Medical user data classified storage system and method based on statistical algorithm

Abstract

The invention discloses a medical user data classified storage system and method based on a statistical algorithm, and relates to the technical field of classified storage, and the method comprises the steps: analyzing user hospitalization data in medical user data based on the statistical algorithm, and obtaining a hospitalization duration interval and a conventional urgency ratio; acquiring a hospitalization expectation interval based on the conventional urgency ratio; performing hospitable classification on the medical users based on the medical user data of the medical users; the method is used for solving the problems that in an existing medical user data classified storage method, when a user needs to be hospitalized, the medical user data cannot be classified for judging whether the user can be hospitalized in time or not based on a medical department of the user, so that more time is wasted for checking whether the user can be hospitalized or not when the user is hospitalized; and the diagnosis is delayed.

Description

Technical Field

[0001] The present invention relates to the technical field of classified storage, and in particular to a medical user data classified storage system and method based on statistical algorithms. Background Art

[0002] Medical user data refers to various patient-related information collected and processed in the medical field, mainly including patient personal information, medical history information, diagnosis information, treatment process information, examination results and other data. Medical user data is mainly collected through medical information systems, electronic medical record systems and other channels to help doctors better understand patients' health conditions, develop reasonable treatment plans, and optimize the management and allocation of medical resources; the storage categories of medical user data mainly include structured data, unstructured data, semi-structured data and time series data.

[0003] The existing method for classification and storage of medical user data is usually to review the medical user data, classify it using an existing classification model, and store the classified medical user data. The storage method is usually online or archived storage based on the usage of the data. Although this improved method can effectively store medical data, it is relatively conventional in classification and can only be classified and stored online or archived based on whether it is time-effective. When a user needs to be hospitalized, the user's medical data still needs to be retrieved from the database and judged when applying for hospitalization only through conventional classification methods. It is impossible to classify the medical user data for the purpose of judging whether the user can be admitted to the hospital in time based on the user's medical department, resulting in a waste of time when the user is admitted to the hospital to verify whether he can be admitted to the hospital, which delays the diagnosis. For example, in the publication number CN1095584 The patent application of 61A discloses a method and device for classifying and storing medical data. The solution is to review, classify and score the medical data, and then classify and store the medical data according to the scoring results, so as to improve the accuracy of the classification and storage of medical data, reduce the consumption of data running space, and improve the efficiency of data reuse. Other classification and storage methods for medical user data are usually improvements in requesting and retrieving data, but they still cannot solve the problem that when a user needs to be hospitalized, the user's medical data needs to be retrieved from the database and judged, and the medical user data cannot be classified for the purpose of judging whether the user can be admitted to the hospital in time based on the user's medical department, resulting in a lot of time wasted on verifying whether the user can be admitted to the hospital when the user is admitted, which delays the diagnosis. In view of this, it is necessary to improve the existing classification and storage methods for medical user data. Summary of the invention

[0004] The present invention aims to solve one of the technical problems in the prior art to at least a certain extent, by proposing a medical user data classification and storage system and method based on a statistical algorithm, so as to solve the problem that in the existing medical user data classification and storage method, when a user needs to be hospitalized, the user's medical data needs to be retrieved from the database and judged, and the medical user data cannot be classified for the purpose of judging whether the user can be admitted to the hospital in time based on the user's medical department, resulting in a lot of time wasted on verifying whether the user can be admitted to the hospital when the user is admitted, thereby delaying the diagnosis.

[0005] To achieve the above-mentioned purpose, in a first aspect, the present application provides a medical user data classification storage system based on a statistical algorithm, including a medical cycle analysis module, a hospitalization duration interval calibration module and a medical user classification module;

[0006] The medical cycle analysis module is used to obtain medical user data, and analyze the user hospitalization data in the medical user data based on statistical algorithms, and obtain the hospitalization duration interval and conventional expedited ratio of each medical department based on the analysis results;

[0007] The hospitalization time interval calibration module is used to adjust the hospitalization time interval of each medical department based on the conventional expedited ratio, and obtain the expected hospitalization interval of each medical department based on the adjustment result;

[0008] The medical user classification module is used to obtain the departments to be classified based on the medical user data of the medical users, and classify the medical users for hospitalization based on the expected hospitalization interval.

[0009] Furthermore, the medical cycle classification module includes an inpatient data analysis unit and an inpatient duration interval acquisition unit. The inpatient data analysis unit is configured with an inpatient data analysis strategy. The inpatient data analysis strategy includes:

[0010] Obtain medical user data based on the hospital database; obtain hospitalization data of all users in the medical user data based on data extraction, and record it as user hospitalization data; for the user hospitalization data corresponding to any user, record the single hospitalization data in the user hospitalization data as single hospitalization data;

[0011] For any single hospitalization data, the department where the user registered in the single hospitalization data is recorded as the hospitalization department, and the duration of the user's hospitalization is recorded as the hospitalization duration; when the bed in the single hospitalization data is an emergency bed, the single hospitalization data is recorded as emergency hospitalization data; when the bed in the single hospitalization data is not an emergency bed, the single hospitalization data is recorded as regular hospitalization data;

[0012] The hospitalization department and hospitalization duration of all single hospitalization data of all users are obtained, and all single hospitalization data are marked as urgent hospitalization data or routine hospitalization data.

[0013] Furthermore, the hospitalization data analysis strategy also includes:

[0014] The number of all different inpatient departments corresponding to all single hospitalization data is recorded as c; c databases are established and recorded as department databases KS 1 To department database KS c , among all single hospitalization data, single hospitalization data with the same hospitalization department are put into the same department database KS;

[0015] For any department database KS: the value of dividing the number of routine hospitalization data in the department database KS by the number of expedited hospitalization data is recorded as the routine-expedited ratio, and the hospitalization department of the single hospitalization data in the department database KS is recorded as the medical department of the department database KS; a plane rectangular coordinate system is established, recorded as the department hospitalization analysis coordinate system, wherein the coordinate points to the right of the coordinate origin in the X-axis of the department hospitalization analysis coordinate system are filled in with the names of the single hospitalization data in the department database KS in sequence, and the unit of the Y-axis of the department hospitalization analysis coordinate system is time; based on the length of hospitalization in each single hospitalization data, punctuation is performed in the department hospitalization analysis coordinate system and recorded as the hospitalization point.

[0016] Furthermore, the hospitalization duration interval acquisition unit is configured with a hospitalization duration interval acquisition strategy, and the hospitalization duration interval acquisition strategy includes:

[0017] The curve obtained by fitting all hospitalization points is recorded as the hospitalization fluctuation curve, and the point with the largest absolute value of the slope in the hospitalization fluctuation curve is recorded as the hospitalization extreme change point; the difference between the ordinate of the hospitalization point closest to the left side of the hospitalization extreme change point and the ordinate of the hospitalization point closest to the right side of the hospitalization extreme change point is recorded as the extreme change time difference;

[0018] The hospitalization point with the smallest horizontal coordinate is recorded as hospitalization point LE, and the hospitalization point with the largest vertical coordinate is recorded as hospitalization point RE; all hospitalization points are fitted into a line segment, which is recorded as the hospitalization fitting line segment, wherein the horizontal coordinates of the leftmost point and the rightmost point of the hospitalization fitting line segment are respectively the same as the horizontal coordinates of the hospitalization point LE and the hospitalization point RE; the vertical coordinate of the midpoint of the hospitalization fitting line segment is marked as the average duration; the difference between the vertical coordinates of the leftmost point and the rightmost point of the hospitalization fitting line segment and the average duration is obtained respectively, and the largest difference is recorded as the conventional duration difference;

[0019] when When to Recorded as the hospitalization duration interval of the medical department in the department database KS; When HR to H+R are recorded as the hospitalization duration interval of the medical department in the department database KS, where H is the average duration, R is the conventional duration difference, and T is the extreme duration difference;

[0020] Obtain the hospitalization duration intervals and routine emergency ratios of all medical departments in the department database KS.

[0021] Furthermore, the hospitalization duration interval calibration module includes a duration interval calibration unit, and the duration interval calibration unit is configured with a duration interval calibration strategy, and the duration interval calibration strategy includes:

[0022] A plane rectangular coordinate system is established, which is recorded as the expected analysis coordinate system, wherein the coordinate points to the right of the coordinate origin in the X-axis of the expected analysis coordinate system are filled with the names of the medical departments in the department database KS in sequence, and the Y-axis of the expected analysis coordinate system is a constant axis;

[0023] For the name α of any medical department, the line segment between the points with vertical coordinates y1 and y2 in the straight line X=α is recorded as the calibration time segment, where y1 is the minimum value of the medical department's hospitalization time interval multiplied by the medical department's conventional expedited ratio, and y2 is the maximum value of the medical department's hospitalization time interval multiplied by the conventional expedited ratio.

[0024] Furthermore, the duration interval calibration strategy also includes:

[0025] Obtain the calibration time segments corresponding to all medical departments in the desired analysis coordinate system, and use the time calibration algorithm to obtain the time calibration parameters of each calibration time segment. The time calibration algorithm is: Among them, F is the time calibration parameter, f max is the ordinate of the highest point in all calibration time segments, f min is the ordinate of the lowest point in all calibration time segments, g 1 is the ordinate of the highest point of the calibration time segment, g 3 is the ordinate of the lowest point of the calibration time segment, g 2 is the maximum value of the hospitalization duration interval of the medical department corresponding to the calibration duration line segment, g 4 is the minimum value of the hospitalization duration interval of the medical department corresponding to the calibration duration line segment;

[0026] The average value of the duration calibration parameters of all calibration duration segments is obtained and recorded as the calibration measurement value. The medical departments corresponding to the calibration duration segments whose duration calibration parameters are greater than or equal to the calibration measurement value are recorded as high-fluctuation departments; the medical departments corresponding to the calibration duration segments whose duration calibration parameters are less than the calibration measurement value are recorded as low-fluctuation departments.

[0027] Furthermore, the duration interval calibration strategy also includes:

[0028] For high-fluctuation departments: subtract the duration calibration parameter of the high-fluctuation department from the maximum value of the duration interval of the high-fluctuation department, add the duration calibration parameter of the high-fluctuation department to the minimum value of the duration interval of the high-fluctuation department, and record the obtained duration interval as the expected hospitalization interval of the high-fluctuation department;

[0029] For low-fluctuation departments: subtract the duration calibration parameter of the low-fluctuation department from the minimum value of the low-fluctuation department's hospitalization duration interval, add the duration calibration parameter of the low-fluctuation department to the maximum value of the low-fluctuation department's hospitalization duration interval, and record the resulting hospitalization duration interval as the expected hospitalization interval of the low-fluctuation department.

[0030] Furthermore, the medical user analysis module includes a medical user analysis unit, and the medical user classification unit is configured with a medical user classification strategy, and the medical user classification strategy includes:

[0031] Based on the medical user data of the medical user, the department where the medical user is located is obtained and recorded as the department to be classified; the medical user is classified as hospitalizable, and the hospitalizable classification includes:

[0032] When the department to be classified is a high-fluctuation department and there are no vacant beds, the medical user is classified into a category that cannot be hospitalized and the maximum value of the expected hospitalization interval of the department to be classified is recorded as the maximum waiting time;

[0033] When the department to be classified is a high-fluctuation department and there are vacant beds, the medical user is classified into the category that can be hospitalized and the minimum value of the expected hospitalization interval of the department to be classified is recorded as the shortest non-admission time.

[0034] Furthermore, the hospitalization categories include:

[0035] When the department to be classified is a low-fluctuation department and there are no vacant beds, the medical user is classified into a category that cannot be hospitalized and the maximum value of the expected hospitalization interval of the department to be classified is recorded as the maximum waiting time;

[0036] When the department to be classified is a low-fluctuation department and there are vacant beds, the medical user will be classified into the category that can be hospitalized.

[0037] In a second aspect, the present application also provides a medical user data classification storage method based on a statistical algorithm, comprising:

[0038] Step S1, obtaining medical user data, and analyzing the user hospitalization data in the medical user data based on a statistical algorithm, and obtaining the hospitalization duration interval and conventional expedited ratio of each medical department based on the analysis results;

[0039] Step S2, adjusting the hospitalization duration interval of each medical department based on the conventional expedited ratio, and obtaining the expected hospitalization interval of each medical department based on the adjustment result;

[0040] Step S3, obtaining the departments to be classified based on the medical user data of the medical user, and classifying the medical users for hospitalization based on the expected hospitalization interval.

[0041] Beneficial effects of the present invention: The present application first obtains medical user data, and analyzes the user hospitalization data in the medical user data based on a statistical algorithm, and obtains the hospitalization time interval and the conventional expedited ratio of each medical department based on the analysis result. The advantage of this is that by obtaining the hospitalization time interval and the conventional expedited ratio of each medical department, the hospitalization time of patients in each medical department and the ratio of expedited beds to conventional beds in the medical department can be obtained, which is helpful for subsequent analysis. When the user applies for hospitalization, the medical user data is effectively classified based on the medical department where the user is located, so as to reduce the verification time when the user applies for hospitalization and improve the efficiency of the user's hospitalization;

[0042] The present application also adjusts the hospitalization time interval of each medical department based on the conventional expedited comparison, and obtains the expected hospitalization time interval of each medical department based on the adjustment result; finally, the department to be classified is obtained based on the medical user data of the medical user, and the medical users are classified as hospitalizable based on the expected hospitalization time interval. The advantage of this is that by obtaining the expected hospitalization time interval, the hospitalization time interval of the medical department obtained above can be calibrated to better reflect the actual hospitalization time of patients in each medical department, thereby making it more accurate when classifying the medical user data as hospitalizable, and helping users to efficiently determine whether they can be hospitalized and how long they can be hospitalized. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 is a flow chart of the steps of the method of the present invention;

[0044] Figure 2 is a functional block diagram of the system of the present invention;

[0045] Figure 3 It is a schematic diagram of obtaining the extreme change time difference of the present invention. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] Example 1, please refer to Figure 1 As shown, the present application provides a medical user data classification storage method based on a statistical algorithm, comprising:

[0048] Step S1, obtaining medical user data, and analyzing the user hospitalization data in the medical user data based on a statistical algorithm, and obtaining the hospitalization duration interval and conventional expedited ratio of each medical department based on the analysis results;

[0049] Step S1 includes: step S101, obtaining medical user data based on a hospital database; obtaining hospitalization data of all users in the medical user data based on data extraction, and recording it as user hospitalization data; for the user hospitalization data corresponding to any user, recording the single hospitalization data in the user hospitalization data as single hospitalization data;

[0050] Step S102: for any single hospitalization data, the department where the user registered in the single hospitalization data is recorded as the hospitalization department, and the duration of the user's hospitalization is recorded as the hospitalization duration; when the bed in the single hospitalization data is an emergency bed, the single hospitalization data is recorded as emergency hospitalization data; when the bed in the single hospitalization data is not an emergency bed, the single hospitalization data is recorded as regular hospitalization data;

[0051] In the specific implementation process, for example, in a data processing, in the single hospitalization data obtained, the department where the user registered is orthopedics, and the user's hospitalization duration is 40 days, and the bed where the user stayed is an emergency bed, then the single hospitalization data can be recorded as emergency hospitalization data, the hospitalization department is recorded as orthopedics, and the hospitalization duration is recorded as 40 days; by extracting and marking the data in the single hospitalization data, it is helpful to accurately obtain the hospitalization duration range and conventional emergency ratio of each medical department when analyzing each medical department later, so as to provide accurate classification data when classifying the user's medical user data;

[0052] Step S103, obtaining the hospitalization department and hospitalization duration of all single hospitalization data of all users, and marking all single hospitalization data as expedited hospitalization data or regular hospitalization data;

[0053] Step S104: record the number of all different hospitalization departments corresponding to all single hospitalization data as c; establish c databases and record them as department databases KS 1 To department database KS c , among all single hospitalization data, single hospitalization data with the same hospitalization department are put into the same department database KS;

[0054] In the specific implementation process, for example, in a data processing, the hospitalization departments of all single hospitalization data are orthopedics, urology, dermatology, orthopedics, otolaryngology and stomatology. Through analysis, it can be obtained that all different hospitalization departments are orthopedics, urology, dermatology, otolaryngology and stomatology. The value of c is 5, and 5 databases should be established and recorded as department databases KS 1 To department database KS 5 ;

[0055] Step S105, for any department database KS: the value of dividing the number of routine hospitalization data in the department database KS by the number of expedited hospitalization data is recorded as the routine expedited ratio, and the hospitalization department of the single hospitalization data in the department database KS is recorded as the medical department of the department database KS; a plane rectangular coordinate system is established, recorded as the department hospitalization analysis coordinate system, wherein the coordinate points to the right of the coordinate origin in the X-axis of the department hospitalization analysis coordinate system are filled with the names of the single hospitalization data in the department database KS in sequence, and the unit of the Y-axis of the department hospitalization analysis coordinate system is time; based on the hospitalization duration in each single hospitalization data, punctuation is performed in the department hospitalization analysis coordinate system and recorded as the hospitalization point;

[0056] In the specific implementation process, for example, when processing the number of households, the department hospitalization analysis coordinate system is obtained as follows Figure 3 As shown, DK1 to DK4 are the names of single hospitalization data in the department database KS; points ZY1 to ZY4 corresponding to the center of the circle ○ are hospitalization points; the curve ZB1 obtained by fitting points ZY1 to ZY4 is the hospitalization fluctuation curve, and through analysis, it can be obtained that point KB is the point with the largest absolute value of the slope in the hospitalization fluctuation curve, and the difference between the ordinates of point ZY1 and point ZY2 can be recorded as the extreme change time difference; by obtaining the extreme change time difference, the difference between the two points with the largest fluctuation in the hospitalization point can be obtained, and by comparing the extreme change time difference with the conventional time difference, the obtained hospitalization time interval can be made more consistent with the actual hospitalization time of patients in the medical department;

[0057] Step S106, the curve obtained by fitting all hospitalization points is recorded as the hospitalization fluctuation curve, and the point with the largest absolute value of the slope in the hospitalization fluctuation curve is recorded as the hospitalization extreme change point; the difference between the ordinate of the hospitalization point closest to the left side of the hospitalization extreme change point and the ordinate of the hospitalization point closest to the right side of the hospitalization extreme change point is recorded as the extreme change time difference;

[0058] Step S107, record the hospitalization point with the smallest horizontal coordinate as hospitalization point LE, and record the hospitalization point with the largest vertical coordinate as hospitalization point RE; fit all hospitalization points into a line segment, which is recorded as the hospitalization fitting line segment, wherein the horizontal coordinates of the leftmost point and the rightmost point of the hospitalization fitting line segment are respectively the same as the horizontal coordinates of the hospitalization point LE and the hospitalization point RE; mark the vertical coordinate of the midpoint of the hospitalization fitting line segment as the average duration; respectively obtain the difference between the vertical coordinates of the leftmost point and the rightmost point of the hospitalization fitting line segment and the average duration, and record the largest difference as the regular duration difference;

[0059] Step S108, when When to Recorded as the hospitalization duration interval of the medical department in the department database KS; When HR to H+R are recorded as the hospitalization duration interval of the medical department in the department database KS, where H is the average duration, R is the conventional duration difference, and T is the extreme duration difference;

[0060] In the specific implementation process, for example, in a data processing, the extreme change duration difference is 10 days, the normal duration difference is 5 days, and the average duration is 30 days. Then, through analysis, we can get The hospitalization duration ranges from 25 to 35 days, indicating that the hospitalization duration of patients in the medical department is from 25 to 35 days;

[0061] Step S109, obtaining the hospitalization duration intervals and conventional expedited ratios of all medical departments in the department database KS.

[0062] Step S2, adjusting the hospitalization duration interval of each medical department based on the conventional expedited ratio, and obtaining the expected hospitalization interval of each medical department based on the adjustment result;

[0063] Step S2 includes: Step S201, establishing a plane rectangular coordinate system, recorded as the expected analysis coordinate system, wherein the coordinate points to the right of the coordinate origin in the X-axis of the expected analysis coordinate system are filled with the names of the medical departments in the department database KS in sequence, and the Y-axis of the expected analysis coordinate system is a constant axis;

[0064] Step S202, for any medical department name α, record the line segment between the points y1 and y2 with ordinates in the straight line X=α as the calibration time segment, where y1 is the minimum value of the hospitalization time interval of the medical department multiplied by the conventional expedited ratio of the medical department, and y2 is the maximum value of the hospitalization time interval of the medical department multiplied by the conventional expedited ratio;

[0065] In the specific implementation process, for example, when processing the number of households, the hospitalization time of the medical department is between 25 days and 35 days, and the value of the conventional expedited ratio is 0.2. Then, by calculation, y1 is 5 and y2 is 7; the two endpoints of the calibration time segment are (α, 5) and (α, 7);

[0066] Step S203, obtaining the calibration time segments corresponding to all medical departments in the desired analysis coordinate system, and using the time calibration algorithm to obtain the time calibration parameters of each calibration time segment, the time calibration algorithm is: Among them, F is the time calibration parameter, f max is the ordinate of the highest point in all calibration time segments, f min is the ordinate of the lowest point in all calibration time segments, g 1 is the ordinate of the highest point of the calibration time segment, g 3 is the ordinate of the lowest point of the calibration time segment, g 2 is the maximum value of the hospitalization duration interval of the medical department corresponding to the calibration duration line segment, g 4 is the minimum value of the hospitalization duration interval of the medical department corresponding to the calibration duration line segment;

[0067] In the specific implementation process, for example, in one data processing, the ordinate of the highest point in all calibrated time segments is 20, the ordinate of the lowest point in all calibrated time segments is 2, the ordinate of the highest point in the calibrated time segment is 7, the ordinate of the lowest point in the calibrated time segment is 5, the maximum value of the hospitalization time interval of the medical department corresponding to the calibrated time segment is 35, and the minimum value of the hospitalization time interval of the medical department corresponding to the calibrated time segment is 25. Then, it can be calculated that the time calibration parameter is 15.6; by obtaining the time calibration parameter, the fluctuation of the hospitalization time of a single medical department compared with all medical departments can be obtained. The larger the standard time parameter, the greater the fluctuation of the hospitalization time of patients in the medical department, and the smaller the standard time parameter, the smaller the fluctuation of the hospitalization time of patients in the medical department;

[0068] Step S204, obtaining the average value of the duration calibration parameters of all calibration duration segments, and recording it as the calibration measurement value, recording the medical departments corresponding to the calibration duration segments whose duration calibration parameters are greater than or equal to the calibration measurement value as high-fluctuation departments; recording the medical departments corresponding to the calibration duration segments whose duration calibration parameters are less than the calibration measurement value as low-fluctuation departments;

[0069] Step S205, for high-fluctuation departments: subtract the duration calibration parameter of the high-fluctuation department from the maximum value of the duration interval of the high-fluctuation department, add the duration calibration parameter of the high-fluctuation department to the minimum value of the duration interval of the high-fluctuation department, and record the obtained duration interval of the high-fluctuation department as the expected duration interval of the high-fluctuation department;

[0070] In the specific implementation process, for example, when processing data once, the calibration measurement value obtained is 20, and the time calibration parameter of the medical department is 15.6, which means that the fluctuation of the hospitalization time of patients in the medical department is smaller than that of patients in all medical departments, so the medical department can be recorded as a low-fluctuation department; through data acquisition, the hospitalization time range of the medical department is 25 days to 35 days, and through analysis, it can be obtained that the adjusted expected hospitalization range is 9.4 days to 50.6 days; in actual application, when the expected hospitalization range is negative, the minimum value of the expected hospitalization range should be adjusted to 0;

[0071] Step S206, for low fluctuation departments: subtract the time calibration parameter of the low fluctuation department from the minimum value of the low fluctuation department's hospitalization time interval, add the time calibration parameter of the low fluctuation department to the maximum value of the low fluctuation department's hospitalization time interval, and record the obtained hospitalization time interval as the expected hospitalization interval of the low fluctuation department.

[0072] Step S3, based on the medical user data of the medical user, the department to be classified is obtained, and the medical user is classified as hospitalized based on the expected hospitalization interval; based on the medical user data of the medical user, the department where the medical user is located is obtained, and recorded as the department to be classified; the medical user is classified as hospitalized, and the hospitalized classification includes:

[0073] Step S301, when the department to be classified is a high-fluctuation department and there are no vacant beds, the medical user is classified into a category that cannot be hospitalized and the maximum value of the expected hospitalization interval of the department to be classified is recorded as the maximum waiting time;

[0074] Step S302: When the department to be classified is a high-fluctuation department and there are vacant beds, the medical user is classified into a category that can be hospitalized and the minimum value of the expected hospitalization interval of the department to be classified is recorded as the shortest non-admission time;

[0075] In the specific implementation process, for example, during a data processing, if the department to be classified is a high-fluctuation department and there are no vacant beds, it means that the user cannot be admitted in time. However, since the hospitalization time of patients in this medical department fluctuates greatly, the maximum value of the expected hospitalization interval of the department to be classified can be provided to the user and medical staff as the maximum waiting time for reference, so as to make timely admission adjustments;

[0076] Step S303: when the department to be classified is a low-fluctuation department and there are no vacant beds, the medical user is classified into a category that cannot be hospitalized and the maximum value of the expected hospitalization interval of the department to be classified is recorded as the maximum waiting time;

[0077] Step S304: When the department to be classified is a low-fluctuation department and there are vacant beds, the medical user is classified into a category that can be hospitalized.

[0078] Example 2, please refer to Figure 2 As shown, the present application also provides a medical user data classification storage system based on a statistical algorithm, characterized in that it includes a medical cycle analysis module, a hospitalization duration interval calibration module and a medical user classification module;

[0079] The medical cycle analysis module is used to obtain medical user data, and analyze the user hospitalization data in the medical user data based on statistical algorithms, and obtain the hospitalization duration interval and conventional expedited ratio of each medical department based on the analysis results;

[0080] The medical cycle classification module includes an inpatient data analysis unit and an inpatient duration interval acquisition unit. The inpatient data analysis unit is configured with an inpatient data analysis strategy, which includes:

[0081] Obtain medical user data based on the hospital database; obtain hospitalization data of all users in the medical user data based on data extraction, and record it as user hospitalization data; for the user hospitalization data corresponding to any user, record the single hospitalization data in the user hospitalization data as single hospitalization data;

[0082] For any single hospitalization data, the department where the user registered in the single hospitalization data is recorded as the hospitalization department, and the duration of the user's hospitalization is recorded as the hospitalization duration; when the bed in the single hospitalization data is an emergency bed, the single hospitalization data is recorded as emergency hospitalization data; when the bed in the single hospitalization data is not an emergency bed, the single hospitalization data is recorded as regular hospitalization data;

[0083] Obtain the hospitalization department and hospitalization duration of all single hospitalization data of all users, and mark all single hospitalization data as emergency hospitalization data or routine hospitalization data;

[0084] The number of all different inpatient departments corresponding to all single hospitalization data is recorded as c; c databases are established and recorded as department databases KS 1 To department database KS c , among all single hospitalization data, single hospitalization data with the same hospitalization department are put into the same department database KS;

[0085] For any department database KS: the value of dividing the number of routine hospitalization data in the department database KS by the number of expedited hospitalization data is recorded as the routine-expedited ratio, and the hospitalization department of the single hospitalization data in the department database KS is recorded as the medical department of the department database KS; a plane rectangular coordinate system is established, recorded as the department hospitalization analysis coordinate system, wherein the coordinate points to the right of the coordinate origin in the X-axis of the department hospitalization analysis coordinate system are filled in with the names of the single hospitalization data in the department database KS in sequence, and the unit of the Y-axis of the department hospitalization analysis coordinate system is time; based on the length of hospitalization in each single hospitalization data, punctuation is performed in the department hospitalization analysis coordinate system and recorded as the hospitalization point.

[0086] The hospitalization time interval acquisition unit is configured with a hospitalization time interval acquisition strategy, which includes:

[0087] The curve obtained by fitting all hospitalization points is recorded as the hospitalization fluctuation curve, and the point with the largest absolute value of the slope in the hospitalization fluctuation curve is recorded as the hospitalization extreme change point; the difference between the ordinate of the hospitalization point closest to the left side of the hospitalization extreme change point and the ordinate of the hospitalization point closest to the right side of the hospitalization extreme change point is recorded as the extreme change time difference;

[0088] The hospitalization point with the smallest horizontal coordinate is recorded as hospitalization point LE, and the hospitalization point with the largest vertical coordinate is recorded as hospitalization point RE; all hospitalization points are fitted into a line segment, which is recorded as the hospitalization fitting line segment, wherein the horizontal coordinates of the leftmost point and the rightmost point of the hospitalization fitting line segment are respectively the same as the horizontal coordinates of the hospitalization point LE and the hospitalization point RE; the vertical coordinate of the midpoint of the hospitalization fitting line segment is marked as the average duration; the difference between the vertical coordinates of the leftmost point and the rightmost point of the hospitalization fitting line segment and the average duration is obtained respectively, and the largest difference is recorded as the conventional duration difference;

[0089] when When to Recorded as the hospitalization duration interval of the medical department in the department database KS; When HR to H+R are recorded as the hospitalization duration interval of the medical department in the department database KS, where H is the average duration, R is the conventional duration difference, and T is the extreme duration difference;

[0090] Obtain the hospitalization duration intervals and routine emergency ratios of all medical departments in the department database KS.

[0091] The hospitalization time interval calibration module is used to adjust the hospitalization time interval of each medical department based on the conventional expedited ratio, and obtain the expected hospitalization interval of each medical department based on the adjustment result;

[0092] The hospitalization duration interval calibration module includes a duration interval calibration unit, and the duration interval calibration unit is configured with a duration interval calibration strategy. The duration interval calibration strategy includes:

[0093] A plane rectangular coordinate system is established, which is recorded as the expected analysis coordinate system, wherein the coordinate points to the right of the coordinate origin in the X-axis of the expected analysis coordinate system are filled with the names of the medical departments in the department database KS in sequence, and the Y-axis of the expected analysis coordinate system is a constant axis;

[0094] For any medical department name α, the line segment between the points with ordinates y1 and y2 in the straight line X=α is recorded as the calibration time segment, where y1 is the minimum value of the hospitalization time interval of the medical department multiplied by the value of the conventional expedited ratio of the medical department, and y2 is the maximum value of the hospitalization time interval of the medical department multiplied by the value of the conventional expedited ratio;

[0095] Obtain the calibration time segments corresponding to all medical departments in the desired analysis coordinate system, and use the time calibration algorithm to obtain the time calibration parameters of each calibration time segment. The time calibration algorithm is: Among them, F is the time calibration parameter, f max is the ordinate of the highest point in all calibration time segments, f min is the ordinate of the lowest point in all calibration time segments, g 1 is the ordinate of the highest point of the calibration time segment, g 3 is the ordinate of the lowest point of the calibration time segment, g 2 is the maximum value of the hospitalization duration interval of the medical department corresponding to the calibration duration line segment, g 4 is the minimum value of the hospitalization duration interval of the medical department corresponding to the calibration duration line segment;

[0096] The average value of the time calibration parameters of all calibration time segments is obtained and recorded as the calibration measurement value, and the medical departments corresponding to the calibration time segments whose time calibration parameters are greater than or equal to the calibration measurement value are recorded as high-fluctuation departments; the medical departments corresponding to the calibration time segments whose time calibration parameters are less than the calibration measurement value are recorded as low-fluctuation departments;

[0097] For high-fluctuation departments: subtract the duration calibration parameter of the high-fluctuation department from the maximum value of the duration interval of the high-fluctuation department, add the duration calibration parameter of the high-fluctuation department to the minimum value of the duration interval of the high-fluctuation department, and record the obtained duration interval as the expected hospitalization interval of the high-fluctuation department;

[0098] For low-fluctuation departments: subtract the duration calibration parameter of the low-fluctuation department from the minimum value of the low-fluctuation department's hospitalization duration interval, add the duration calibration parameter of the low-fluctuation department to the maximum value of the low-fluctuation department's hospitalization duration interval, and record the resulting hospitalization duration interval as the expected hospitalization interval of the low-fluctuation department.

[0099] The medical user classification module is used to obtain the department to be classified based on the medical user data of the medical user, and classify the medical user as hospitalizable based on the expected hospitalization interval;

[0100] The medical user analysis module includes a medical user analysis unit. The medical user classification unit is configured with a medical user classification strategy. The medical user classification strategy includes:

[0101] Based on the medical user data of the medical user, the department where the medical user is located is obtained and recorded as the department to be classified; the medical user is classified as hospitalizable, and the hospitalizable classification includes:

[0102] When the department to be classified is a high-fluctuation department and there are no vacant beds, the medical user is classified into a category that cannot be hospitalized and the maximum value of the expected hospitalization interval of the department to be classified is recorded as the maximum waiting time;

[0103] When the department to be classified is a high-fluctuation department and there are vacant beds, the medical user is classified into the category that can be hospitalized and the minimum value of the expected hospitalization interval of the department to be classified is recorded as the shortest non-admission time;

[0104] When the department to be classified is a low-fluctuation department and there are no vacant beds, the medical user is classified into a category that cannot be hospitalized and the maximum value of the expected hospitalization interval of the department to be classified is recorded as the maximum waiting time;

[0105] When the department to be classified is a low-fluctuation department and there are vacant beds, the medical user will be classified into the category that can be hospitalized.

[0106] Working principle: First, obtain medical user data, and analyze the user hospitalization data in the medical user data based on statistical algorithms. Based on the analysis results, obtain the hospitalization time range and conventional expedited ratio of each medical department. Then, adjust the hospitalization time range of each medical department based on the conventional expedited ratio, and obtain the expected hospitalization range of each medical department based on the adjustment results. Finally, obtain the department to be classified based on the medical user data of the medical user, and classify the medical users who can be hospitalized based on the expected hospitalization range.

[0107] Through the description of the above implementation methods, the embodiments of the present invention can be provided as methods, systems or computer program products. Based on such an understanding, the above technical solutions can be essentially or partly contributed to the prior art in the form of software products, which can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and include several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0108] In the embodiments provided in the present application, it should be understood that the disclosed system or method can be implemented in other ways. The embodiments described above are merely schematic. For example, the division of modules or units is only a logical function division. There may be other division methods in actual implementation. For example, multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of systems, modules and units can be electrical, mechanical or other forms.

[0109] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A medical user data classification storage system based on statistical algorithms, characterized in that: It includes the medical cycle analysis module, hospitalization duration interval calibration module and medical user classification module; The medical cycle analysis module is used to obtain medical user data, and analyze the user hospitalization data in the medical user data based on statistical algorithms, and obtain the hospitalization duration interval and conventional expedited ratio of each medical department based on the analysis results; The hospitalization time interval calibration module is used to adjust the hospitalization time interval of each medical department based on the conventional expedited ratio, and obtain the expected hospitalization interval of each medical department based on the adjustment result; The medical user classification module is used to obtain the departments to be classified based on the medical user data of the medical users, and classify the medical users for hospitalization based on the expected hospitalization interval.

2. According to the medical user data classification storage system based on statistical algorithm according to claim 1, it is characterized in that: The medical cycle classification module includes an inpatient data analysis unit and an inpatient duration interval acquisition unit. The inpatient data analysis unit is configured with an inpatient data analysis strategy, which includes: Obtain medical user data based on the hospital database; obtain hospitalization data of all users in the medical user data based on data extraction, and record it as user hospitalization data; for the user hospitalization data corresponding to any user, record the single hospitalization data in the user hospitalization data as single hospitalization data; For any single hospitalization data, the department where the user registered in the single hospitalization data is recorded as the hospitalization department, and the duration of the user's hospitalization is recorded as the hospitalization duration; when the bed in the single hospitalization data is an emergency bed, the single hospitalization data is recorded as emergency hospitalization data; when the bed in the single hospitalization data is not an emergency bed, the single hospitalization data is recorded as regular hospitalization data; The hospitalization department and hospitalization duration of all single hospitalization data of all users are obtained, and all single hospitalization data are marked as urgent hospitalization data or routine hospitalization data.

3. A medical user data classification storage system based on statistical algorithm according to claim 2, characterized in that: Hospitalization data analysis strategies also include: The number of all different inpatient departments corresponding to all single hospitalization data is recorded as c; c databases are established and recorded as department database KS1 to department database KS c , among all single hospitalization data, single hospitalization data with the same hospitalization department are put into the same department database KS; For any department database KS: the value of dividing the number of routine hospitalization data in the department database KS by the number of expedited hospitalization data is recorded as the routine-expedited ratio, and the hospitalization department of the single hospitalization data in the department database KS is recorded as the medical department of the department database KS; a plane rectangular coordinate system is established, recorded as the department hospitalization analysis coordinate system, wherein the coordinate points to the right of the coordinate origin in the X-axis of the department hospitalization analysis coordinate system are filled in with the names of the single hospitalization data in the department database KS in sequence, and the unit of the Y-axis of the department hospitalization analysis coordinate system is time; based on the length of hospitalization in each single hospitalization data, punctuation is performed in the department hospitalization analysis coordinate system and recorded as the hospitalization point.

4. A medical user data classification storage system based on statistical algorithm according to claim 3, characterized in that: The hospitalization time interval acquisition unit is configured with a hospitalization time interval acquisition strategy, which includes: The curve obtained by fitting all hospitalization points is recorded as the hospitalization fluctuation curve, and the point with the largest absolute value of the slope in the hospitalization fluctuation curve is recorded as the hospitalization extreme change point; the difference between the ordinate of the hospitalization point closest to the left side of the hospitalization extreme change point and the ordinate of the hospitalization point closest to the right side of the hospitalization extreme change point is recorded as the extreme change time difference; The hospitalization point with the smallest horizontal coordinate is recorded as hospitalization point LE, and the hospitalization point with the largest vertical coordinate is recorded as hospitalization point RE; all hospitalization points are fitted into a line segment, which is recorded as the hospitalization fitting line segment, wherein the horizontal coordinates of the leftmost point and the rightmost point of the hospitalization fitting line segment are respectively the same as the horizontal coordinates of the hospitalization point LE and the hospitalization point RE; the vertical coordinate of the midpoint of the hospitalization fitting line segment is marked as the average duration; the difference between the vertical coordinates of the leftmost point and the rightmost point of the hospitalization fitting line segment and the average duration is obtained respectively, and the largest difference is recorded as the conventional duration difference; when When to Recorded as the hospitalization duration interval of the medical department in the department database KS; When HR to H+R are recorded as the hospitalization duration interval of the medical department in the department database KS, where H is the average duration, R is the conventional duration difference, and T is the extreme duration difference; Obtain the hospitalization duration intervals and routine emergency ratios of all medical departments in the department database KS.

5. A medical user data classification storage system based on statistical algorithm according to claim 4, characterized in that: The hospitalization duration interval calibration module includes a duration interval calibration unit, and the duration interval calibration unit is configured with a duration interval calibration strategy. The duration interval calibration strategy includes: A plane rectangular coordinate system is established, which is recorded as the expected analysis coordinate system, wherein the coordinate points to the right of the coordinate origin in the X-axis of the expected analysis coordinate system are filled with the names of the medical departments in the department database KS in sequence, and the Y-axis of the expected analysis coordinate system is a constant axis; For the name α of any medical department, the line segment between the points with vertical coordinates y1 and y2 in the straight line X=α is recorded as the calibration time segment, where y1 is the minimum value of the medical department's hospitalization time interval multiplied by the medical department's conventional expedited ratio, and y2 is the maximum value of the medical department's hospitalization time interval multiplied by the conventional expedited ratio.

6. A medical user data classification storage system based on statistical algorithm according to claim 5, characterized in that: The duration interval calibration strategy also includes: Obtain the calibration time segments corresponding to all medical departments in the desired analysis coordinate system, and use the time calibration algorithm to obtain the time calibration parameters of each calibration time segment. The time calibration algorithm is: Among them, F is the time calibration parameter, f max is the ordinate of the highest point in all calibration time segments, f min is the ordinate of the lowest point in all calibration time segments, g1 is the ordinate of the highest point in the calibration time segment, g3 is the ordinate of the lowest point in the calibration time segment, g2 is the maximum value of the hospitalization time interval of the medical department corresponding to the calibration time segment, and g4 is the minimum value of the hospitalization time interval of the medical department corresponding to the calibration time segment; The average value of the duration calibration parameters of all calibration duration segments is obtained and recorded as the calibration measurement value. The medical departments corresponding to the calibration duration segments whose duration calibration parameters are greater than or equal to the calibration measurement value are recorded as high-fluctuation departments; the medical departments corresponding to the calibration duration segments whose duration calibration parameters are less than the calibration measurement value are recorded as low-fluctuation departments.

7. A medical user data classification storage system based on statistical algorithm according to claim 6, characterized in that: The duration interval calibration strategy also includes: For high-fluctuation departments: subtract the duration calibration parameter of the high-fluctuation department from the maximum value of the duration interval of the high-fluctuation department, add the duration calibration parameter of the high-fluctuation department to the minimum value of the duration interval of the high-fluctuation department, and record the obtained duration interval as the expected hospitalization interval of the high-fluctuation department; For low-fluctuation departments: subtract the duration calibration parameter of the low-fluctuation department from the minimum value of the low-fluctuation department's hospitalization duration interval, add the duration calibration parameter of the low-fluctuation department to the maximum value of the low-fluctuation department's hospitalization duration interval, and record the resulting hospitalization duration interval as the expected hospitalization interval of the low-fluctuation department.

8. The medical user data classification storage system based on statistical algorithm according to claim 1, characterized in that: The medical user analysis module includes a medical user analysis unit. The medical user classification unit is configured with a medical user classification strategy. The medical user classification strategy includes: Based on the medical user data of the medical user, the department where the medical user is located is obtained and recorded as the department to be classified; the medical user is classified as hospitalizable, and the hospitalizable classification includes: When the department to be classified is a high-fluctuation department and there are no vacant beds, the medical user is classified into a category that cannot be hospitalized and the maximum value of the expected hospitalization interval of the department to be classified is recorded as the maximum waiting time; When the department to be classified is a high-fluctuation department and there are vacant beds, the medical user is classified into the category that can be hospitalized and the minimum value of the expected hospitalization interval of the department to be classified is recorded as the shortest non-admission time.

9. The medical user data classification storage system based on statistical algorithm according to claim 1, characterized in that: Other categories that can be hospitalized include: When the department to be classified is a low-fluctuation department and there are no vacant beds, the medical user is classified into a category that cannot be hospitalized and the maximum value of the expected hospitalization interval of the department to be classified is recorded as the maximum waiting time; When the department to be classified is a low-fluctuation department and there are vacant beds, the medical user will be classified into the category that can be hospitalized.

10. A medical user data classification storage method based on a statistical algorithm, used to implement a medical user data classification storage system based on a statistical algorithm as described in any one of claims 1 to 9, characterized in that: include: Step S1, obtaining medical user data, and analyzing the user hospitalization data in the medical user data based on a statistical algorithm, and obtaining the hospitalization duration interval and conventional expedited ratio of each medical department based on the analysis results; Step S2, adjusting the hospitalization duration interval of each medical department based on the conventional expedited ratio, and obtaining the expected hospitalization interval of each medical department based on the adjustment result; Step S3, obtaining the departments to be classified based on the medical user data of the medical user, and classifying the medical users for hospitalization based on the expected hospitalization interval.

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