located at, measurement value}, where the belonging indicates that the lesion belongs to the patient HZ a , the located indicates that the lesion is located at the lesion position in the B-ultrasound image, and the measured value indicates the lesion size measured by the lesion.

[0102] Furthermore, the efficacy evaluation module includes an efficacy prediction and lesion change rate calculation unit and a comprehensive efficacy evaluation value calculation unit.

[0103] The efficacy prediction and lesion change rate calculation unit: adds the second entity set and the second relationship set to the initial knowledge graph G = {FE, FR} to form the final patient personalized knowledge graph, denoted as HG = {FE∪SE, FR∪SR}; constructs a time series, denoted as SJ = {t|t∈[1,T]}, where t represents the tth time point and T represents the total number of time points, and adds a time series period label to the lesion L, denoted as periodic lesion L t ; Calculate the patient's HZ a The predictive value of the efficacy P(HZ a ,S i ,T b ,D c ).

[0104] The comprehensive efficacy evaluation value calculation unit calculates the periodic lesion L at time point t. t The lesion change rate ΔL t Based on the predicted value of efficacy P(HZa ,S i ,T b ,D c ) and the lesion change rate ΔL t , calculate the patient's HZ a The comprehensive efficacy evaluation value R(HZ a ,S i ,T b ,D c ,t).

[0105] Furthermore, the personalized efficacy optimization module includes a knowledge graph updating unit and an optimal treatment plan recommendation unit.

[0106] The knowledge graph updating unit: based on the comprehensive efficacy evaluation value R(HZ a ,S i ,T b ,D c ,t), construct the efficacy evaluation set PG={[S i ,T b ,D c ,R(HZ a ,S i ,T b ,D c ,t)]}, update the efficacy evaluation set PG to the patient personalized knowledge graph HG, the update formula is: TG = HG ∪ PG, where TG represents the updated patient personalized knowledge graph; based on the updated patient personalized knowledge graph TG, preset patient HZ a With patient HZ o The similarity between them is denoted as sim(HZ a ,HZ o ), where HZ o represents the oth patient, and o≠a; based on patient HZ a With patient HZ o The similarity between sim(HZ a ,HZ o ), calculate the patient's HZ a With patient HZ o The optimized efficacy prediction value R opt (HZ a ,S i ).

[0107] The optimal treatment plan recommendation unit: based on the patient's HZ a With patient HZ o The optimized efficacy prediction value R opt (HZ a ,S i ), calculate the optimal treatment plan B(T b,D c ); if the patient HZ a With patient HZ o The optimized efficacy prediction value R opt (HZ a ,S i ) is greater than the patient's HZ a The comprehensive efficacy evaluation value R(HZ a ,S i ,T b ,D c ,t), then it is the patient HZ a Recommend the best treatment plan B(T b ,D c ).

[0108] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0109] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A multi-modal driven knowledge graph therapeutic effect auxiliary supervision method, characterized in that: The method comprises the following steps: Step S1: Establish a patient database, construct the patient's electronic medical record data set and B-ultrasound image data set; extract the entities and relationships between the patient and the electronic medical record; Step S2: construct a first entity set and a first relationship set between the patient and the electronic medical record, and construct an initial knowledge graph; use image analysis technology to extract the lesion area in the B-ultrasound image, and construct a second entity set and a second relationship set between the patient and the B-ultrasound image; Step S3: adding the second entity set and the second relationship set to the initial knowledge graph to form a personalized knowledge graph for the patient; attaching a time series period label to the lesion, recording it as a periodic lesion; calculating the patient's efficacy prediction value; calculating the lesion change rate of the periodic lesion; and calculating the patient's comprehensive efficacy evaluation value based on the efficacy prediction value and the lesion change rate; Step S4: Based on the comprehensive efficacy evaluation value, construct an efficacy evaluation set and update it to the patient's personalized knowledge graph; calculate the optimized efficacy prediction value between different patients; based on the optimized efficacy prediction value, calculate the optimal treatment plan.

2. According to claim 1, a multi-modal driven knowledge graph therapeutic effect auxiliary supervision method is characterized in that: The specific implementation process of step S1 includes the following steps: Establish a patient database, denoted as HZ = {HZ a |a∈[1,A]}, where HZ a represents the ath patient, A represents the total number of patients; obtain the patient HZ a The electronic medical record data and B-ultrasound image data are used to construct the electronic medical record data set DB = {DB a |a∈[1,A]} and B-ultrasound image dataset BC={BC a |a∈[1,A]}, where DB a represents the electronic medical record data of the ath patient, BC a represents the B-ultrasound image data of the ath patient; the electronic medical record data includes patient HZ a symptoms, treatment options and medications; Based on the electronic medical record dataset and the patient database, entities and relationships between patients and electronic medical records are extracted, and the entities include patient HZ a 、Symptoms i Treatment plan b and drug D c , where S i represents the ith symptom, T b represents the bth treatment plan, D c represents the cth drug; the relationship includes suffering from, receiving and using; the suffering from represents the patient HZ a Symptoms i The relationship between the patient and the patient, the acceptance indicates that the patient HZ a Treatment plan T b The acceptance relationship between the patient HZ a With drug D c The usage relationship between them.

3. According to claim 2, a multi-modal driven knowledge graph therapeutic effect auxiliary supervision method is characterized in that: The specific implementation process of step S2 includes the following steps: Construct the first entity set and the first relationship set between the patient and the electronic medical record, respectively denoted as the first entity set FE = {patient HZ a 、Symptoms i Treatment plan b , Drug D c } and the first relationship set FR = {suffering from, accepting, using}, based on the first entity set E and the first relationship set R, construct an initial knowledge graph, denoted as G = {FE, FR}; The image analysis technology is used to extract the lesion area L in the B-ultrasound image, and the position and size of the lesion are recorded. The lesion area refers to the abnormal area of ​​the tissue and organ displayed on the B-ultrasound image; the second entity set and the second relationship set between the patient and the B-ultrasound image are constructed, which are respectively recorded as the second entity set SE = {patient HZ a , lesion location, lesion size} and a second relationship set SR = {belongs to, is located at, measurement value}, where the belonging indicates that the lesion belongs to the patient HZ a , the located indicates that the lesion is located at the lesion position in the B-ultrasound image, and the measured value indicates the lesion size measured by the lesion.

4. According to claim 3, a multi-modal driven knowledge graph therapeutic effect auxiliary supervision method is characterized in that: The specific implementation process of step S3 includes the following steps: The second entity set and the second relationship set are added to the initial knowledge graph G = {FE, FR} to form the final patient personalized knowledge graph, denoted as HG = {FE∪SE, FR∪SR}; a time series is constructed, denoted as SJ = {t|t∈[1,T]}, where t represents the tth time point and T represents the total number of time points. A time series period label is added to the lesion L, denoted as periodic lesion L t ; Calculate patient HZ a The predictive value of the efficacy P(HZ a ,S i ,T b ,D c ), the calculation formula is as follows: P(HZ a ,S i ,T b ,D c )=α1·f(S i )+α2·f(T b )+α3·f(D c ); f(D c )=d c ·p c ·e c ; Among them, f(S i ) represents the characteristic function of the disease symptoms, f(T b ) represents the characteristic function of the treatment plan, f(D c ) represents the drug characteristic function, α1, α2 and α3 represent the weights of the preset symptoms, treatment plans and drugs respectively, and s i Indicates symptoms of illness i The severity of i Indicates the duration of symptoms, f i Indicates the frequency of symptoms, I b Indicates the intensity of the treatment plan, E b Indicates the suitability of the treatment method, D b Indicates the duration of the treatment course, d c Indicates drug D c The dose, p c represents the frequency of drug use, e c It represents the efficacy score of a drug based on clinical data; Calculate the periodic lesion L at time point t t The lesion change rate ΔL t , the calculation formula is as follows: Where, ΔL t Indicates periodic lesion L t The lesion change rate, L1 represents the initial lesion size before treatment, and t≠1; Based on the efficacy prediction value P(HZ a ,S i ,T b ,D c ) and the lesion change rate ΔL t , calculate the patient's HZ a The comprehensive efficacy evaluation value R(HZ a ,S i ,T b ,D c ,t), the calculation formula is as follows: R(HZ a ,S i ,T b ,D c ,t)=β·P(HZ a ,S i ,T b ,D c )+γ·ΔL t ; Wherein, β and γ represent preset parameters.

5. According to claim 4, a multi-modal driven knowledge graph therapeutic effect auxiliary supervision method is characterized in that: The specific implementation process of step S4 includes the following steps: Based on the comprehensive efficacy evaluation value R(HZ a ,S i ,T b ,D c ,t), construct the efficacy evaluation set PG={[S i ,T b ,D c ,R(HZ a ,S i ,T b ,D c ,t)]}, update the efficacy evaluation set PG to the patient personalized knowledge graph HG, the update formula is: TG = HG ∪ PG, where TG represents the updated patient personalized knowledge graph; Based on the updated patient personalized knowledge graph TG, the patient HZ is preset a With patient HZ o The similarity between them is denoted as sim(HZ a ,HZ o ), where HZ o represents the oth patient, and o≠a; based on patient HZ a With patient HZ o The similarity between sim(HZ a ,HZ o ), calculate the patient's HZ a With patient HZ o The optimized efficacy prediction value is calculated as follows: R opt (HZ a ,S i )=sim(HZ a ,HZ o )·R(HZ o ,S i ,T b ,D c ,t); Among them, R(HZ o ,S i ,T b ,D c ,t) represents the patient HZ o The comprehensive efficacy evaluation value, R opt (HZ a ,S i ) indicates patient HZ a With patient HZ o The optimized efficacy prediction value of Based on the patient's HZ a With patient HZ o The optimized efficacy prediction value R opt (HZ a ,S i ), calculate the optimal treatment plan, the calculation formula is as follows: Among them, B(T b ,D c ) represents the optimal treatment plan; If the patient HZ a With patient HZ o The optimized efficacy prediction value R opt (HZ a ,S i ) is greater than the patient's HZ a The comprehensive efficacy evaluation value R(HZ a ,S i ,T b ,D c ,t), then it is the patient HZ a Recommend the best treatment plan B(T b ,D c ).

6. A multi-modal driven knowledge graph therapeutic effect auxiliary supervision system, characterized in that: The system includes: a patient data management module, a knowledge graph construction module, an efficacy evaluation module and a personalized efficacy optimization module; The patient data management module: establishes a patient database, constructs a patient electronic medical record data set and a B-ultrasound image data set; extracts entities and relationships between patients and electronic medical records; The knowledge graph construction module: constructs a first entity set and a first relationship set between the patient and the electronic medical record, and constructs an initial knowledge graph; uses image analysis technology to extract the lesion area in the B-ultrasound image, and constructs a second entity set and a second relationship set between the patient and the B-ultrasound image; The efficacy evaluation module: adds the second entity set and the second relationship set to the initial knowledge graph to form a patient personalized knowledge graph; adds a time series period label to the lesion, recording it as a periodic lesion; calculates the patient's efficacy prediction value; calculates the lesion change rate of the periodic lesion; and calculates the patient's comprehensive efficacy evaluation value based on the efficacy prediction value and the lesion change rate; The personalized efficacy optimization module: constructs an efficacy evaluation set based on the comprehensive efficacy evaluation value, and updates it to the patient's personalized knowledge graph; calculates the optimized efficacy prediction value between different patients; and calculates the optimal treatment plan based on the optimized efficacy prediction value.

7. The multi-modal driven knowledge graph therapeutic effect auxiliary monitoring system according to claim 6 is characterized by: The patient data management module includes a database construction unit and an entity and relationship extraction unit; The database construction unit: establishes a patient database, denoted as HZ = {HZ a |a∈[1,A]}, where HZ a represents the ath patient, A represents the total number of patients; obtain the patient HZ a The electronic medical record data and B-ultrasound image data are used to construct the electronic medical record data set DB = {DB a |a∈[1,A]} and B-ultrasound image dataset BC={BC a |a∈[1,A]}, where DB a represents the electronic medical record data of the ath patient, BC a represents the B-ultrasound image data of the ath patient; the electronic medical record data includes patient HZ a symptoms, treatment options and medications; The entity and relationship extraction unit: based on the electronic medical record data set and the patient database, extracts the entities and relationships between the patient and the electronic medical records, the entities include the patient HZ a 、Symptoms i Treatment plan b and drug D c , where S i represents the ith symptom, T b represents the bth treatment plan, D c represents the cth drug; the relationship includes suffering from, receiving and using; the suffering from represents the patient HZ a Symptoms i The relationship between the patient and the patient, the acceptance indicates that the patient HZ a Treatment plan T b The acceptance relationship between the patient HZ a With drug D c The usage relationship between them.

8. The multi-modal driven knowledge graph therapeutic effect auxiliary monitoring system according to claim 7 is characterized by: The knowledge graph construction module includes a first entity set and a first relationship set construction unit and a second entity set and a second relationship set construction unit; The first entity set and the first relationship set construction unit: constructs the first entity set and the first relationship set between the patient and the electronic medical record, respectively denoted as the first entity set FE = {patient HZ a 、Symptoms i Treatment plan b , Drug D c } and the first relationship set FR = {suffering from, accepting, using}, based on the first entity set E and the first relationship set R, construct an initial knowledge graph, denoted as G = {FE, FR}; The second entity set and the second relationship set construction unit: use image analysis technology to extract the lesion area L in the B-ultrasound image, and record the position and size of the lesion, wherein the lesion area refers to the abnormal area of ​​the tissue and organ displayed on the B-ultrasound image; construct the second entity set and the second relationship set between the patient and the B-ultrasound image, respectively recorded as the second entity set SE = {patient HZ a , lesion location, lesion size} and a second relationship set SR = {belongs to, is located at, measurement value}, where the belonging indicates that the lesion belongs to the patient HZ a , the located indicates that the lesion is located at the lesion position in the B-ultrasound image, and the measured value indicates the lesion size measured by the lesion.

9. The multi-modal driven knowledge graph therapeutic effect auxiliary monitoring system according to claim 8 is characterized by: The efficacy evaluation module includes an efficacy prediction and lesion change rate calculation unit and a comprehensive efficacy evaluation value calculation unit; The efficacy prediction and lesion change rate calculation unit: adds the second entity set and the second relationship set to the initial knowledge graph G = {FE, FR} to form the final patient personalized knowledge graph, denoted as HG = {FE∪SE, FR∪SR}; constructs a time series, denoted as SJ = {t|t∈[1,T]}, where t represents the tth time point and T represents the total number of time points, and adds a time series period label to the lesion L, denoted as periodic lesion L t ; Calculate the patient's HZ a The predictive value of the efficacy P(HZ a ,S i ,T b ,D c ); The comprehensive efficacy evaluation value calculation unit calculates the periodic lesion L at time point t. t The lesion change rate ΔL t Based on the predicted value of efficacy P(HZ a ,S i ,T b ,D c ) and the lesion change rate ΔL t , calculate the patient's HZ a The comprehensive efficacy evaluation value R(HZ a ,S i ,T b ,D c ,t).

10. The multi-modal driven knowledge graph therapeutic effect auxiliary monitoring system according to claim 9 is characterized in that: The personalized efficacy optimization module includes a knowledge graph updating unit and an optimal treatment plan recommendation unit; The knowledge graph updating unit: based on the comprehensive efficacy evaluation value R(HZ a ,S i ,T b ,D c ,t), construct the efficacy evaluation set PG={[S i ,T b ,D c ,R(HZ a ,S i ,T b ,D c ,t)]}, update the efficacy evaluation set PG to the patient personalized knowledge graph HG, the update formula is: TG = HG ∪ PG, where TG represents the updated patient personalized knowledge graph; based on the updated patient personalized knowledge graph TG, preset patient HZ a With patient HZ o The similarity between them is denoted as sim(HZ a ,HZ o ), where HZ o represents the oth patient, and o≠a; based on patient HZ a With patient HZ o The similarity between sim(HZ a ,HZ o ), calculate the patient's HZ a With patient HZ o The optimized efficacy prediction value R opt (HZ a ,S i ); The optimal treatment plan recommendation unit: based on the patient's HZ a With patient HZ o The optimized efficacy prediction value R opt (HZ a ,S i ), calculate the optimal treatment plan B(T b ,D c ); if the patient HZ a With patient HZ o The optimized efficacy prediction value R opt (HZ a ,S i ) is greater than the patient's HZ a The comprehensive efficacy evaluation value R(HZ a ,S i ,T b ,D c ,t), then it is the patient HZ a Recommend the best treatment plan B(T b ,D c ).

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