Electronic system for evaluating medication compliance of schizophrenia patient based on big data

Abstract

The invention discloses a schizophrenia patient medication compliance evaluation electronic system based on big data, and relates to the technical field of evaluation.The schizophrenia patient medication compliance evaluation electronic system comprises a data monitoring module, a data analysis module, a treatment adjustment module and a database, and is characterized in that the heart rate, blood pressure and body feedback of each patient after each time of medication in each treatment time period are detected; the method comprises the following steps: analyzing the medication compliance coefficient and the body feedback condition of each patient according to the treatment time of each patient, the blood concentration and the disease recovery score of each patient by a doctor, judging whether each patient needs to adjust the used medicine in each treatment time period, and after each treatment time period of each patient in a hospital is finished, judging whether the patient needs to adjust the used medicine. Whether each patient can go home for treatment or not is judged according to the medication compliance coefficient of each patient in each treatment time period, the illness state recovery score and the historical illness condition after each time of going home, the illness state stability of the patient after going home for treatment is guaranteed, and the accuracy of medication compliance coefficient analysis of the patient and the effectiveness of medication treatment are guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of evaluation technology, and in particular to an electronic system for evaluating medication compliance of schizophrenia patients based on big data. Background Art

[0002] In the field of mental illness treatment, schizophrenia is a serious mental disorder. Patients usually face challenges in cognition, emotion, and behavior. Drug therapy is the core means of managing the disease, but medication compliance of schizophrenia patients is an important problem faced in clinical treatment. Low medication compliance may lead to worsening symptoms, increased relapse and hospitalization rates, and even increase the risk of suicide.

[0003] The traditional electronic medication compliance assessment system for schizophrenia patients analyzes the patient's medication compliance coefficient based on the number of times the patient takes the medicine during the patient's hospitalization. At the same time, the doctor determines whether the patient can be discharged and go home for treatment based on the patient's recovery. Obviously, this electronic medication compliance assessment system for schizophrenia patients has at least the following deficiencies: 1. The traditional electronic medication compliance assessment system for schizophrenia patients only analyzes the patient's medication compliance coefficient based on the number of times the patient takes the medicine. When the patient takes the medicine, there is a lack of judgment on whether the patient takes the medicine as required, and the accuracy of the patient's medication compliance coefficient analysis cannot be guaranteed.

[0004] 2. The traditional electronic system for assessing medication compliance in schizophrenia patients lacks analysis of the patient's physical feedback during hospitalization, and cannot guarantee the effectiveness of the patient's medication treatment.

[0005] 3. After the patient's hospitalization treatment is completed, the traditional electronic system for evaluating medication compliance for schizophrenia patients determines whether the patient can be discharged and returned home for treatment based on the patient's condition recovery. However, it lacks analysis of changes in the patient's condition after returning home for treatment, and cannot guarantee the stability of the patient's condition after returning home for treatment. Summary of the Invention

[0006] In view of the above-mentioned technical deficiencies, the purpose of the present invention is to provide an electronic system for evaluating medication compliance of schizophrenia patients based on big data.

[0007] To solve the above technical problems, the present invention adopts the following technical solution: The present invention provides an electronic system for evaluating medication compliance of schizophrenia patients based on big data, including the following modules: a data monitoring module, a data analysis module, a treatment adjustment module and a database.

[0008] The data monitoring module is used to detect the heart rate, blood pressure, and physical feedback of each patient after each medication during each treatment period, as well as the blood drug concentration of each patient and the doctor's score of the patient's condition recovery.

[0009] The data analysis module includes a compliance analysis unit and a body feedback analysis unit.

[0010] The compliance analysis unit is used to obtain the heart rate, blood pressure and blood drug concentration of each patient after each medication in each treatment time period, and analyze the medication compliance coefficient of each patient in each treatment time period.

[0011] The physical feedback analysis unit is used to obtain physical feedback from each patient after each medication in each treatment time period, analyze whether each patient needs to adjust the medication used in each treatment time period, and refer to each patient who needs to adjust the medication as an adjusted patient. The feedback from each adjusted patient is fed back to the doctor, and the doctor adjusts the medication of each adjusted patient based on the physical feedback from the patient.

[0012] The treatment adjustment module is used to obtain the medication compliance coefficient and disease recovery score of each patient in each treatment period, determine whether each patient can go home for treatment, and feedback each patient who can go home for treatment to the doctor.

[0013] The database is used to store the effective blood drug concentration range, the heart rate change difference and blood pressure change difference of each patient when taking the medicine according to the doctor's instructions, the initial heart rate and initial blood pressure of each patient, and the historical symptoms of each patient after returning home for treatment.

[0014] The beneficial effects of the present invention are: 1. The present invention provides an electronic system for evaluating medication compliance of schizophrenia patients based on big data, which detects the heart rate, blood pressure, and physical feedback of each patient after each medication in each treatment time period, as well as the blood drug concentration and the doctor's score of the patient's condition recovery, analyzes each patient's medication compliance coefficient and physical feedback, and judges whether each patient needs to adjust the medication used in each treatment time period. When each patient's treatment time period in the hospital ends, it judges whether each patient can go home for treatment based on the patient's medication compliance coefficient, condition recovery score and historical symptoms after returning home in each treatment time period, thereby ensuring the stability of the patient's condition after returning home for treatment, and ensuring the accuracy of the patient's medication compliance coefficient analysis and the effectiveness of medication treatment.

[0015] 2. In each treatment time period, the present invention obtains the blood drug concentration of each patient at each blood drug test, the heart rate and blood pressure after each medication, and the marking time of each patient. At the same time, it analyzes whether each medication of each patient is qualified, and each medication that is unqualified is called an unqualified medication. The number of unqualified medications and the marking time of each patient are obtained, and the duration of the treatment time period is obtained from the database. According to the number of unqualified medications, the marking time and the duration of the treatment time period of each patient, the medication compliance coefficient of each patient is determined, thereby ensuring the accuracy of the patient medication compliance coefficient analysis.

[0016] 3. Within each treatment time period, the present invention sets each sub-time period according to a preset time threshold, obtains the sleep duration, deep sleep duration, weight and real-time skin conductance value of each patient in each sub-time period, analyzes the physical feedback of each patient in each sub-time period, and simultaneously analyzes the treatment effect of each patient in each sub-time period. If a patient has a sub-time period with poor treatment effect, it means that the patient needs to adjust the medication used during the treatment time period. If a patient has a good treatment effect in each sub-time period, it means that the patient does not need to adjust the medication used during the treatment time period, thereby ensuring the effectiveness of the patient's medication treatment.

[0017] 4. After each treatment period of each patient in the hospital ends, the present invention obtains the medication compliance coefficient and the disease recovery score of each patient in each treatment period, and determines the medication compliance return value and the disease recovery score return value of each patient. At the same time, the patient level of each patient and the disease condition of each patient after each historical return home treatment are determined. According to the patient level of each patient and the disease condition of each patient after each historical return home treatment, it is judged whether each patient can go home for treatment, thereby ensuring the stability of the patient's condition after returning home for treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 This is a schematic diagram of the system structure connection of the present invention. DETAILED DESCRIPTION

[0020] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0021] See also Figure 1 As shown, the present invention provides an electronic system for evaluating medication compliance of schizophrenia patients based on big data, including the following modules: a data monitoring module, a data analysis module, a treatment adjustment module and a database.

[0022] The data monitoring module is connected to the data analysis module, the data analysis module is connected to the treatment adjustment module, and the database is connected to the data analysis module and the treatment adjustment module.

[0023] The data monitoring module is used to detect the heart rate, blood pressure, and physical feedback of each patient after each medication during each treatment period, as well as the blood drug concentration of each patient and the doctor's score of the patient's condition recovery.

[0024] The data analysis module includes a compliance analysis unit and a body feedback analysis unit.

[0025] The compliance analysis unit is used to obtain the heart rate, blood pressure and blood drug concentration of each patient after each medication in each treatment time period, and analyze the medication compliance coefficient of each patient in each treatment time period.

[0026] In a specific embodiment, the analysis of the medication compliance coefficient of each patient in each treatment time period is as follows: in each treatment time period, the blood drug concentration of each patient at each blood drug test, the heart rate and blood pressure after each medication are obtained, and the marking time of each patient is obtained based on the blood drug concentration of each patient at each blood drug test. Based on the heart rate and blood pressure of the patient after each medication, it is analyzed whether each medication of each patient is qualified, and each medication that is unqualified is called an unqualified medication. The number of unqualified medications and the marking time of each patient are obtained, and the duration of the treatment time period is obtained from the database. Based on the number of unqualified medications, the marking time and the duration of the treatment time period of each patient, the medication compliance coefficient of each patient is determined. This method is used to analyze the medication compliance coefficient of each patient in each treatment time period.

[0027] It should be noted that the number of unqualified medications, marking time and treatment time period of each patient are normalized. Where A a,b represents the number of times the ath patient failed to take the medication during the bth treatment period, B a,b represents the marking time of the ath patient in the bth treatment period, B′ a,b represents the duration of the bth treatment period for the ath patient, α a represents the medication compliance coefficient of the ath patient in the bth treatment period, a represents the number of each patient, b represents the number of each treatment period, and both a and b are positive integers.

[0028] In the above, the specific process of obtaining the marked time of each patient is as follows: obtaining the effective blood drug concentration range from the database, comparing the blood drug concentration of each patient at each blood drug test with the effective blood drug concentration range, if the blood drug concentration range of a patient at a certain blood drug test is not within the effective blood drug concentration range, then the blood drug concentration of the patient at the time of the blood drug test is unreasonable, and the blood drug concentration test is called the marked blood drug concentration test, and the marked blood drug concentration tests of each patient are obtained in this way.

[0029] It should be noted that different drugs have different effective blood concentrations. For example, the effective blood concentration range of clozapine is 300ng / mL-600ng / mL, the effective blood concentration range of olanzapine is 20ng / mL-80ng / mL, and the effective blood concentration range of quetiapine is 200ng / mL-600ng / mL.

[0030] In each patient, the time interval between each marker blood drug concentration and the previous marker blood drug concentration is obtained, and the time intervals are added together to obtain the marking time, thereby obtaining the marking time of each patient.

[0031] In the above, the analysis of whether each patient's medication is qualified is carried out as follows: the heart rate change difference and blood pressure change difference of each patient when taking the medication according to the doctor's instructions are obtained from the database, and the heart rate difference range and blood pressure difference range of each patient are obtained by comparison. At the same time, the initial heart rate and initial blood pressure of each patient are obtained from the database.

[0032] It should be noted that before the patient receives treatment, the patient's heart rate and blood pressure are measured and used as the patient's initial heart rate and initial blood pressure.

[0033] For each patient, the heart rate and blood pressure after each medication are obtained and compared with the patient's initial heart rate and initial blood pressure. If the patient's heart rate after a certain medication is not higher than the initial heart rate or the blood pressure after medication is not higher than the initial heart rate, it means that the patient's medication is unqualified. If the patient's heart rate after a certain medication is higher than the initial heart rate and the blood pressure after medication is higher than the initial blood pressure, the patient's heart rate change difference and blood pressure change difference after the medication are obtained, and compared with the patient's heart rate difference range and blood pressure difference change range. If the patient's heart rate change difference is not within the heart rate difference range, or the blood pressure change difference is not within the blood pressure difference range, it means that the patient's medication is unqualified. If the patient's heart rate change difference is within the heart rate difference range, and the blood pressure change difference is within the blood pressure difference range, it means that the patient's medication is qualified. This method is used to analyze whether each patient's medication is qualified.

[0034] It should be noted that the difference in heart rate change after medication is obtained by subtracting the patient's heart rate after medication from the patient's initial heart rate, and the difference in blood pressure change after medication is obtained by subtracting the patient's blood pressure after medication from the patient's initial blood pressure.

[0035] The physical feedback analysis unit is used to obtain physical feedback from each patient after each medication in each treatment time period, analyze whether each patient needs to adjust the medication used in each treatment time period, and refer to each patient who needs to adjust the medication as an adjusted patient. The feedback from each adjusted patient is fed back to the doctor, and the doctor adjusts the medication of each adjusted patient based on the physical feedback from the patient.

[0036] In a specific embodiment, the analysis of whether each patient needs to adjust the medication used in each treatment time period is as follows: within each treatment time period, each sub-time period is set according to a preset time threshold, the sleep duration, deep sleep duration, weight and real-time skin conductance value of each patient in each sub-time period are obtained, the physical feedback of each patient in each sub-time period is analyzed, and the treatment effect of each patient in each sub-time period is analyzed.

[0037] It should be noted that the preset duration threshold is a standard value used to evaluate whether the sub-time period division is reasonable, and is set by medical personnel.

[0038] If a patient has a sub-time period with poor treatment effect, it means that the patient needs to adjust the medication used during the treatment period. If a patient has a good treatment effect in each sub-time period, it means that the patient does not need to adjust the medication used during the treatment period. This method is used to analyze whether each patient needs to adjust the medication used in each treatment period.

[0039] In the above, the physical feedback of each patient in each sub-time period is analyzed as follows: the difference in skin conductance values ​​at each adjacent moment is calculated and compared with a preset difference threshold. If the difference in skin conductance values ​​at a certain adjacent moment is greater than the preset difference threshold, it means that the patient's mood fluctuates greatly, and this adjacent moment is called a marked adjacent moment. In this way, the marked adjacent moments of each patient in each sub-time period are obtained, and the deep sleep time ratio and weight change value of each patient in each sub-time period are calculated to determine the physical feedback coefficient of each patient in each sub-time period.

[0040] It should be noted that the preset difference threshold is the critical value used to judge whether the patient's mood fluctuations are large. The difference in skin conductance values ​​of each patient during each historical attack is obtained from the database, and the average value is calculated and used as the preset difference threshold.

[0041] It should also be noted that the sleep duration, deep sleep duration, weight, real-time skin conductance value, initial weight and optimal sleep duration of each patient in each sub-time period were normalized. Where C a,c represents the proportion of deep sleep time of the ath patient in the cth sub-time period, E″ a,c represents the deep sleep duration of the ath patient in the cth sub-period, D a,c represents the weight change of the ath patient in the cth sub-time period. When c>1, D a,c =D′ a,c -D′ a,c-1 , D′ a,c represents the weight of the ath patient in the cth sub-time period, D′ a,c-1 represents the weight of the ath patient in the c-1th sub-time period. When c=1, D a,c =D″′ a -D′ a,c , D″′ a represents the initial weight of the ath patient, F a,c represents the total number of marked adjacent moments of the ath patient in the cth sub-time period, E′ represents the optimal sleep duration, β a,c represents the body feedback coefficient of the a-th patient in the c-th sub-time period, where c represents the number of each sub-time period and is a positive integer.

[0042] Among them, the patient's weight was measured before the patient received treatment and used as the patient's initial weight. The optimal sleep time was 9 hours.

[0043] The physical feedback coefficient of each patient in each sub-time period is compared with the preset physical feedback coefficient threshold. If the physical feedback coefficient of a patient in a sub-time period is greater than the preset physical feedback coefficient threshold, it means that the patient's physical feedback in this sub-time period is good. If the physical feedback coefficient of a patient in a sub-time period is lower than the preset physical feedback coefficient threshold, it means that the patient's physical feedback in this sub-time period is poor. This method is used to analyze the physical feedback of each patient in each sub-time period.

[0044] It should be noted that the preset body feedback coefficient threshold is a critical value used to evaluate whether the patient's body feedback is good. Patients with good historical body feedback are obtained from the database and referred to as marked patients. The body feedback coefficient of each marked patient is calculated, and the average value of the body feedback coefficient of each marked patient is calculated at the same time, and the average value is used as the preset body feedback coefficient threshold.

[0045] In the above, the specific process of analyzing the treatment effect of each patient in each sub-time period is as follows: for each patient, each sub-time period is numbered in chronological order, and the physical feedback of the sub-time period numbered 1 is obtained. If the physical feedback of the sub-time period numbered 1 is good, it means that the patient's treatment effect in this sub-time period is good. If the physical feedback of the sub-time period numbered 1 is poor, it means that the patient's treatment effect in this sub-time period is poor. Then, the physical feedback of the sub-time period numbered 2 is obtained. If the physical feedback of the sub-time period numbered 2 is good, it means that the patient's treatment effect in this sub-time period is good. If the physical feedback of the sub-time period numbered 2 is poor, then according to the physical feedback coefficient of the sub-time period numbered 1 and the physical feedback coefficient of the sub-time period numbered 2, the treatment effect of the patient in the sub-time period numbered 2 is analyzed. Then, the sub-time period numbered 3 is obtained, and according to the above analysis method, the treatment effect of the patient in the sub-time period numbered 3 is analyzed. In this way, the treatment effect of each patient in each sub-time period is analyzed.

[0046] In the above, the treatment effect of the patient in the sub-time period numbered 2 is analyzed, and the specific process is as follows: obtain the patient's physical feedback coefficient in the sub-time period numbered 1 and the physical feedback coefficient in the sub-time period numbered 2, and compare them. If the physical feedback coefficient in the sub-time period numbered 2 is less than the physical feedback coefficient in the sub-time period numbered 1, it means that the treatment effect of the patient in the sub-time period numbered 2 is poor. If the physical feedback coefficient in the sub-time period numbered 2 is greater than the physical feedback coefficient in the sub-time period numbered 1, the difference between the physical feedback coefficient in the sub-time period numbered 2 and the physical feedback coefficient in the sub-time period numbered 1 is calculated, and the difference is compared with the preset coefficient difference threshold. If the difference is less than the preset coefficient difference threshold, it means that the treatment effect of the patient in the sub-time period numbered 2 is poor. If the difference is greater than the preset coefficient difference threshold, it means that the treatment effect of the patient in the sub-time period numbered 2 is good.

[0047] It should be noted that the preset coefficient difference threshold is a boundary value used to analyze whether the patient's treatment effect is good, and is set by medical staff.

[0048] The treatment adjustment module is used to obtain the medication compliance coefficient and disease recovery score of each patient in each treatment period, determine whether each patient can go home for treatment, and feedback each patient who can go home for treatment to the doctor.

[0049] In a specific embodiment, the specific process of determining whether each patient can go home for treatment is as follows: after each treatment period of each patient in the hospital ends, the medication compliance coefficient and the disease recovery score of each patient in each treatment period are obtained, and the medication compliance return value and the disease recovery score return value of each patient are determined, and the patient level of each patient is determined at the same time.

[0050] It should be noted that when b>1, Where d represents the total number of treatment time periods, d is a positive integer, and α a,d represents the medication compliance coefficient of the ath patient in the dth treatment period, χ a represents the medication compliance return value of the ath patient, α′ represents the preset medication compliance coefficient threshold, Where G a,b represents the recovery score of the ath patient in the bth treatment period, G a,b-1 represents the disease recovery score of the ath patient in the b-1th treatment period, G a,d represents the disease recovery score of the ath patient in the dth treatment period, G′ represents the preset disease recovery score threshold, δ a Represents the return value of the recovery score of the ath patient. When b=1,

[0051] Among them, the preset medication compliance coefficient threshold is the minimum value used to evaluate whether the patient's medication compliance is high. The patients with high historical medication compliance are obtained from the database and are referred to as compliant patients. The medication compliance coefficient of each compliant patient is calculated, and the average value is calculated. The average value is used as the preset medication compliance coefficient threshold. When the patient's medication compliance coefficient is higher than the preset medication compliance coefficient threshold, it means that the patient's medication compliance is high. When the patient's medication compliance coefficient is lower than the preset medication compliance coefficient threshold, it means that the patient's medication compliance is low.

[0052] It should also be noted that the preset disease recovery score threshold is the critical value used to judge whether the patient's disease recovery is good. It is set by medical staff. When the patient's disease recovery score is lower than the preset disease recovery score threshold, it means that the patient's disease recovery is poor. When the patient's disease recovery score is higher than the preset disease recovery score threshold, it means that the patient's disease recovery is good.

[0053] Obtain the patient level of each patient. When a patient's patient level is level one, it means that the patient can go home for treatment. When a patient's patient level is level two or level three, obtain the patient's historical symptoms after each return home treatment from the database, and calculate the patient's symptom deterioration ratio, and compare the patient's symptom deterioration ratio with the preset ratio threshold. If the patient's symptom deterioration ratio is less than the preset ratio threshold, it means that the patient can go home for treatment. If the patient's symptom deterioration ratio is greater than the preset ratio threshold, it means that the patient cannot go home for treatment. When a patient's patient level is level four, it means that the patient cannot go home for treatment. This method is used to determine whether each patient can go home for treatment.

[0054] It should be noted that the patient's condition includes deterioration and relief, and the number of times the condition worsened after returning home was counted. The number of times the condition worsened after returning home was divided by the number of times the patient went home for treatment in history to obtain the proportion of deterioration.

[0055] It should also be noted that the preset ratio threshold is the critical value used to analyze whether the patient can go home for treatment. The preset ratio threshold is

[0056] In the above, the specific process of determining the patient level of each patient is as follows: obtain the medication compliance return value and the disease recovery score return value of each patient. If the medication compliance return value of a patient is 1 and the disease recovery score return value is 1, then the patient level of the patient is divided into level one. If the medication compliance return value of a patient is 0 and the disease recovery score return value is 1, then the patient level of the patient is divided into level two. If the medication compliance return value of a patient is 1 and the disease recovery score return value is 0, then the patient level of the patient is divided into level three. If the medication compliance return value of a patient is 0 and the disease recovery score return value is 0, then the patient level of the patient is divided into level four.

[0057] The database is used to store the effective blood drug concentration range, the heart rate change difference and blood pressure change difference of each patient when taking the medicine according to the doctor's instructions, the initial heart rate and initial blood pressure of each patient, and the historical symptoms of each patient after returning home for treatment.

[0058] The embodiment of the present invention detects the heart rate, blood pressure, and physical feedback of each patient after each medication in each treatment time period, as well as the blood drug concentration and the doctor's score of each patient's condition recovery, analyzes each patient's medication compliance coefficient and physical feedback, and determines whether each patient needs to adjust the medication used in each treatment time period. When each patient's treatment time period in the hospital ends, after each patient's medication compliance coefficient in each treatment time period, the patient's condition recovery score and historical symptoms after returning home are determined to determine whether each patient can go home for treatment, thereby ensuring the stability of the patient's condition after returning home for treatment, and ensuring the accuracy of the patient's medication compliance coefficient analysis and the effectiveness of medication treatment.

[0059] The above content is merely an example and explanation of the concept of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined in this specification, they should all fall within the scope of protection of the present invention.

Claims

1. An electronic system for evaluating medication compliance in schizophrenia patients based on big data, characterized by: Includes the following modules: The data monitoring module is used to detect each patient's heart rate, blood pressure, and physical feedback after each medication during each treatment period, as well as each patient's blood drug concentration and the doctor's assessment of the patient's recovery score; The data analysis module includes a compliance analysis unit and a physical feedback analysis unit: The compliance analysis unit is used to obtain the heart rate, blood pressure and blood drug concentration of each patient after each medication in each treatment period, and analyze the medication compliance coefficient of each patient in each treatment period; The physical feedback analysis unit is used to obtain physical feedback from each patient after each medication in each treatment period, analyze whether each patient needs to adjust the medication in each treatment period, and refer to each patient who needs to adjust the medication as an adjustment patient. The feedback from each adjustment patient is fed back to the doctor, and the doctor adjusts the medication of each adjustment patient according to the physical feedback of each adjustment patient. The treatment adjustment module is used to obtain the medication compliance coefficient and disease recovery score of each patient in each treatment period, determine whether each patient can go home for treatment, and provide feedback to the doctor on each patient who can go home for treatment; The database is used to store the effective blood drug concentration range, the heart rate change difference and blood pressure change difference of each patient when taking the medicine according to the doctor's instructions, the initial heart rate and initial blood pressure of each patient, and the historical symptoms of each patient after returning home for treatment.

2. The electronic system for evaluating medication compliance of schizophrenia patients based on big data according to claim 1 is characterized in that: The specific process of analyzing the medication compliance coefficient of each patient in each treatment period is as follows: During each treatment time period, the blood drug concentration of each patient at each blood drug test, the heart rate and blood pressure after each medication are obtained. Based on the blood drug concentration of each patient at each blood drug test, the marking time of each patient is obtained. Based on the heart rate and blood pressure of the patient after each medication, whether each medication of each patient is qualified is analyzed. Each medication that is unqualified is called an unqualified medication. The number of unqualified medications and the marking time of each patient are obtained, and the duration of the treatment time period is obtained from the database. Based on the number of unqualified medications, the marking time and the duration of the treatment time period of each patient, the patient's medication compliance coefficient is determined. This method is used to analyze the medication compliance coefficient of each patient in each treatment time period.

3. The electronic system for evaluating medication compliance of schizophrenia patients based on big data according to claim 2 is characterized in that: The specific process of obtaining the marking duration of each patient is as follows: Obtain the effective blood drug concentration range from the database, compare the blood drug concentration of each patient at each blood drug test with the effective blood drug concentration range, if the blood drug concentration range of a patient at a certain blood drug test is not within the effective blood drug concentration range, then the patient's blood drug concentration at that blood drug test is unreasonable, and this blood drug concentration test is called a marker blood drug concentration test, and in this way, obtain each marker blood drug concentration test of each patient; In each patient, the time interval between each marker blood drug concentration and the previous marker blood drug concentration is obtained, and the time intervals are added together to obtain the marking time, thereby obtaining the marking time of each patient.

4. The electronic system for evaluating medication compliance of schizophrenia patients based on big data according to claim 2 is characterized in that: The specific process of analyzing whether each patient's medication is qualified is as follows: Obtain the heart rate change difference and blood pressure change difference of each patient at each time of taking the medicine according to the doctor's instructions from the database, and compare them to obtain the heart rate difference range and blood pressure difference range of each patient. At the same time, obtain the initial heart rate and initial blood pressure of each patient from the database; For each patient, the heart rate and blood pressure after each medication are obtained and compared with the patient's initial heart rate and initial blood pressure. If the patient's heart rate after a certain medication is not higher than the initial heart rate or the blood pressure after medication is not higher than the initial heart rate, it means that the patient's medication is unqualified. If the patient's heart rate after a certain medication is higher than the initial heart rate and the blood pressure after medication is higher than the initial blood pressure, the patient's heart rate change difference and blood pressure change difference after the medication are obtained, and compared with the patient's heart rate difference range and blood pressure difference change range. If the patient's heart rate change difference is not within the heart rate difference range, or the blood pressure change difference is not within the blood pressure difference range, it means that the patient's medication is unqualified. If the patient's heart rate change difference is within the heart rate difference range, and the blood pressure change difference is within the blood pressure difference range, it means that the patient's medication is qualified. This method is used to analyze whether each patient's medication is qualified.

5. The electronic system for evaluating medication compliance of schizophrenia patients based on big data according to claim 1 is characterized in that: The specific process of analyzing whether each patient needs to adjust the medication during each treatment period is as follows: During each treatment period, sub-periods are set according to the preset duration threshold, and the sleep duration, deep sleep duration, weight, and real-time skin conductance value of each patient in each sub-period are obtained. The patient's physical feedback in each sub-period is analyzed, and the treatment effect of each patient in each sub-period is analyzed; If a patient has a sub-time period with poor treatment effect, it means that the patient needs to adjust the medication used during the treatment period. If a patient has a good treatment effect in each sub-time period, it means that the patient does not need to adjust the medication used during the treatment period. This method is used to analyze whether each patient needs to adjust the medication used in each treatment period.

6. The electronic system for evaluating medication compliance of schizophrenia patients based on big data according to claim 5 is characterized in that: The specific process of analyzing the physical feedback of each patient in each sub-time period is as follows: The difference in skin conductance values ​​between adjacent moments is calculated and compared with a preset difference threshold. If the difference in skin conductance values ​​between adjacent moments is greater than the preset difference threshold, it indicates that the patient's mood fluctuates significantly, and this adjacent moment is called a marked adjacent moment. This method is used to obtain the marked collection moments for each patient in each sub-time period, and the deep sleep duration and weight change of each patient in each sub-time period are calculated to determine the body feedback coefficient of each patient in each sub-time period. The physical feedback coefficient of each patient in each sub-time period is compared with the preset physical feedback coefficient threshold. If the physical feedback coefficient of a patient in a sub-time period is greater than the preset physical feedback coefficient threshold, it means that the patient's physical feedback in this sub-time period is good. If the physical feedback coefficient of a patient in a sub-time period is lower than the preset physical feedback coefficient threshold, it means that the patient's physical feedback in this sub-time period is poor. This method is used to analyze the physical feedback of each patient in each sub-time period.

7. The electronic system for evaluating medication compliance of schizophrenia patients based on big data according to claim 5 is characterized in that: The specific process of analyzing the treatment effect of each patient in each sub-time period is as follows: For each patient, each sub-time period is numbered in chronological order, and the physical feedback of the sub-time period numbered 1 is obtained. If the physical feedback of the sub-time period numbered 1 is good, it means that the patient's treatment effect in this sub-time period is good; if the physical feedback of the sub-time period numbered 1 is poor, it means that the patient's treatment effect in this sub-time period is poor; then the physical feedback of the sub-time period numbered 2 is obtained. If the physical feedback of the sub-time period numbered 2 is good, it means that the patient's treatment effect in this sub-time period is good; if the physical feedback of the sub-time period numbered 2 is poor, then the treatment effect of the patient in the sub-time period numbered 2 is analyzed based on the physical feedback coefficient of the sub-time period numbered 1 and the physical feedback coefficient of the sub-time period numbered 2; then the sub-time period numbered 3 is obtained, and the treatment effect of the patient in the sub-time period numbered 3 is analyzed according to the above analysis method. This method is used to analyze the treatment effect of each patient in each sub-time period.

8. The electronic system for evaluating medication compliance of schizophrenia patients based on big data according to claim 7 is characterized in that: Analyze the treatment effect of the patient in the sub-time period numbered 2. The specific process is as follows: Obtain the patient's physical feedback coefficient in the sub-time period numbered 1 and the physical feedback coefficient in the sub-time period numbered 2, and compare them. If the physical feedback coefficient in the sub-time period numbered 2 is smaller than the physical feedback coefficient in the sub-time period numbered 1, it means that the treatment effect of the patient in the sub-time period numbered 2 is poor. If the physical feedback coefficient in the sub-time period numbered 2 is greater than the physical feedback coefficient in the sub-time period numbered 1, calculate the difference between the physical feedback coefficient in the sub-time period numbered 2 and the physical feedback coefficient in the sub-time period numbered 1, and compare the difference with the preset coefficient difference threshold. If the difference is smaller than the preset coefficient difference threshold, it means that the treatment effect of the patient in the sub-time period numbered 2 is poor. If the difference is greater than the preset coefficient difference threshold, it means that the treatment effect of the patient in the sub-time period numbered 2 is good.

9. The electronic system for evaluating medication compliance of schizophrenia patients based on big data according to claim 1, characterized in that: The specific process of determining whether each patient can go home for treatment is as follows: After each treatment period of each patient in the hospital is completed, the medication compliance coefficient and the disease recovery score of each patient in each treatment period are obtained, and the medication compliance return value and disease recovery score return value of each patient are determined, and the patient level of each patient is determined at the same time; Obtain the patient level of each patient. When a patient's patient level is level one, it means that the patient can go home for treatment. When a patient's patient level is level two or level three, obtain the patient's historical symptoms after each return home treatment from the database, and calculate the patient's symptom deterioration ratio, and compare the patient's symptom deterioration ratio with the preset ratio threshold. If the patient's symptom deterioration ratio is less than the preset ratio threshold, it means that the patient can go home for treatment. If the patient's symptom deterioration ratio is greater than the preset ratio threshold, it means that the patient cannot go home for treatment. When a patient's patient level is level four, it means that the patient cannot go home for treatment. This method is used to determine whether each patient can go home for treatment.

10. The electronic system for evaluating medication compliance of schizophrenia patients based on big data according to claim 9, characterized in that: The specific process of determining the patient grade of each patient is as follows: Get the medication compliance return value and disease recovery score return value of each patient. If the medication compliance return value of a patient is 1 and the disease recovery score return value is 1, the patient level of the patient is divided into level one. If the medication compliance return value of a patient is 0 and the disease recovery score return value is 1, the patient level of the patient is divided into level two. If the medication compliance return value of a patient is 1 and the disease recovery score return value is 0, the patient level of the patient is divided into level three. If the medication compliance return value of a patient is 0 and the disease recovery score return value is 0, the patient level of the patient is divided into level four.

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