CN116844732B - Hypertension diagnosis and treatment data distributed regulation and control system and method based on big data analysis - Google Patents
Hypertension diagnosis and treatment data distributed regulation and control system and method based on big data analysis Download PDFInfo
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Abstract
The invention relates to the technical field of diagnosis and treatment data analysis, in particular to a distributed regulation and control system and a method for high blood pressure diagnosis and treatment data based on big data analysis. According to the invention, through analyzing the historical monitoring condition of the hypertension patient and the diagnosis and treatment condition of each diagnosis and treatment doctor, the screening of the diagnosis and treatment doctor can be realized, and the influence of the diagnosis and treatment scheme of the diagnosis and treatment doctor on the hypertension patient is reduced; and the diagnosis waiting data of each diagnosis and treatment doctor are combined, so that the distributed regulation and control of the diagnosis and treatment doctor's diagnosis waiting data is realized.
Description
Technical Field
The invention relates to the technical field of diagnosis and treatment data analysis, in particular to a distributed regulation and control system and method for hypertension diagnosis and treatment data based on big data analysis.
Background
Along with the continuous improvement of social level, hypertension gradually shows a younger trend, and is one of the most common chronic diseases, and is the most important risk factor for heart disease, cerebral apoplexy, nephrosis and death. The death caused by cardiovascular and cerebrovascular diseases in China accounts for more than 40% of the total death of residents, and about 70% of cerebral apoplexy death and about 50% of myocardial infarction are closely related to hypertension. To enhance the supervision of the state of the patient in hypertension, the patient in hypertension usually needs to visit the hospital once at intervals.
In a hospital, as different doctors have different diagnosis and treatment schemes aiming at the same illness state, the different diagnosis and treatment schemes possibly have conflict influence, so that the diagnosis and treatment schemes cannot be changed at will in the diagnosis and treatment process of patients, and the doctor who makes a doctor visit is necessary for pre-screening; however, when the existing patient diagnoses registration, registration objects are subjectively screened by the patient and are not suitable for the own illness state of the patient, and the distribution of the waiting patients of different doctors is also unbalanced, so that a part of doctors have more waiting patients, but a part of doctors do not have the condition of waiting patients, and medical resource waste is caused.
Disclosure of Invention
The invention aims to provide a distributed regulation and control system and a distributed regulation and control method for hypertension diagnosis and treatment data based on big data analysis, so as to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: the distributed regulation and control method for the hypertension diagnosis and treatment data based on big data analysis comprises the following steps:
s1, acquiring historical diagnosis and treatment data of a patient to be subjected to hypertension, and generating diagnosis and treatment characteristics of the patient to be subjected to hypertension, wherein the diagnosis and treatment characteristics comprise life habit characteristics, diagnosis and treatment monitoring data characteristics and diagnosis and treatment scheme characteristics;
s2, acquiring a matching value between diagnosis and treatment monitoring data characteristics of patients in the historical data and diagnosis and treatment monitoring data characteristics of patients with hypertension to be detected, screening diagnosis and treatment doctors and corresponding diagnosis and treatment schemes corresponding to all historical users with the matching value larger than a threshold value, and obtaining diagnosis and treatment scheme characteristics corresponding to each screened diagnosis and treatment scheme; matching interference values between the obtained diagnosis and treatment scheme characteristics and the diagnosis and treatment scheme characteristics of the hypertension patient to be detected; acquiring an adaptation evaluation value between life habit characteristics of a patient to be measured and diagnosis and treatment scheme characteristics of the patient to be measured and diagnosis and treatment scheme characteristics obtained by matching;
S3, predicting comprehensive diagnosis and treatment analysis values between each matched diagnosis and treatment scheme and the hypertension patient to be detected in the S2 by combining the interference values and the adaptive evaluation value conditions analyzed in the S2, and generating diagnosis and treatment registration candidate sequences of the hypertension patient to be detected according to the sequence from small to large of the comprehensive diagnosis and treatment analysis values;
s4, acquiring waiting data of each corresponding doctor when registering the hypertension patient to be detected, obtaining an optimal diagnosis and treatment registering object of the hypertension patient to be detected, recommending the optimal diagnosis and treatment registering object to the hypertension patient to be detected, and sending diagnosis and treatment characteristics of the hypertension patient to be detected to the corresponding optimal diagnosis and treatment registering object.
According to the invention, through analyzing the historical monitoring condition of the hypertension patient and the diagnosis and treatment condition of each diagnosis and treatment doctor, the screening of the diagnosis and treatment doctor can be realized, so that the diagnosis and treatment scheme of the diagnosis and treatment registration object of the hypertension patient can meet the self requirement of the hypertension patient, and the influence of the diagnosis and treatment scheme of the diagnosis and treatment doctor on the hypertension patient is reduced; the optimal diagnosis and registration object recommendation of the hypertension patients is realized by combining the waiting data of each doctor, the distributed regulation and control of the waiting data of the doctor is realized, the situation that the waiting number of the doctor is unevenly distributed and the workload of the doctor is unbalanced are avoided, the requirement of timely diagnosing the hypertension patients is met, and the diagnosing workload of the doctor is balanced.
Further, the lifestyle characteristic in S1 is a characteristic value corresponding to diet and exercise in a database during two adjacent monitoring blood pressure periods of the patient to be tested, wherein the characteristic value corresponding to diet and exercise in the database during two adjacent monitoring blood pressure periods of the patient to be tested is equal to the sum of the diet characteristic value and the exercise characteristic value, the diet characteristic value represents an average value of values queried in the database for each daily diet state during the two adjacent monitoring blood pressure periods of the patient to be tested, and the exercise characteristic value represents an average value of values queried in the database for each daily exercise state during the two adjacent monitoring blood pressure periods of the patient to be tested;
the invention includes the dietary state including the calculation of protein intake, sodium salt and edible oil and the type of food consumed in one day, the blood pressure value monitored by the patient with hypertension to be measured in the database is different and the corresponding inquiry value is different when the dietary state is the same, and each dietary state corresponds to the unique inquiry value when each blood pressure value in the database is unchanged; the exercise state comprises an exercise mode and exercise time, the blood pressure values monitored by the patient to be tested before in the database are different, the corresponding inquiry values are different when the exercise states are the same, and each exercise state corresponds to a unique inquiry value under the condition that each blood pressure value in the database is unchanged.
The diagnosis and treatment monitoring data is characterized by comprising a set formed by the previous n times of blood pressure monitoring results in the historical diagnosis and treatment data of a patient with hypertension to be detected, the time length from the time corresponding to the previous n times of blood pressure monitoring results to the time corresponding to the previous 1 times of blood pressure monitoring results is marked as T, and the n is a constant preset in a database;
the diagnosis and treatment scheme is characterized by comprising a set of diagnosis and treatment information corresponding to the latest diagnosis and treatment scheme in historical diagnosis and treatment data of patients with hypertension to be detected, wherein the diagnosis and treatment information comprises edible diagnosis and treatment medicines and diagnosis and treatment suggestions.
Further, the method for obtaining the diagnosis and treatment scheme characteristics corresponding to each diagnosis and treatment scheme screened in S2 includes the following steps:
s201, obtaining a matching value between the diagnosis and treatment monitoring data characteristics of the patient in the historical data and the diagnosis and treatment monitoring data characteristics of the hypertension patient to be detected,
constructing a plane rectangular coordinate system, wherein the plane rectangular coordinate system is a coordinate system formed by taking standard interval duration as an x axis and taking blood pressure monitoring data as a y axis; obtaining fold lines corresponding to each patient diagnosis and treatment monitoring data characteristic in historical data, and marking the fold lines as first characteristic fold lines, wherein each blood pressure monitoring result in each patient diagnosis and treatment monitoring data characteristic corresponds to one node in the first characteristic fold lines, the first characteristic fold lines are connecting lines of all adjacent nodes in a plane rectangular coordinate system, and the standard interval duration is a difference value between time corresponding to each blood pressure monitoring data in the corresponding patient diagnosis and treatment monitoring data characteristic and time corresponding to the first blood pressure monitoring data;
Marking a fold line corresponding to the diagnosis and treatment monitoring data characteristics of the hypertension patient to be detected as a second characteristic fold line;
the method comprises the steps of marking a function corresponding to a first characteristic fold line corresponding to the characteristics of the ith patient diagnosis and treatment monitoring data in historical data as Fi (x), and marking a function corresponding to a second characteristic fold line corresponding to the characteristics of the patient diagnosis and treatment monitoring data to be measured as F (x);
shifting the function F (x) left and right, and when the length of the x value range overlapping the F (x) after shifting and the function Fi (x) is T, marking the F (x) after shifting as Fp (x), marking the overlapping x value range as [ x1, x1+ T ], calculating the data deviation between Fp (x) and Fi (x) in the range of [ x1, x1+ T ], marking as Pi,
the pi= [ ≡ x＝x1 x＝x1+T |Fp(x)-Fi(x)|dx]/[∫ x＝x1 x＝x1+T |Fp(x)|dx]；
The matching value between the ith patient diagnosis and treatment monitoring data characteristic and the diagnosis and treatment monitoring data characteristic of the hypertension patient to be detected in the historical data is equal to the minimum value of each Pi respectively corresponding to different values of x 1;
s202, screening diagnosis and treatment doctors and corresponding diagnosis and treatment schemes corresponding to all historical users with matching values larger than a threshold, and binding the obtained diagnosis and treatment doctors with the corresponding diagnosis and treatment schemes, wherein the threshold is a preset constant in a database;
s203, acquiring a set of diagnosis and treatment information corresponding to each diagnosis and treatment scheme screened in S202, and obtaining diagnosis and treatment scheme characteristics corresponding to each diagnosis and treatment scheme screened.
According to the invention, the diagnosis and treatment scheme characteristics corresponding to each diagnosis and treatment scheme are obtained, namely, diagnosis and treatment doctors and diagnosis and treatment schemes corresponding to each patient with similar diagnosis and treatment detection data characteristics of the hypertension patient to be detected are obtained, so that the adaptation evaluation condition of each diagnosis and treatment scheme and the hypertension patient to be detected can be analyzed in the subsequent steps, and data support is provided for the subsequent acquisition of the optimal diagnosis and treatment registration object of the hypertension patient to be detected.
Further, when the interference value between the diagnosis and treatment scheme feature obtained by matching and the diagnosis and treatment scheme feature of the patient with hypertension to be detected in the step S2 is obtained, corresponding medicine contraindications of diagnosis and treatment medicines corresponding to diagnosis and treatment information in the diagnosis and treatment scheme feature obtained by matching are obtained, medicine interference values corresponding to each medicine contraindication in a database preset form are queried, and unique medicine interference values corresponding to each medicine contraindication of the diagnosis and treatment medicines in the database preset form are obtained; acquiring an intersection of the acquired drug contraindications and components of the diagnosis and treatment drugs in the diagnosis and treatment scheme characteristics of the patients with hypertension to be detected, and taking the sum of drug interference values corresponding to all elements in the acquired intersection as an interference value between the matched diagnosis and treatment scheme characteristics of the patients with hypertension to be detected and the diagnosis and treatment scheme characteristics of the patients with hypertension to be detected;
The method for acquiring the adaptation evaluation value between the life habit characteristics of the patient to be hypertension and the diagnosis and treatment scheme characteristics obtained by matching in the S2 comprises the following steps:
s211, acquiring life habit characteristics of a patient to be treated with hypertension, marking as Q1, acquiring diagnosis and treatment suggestions in diagnosis and treatment scheme characteristics of the patient to be treated with hypertension and matching diagnosis and treatment suggestions in the obtained diagnosis and treatment scheme characteristics, marking life habit characteristics corresponding to diet and movement conditions in the diagnosis and treatment suggestions in the diagnosis and treatment scheme characteristics of the patient to be treated with hypertension as Q2, and marking life habit characteristics corresponding to diet and movement conditions in the diagnosis and treatment suggestions in the k-th diagnosis and treatment scheme characteristics to be matched as Q3 k ；
S212, acquiring an adaptation evaluation value between Q1 and Q2, which is marked as G1, and acquiring Q1 and Q3 k The adaptation evaluation value between the two is denoted as G2, wherein G1 = R { Q2}/Q1, and G2 is as follows k ＝R{Q3 k A }/Q1, said Q1+.0,
wherein R { } represents the adaptation evaluation function when the variable Q2 or Q3 within the adaptation evaluation function k When the value is smaller than Q1, the corresponding value of the corresponding variable in the adaptive evaluation function is Q1, and when the variable Q2 or Q3 in the adaptive evaluation function k When the value of the corresponding variable in the adaptive evaluation function is greater than or equal to Q1, the corresponding value of the corresponding variable is the value of the corresponding variable, R { Q2} and R { Q3 }, respectively k Q2 and Q3 in } k Are all variables of the adaptive evaluation function;
further, the method for predicting the comprehensive diagnosis and treatment analysis value between each matched diagnosis and treatment scheme and the hypertension patient to be measured in the step S3 comprises the following steps:
s31, acquiring life habit characteristics Q2 corresponding to diet and exercise conditions in diagnosis and treatment proposals in diagnosis and treatment scheme characteristics of patients with hypertension to be treated, and acquiring life habit characteristics Q3 corresponding to diet and exercise conditions in diagnosis and treatment proposals in the k diagnosis and treatment scheme characteristics which are matched k ；
S32, obtaining an interference value between the matched diagnosis and treatment scheme characteristic and the diagnosis and treatment scheme characteristic of the hypertension patient to be detected, and marking the interference value between the matched kth diagnosis and treatment scheme characteristic and the diagnosis and treatment scheme characteristic of the hypertension patient to be detected as Hk;
s33, obtaining a comprehensive diagnosis and treatment analysis value between the k-th diagnosis and treatment scheme and the hypertension patient to be tested, wherein the comprehensive diagnosis and treatment analysis value is marked as Wk, and Wk=a1.Hk+a2 (Q3 k -Q2), wherein a1 and a2 are both constants preset in the database;
when diagnosis and treatment registration candidate sequences of patients with hypertension to be detected are generated according to the sequence from small to large of the comprehensive diagnosis and treatment analysis values, elements in the diagnosis and treatment registration candidate sequences are diagnosis and treatment doctors bound by corresponding diagnosis and treatment schemes corresponding to the comprehensive diagnosis and treatment analysis values.
The invention obtains diagnosis and treatment registration candidate sequences of patients to be subjected to hypertension, which are used for obtaining the matched priority levels between each diagnosis and treatment scheme and the patients to be subjected to hypertension, wherein the earlier elements in the obtained diagnosis and treatment registration candidate sequences of the patients to be subjected to hypertension are more in line with the requirements of the patients to be subjected to hypertension on treatment, and theoretically are more in line with the selection of the optimal diagnosis and treatment registration objects of the patients to be subjected to hypertension.
Further, the method for obtaining the optimal diagnosis and treatment registration object of the hypertension patient to be detected in the step S4 comprises the following steps:
s41, acquiring a diagnosis and treatment registration candidate sequence of a patient with hypertension to be detected, and marking the diagnosis and treatment registration candidate sequence as E;
s42, acquiring waiting data of each doctor corresponding to registering of the patient to be treated with hypertension, and binding the acquired waiting data of each doctor with the doctor corresponding to each element in E respectively to acquire a waiting data sequence of the registered doctor of the patient to be treated with hypertension, wherein the waiting data represents the ratio of the number of waiting persons to the number of waiting persons in the average unit time of the corresponding doctor in the historical data;
s43, obtaining an optimal diagnosis and treatment registration object of the hypertension patient to be detected, wherein the optimal diagnosis and treatment registration object is the minimum value of the product of the serial numbers corresponding to all elements in the diagnosis and treatment registration doctor waiting data sequence of the hypertension patient to be detected and the waiting data corresponding to the corresponding elements;
The optimal diagnosis and treatment registration object of the hypertension patient to be detected corresponds to a doctor.
When the optimal diagnosis and treatment registration objects of the patients to be tested are obtained, the invention combines the waiting data of each doctor and the diagnosis and treatment registration candidate sequence of the patients to be tested to realize the distributed regulation and control of the hypertension diagnosis and treatment data, the regulated and control objects are the final diagnosis and treatment registration objects recommended by the patients to be tested, and the distributed regulation and control is realized in the balance of the waiting data in the doctors meeting the registration objects of the patients to be tested (so that the corresponding waiting data of each doctor meeting the registration objects of the patients to be tested does not have larger difference, the requirement of timely treatment of the patients to be tested is met to a certain extent, the waiting time of the patients to be tested is shortened, the treatment workload of the doctors is balanced, and the condition that the waiting number distribution of the doctors is uneven is avoided).
Hypertension diagnosis and treatment data distributed regulation and control system based on big data analysis, wherein the system comprises the following modules:
the diagnosis and treatment characteristic generation module is used for acquiring historical diagnosis and treatment data of patients to be subjected to hypertension and generating diagnosis and treatment characteristics of the patients to be subjected to hypertension, wherein the diagnosis and treatment characteristics comprise life habit characteristics, diagnosis and treatment monitoring data characteristics and diagnosis and treatment scheme characteristics;
The diagnosis and treatment scheme analysis module acquires matching values between diagnosis and treatment monitoring data characteristics of patients in the historical data and diagnosis and treatment monitoring data characteristics of patients with hypertension to be detected, screens diagnosis and treatment doctors and corresponding diagnosis and treatment schemes corresponding to all historical users with the matching values larger than a threshold value, and obtains diagnosis and treatment scheme characteristics corresponding to each screened diagnosis and treatment scheme; matching interference values between the obtained diagnosis and treatment scheme characteristics and the diagnosis and treatment scheme characteristics of the hypertension patient to be detected; acquiring an adaptation evaluation value between life habit characteristics of a patient to be measured and diagnosis and treatment scheme characteristics of the patient to be measured and diagnosis and treatment scheme characteristics obtained by matching;
the candidate sequence prediction module is used for predicting comprehensive diagnosis and treatment analysis values between each matched diagnosis and treatment scheme in the diagnosis and treatment scheme analysis module and a hypertension patient to be detected by combining the interference value and the adaptation evaluation value condition analyzed in the diagnosis and treatment scheme analysis module, and generating diagnosis and treatment registration candidate sequences of the hypertension patient to be detected according to the sequence from the small comprehensive diagnosis and treatment analysis values;
the optimal diagnosis and treatment registration object screening module acquires the waiting data of each corresponding diagnosis and treatment doctor when the hypertension patient to be detected registers, obtains the optimal diagnosis and treatment registration object of the hypertension patient to be detected, recommends the optimal diagnosis and treatment registration object to the hypertension patient to be detected, and sends the diagnosis and treatment characteristics of the hypertension patient to be detected to the corresponding optimal diagnosis and treatment registration object.
Furthermore, the diagnosis and treatment scheme analysis module comprises a feature matching analysis unit, a diagnosis and treatment scheme feature extraction unit, a feature interference analysis unit and an adaptation evaluation unit,
the characteristic matching analysis unit acquires a matching value between the diagnosis and treatment monitoring data characteristics of the patient and the diagnosis and treatment monitoring data characteristics of the hypertension patient to be detected in the historical data;
the diagnosis and treatment scheme feature extraction unit screens diagnosis and treatment doctors and corresponding diagnosis and treatment schemes corresponding to all historical users with matching values larger than a threshold value to obtain diagnosis and treatment scheme features corresponding to each screened diagnosis and treatment scheme;
the characteristic interference analysis unit is used for analyzing and matching interference values between the obtained diagnosis and treatment scheme characteristics and the diagnosis and treatment scheme characteristics of the patients with hypertension to be detected;
the adaptation evaluation unit acquires adaptation evaluation values between life habit characteristics of the patient to be hypertension and diagnosis and treatment scheme characteristics obtained by matching the life habit characteristics of the patient to be hypertension.
Compared with the prior art, the invention has the following beneficial effects: according to the invention, through analyzing the historical monitoring condition of the hypertension patient and the diagnosis and treatment condition of each diagnosis and treatment doctor, the screening of the diagnosis and treatment doctor can be realized, so that the diagnosis and treatment scheme of the diagnosis and treatment registration object of the hypertension patient can meet the self requirement of the hypertension patient, and the influence of the diagnosis and treatment scheme of the diagnosis and treatment doctor on the hypertension patient is reduced; the optimal diagnosis and registration object recommendation of the hypertension patients is realized by combining the waiting data of each diagnosis and treatment doctor, the distributed regulation and control of the waiting data of the diagnosis and treatment doctor is realized, the situations of uneven distribution of the waiting number of the diagnosis and treatment doctor and unbalanced workload of the diagnosis and treatment doctor are avoided, the requirement of timely diagnosing the hypertension patients is met, and the diagnosing workload of the diagnosis and treatment doctor is balanced.
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The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
fig. 1 is a schematic structural diagram of a distributed control method of hypertension diagnosis and treatment data based on big data analysis;
fig. 2 is a schematic flow chart of the distributed regulation and control system of the hypertension diagnosis and treatment data based on big data analysis.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the present invention provides the following technical solutions: the distributed regulation and control method for the hypertension diagnosis and treatment data based on big data analysis comprises the following steps:
s1, acquiring historical diagnosis and treatment data of a patient to be subjected to hypertension, and generating diagnosis and treatment characteristics of the patient to be subjected to hypertension, wherein the diagnosis and treatment characteristics comprise life habit characteristics, diagnosis and treatment monitoring data characteristics and diagnosis and treatment scheme characteristics;
The life habit features in the S1 are feature values corresponding to diet and exercise in a database during two adjacent monitoring blood pressure periods of the patient to be tested, wherein the feature values corresponding to diet and exercise in the database during two adjacent monitoring blood pressure periods of the patient to be tested are equal to the sum of the diet feature values and exercise feature values, the diet feature values represent average values of query values in the database respectively for diet states of the patient to be tested during the two adjacent monitoring blood pressure periods (the diet states comprise food types for protein intake calculation, sodium salt and edible oil calculation and eating in one day, the query values corresponding to the blood pressure values of the patient to be tested in the database are different and the diet states are the same, each diet state corresponds to a unique query value under the condition that each blood pressure value in the database is unchanged, the exercise state of the patient to be tested during the two adjacent monitoring blood pressure periods of the patient to be tested respectively corresponds to the average values in the database (the exercise state comprises exercise mode and exercise time, the blood pressure values of the patient to be tested in the database are different and the blood pressure states of the patient to be tested correspond to each unique query value under the condition that each data is different);
The diagnosis and treatment monitoring data is characterized by comprising a set formed by the previous n times of blood pressure monitoring results in the historical diagnosis and treatment data of a patient with hypertension to be detected, the time length from the time corresponding to the previous n times of blood pressure monitoring results to the time corresponding to the previous 1 times of blood pressure monitoring results is marked as T, and the n is a constant preset in a database;
the diagnosis and treatment scheme is characterized by comprising a set of diagnosis and treatment information corresponding to the latest diagnosis and treatment scheme in historical diagnosis and treatment data of patients with hypertension to be detected, wherein the diagnosis and treatment information comprises edible diagnosis and treatment medicines and diagnosis and treatment suggestions.
S2, acquiring a matching value between diagnosis and treatment monitoring data characteristics of patients in the historical data and diagnosis and treatment monitoring data characteristics of patients with hypertension to be detected, screening diagnosis and treatment doctors and corresponding diagnosis and treatment schemes corresponding to all historical users with the matching value larger than a threshold value, and obtaining diagnosis and treatment scheme characteristics corresponding to each screened diagnosis and treatment scheme; matching interference values between the obtained diagnosis and treatment scheme characteristics and the diagnosis and treatment scheme characteristics of the hypertension patient to be detected; acquiring an adaptation evaluation value between life habit characteristics of a patient to be measured and diagnosis and treatment scheme characteristics of the patient to be measured and diagnosis and treatment scheme characteristics obtained by matching;
the method for obtaining the diagnosis and treatment scheme characteristics corresponding to each screened diagnosis and treatment scheme in the S2 comprises the following steps:
S201, obtaining a matching value between the diagnosis and treatment monitoring data characteristics of the patient in the historical data and the diagnosis and treatment monitoring data characteristics of the hypertension patient to be detected,
constructing a plane rectangular coordinate system, wherein the plane rectangular coordinate system is a coordinate system formed by taking standard interval duration as an x axis and taking blood pressure monitoring data as a y axis; obtaining fold lines corresponding to each patient diagnosis and treatment monitoring data characteristic in historical data, and marking the fold lines as first characteristic fold lines, wherein each blood pressure monitoring result in each patient diagnosis and treatment monitoring data characteristic corresponds to one node in the first characteristic fold lines, the first characteristic fold lines are connecting lines of all adjacent nodes in a plane rectangular coordinate system, and the standard interval duration is a difference value between time corresponding to each blood pressure monitoring data in the corresponding patient diagnosis and treatment monitoring data characteristic and time corresponding to the first blood pressure monitoring data;
marking a fold line corresponding to the diagnosis and treatment monitoring data characteristics of the hypertension patient to be detected as a second characteristic fold line;
the method comprises the steps of marking a function corresponding to a first characteristic fold line corresponding to the characteristics of the ith patient diagnosis and treatment monitoring data in historical data as Fi (x), and marking a function corresponding to a second characteristic fold line corresponding to the characteristics of the patient diagnosis and treatment monitoring data to be measured as F (x);
Shifting the function F (x) left and right, and when the length of the x value range overlapping the F (x) after shifting and the function Fi (x) is T, marking the F (x) after shifting as Fp (x), marking the overlapping x value range as [ x1, x1+ T ], calculating the data deviation between Fp (x) and Fi (x) in the range of [ x1, x1+ T ], marking as Pi,
the pi= [ ≡ x＝x1 x＝x1+T |Fp(x)-Fi(x)|dx]/[∫ x＝x1 x＝x1+T |Fp(x)|dx]；
The matching value between the ith patient diagnosis and treatment monitoring data characteristic and the diagnosis and treatment monitoring data characteristic of the hypertension patient to be detected in the historical data is equal to the minimum value of each Pi respectively corresponding to different values of x 1;
s202, screening diagnosis and treatment doctors and corresponding diagnosis and treatment schemes corresponding to all historical users with matching values larger than a threshold, and binding the obtained diagnosis and treatment doctors with the corresponding diagnosis and treatment schemes, wherein the threshold is a preset constant in a database;
s203, acquiring a set of diagnosis and treatment information corresponding to each diagnosis and treatment scheme screened in S202, and obtaining diagnosis and treatment scheme characteristics corresponding to each diagnosis and treatment scheme screened.
When the interference value between the diagnosis and treatment scheme characteristics of the hypertension patient to be detected is matched in the S2, corresponding medicine contraindications of diagnosis and treatment medicines corresponding to diagnosis and treatment information in the diagnosis and treatment scheme characteristics are obtained, medicine interference values corresponding to each medicine contraindication in a database preset form are queried, and unique medicine interference values corresponding to each medicine contraindication of the diagnosis and treatment medicines in the database preset form are obtained; acquiring an intersection of the acquired drug contraindications and components of the diagnosis and treatment drugs in the diagnosis and treatment scheme characteristics of the patients with hypertension to be detected, and taking the sum of drug interference values corresponding to all elements in the acquired intersection as an interference value between the matched diagnosis and treatment scheme characteristics of the patients with hypertension to be detected and the diagnosis and treatment scheme characteristics of the patients with hypertension to be detected;
The method for acquiring the adaptation evaluation value between the life habit characteristics of the patient to be hypertension and the diagnosis and treatment scheme characteristics obtained by matching in the S2 comprises the following steps:
s211, acquiring life habit characteristics of a patient to be treated with hypertension, marking as Q1, acquiring diagnosis and treatment suggestions in diagnosis and treatment scheme characteristics of the patient to be treated with hypertension and matching diagnosis and treatment suggestions in the obtained diagnosis and treatment scheme characteristics, marking life habit characteristics corresponding to diet and movement conditions in the diagnosis and treatment suggestions in the diagnosis and treatment scheme characteristics of the patient to be treated with hypertension as Q2, and marking life habit characteristics corresponding to diet and movement conditions in the diagnosis and treatment suggestions in the k-th diagnosis and treatment scheme characteristics to be matched as Q3 k ；
S212, acquiring an adaptation evaluation value between Q1 and Q2, which is marked as G1, and acquiring Q1 and Q3 k The adaptation evaluation value between the two is denoted as G2, wherein G1 = R { Q2}/Q1, and G2 is as follows k ＝R{Q3 k A }/Q1, said Q1+.0,
wherein R { } represents the adaptation evaluation function when the variable Q2 or Q3 within the adaptation evaluation function k When the value is smaller than Q1, the corresponding value of the corresponding variable in the adaptation evaluation function is Q1, and when the value in the adaptation evaluation function is smaller than Q1Variable Q2 or Q3 k When the value of the corresponding variable in the adaptive evaluation function is greater than or equal to Q1, the corresponding value of the corresponding variable is the value of the corresponding variable, R { Q2} and R { Q3 }, respectively k Q2 and Q3 in } k Are all variables of the adaptive evaluation function;
s3, predicting comprehensive diagnosis and treatment analysis values between each matched diagnosis and treatment scheme and the hypertension patient to be detected in the S2 by combining the interference values and the adaptive evaluation value conditions analyzed in the S2, and generating diagnosis and treatment registration candidate sequences of the hypertension patient to be detected according to the sequence from small to large of the comprehensive diagnosis and treatment analysis values;
the method for predicting the comprehensive diagnosis and treatment analysis value between each matched diagnosis and treatment scheme and the hypertension patient to be detected in the S3 comprises the following steps:
s31, acquiring life habit characteristics Q2 corresponding to diet and exercise conditions in diagnosis and treatment proposals in diagnosis and treatment scheme characteristics of patients with hypertension to be treated, and acquiring life habit characteristics Q3 corresponding to diet and exercise conditions in diagnosis and treatment proposals in the k diagnosis and treatment scheme characteristics which are matched k ；
S32, obtaining an interference value between the matched diagnosis and treatment scheme characteristic and the diagnosis and treatment scheme characteristic of the hypertension patient to be detected, and marking the interference value between the matched kth diagnosis and treatment scheme characteristic and the diagnosis and treatment scheme characteristic of the hypertension patient to be detected as Hk;
s33, obtaining a comprehensive diagnosis and treatment analysis value between the k-th diagnosis and treatment scheme and the hypertension patient to be tested, wherein the comprehensive diagnosis and treatment analysis value is marked as Wk, and Wk=a1.Hk+a2 (Q3 k -Q2), wherein a1 and a2 are both constants preset in the database;
when diagnosis and treatment registration candidate sequences of patients with hypertension to be detected are generated according to the sequence from small to large of the comprehensive diagnosis and treatment analysis values, elements in the diagnosis and treatment registration candidate sequences are diagnosis and treatment doctors bound by corresponding diagnosis and treatment schemes corresponding to the comprehensive diagnosis and treatment analysis values.
S4, acquiring waiting data of each corresponding doctor when registering the hypertension patient to be detected, obtaining an optimal diagnosis and treatment registering object of the hypertension patient to be detected, recommending the optimal diagnosis and treatment registering object to the hypertension patient to be detected, and sending diagnosis and treatment characteristics of the hypertension patient to be detected to the corresponding optimal diagnosis and treatment registering object.
The method for obtaining the optimal diagnosis and treatment registration object of the hypertension patient to be detected in the step S4 comprises the following steps:
s41, acquiring a diagnosis and treatment registration candidate sequence of a patient with hypertension to be detected, and marking the diagnosis and treatment registration candidate sequence as E;
s42, acquiring waiting data of each doctor corresponding to registering of the patient to be treated with hypertension, and binding the acquired waiting data of each doctor with the doctor corresponding to each element in E respectively to acquire a waiting data sequence of the registered doctor of the patient to be treated with hypertension, wherein the waiting data represents the ratio of the number of waiting persons to the number of waiting persons in the average unit time of the corresponding doctor in the historical data;
S43, obtaining an optimal diagnosis and treatment registration object of the hypertension patient to be detected, wherein the optimal diagnosis and treatment registration object is the minimum value of the product of the serial numbers corresponding to all elements in the diagnosis and treatment registration doctor waiting data sequence of the hypertension patient to be detected and the waiting data corresponding to the corresponding elements;
the optimal diagnosis and treatment registration object of the hypertension patient to be detected corresponds to a doctor.
In this embodiment, if the diagnosis and treatment candidate sequence E of the patient to be tested is { M1, M2, M3}, where M1, M2 and M3 respectively represent different doctors;
if the hypertension patient to be measured registers, the corresponding waiting data of each diagnosis and treatment doctor are as follows:
the number of waiting persons corresponding to M1 is 9, the number of waiting persons in the average unit time of the diagnostician M1 in the historical data is 3, and the unit time in the embodiment represents one hour;
the number of the waiting persons corresponding to M2 is 10, and the number of the waiting persons in the average unit time of the diagnosis doctor M2 in the historical data is 4;
the number of the waiting persons corresponding to M3 is 6, and the number of the waiting persons in the average unit time of the diagnosis doctor M3 in the historical data is 2;
the waiting data corresponding to M1 is 9++3=3; the waiting data corresponding to M2 is 10++4=2.5; the waiting data corresponding to M3 is 6 +.2=3;
obtaining a diagnosis and treatment registration doctor waiting data sequence {3,2.5,3}, wherein the serial number corresponding to 2.5 is 2, and the serial numbers corresponding to 3 are 1 and 3;
Because 1×3 is less than 2.5× 2 is less than 3×3, the optimal diagnosis and treatment registration object of the hypertension patient to be detected is M1;
the invention obtains the minimum value of the product of the serial numbers corresponding to each element in the diagnosis and treatment doctor waiting data sequence of the patient to be treated with hypertension and the waiting data corresponding to the corresponding element, and if the obtained minimum values are a plurality of, the optimal diagnosis and treatment registration objects of the patient to be treated with hypertension are also a plurality of.
As shown in fig. 2, the system for distributed regulation and control of hypertension diagnosis and treatment data based on big data analysis comprises the following modules:
the diagnosis and treatment characteristic generation module is used for acquiring historical diagnosis and treatment data of patients to be subjected to hypertension and generating diagnosis and treatment characteristics of the patients to be subjected to hypertension, wherein the diagnosis and treatment characteristics comprise life habit characteristics, diagnosis and treatment monitoring data characteristics and diagnosis and treatment scheme characteristics;
the diagnosis and treatment scheme analysis module acquires matching values between diagnosis and treatment monitoring data characteristics of patients in the historical data and diagnosis and treatment monitoring data characteristics of patients with hypertension to be detected, screens diagnosis and treatment doctors and corresponding diagnosis and treatment schemes corresponding to all historical users with the matching values larger than a threshold value, and obtains diagnosis and treatment scheme characteristics corresponding to each screened diagnosis and treatment scheme; matching interference values between the obtained diagnosis and treatment scheme characteristics and the diagnosis and treatment scheme characteristics of the hypertension patient to be detected; acquiring an adaptation evaluation value between life habit characteristics of a patient to be measured and diagnosis and treatment scheme characteristics of the patient to be measured and diagnosis and treatment scheme characteristics obtained by matching;
The candidate sequence prediction module is used for predicting comprehensive diagnosis and treatment analysis values between each matched diagnosis and treatment scheme in the diagnosis and treatment scheme analysis module and a hypertension patient to be detected by combining the interference value and the adaptation evaluation value condition analyzed in the diagnosis and treatment scheme analysis module, and generating diagnosis and treatment registration candidate sequences of the hypertension patient to be detected according to the sequence from the small comprehensive diagnosis and treatment analysis values;
the optimal diagnosis and treatment registration object screening module acquires the waiting data of each corresponding diagnosis and treatment doctor when the hypertension patient to be detected registers, obtains the optimal diagnosis and treatment registration object of the hypertension patient to be detected, recommends the optimal diagnosis and treatment registration object to the hypertension patient to be detected, and sends the diagnosis and treatment characteristics of the hypertension patient to be detected to the corresponding optimal diagnosis and treatment registration object.
The diagnosis and treatment scheme analysis module comprises a feature matching analysis unit, a diagnosis and treatment scheme feature extraction unit, a feature interference analysis unit and an adaptation evaluation unit,
the characteristic matching analysis unit acquires a matching value between the diagnosis and treatment monitoring data characteristics of the patient and the diagnosis and treatment monitoring data characteristics of the hypertension patient to be detected in the historical data;
the diagnosis and treatment scheme feature extraction unit screens diagnosis and treatment doctors and corresponding diagnosis and treatment schemes corresponding to all historical users with matching values larger than a threshold value to obtain diagnosis and treatment scheme features corresponding to each screened diagnosis and treatment scheme;
The characteristic interference analysis unit is used for analyzing and matching interference values between the obtained diagnosis and treatment scheme characteristics and the diagnosis and treatment scheme characteristics of the patients with hypertension to be detected;
the adaptation evaluation unit acquires adaptation evaluation values between life habit characteristics of the patient to be hypertension and diagnosis and treatment scheme characteristics obtained by matching the life habit characteristics of the patient to be hypertension
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (6)
1. The distributed regulation and control method for the hypertension diagnosis and treatment data based on big data analysis is characterized by comprising the following steps of:
s1, acquiring historical diagnosis and treatment data of a patient to be subjected to hypertension, and generating diagnosis and treatment characteristics of the patient to be subjected to hypertension, wherein the diagnosis and treatment characteristics comprise life habit characteristics, diagnosis and treatment monitoring data characteristics and diagnosis and treatment scheme characteristics;
s2, acquiring a matching value between diagnosis and treatment monitoring data characteristics of patients in the historical data and diagnosis and treatment monitoring data characteristics of patients with hypertension to be detected, screening diagnosis and treatment doctors and corresponding diagnosis and treatment schemes corresponding to all historical users with the matching value larger than a threshold value, and obtaining diagnosis and treatment scheme characteristics corresponding to each screened diagnosis and treatment scheme; matching interference values between the obtained diagnosis and treatment scheme characteristics and the diagnosis and treatment scheme characteristics of the hypertension patient to be detected; acquiring an adaptation evaluation value between life habit characteristics of a patient to be measured and diagnosis and treatment scheme characteristics of the patient to be measured and diagnosis and treatment scheme characteristics obtained by matching;
s3, predicting comprehensive diagnosis and treatment analysis values between each matched diagnosis and treatment scheme and the hypertension patient to be detected in the S2 by combining the interference values and the adaptive evaluation value conditions analyzed in the S2, and generating diagnosis and treatment registration candidate sequences of the hypertension patient to be detected according to the sequence from small to large of the comprehensive diagnosis and treatment analysis values;
S4, acquiring waiting data of each corresponding doctor when registering a patient with hypertension to be detected, obtaining an optimal diagnosis and treatment registering object of the patient with hypertension to be detected, recommending the optimal diagnosis and treatment registering object to the patient with hypertension to be detected, and sending diagnosis and treatment characteristics of the patient with hypertension to be detected to the corresponding optimal diagnosis and treatment registering object;
the life habit features in the S1 are feature values corresponding to diet and exercise in a database during the period of monitoring blood pressure of the patient with hypertension to be detected, wherein the feature values corresponding to diet and exercise in the database during the period of monitoring blood pressure of the patient with hypertension to be detected are equal to the sum of the diet feature values and the exercise feature values, the diet feature values represent average values of query values in the database respectively for diet states of the patient with hypertension to be detected during the period of monitoring blood pressure of the patient with hypertension to be detected, and the exercise feature values represent average values of query values corresponding to motion states of the patient with hypertension to be detected during the period of monitoring blood pressure of the patient to be detected;
the diagnosis and treatment monitoring data is characterized by comprising a set formed by the previous n times of blood pressure monitoring results in the historical diagnosis and treatment data of a patient with hypertension to be detected, the time length from the time corresponding to the previous n times of blood pressure monitoring results to the time corresponding to the previous 1 times of blood pressure monitoring results is marked as T, and the n is a constant preset in a database;
The diagnosis and treatment scheme is characterized by comprising a set of diagnosis and treatment information corresponding to the latest diagnosis and treatment scheme in historical diagnosis and treatment data of patients with hypertension to be detected, wherein the diagnosis and treatment information comprises edible diagnosis and treatment medicines and diagnosis and treatment suggestions;
the method for obtaining the diagnosis and treatment scheme characteristics corresponding to each screened diagnosis and treatment scheme in the S2 comprises the following steps:
s201, obtaining a matching value between the diagnosis and treatment monitoring data characteristics of the patient in the historical data and the diagnosis and treatment monitoring data characteristics of the hypertension patient to be detected,
constructing a plane rectangular coordinate system, wherein the plane rectangular coordinate system is a coordinate system formed by taking standard interval duration as an x axis and taking blood pressure monitoring data as a y axis; obtaining fold lines corresponding to each patient diagnosis and treatment monitoring data characteristic in historical data, and marking the fold lines as first characteristic fold lines, wherein each blood pressure monitoring result in each patient diagnosis and treatment monitoring data characteristic corresponds to one node in the first characteristic fold lines, the first characteristic fold lines are connecting lines of all adjacent nodes in a plane rectangular coordinate system, and the standard interval duration is a difference value between time corresponding to each blood pressure monitoring data in the corresponding patient diagnosis and treatment monitoring data characteristic and time corresponding to the first blood pressure monitoring data;
Marking a fold line corresponding to the diagnosis and treatment monitoring data characteristics of the hypertension patient to be detected as a second characteristic fold line;
the method comprises the steps of marking a function corresponding to a first characteristic fold line corresponding to the characteristics of the ith patient diagnosis and treatment monitoring data in historical data as Fi (x), and marking a function corresponding to a second characteristic fold line corresponding to the characteristics of the patient diagnosis and treatment monitoring data to be measured as F (x);
shifting the function F (x) left and right, and when the length of the x value range overlapping the F (x) after shifting and the function Fi (x) is T, marking the F (x) after shifting as Fp (x), marking the overlapping x value range as [ x1, x1+ T ], calculating the data deviation between Fp (x) and Fi (x) in the range of [ x1, x1+ T ], marking as Pi,
the pi= [ ≡ x＝x1 x＝x1+T |Fp(x)-Fi(x)|dx]/[∫ x＝x1 x＝x1+T |Fp(x)|dx]；
The matching value between the ith patient diagnosis and treatment monitoring data characteristic and the diagnosis and treatment monitoring data characteristic of the hypertension patient to be detected in the historical data is equal to the minimum value of each Pi respectively corresponding to different values of x 1;
s202, screening diagnosis and treatment doctors and corresponding diagnosis and treatment schemes corresponding to all historical users with matching values larger than a threshold, and binding the obtained diagnosis and treatment doctors with the corresponding diagnosis and treatment schemes, wherein the threshold is a preset constant in a database;
s203, acquiring a set of diagnosis and treatment information corresponding to each diagnosis and treatment scheme screened in S202, and obtaining diagnosis and treatment scheme characteristics corresponding to each diagnosis and treatment scheme screened.
2. The distributed regulation and control method for the hypertension diagnosis and treatment data based on big data analysis according to claim 1, wherein the method is characterized in that: when the interference value between the diagnosis and treatment scheme characteristics of the hypertension patient to be detected is matched in the S2, corresponding medicine contraindications of diagnosis and treatment medicines corresponding to diagnosis and treatment information in the diagnosis and treatment scheme characteristics are obtained, medicine interference values corresponding to each medicine contraindication in a database preset form are queried, and unique medicine interference values corresponding to each medicine contraindication of the diagnosis and treatment medicines in the database preset form are obtained; acquiring an intersection of the acquired drug contraindications and components of the diagnosis and treatment drugs in the diagnosis and treatment scheme characteristics of the patients with hypertension to be detected, and taking the sum of drug interference values corresponding to all elements in the acquired intersection as an interference value between the matched diagnosis and treatment scheme characteristics of the patients with hypertension to be detected and the diagnosis and treatment scheme characteristics of the patients with hypertension to be detected;
the method for acquiring the adaptation evaluation value between the life habit characteristics of the patient to be hypertension and the diagnosis and treatment scheme characteristics obtained by matching in the S2 comprises the following steps:
s211, acquiring life habit characteristics of a patient to be treated with hypertension, marking as Q1, acquiring diagnosis and treatment suggestions in diagnosis and treatment scheme characteristics of the patient to be treated with hypertension and matching diagnosis and treatment suggestions in the obtained diagnosis and treatment scheme characteristics, marking life habit characteristics corresponding to diet and movement conditions in the diagnosis and treatment suggestions in the diagnosis and treatment scheme characteristics of the patient to be treated with hypertension as Q2, and marking life habit characteristics corresponding to diet and movement conditions in the diagnosis and treatment suggestions in the k-th diagnosis and treatment scheme characteristics to be matched as Q3 k ；
S212, acquiring an adaptation evaluation value between Q1 and Q2, which is marked as G1, and acquiring Q1 and Q3 k The adaptation evaluation value between the two is denoted as G2, wherein G1 = R { Q2}/Q1, and G2 is as follows k ＝R{Q3 k A }/Q1, said Q1+.0,
wherein R { } represents the adaptation evaluation function when the variable Q2 or Q3 within the adaptation evaluation function k When the value is smaller than Q1, the corresponding value of the corresponding variable in the adaptive evaluation function is Q1, and when the variable Q2 or Q3 in the adaptive evaluation function k When the value of the corresponding variable in the adaptive evaluation function is greater than or equal to Q1, the corresponding value of the corresponding variable is the value of the corresponding variable, R { Q2} and R { Q3 }, respectively k Q2 and Q3 in } k Are variables of the adaptation evaluation function.
3. The distributed regulation and control method for the hypertension diagnosis and treatment data based on big data analysis according to claim 2, wherein the method is characterized in that: the method for predicting the comprehensive diagnosis and treatment analysis value between each matched diagnosis and treatment scheme and the hypertension patient to be detected in the S3 comprises the following steps:
s31, acquiring life habit characteristics Q2 corresponding to diet and movement conditions in diagnosis and treatment suggestions in diagnosis and treatment scheme characteristics of patients with hypertension to be treated, and acquiring a matched first stepLife habit characteristics Q3 corresponding to diet and exercise conditions in diagnosis and treatment advice in k diagnosis and treatment scheme characteristics k ；
S32, obtaining an interference value between the matched diagnosis and treatment scheme characteristic and the diagnosis and treatment scheme characteristic of the hypertension patient to be detected, and marking the interference value between the matched kth diagnosis and treatment scheme characteristic and the diagnosis and treatment scheme characteristic of the hypertension patient to be detected as Hk;
S33, obtaining a comprehensive diagnosis and treatment analysis value between the k-th diagnosis and treatment scheme and the hypertension patient to be tested, wherein the comprehensive diagnosis and treatment analysis value is marked as Wk, and Wk=a1.Hk+a2 (Q3 k -Q2), wherein a1 and a2 are both constants preset in the database;
when diagnosis and treatment registration candidate sequences of patients with hypertension to be detected are generated according to the sequence from small to large of the comprehensive diagnosis and treatment analysis values, elements in the diagnosis and treatment registration candidate sequences are diagnosis and treatment doctors bound by corresponding diagnosis and treatment schemes corresponding to the comprehensive diagnosis and treatment analysis values.
4. The distributed regulation and control method for the hypertension diagnosis and treatment data based on big data analysis according to claim 1, wherein the method is characterized in that: the method for obtaining the optimal diagnosis and treatment registration object of the hypertension patient to be detected in the step S4 comprises the following steps:
s41, acquiring a diagnosis and treatment registration candidate sequence of a patient with hypertension to be detected, and marking the diagnosis and treatment registration candidate sequence as E;
s42, acquiring waiting data of each doctor corresponding to registering of the patient to be treated with hypertension, and binding the acquired waiting data of each doctor with the doctor corresponding to each element in E respectively to acquire a waiting data sequence of the registered doctor of the patient to be treated with hypertension, wherein the waiting data represents the ratio of the number of waiting persons to the number of waiting persons in the average unit time of the corresponding doctor in the historical data;
S43, obtaining an optimal diagnosis and treatment registration object of the hypertension patient to be detected, wherein the optimal diagnosis and treatment registration object is the minimum value of the product of the serial numbers corresponding to all elements in the diagnosis and treatment registration doctor waiting data sequence of the hypertension patient to be detected and the waiting data corresponding to the corresponding elements;
the optimal diagnosis and treatment registration object of the hypertension patient to be detected corresponds to a doctor.
5. The distributed regulation and control system for the hypertension diagnosis and treatment data based on big data analysis, which is realized based on the distributed regulation and control method for the hypertension diagnosis and treatment data based on big data analysis according to any one of claims 1 to 4, is characterized by comprising the following modules:
the diagnosis and treatment characteristic generation module is used for acquiring historical diagnosis and treatment data of patients to be subjected to hypertension and generating diagnosis and treatment characteristics of the patients to be subjected to hypertension, wherein the diagnosis and treatment characteristics comprise life habit characteristics, diagnosis and treatment monitoring data characteristics and diagnosis and treatment scheme characteristics;
the diagnosis and treatment scheme analysis module acquires matching values between diagnosis and treatment monitoring data characteristics of patients in the historical data and diagnosis and treatment monitoring data characteristics of patients with hypertension to be detected, screens diagnosis and treatment doctors and corresponding diagnosis and treatment schemes corresponding to all historical users with the matching values larger than a threshold value, and obtains diagnosis and treatment scheme characteristics corresponding to each screened diagnosis and treatment scheme; matching interference values between the obtained diagnosis and treatment scheme characteristics and the diagnosis and treatment scheme characteristics of the hypertension patient to be detected; acquiring an adaptation evaluation value between life habit characteristics of a patient to be measured and diagnosis and treatment scheme characteristics of the patient to be measured and diagnosis and treatment scheme characteristics obtained by matching;
The candidate sequence prediction module is used for predicting comprehensive diagnosis and treatment analysis values between each matched diagnosis and treatment scheme in the diagnosis and treatment scheme analysis module and a hypertension patient to be detected by combining the interference value and the adaptation evaluation value condition analyzed in the diagnosis and treatment scheme analysis module, and generating diagnosis and treatment registration candidate sequences of the hypertension patient to be detected according to the sequence from the small comprehensive diagnosis and treatment analysis values;
the optimal diagnosis and treatment registration object screening module acquires the waiting data of each corresponding diagnosis and treatment doctor when the hypertension patient to be detected registers, obtains the optimal diagnosis and treatment registration object of the hypertension patient to be detected, recommends the optimal diagnosis and treatment registration object to the hypertension patient to be detected, and sends the diagnosis and treatment characteristics of the hypertension patient to be detected to the corresponding optimal diagnosis and treatment registration object.
6. The big data analysis-based distributed regulation and control system for hypertension diagnosis and treatment data according to claim 5, wherein: the diagnosis and treatment scheme analysis module comprises a feature matching analysis unit, a diagnosis and treatment scheme feature extraction unit, a feature interference analysis unit and an adaptation evaluation unit,
the characteristic matching analysis unit acquires a matching value between the diagnosis and treatment monitoring data characteristics of the patient and the diagnosis and treatment monitoring data characteristics of the hypertension patient to be detected in the historical data;
The diagnosis and treatment scheme feature extraction unit screens diagnosis and treatment doctors and corresponding diagnosis and treatment schemes corresponding to all historical users with matching values larger than a threshold value to obtain diagnosis and treatment scheme features corresponding to each screened diagnosis and treatment scheme;
the characteristic interference analysis unit is used for analyzing and matching interference values between the obtained diagnosis and treatment scheme characteristics and the diagnosis and treatment scheme characteristics of the patients with hypertension to be detected;
the adaptation evaluation unit acquires adaptation evaluation values between life habit characteristics of the patient to be hypertension and diagnosis and treatment scheme characteristics obtained by matching the life habit characteristics of the patient to be hypertension.
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