CN112001805A - Medical insurance data processing method, device, equipment and medium based on fixed time window - Google Patents
Medical insurance data processing method, device, equipment and medium based on fixed time window Download PDFInfo
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Abstract
The invention relates to the technical field of artificial intelligence, is applied to the field of intelligent medical treatment, and discloses a medical insurance data processing method, a device, equipment and a medium based on a fixed time window, wherein the method comprises the steps of acquiring the triggering time point and the triggering total times of medical insurance use events in a preset time period; displaying the medical insurance use events and the trigger time points thereof on a preset time axis, and acquiring the time interval between two adjacent medical insurance use events on the preset time axis; cutting a preset time axis into a plurality of sub time axes according to a preset fixed time window and a time interval; recording the sub time axis containing the maximum total number of medical insurance use events as a first sub time axis; determining a first densest frequency corresponding to the first sub-time axis according to a preset fixed time window and a preset frequency determination method; and when the first most dense frequency meets the preset frequency standard, prompting that the medical insurance use event in the preset time period is abnormal. The invention improves the abnormal data processing efficiency.
Description
Technical Field
The invention relates to the field of data processing, in particular to a medical insurance data processing method, device, equipment and medium based on a fixed time window.
Background
With the development of scientific technology, data processing technologies such as special data query and abnormal data processing are also developed. Data processing techniques are also used in different fields, such as the medical field, the field of application programs, etc.
In the medical field, abnormal data query is often required for medical insurance data, for example, the number of times of medical treatment of a patient in the same department within a week is queried on the premise of using medical insurance; or on the premise of using the medical insurance, the patient takes the medicine within one week, and the medical insurance data of the patient is judged to be abnormal according to the data. The prior art generally adopts a sliding time window method to implement. The sliding time window method is that for a preset interested time window, on a time axis constructed based on the time point of the occurrence of an event, the time window is slid one time unit from left to right, and then the number of the events contained in the current time window is obtained. The method has the following defects: according to the method, the result corresponding to each time window can be obtained only by exhaustively exhausting the whole time shaft, the detection time is long, and the detection efficiency is low.
Disclosure of Invention
The embodiment of the invention provides a medical insurance data processing method, device, equipment and medium based on a fixed time window, and aims to solve the problems of long detection time and low detection efficiency.
A medical insurance data processing method based on a fixed time window comprises the following steps:
acquiring a triggering time point of a medical insurance use event in a preset time period and the total triggering times of the medical insurance use event;
displaying the medical insurance use events and the trigger time points thereof on a preset time axis according to a preset display rule, and acquiring time intervals between all two adjacent medical insurance use events on the preset time axis;
cutting the preset time shaft into a plurality of sub time shafts according to a preset fixed time window and the time interval;
acquiring the total number of the medical insurance use events contained in each sub time axis, and recording the sub time axis containing the maximum total number of the medical insurance use events as a first sub time axis;
determining a first densest frequency corresponding to the first sub-time axis according to the preset fixed time window and a preset frequency determination method; the first most frequent representation corresponds to the preset fixed time window, and the maximum frequency of the medical insurance usage event in the first sub-time axis is triggered;
and when the first most dense frequency meets a preset frequency standard, prompting that the medical insurance use event in the preset time period is abnormal.
A fixed time window based medical insurance data processing apparatus, comprising:
the data acquisition module is used for acquiring the triggering time point of the medical insurance usage event and the total triggering times of the medical insurance usage event within a preset time period;
the data display module is used for displaying the medical insurance usage events and the trigger time points thereof on a preset time axis according to preset display rules and acquiring time intervals between all two adjacent medical insurance usage events on the preset time axis;
the time axis cutting module is used for cutting the preset time axis into a plurality of sub time axes according to a preset fixed time window and the time interval;
the first sub-timeline determining module is used for acquiring the total number of the medical insurance use events contained in each sub-timeline, and recording the sub-timeline containing the maximum total number of the medical insurance use events as the first sub-timeline;
the first frequency determining module is used for determining a first densest frequency corresponding to the first sub time axis according to the preset fixed time window and a preset frequency determining method; the first most frequent representation corresponds to the preset fixed time window, and the maximum frequency of the medical insurance usage event in the first sub-time axis is triggered;
and the first abnormity prompting module is used for prompting that the medical insurance use event in the preset time period is abnormal when the first most dense frequency meets a preset frequency standard.
A computer device comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, wherein the processor realizes the medical insurance data processing method based on the fixed time window when executing the computer program.
A computer-readable storage medium, which stores a computer program, which, when executed by a processor, implements the fixed time window-based medical insurance data processing method described above.
According to the medical insurance data processing method, device, equipment and medium based on the fixed time window, the triggering time point of the medical insurance use event and the total triggering times of the medical insurance use event in a preset time period are obtained; displaying the medical insurance use events and the trigger time points thereof on a preset time axis according to a preset display rule, and acquiring time intervals between all two adjacent medical insurance use events on the preset time axis; cutting the preset time shaft into a plurality of sub time shafts according to a preset fixed time window and the time interval; acquiring the total number of the medical insurance use events contained in each sub time axis, and recording the sub time axis containing the maximum total number of the medical insurance use events as a first sub time axis; determining a first densest frequency corresponding to the first sub-time axis according to the preset fixed time window and a preset frequency determination method; the first most frequent representation corresponds to the preset fixed time window, and the maximum frequency of the medical insurance usage event in the first sub-time axis is triggered; and when the first most dense frequency meets a preset frequency standard, prompting that the medical insurance use event in the preset time period is abnormal.
Firstly, cutting a preset time shaft by a time interval on the time shaft and a preset fixed time window; the preset time axis is effectively pruned without exhaustively listing the time axis, so that the time for processing abnormal data is saved; secondly, the first sub-time shaft with the most medical insurance use events is detected for the first time, when the first densest frequency corresponding to the first sub-time shaft meets a preset frequency standard, the medical insurance use events are judged to have abnormal phenomena in a preset time period, the rest sub-time shafts can be deleted, the detection time is shortened, and the system calculation amount is reduced, so that whether the abnormal phenomena exist in the preset time period can be more quickly verified; by the method, whether abnormal phenomena occur or not can be detected more quickly in the event of medical insurance application with large data volume, so that the construction of a smart city is promoted.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments of the present invention will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive labor.
FIG. 1 is a schematic diagram of an application environment of a fixed time window-based medical insurance data processing method according to an embodiment of the present invention;
FIG. 2 is a flowchart of a method for processing medical insurance data based on a fixed time window according to an embodiment of the present invention;
FIG. 3 is a flowchart of step S30 in the method for processing medical insurance data based on the fixed time window according to an embodiment of the present invention;
FIG. 4 is a flowchart of step S40 in the method for processing medical insurance data based on the fixed time window according to the embodiment of the present invention;
FIG. 5 is a flowchart of step S50 in the method for processing medical insurance data based on the fixed time window according to the embodiment of the present invention;
FIG. 6 is a schematic block diagram of a medical insurance data processing apparatus based on a fixed time window according to an embodiment of the present invention;
FIG. 7 is a schematic block diagram of a timeline slicing module in a fixed time window based medical insurance data processing apparatus according to an embodiment of the present invention;
FIG. 8 is a schematic block diagram of a first sub-timeline determining module in the medical insurance data processing apparatus based on the fixed time window according to the embodiment of the present invention;
FIG. 9 is a schematic block diagram of a first frequency determination module in a fixed time window based medical insurance data processing apparatus according to an embodiment of the present invention;
FIG. 10 is a schematic diagram of a computer device according to an embodiment of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The medical insurance data processing method based on the fixed time window provided by the embodiment of the invention can be applied to the application environment shown in figure 1. Specifically, the medical insurance data processing method based on the fixed time window is applied to a medical insurance data processing system based on the fixed time window, the medical insurance data processing system based on the fixed time window comprises a client and a server as shown in fig. 1, and the client and the server are in communication through a network and are used for solving the problems of long detection time and low detection efficiency. The client is also called a user side, and refers to a program corresponding to the server and providing local services for the client. The client may be installed on, but is not limited to, various personal computers, laptops, smartphones, tablets, and portable wearable devices. The server may be implemented as a stand-alone server or as a server cluster consisting of a plurality of servers.
In one embodiment, as shown in fig. 2, a medical insurance data processing method based on a fixed time window is provided and applied in the server shown in fig. 1, and the method includes the following steps:
s10: acquiring the triggering time point of a medical insurance use event in a preset time period and the total triggering times of the medical insurance use event.
The preset time period can be set according to actual scene requirements, and exemplarily, the preset time period can be one week or one month. Illustratively, a medical insurance usage event may be a patient visit record to the same department; or the number of times the patient takes the medicine. The triggering time point refers to a time point corresponding to the occurrence of the medical insurance usage event monitored in a preset time period. The total number of triggers refers to the total number of triggers for a medical insurance usage event within a preset time period.
S20: and displaying the medical insurance use events and the trigger time points thereof on a preset time axis according to preset display rules, and acquiring time intervals between all two adjacent medical insurance use events on the preset time axis.
The preset display rule can be that each medical insurance use event is sequenced according to the sequence of the trigger time points corresponding to each medical insurance use event, and the interval between the medical insurance use events is the time interval between the trigger time points corresponding to the medical insurance use events. That is, each coordinate point on the preset time axis represents a time point corresponding to each medical insurance use event.
Specifically, after the triggering time points of the medical insurance usage events and the total triggering times of the medical insurance usage events within the preset time period are obtained, each medical insurance usage event is displayed on the preset time axis according to the sequence of the triggering time points corresponding to the medical insurance usage events, so that the time points corresponding to each medical insurance usage event and the total triggering times of the medical insurance usage events (namely the number of coordinate points on the preset time axis) can be clearly known on the preset time axis, and the time intervals between all two adjacent medical insurance usage events on the preset time axis are obtained.
S30: and cutting the preset time shaft into a plurality of sub time shafts according to a preset fixed time window and the time interval.
The preset fixed time window refers to a fixed monitoring window, namely the trigger frequency of the medical insurance use event in the preset fixed time window can be used for judging whether the medical insurance use event is abnormal or not, and the preset fixed time window can be set according to an actual application scene. The sub-time axis refers to a time axis obtained by cutting and dividing a preset time axis, that is, the sub-time axis is a part of the preset time axis.
In one embodiment, as shown in fig. 3, step S30 includes the following steps:
s301: comparing each of the time intervals with the preset fixed time window.
S302: when the time interval is larger than the preset fixed time window, checking whether the time interval is a first time interval; the first time interval is a time interval located at the first position on the preset time axis.
S303: and when the time interval is a first time interval, deleting the first time interval and a first medical insurance use event positioned before the first time interval from the preset time axis.
S304: and when the time interval is not the first time interval, deleting the time interval from the preset time axis, cutting the preset time axis at a position corresponding to the time interval, recording the medical insurance usage event after the time interval as a starting point event of a next sub time axis, and simultaneously recording the medical insurance usage event before the time interval as an ending point event of a previous sub time axis.
Specifically, after the medical insurance usage events and the trigger time points thereof are displayed on a preset time axis according to preset display rules and the time intervals between all two adjacent medical insurance usage events on the preset time axis are acquired, a preset fixed time window is compared with each time interval to determine whether the time intervals are greater than the preset fixed time window.
Because the preset fixed time window is set according to a specific application scenario, when the time interval is greater than the preset fixed time window, the time interval between the trigger time points representing the two adjacent medical insurance usage events corresponding to the time interval is very long and exceeds the monitoring time of the preset fixed time window, that is, the two adjacent medical insurance usage events corresponding to the time interval are represented to be free from abnormality, that is, normal data, so that the time interval is deleted, the time for searching for abnormal data is saved, and the abnormal data processing efficiency is improved.
Furthermore, when the time interval is greater than the preset fixed time window, the time interval needs to be cut on the preset time axis, and one of the two adjacent medical insurance usage events corresponding to the first time interval is the first medical insurance usage event located before the first time interval on the preset time axis, and after the first time interval is cut, the first medical insurance usage event is independently used as a sub-time axis, and therefore, the sub-time axis cannot be abnormal, when the time interval greater than the preset fixed time window is the first time interval, the first time interval and the first medical insurance usage event located before the first time interval are deleted from the preset time axis, and therefore time for processing abnormal data is saved.
Further, when the time interval is not the first time interval, deleting the time interval from the preset time axis, cutting the preset time axis at a position corresponding to the time interval, recording the medical insurance usage event after the time interval as a start event of a next sub time axis, and recording the medical insurance usage event before the time interval as an end event of a previous sub time axis.
Further, when the time interval is an end time interval (that is, a last time interval on a preset time axis), and the end time interval is greater than a preset fixed time window, after the end time interval is cut, a previous medical insurance usage event corresponding to the end time interval is used as an end point event of a previous sub-time axis, and the rest is the last medical insurance usage event, and in the case that the last medical insurance usage event is used as a sub-time axis alone, the sub-time axis is not abnormal, so the end time interval and the last medical insurance usage event located after the end time interval can be deleted from the preset time axis.
In another embodiment, when the time interval is less than or equal to the predetermined fixed time window, the time interval is characterized to conform to the monitoring range of the predetermined fixed time window, and thus the time interval is reserved, i.e., not deleted.
S40: and acquiring the total number of the medical insurance use events contained in each sub-time axis, and recording the sub-time axis containing the maximum total number of the medical insurance use events as a first sub-time axis.
Specifically, after the preset time axis is cut into a plurality of sub time axes according to a preset fixed time window and the time interval, the total number of the medical insurance usage events contained in each sub time axis is acquired, and the sub time axis containing the maximum total number of the medical insurance usage events is recorded as a first sub time axis.
Further, as shown in fig. 4, step S40 further includes the following steps:
s401: and deleting a qualified time axis, wherein the qualified time axis refers to the sub time axis which contains the total number of the medical insurance use events less than the preset number.
The preset number refers to the number of times that the medical insurance use event is allowed to be triggered; illustratively, in a medical scenario, the number of times that a patient visits the same department within a week is allowed to occur at most three times under the premise of using medical insurance; or the number of times of taking the medicine in one week is allowed to occur four times at most.
S402: and recording the sub-time axis containing the maximum total number of medical insurance use events as a first sub-time axis when the number of the sub-time axes after the qualified time axis is deleted is greater than or equal to one.
S403: and when the number of the sub time axes after the qualified time axis is deleted is equal to zero, prompting that no abnormality occurs in the medical insurance use event in the preset time period.
Specifically, after the preset time axis is cut into a plurality of sub time axes according to a preset fixed time window and the time interval, the total number of the medical insurance usage events contained in each sub time axis is acquired; when the total number of the medical insurance use events on any one sub-time axis is less than the preset number, deleting the sub-time axis, namely the qualified time axis; and after deleting all the qualified time axes, detecting the quantity of the remaining sub-time axes, and if the quantity of the remaining sub-time axes is more than or equal to one, namely representing that the unqualified time axes still exist at the moment, recording the sub-time axes containing the maximum total number of medical insurance use events as a first sub-time axis so as to carry out first detection on the first sub-time axis in the subsequent steps.
Further, if the number of the remaining sub-time axes is equal to zero, the representation shows that no unqualified time axis exists at this time, that is, no abnormality occurs in the medical insurance usage events on each sub-time axis, so that the medical insurance usage events can be prompted to be not abnormal within a preset time period.
S50: determining a first densest frequency corresponding to the first sub-time axis according to the preset fixed time window and a preset frequency determination method; the first most frequent representation corresponds to the preset fixed time window, and the maximum frequency of the medical insurance usage event is triggered in the first sub-time axis.
The preset frequency determining method is used for determining the densest frequency on any one sub time axis.
Specifically, after the total number of the medical insurance usage events contained in each sub-time axis is obtained and the sub-time axis containing the maximum total number of the medical insurance usage events is recorded as a first sub-time axis, the maximum frequency of triggering the medical insurance usage events in the first sub-time axis in the preset fixed time window is determined according to the preset fixed time window and a preset frequency determination method, and the maximum frequency is recorded as a first most intensive frequency corresponding to the first sub-time axis.
Further, as shown in fig. 5, step S50 specifically includes the following steps:
s501: selecting a preset number of medical insurance usage events on the first sub-time axis as an initialization element group according to the time sequence, and recording the total duration of time intervals between adjacent medical insurance usage events in the initialization element group as a first accumulated time interval.
The time sequence refers to the sequence of the trigger time points corresponding to the medical insurance use events. The preset number refers to the number of times that the medical insurance use event is allowed to be triggered; illustratively, in a medical scenario, the number of times that a patient visits the same department within a week is allowed to occur at most three times under the premise of using medical insurance; or the number of times of taking the medicine in one week is allowed to occur four times at most. The initialization element group is a combination containing a preset number of medical insurance usage events on the first sub-timeline.
Specifically, after the total number of the medical insurance usage events contained in each sub-time axis is obtained and the sub-time axis containing the largest total number of the medical insurance usage events is recorded as the first sub-time axis, a preset number of medical insurance usage events are selected as the initialization element group according to the sequence of the trigger time points corresponding to the medical insurance usage events on the first sub-time axis. It is to be understood that the first medical insurance usage event and the second medical insurance usage event on the first sub-timeline can be selected as the initialization element group. The first medical insurance usage event and the third medical insurance usage event on the first sub-time axis should not be selected as the initialization element group, because the maximum frequency that needs to be determined in this embodiment is related to each medical insurance usage event, if the medical insurance usage events are not selected continuously, the finally obtained maximum frequency may be wrong, and the accuracy of processing abnormal data is reduced. In this embodiment, the first medical insurance usage event of the first sub-timeline is used as a starting point, that is, a preset number of medical insurance usage events are selected from the first medical insurance usage event as an initialization element group.
S502: and when the first accumulated time interval is smaller than or equal to the preset fixed time window, storing the first accumulated time interval and the initialization element group in an associated manner as first frequency data.
Specifically, a preset number of medical insurance usage events on the first sub-time axis are selected according to a time sequence to serve as an initialization element group, after the total duration of time intervals between adjacent medical insurance usage events in the initialization element group is recorded as a first accumulated time interval, the first accumulated time interval is compared with a preset fixed time window, when the first accumulated time interval is smaller than or equal to the preset fixed time window, the first accumulated time interval is characterized to meet the monitoring requirement of the preset fixed time window, and the first accumulated time interval and the initialization element group are stored as first frequency data in an associated mode.
S503: detecting whether a medical insurance usage event exists after the last medical insurance usage event in the initialization element group.
Specifically, when the first accumulated time interval is less than or equal to the preset fixed time window, after the first accumulated time interval and the initialization element group are stored in an associated manner as first frequency data, whether a medical insurance usage event still exists after the last medical insurance usage event in the initialization element group is detected. Illustratively, assuming that the first sub-timeline includes five medical insurance usage events arranged in chronological order, assuming that the initialization element group includes a first medical insurance usage event, a second medical insurance usage event, and a third medical insurance usage event, there is a medical insurance usage event after the last medical insurance usage event (i.e., the third medical insurance usage event) in the initialization element group.
S504: when no medical insurance usage event exists after the last medical insurance usage event in the initialization element group, recording the first frequency data as the first most dense frequency corresponding to the first sub-timeline.
Specifically, after detecting whether a medical insurance usage event still exists after the last medical insurance usage event in the initialization element group and when the medical insurance usage event does not exist after the last medical insurance usage event in the initialization element group, the first sub-time axis is characterized to be checked completely, and then the first frequency data is recorded as the first most dense frequency corresponding to the first sub-time axis.
Further, after step S503, the method further includes:
s505: when a medical insurance usage event exists after the last medical insurance usage event in the initialization element group, adding the medical insurance usage event after the last medical insurance usage event in the initialization element group into the initialization element group to form a second element group, and recording the total duration of time intervals between adjacent medical insurance usage events in the second element group as a second accumulated time interval.
Specifically, after detecting whether a medical insurance usage event exists after the last medical insurance usage event in the initialization element group, if the medical insurance usage event exists after the last medical insurance usage event in the initialization element group, the first sub-time axis is not verified, the medical insurance usage event after the last medical insurance usage event in the initialization element group is added into the initialization element group to form a second element group, and the total duration of the time interval between adjacent medical insurance usage events in the second element group is recorded as a second accumulated time interval.
S506: and when the second accumulated time interval is smaller than or equal to the preset fixed time window, if no medical insurance using event exists after the last medical insurance using event in the second element group, storing the second accumulated time interval and the second element group in an associated manner as a first most dense frequency corresponding to the first sub-time axis.
Specifically, after a medical insurance usage event after the last medical insurance usage event in the initialization element group is added to the initialization element group to form a second element group, the total duration of a time interval between adjacent medical insurance usage events in the second element group is recorded as a second accumulated time interval, the second accumulated time interval is compared with a preset fixed time window, when the second accumulated time interval is smaller than or equal to the preset fixed time window, whether a medical insurance usage event exists after the last medical insurance usage event in the second element group is detected, and if no medical insurance usage event exists after the last medical insurance usage event in the second element group, the second accumulated time interval and the second element group are stored in an associated manner as a first dense frequency corresponding to the first sub-time axis. It is understood that if the second accumulated time interval corresponding to the second element group is still less than or equal to the preset fixed time window after the new medical insurance usage event is added, the time interval between the medical insurance usage event and other medical insurance usage events is short, and therefore the second accumulated time interval and the second element group are associated and stored as the first intensive frequency corresponding to the first sub-time axis.
Further, if the medical insurance usage event still exists after the last medical insurance usage event in the second element group, the steps S505 to S506 are repeated to obtain a fourth element group, a fifth element group and the like until all the medical insurance usage events in the first sub-time axis are traversed.
In a specific embodiment, after step S501, that is, after selecting a preset number of medical insurance usage events on the first sub-time axis as an initialization element group according to a time sequence, and recording a total duration of a time interval between adjacent medical insurance usage events in the initialization element group as a first accumulated time interval, the method further includes the following steps:
s507: when the first accumulated time interval is larger than the preset fixed time window, if a medical insurance usage event still exists after the last medical insurance usage event in the initialization element group, adding the medical insurance usage event after the last medical insurance usage event in the initialization element group into the initialization element group, deleting the first medical insurance usage event in the initialization element group according to the time sequence to form a third element group, and recording the total duration of the time interval between the adjacent medical insurance usage events in the third element group as a third accumulated time interval.
Specifically, after recording the total duration of the time interval between adjacent medical insurance usage events in the initialization element group as a first accumulated time interval, comparing the first accumulated time interval with a preset fixed time window, and when the first accumulated time interval is greater than the preset fixed time window, detecting whether a medical insurance usage event still exists after the last medical insurance usage event in the initialization element group; when the first accumulated time interval is larger than the preset fixed time window, if a medical insurance usage event still exists after the last medical insurance usage event in the initialization element group, adding the medical insurance usage event after the last medical insurance usage event in the initialization element group into the initialization element group, deleting the first medical insurance usage event in the initialization element group according to the time sequence to form a third element group, and recording the total duration of the time interval between the adjacent medical insurance usage events in the third element group as a third accumulated time interval.
S508: and when the third accumulated time interval is smaller than or equal to the preset fixed time window, the third accumulated time interval and the third element group are associated and stored as third frequency data.
S509: and when no medical insurance using event exists after the last medical insurance using event in the third element group, recording the maximum frequency density in the first frequency data and the third frequency data as the first most dense frequency corresponding to the first sub time axis.
The frequency density refers to a ratio of the number of the medical insurance use events in the element group corresponding to the first frequency data (or the third frequency data) to the accumulated time interval.
Specifically, when the third accumulated time interval is less than or equal to a preset fixed time window, the third accumulated time interval and the third element are stored in an associated manner as third frequency data; detecting whether a medical insurance using event exists after the last medical insurance using event in the third element group, and if the medical insurance using event does not exist after the last medical insurance using event in the third element group, acquiring the frequency density of the first frequency data, namely the ratio of the number of the medical insurance using events in the initialization element group in the first frequency data to the first accumulated time interval; acquiring the frequency density of the third frequency data, namely the ratio of the number of the traditional Chinese medicine guarantee events of the third element group in the third frequency data to the third accumulated time interval; and recording the maximum frequency density in the first frequency data and the third frequency data as a first densest frequency corresponding to the first sub time axis.
In another embodiment, after step S507, that is, after the first accumulated time interval is greater than the preset fixed time window, the method further includes:
and when no medical insurance use event exists after the last medical insurance use event in the initialization element group, prompting that no abnormality occurs in the medical insurance use event in the preset time period.
It can be understood that the initialization element group includes a preset number of medical insurance usage events, that is, the number of medical insurance usage events in the initialization element group is in accordance with the requirement, so that after the first accumulated time interval is greater than the preset fixed time window and no medical insurance usage event exists after the last medical insurance usage time in the initialization element group, the medical insurance usage times on the first sub-time axis are all represented to be abnormal, and therefore, the medical insurance usage events in the preset time period can be timed not to be abnormal.
S60: and when the first most dense frequency meets a preset frequency standard, prompting that the medical insurance use event in the preset time period is abnormal.
The preset frequency standard can be set according to different scenes; assuming that in a medical scenario, the number of times that a patient obtains the same prescription within one month should not exceed five times, and if the number of times exceeds five times, it represents that there is an abnormality such as suspicion of fraud, and the like, the preset frequency criterion is set to be that the number of times that the patient obtains the same prescription within one month exceeds five times.
Specifically, after the first densest frequency corresponding to the first sub-time axis is determined according to the preset fixed time window and the preset frequency determining method, the first densest frequency is compared with a preset frequency standard to determine whether the first densest frequency meets the preset frequency standard, and if the first densest frequency meets the preset frequency standard, an abnormal phenomenon of medical insurance use events in a preset time period is prompted to be checked by a checker.
Further, when the first most dense frequency meets the preset frequency standard, the fact that the medical insurance use event is determined to have an abnormal phenomenon in the preset time period on the first sub-time axis is represented, the remaining sub-time axes can be deleted, and the abnormal data processing time is shortened while the system calculation amount is reduced.
In a specific embodiment, after step S50, that is, after determining the first densest frequency corresponding to the first sub-time axis according to the preset fixed time window and the preset frequency determining method, the method further includes the following steps:
s70: and recording a sub-time axis containing a plurality of total medical insurance use events as a second sub-time axis when the first most dense frequency does not meet the preset frequency standard.
Specifically, after determining a first densest frequency corresponding to the first sub-time axis according to the preset fixed time window and the preset frequency determining method, comparing the first densest frequency with a preset frequency standard, determining whether the first densest frequency meets the preset frequency standard, if the first densest frequency does not meet the preset frequency standard, representing that no abnormal phenomenon occurs in the medical insurance usage event on the first sub-time axis, and therefore, continuously checking whether the abnormal phenomenon occurs in the medical insurance usage event exists in the remaining sub-time axes, and further recording the sub-time axis containing the medical insurance usage event in a large number as a second sub-time axis.
S80: determining a second densest frequency corresponding to the second sub-time axis according to the preset fixed time window and a preset frequency determination method; the second most frequent representation corresponds to the preset fixed time window, and the maximum frequency of the medical insurance usage event is triggered in the second sub-time axis.
Specifically, after recording the sub-time axis containing the total number of times of the medical insurance usage events as a second sub-time axis when the first most intensive frequency does not meet the preset frequency standard, determining that the second sub-time axis has the maximum frequency triggering the medical insurance usage events in the preset fixed time window according to the preset fixed time window and a preset frequency determination method, and recording the maximum frequency as the second most intensive frequency corresponding to the second sub-time axis.
S90: and when the second most dense frequency meets a preset frequency standard, prompting that the medical insurance use event in the preset time period is abnormal.
Specifically, after the second densest frequency corresponding to the second sub-time axis is determined according to the preset fixed time window and the preset frequency determining method, the second densest frequency is compared with a preset frequency standard to determine whether the second densest frequency meets the preset frequency standard, and if the second densest frequency meets the preset frequency standard, an abnormal phenomenon of medical insurance use events in a preset time period is prompted to be checked by a checker.
Further, when the second most dense frequency meets the preset frequency standard, the fact that the medical insurance use event is determined to have an abnormal phenomenon in the preset time period on the second sub-time axis is represented, the remaining sub-time axes can be deleted, and the abnormal data processing time is shortened while the system calculation amount is reduced.
Further, when the second most intensive frequency does not meet the preset frequency standard, checking a sub-time axis containing the third most intensive frequency of the total number of the target time, if the second most intensive frequency does not meet the preset frequency standard, sequentially traversing all the remaining sub-time axes, and when the most intensive frequency corresponding to any one sub-time axis meets the preset frequency standard, stopping the checking process and prompting that the medical insurance use event has an abnormal phenomenon in a preset time period, or when the remaining sub-time axes are zero and the most intensive frequencies corresponding to all the sub-time axes do not meet the preset frequency standard, stopping the checking and prompting that the medical insurance use event has no abnormal phenomenon temporarily in the preset time period.
In this embodiment, first, a preset time axis is cut by dividing a time interval on the time axis by a preset fixed time window; effective pruning is carried out on the preset time axis, so that the time for processing abnormal data is saved; secondly, the first sub-time shaft with the most medical insurance use events is detected for the first time, when the first densest frequency corresponding to the first sub-time shaft meets a preset frequency standard, the medical insurance use events are judged to have abnormal phenomena in a preset time period, the rest sub-time shafts can be deleted, the detection time is shortened, and the system calculation amount is reduced, so that whether the abnormal phenomena exist in the preset time period can be more quickly verified; by the method, whether the abnormal phenomenon occurs or not can be detected more quickly under the condition that a large amount of data exists in the preset time period.
In another embodiment, to ensure privacy and security of the medical insurance usage event in the above embodiments, the medical insurance usage event may be stored in the blockchain. The Block chain (Blockchain) is an encrypted and chained transaction storage structure formed by blocks (blocks).
For example, the header of each block may include hash values of all transactions in the block, and also include hash values of all transactions in the previous block, so as to achieve tamper resistance and forgery resistance of the transactions in the block based on the hash values; newly generated transactions, after being filled into the tiles and passing through the consensus of nodes in the blockchain network, are appended to the end of the blockchain to form a chain growth.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
In an embodiment, a medical insurance data processing device based on a fixed time window is provided, and the medical insurance data processing device based on the fixed time window corresponds to the medical insurance data processing method based on the fixed time window in the above embodiment one to one. As shown in fig. 6, the medical insurance data processing based on the fixed time window includes the following modules:
the data acquisition module 10 is used for acquiring the triggering time points of the medical insurance usage events within a preset time period and the total triggering times of the medical insurance usage events;
the data display module 20 is configured to display the medical insurance usage events and the trigger time points thereof on a preset time axis according to a preset display rule, and acquire time intervals between all two adjacent medical insurance usage events on the preset time axis;
a time axis cutting module 30, configured to cut the preset time axis into a plurality of sub time axes according to a preset fixed time window and the time interval;
the first sub-timeline determining module 40 is configured to obtain the total number of the medical insurance usage events included in each sub-timeline, and record the sub-timeline including the largest total number of the medical insurance usage events as the first sub-timeline;
a first frequency determining module 50, configured to determine a first densest frequency corresponding to the first sub-timeline according to the preset fixed time window and a preset frequency determining method; the first most frequent representation corresponds to the preset fixed time window, and the maximum frequency of the medical insurance usage event in the first sub-time axis is triggered;
and the first abnormity prompting module 60 is used for prompting that the medical insurance usage event in the preset time period is abnormal when the first most dense frequency meets a preset frequency standard.
Preferably, as shown in fig. 7, the time axis cutting module 30 includes the following units:
a data comparing unit 301, configured to compare each time interval with the preset fixed time window;
a position detecting unit 302, configured to check whether the time interval is a first time interval when the time interval is greater than the preset fixed time window; the first time interval is a time interval positioned at the first position on the preset time axis;
a first data deleting unit 303, configured to delete the first time interval and a first medical insurance usage event located before the first time interval from the preset time axis when the time interval is the first time interval;
a second data deleting unit 304, configured to delete the time interval from the preset time axis when the time interval is not the first time interval, cut the preset time axis at a position corresponding to the time interval, record the medical insurance usage event after the time interval as a start event of a next sub time axis, and record the medical insurance usage event before the time interval as an end event of a previous sub time axis.
Preferably, the medical insurance data processing device based on the fixed time window further comprises the following modules:
a second sub-timeline determining module 70, configured to record, as a second sub-timeline, a sub-timeline including a plurality of total medical insurance usage events when the first most dense frequency does not meet a preset frequency standard;
a second frequency determining module 80, configured to determine a second densest frequency corresponding to the second sub-timeline according to the preset fixed time window and a preset frequency determining method; the second most frequent representation corresponds to the preset fixed time window, and the maximum frequency of the medical insurance use event in the second sub-time axis is triggered;
and the second abnormity prompting module 90 is used for prompting that the medical insurance using event in the preset time period is abnormal when the second most dense frequency meets a preset frequency standard.
Preferably, as shown in fig. 8, the first sub timeline determining module 40 includes the following units:
a time axis deleting unit 401, configured to delete a qualified time axis, where the qualified time axis refers to the sub time axis in which the total number of the medical insurance usage events is less than a preset number.
A sub-timeline determining unit 402, configured to record, as a first sub-timeline, a sub-timeline with a maximum total number of medical insurance usage events when the number of the sub-timelines after the deletion of the qualified timeline is greater than or equal to one.
A first exception prompting unit 403, configured to prompt that no exception occurs in the medical insurance usage event within the preset time period when the number of the sub-time axes after the qualified time axis is deleted is equal to zero.
Preferably, as shown in fig. 9, the first frequency determining module 50 includes the following units:
an element group determining unit 501, configured to select, according to a time sequence, a preset number of medical insurance usage events on the first sub-time axis as an initialization element group, and record a total duration of a time interval between adjacent medical insurance usage events in the initialization element group as a first accumulated time interval.
A first frequency data determining unit 502, configured to store the first accumulated time interval and the initialization element group as first frequency data in an associated manner when the first accumulated time interval is less than or equal to the preset fixed time window;
an event detecting unit 503, configured to detect whether there is a medical insurance usage event after the last medical insurance usage event in the initialization element group;
a first frequency determining unit 504, configured to record the first frequency data as a first densest frequency corresponding to the first sub-timeline when no medical insurance usage event exists after a last medical insurance usage event in the initialization element group.
Preferably, the first frequency determining module 50 further includes the following units:
a first element group adjusting unit 505, configured to, when a medical insurance usage event exists after a last medical insurance usage event in the initialization element group, add the medical insurance usage event after the last medical insurance usage event in the initialization element group to form a second element group, and record a total duration of a time interval between adjacent medical insurance usage events in the second element group as a second accumulated time interval;
a second frequency determining unit 506, configured to, when the second accumulated time interval is less than or equal to the preset fixed time window, if no medical insurance usage event exists after the last medical insurance usage event in the second element group, associate and store the second accumulated time interval and the second element group as a first most dense frequency corresponding to the first sub-time axis.
Preferably, the first frequency determining module 50 further includes the following units:
a second element group adjusting unit 507, configured to, when the first accumulated time interval is greater than the preset fixed time window, if a medical insurance usage event still exists after a last medical insurance usage event in the initialization element group, add the medical insurance usage event after the last medical insurance usage event in the initialization element group to the initialization element group, delete the first medical insurance usage event in the initialization element group according to a time sequence to form a third element group, and record a total duration of a time interval between adjacent medical insurance usage events in the third element group as a third accumulated time interval;
a third frequency determining unit 508, configured to associate and store the third accumulated time interval and the third element group as third frequency data when the third accumulated time interval is smaller than or equal to the preset fixed time window;
a fourth frequency determining unit 509, configured to record, as the first densest frequency corresponding to the first sub-time axis, the one with the highest frequency density in the first frequency data and the third frequency data when no medical insurance usage event exists after the last medical insurance usage event in the third element group.
Preferably, the first frequency determining module 50 further includes the following units:
and the second abnormity prompting unit is used for prompting that no abnormity occurs in the medical insurance usage event within the preset time period when no medical insurance usage event exists after the last medical insurance usage event in the initialization element group.
For specific limitations of the medical insurance data processing device based on the fixed time window, reference may be made to the above limitations of the medical insurance data processing method based on the fixed time window, and details thereof are not repeated here. All or part of each module in the medical insurance data processing device based on the fixed time window can be realized by software, hardware and the combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in fig. 10. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for data used in the medical insurance data processing method based on the fixed time window in the embodiment. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a fixed time window based medical insurance data processing method.
In one embodiment, a computer device is provided, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the computer program, the medical insurance data processing method based on the fixed time window in the above embodiments is implemented.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which, when being executed by a processor, implements the fixed-time-window-based medical insurance data processing method in the above-described embodiments.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (10)
1. A medical insurance data processing method based on a fixed time window is characterized by comprising the following steps:
acquiring a triggering time point of a medical insurance use event in a preset time period and the total triggering times of the medical insurance use event;
displaying the medical insurance use events and the trigger time points thereof on a preset time axis according to a preset display rule, and acquiring time intervals between all two adjacent medical insurance use events on the preset time axis;
cutting the preset time shaft into a plurality of sub time shafts according to a preset fixed time window and the time interval;
acquiring the total number of the medical insurance use events contained in each sub time axis, and recording the sub time axis containing the maximum total number of the medical insurance use events as a first sub time axis;
determining a first densest frequency corresponding to the first sub-time axis according to the preset fixed time window and a preset frequency determination method; the first most frequent representation corresponds to the preset fixed time window, and the maximum frequency of the medical insurance usage event in the first sub-time axis is triggered;
and when the first most dense frequency meets a preset frequency standard, prompting that the medical insurance use event in the preset time period is abnormal.
2. The medical insurance data processing method based on the fixed time window as claimed in claim 1, wherein the cutting the preset time axis into a plurality of sub time axes according to the preset fixed time window and the time interval comprises:
comparing each of the time intervals with the preset fixed time window;
when the time interval is larger than the preset fixed time window, checking whether the time interval is a first time interval; the first time interval is a time interval positioned at the first position on the preset time axis;
when the time interval is a first time interval, deleting the first time interval and a first medical insurance use event positioned before the first time interval from the preset time axis;
and when the time interval is not the first time interval, deleting the time interval from the preset time axis, cutting the preset time axis at a position corresponding to the time interval, recording the medical insurance usage event after the time interval as a starting point event of a next sub time axis, and simultaneously recording the medical insurance usage event before the time interval as an ending point event of a previous sub time axis.
3. The medical insurance data processing method based on the fixed time window as claimed in claim 1, wherein after determining the first densest frequency corresponding to the first sub-time axis, further comprising:
when the first most dense frequency does not meet a preset frequency standard, recording a sub-time axis containing a plurality of total medical insurance use events as a second sub-time axis;
determining a second densest frequency corresponding to the second sub-time axis according to the preset fixed time window and a preset frequency determination method; the second most frequent representation corresponds to the preset fixed time window, and the maximum frequency of the medical insurance use event in the second sub-time axis is triggered;
and when the second most dense frequency meets a preset frequency standard, prompting that the medical insurance use event in the preset time period is abnormal.
4. The medical insurance data processing method based on the fixed time window of claim 1, wherein the acquiring the total number of the medical insurance usage events contained in each sub-timeline and recording the sub-timeline containing the maximum total number of the medical insurance usage events as the first sub-timeline further comprises:
deleting a qualified time axis, wherein the qualified time axis refers to the sub time axes of which the total number of the medical insurance use events is less than a preset number;
when the number of the sub-time axes after the qualified time axis is deleted is larger than or equal to one, recording the sub-time axis containing the maximum total number of medical insurance use events as a first sub-time axis;
and when the number of the sub time axes after the qualified time axis is deleted is equal to zero, prompting that no abnormality occurs in the medical insurance use event in the preset time period.
5. The medical insurance data processing method based on the fixed time window as claimed in claim 1, wherein the determining the first densest frequency corresponding to the first sub-time axis according to the preset fixed time window and a preset frequency determining method comprises:
selecting a preset number of medical insurance usage events on the first sub-time axis as an initialization element group according to the time sequence, and recording the total duration of time intervals between adjacent medical insurance usage events in the initialization element group as a first accumulated time interval;
when the first accumulated time interval is smaller than or equal to the preset fixed time window, storing the first accumulated time interval and the initialization element group in an associated mode as first frequency data;
detecting whether a medical insurance usage event exists after the last medical insurance usage event in the initialization element group;
when no medical insurance usage event exists after the last medical insurance usage event in the initialization element group, recording the first frequency data as the first most dense frequency corresponding to the first sub-timeline.
6. The fixed-time-window-based medical insurance data processing method of claim 5, wherein said detecting whether there is a medical insurance usage event after a last medical insurance usage event in the initialization element group further comprises:
when a medical insurance usage event exists after the last medical insurance usage event in the initialization element group, adding the medical insurance usage event after the last medical insurance usage event in the initialization element group into the initialization element group to form a second element group, and recording the total duration of time intervals between adjacent medical insurance usage events in the second element group as a second accumulated time interval;
and when the second accumulated time interval is smaller than or equal to the preset fixed time window, if no medical insurance using event exists after the last medical insurance using event in the second element group, storing the second accumulated time interval and the second element group in an associated manner as a first most dense frequency corresponding to the first sub-time axis.
7. The fixed-time-window-based medical insurance data processing method of claim 5, wherein recording a total duration of time intervals between adjacent medical insurance usage events in the initialization element group as a first cumulative time interval, further comprises:
when the first accumulated time interval is greater than the preset fixed time window, if a medical insurance usage event still exists after the last medical insurance usage event in the initialization element group, adding the medical insurance usage event after the last medical insurance usage event in the initialization element group into the initialization element group, deleting the first medical insurance usage event in the initialization element group according to a time sequence to form a third element group, and recording the total duration of the time interval between adjacent medical insurance usage events in the third element group as a third accumulated time interval;
when the third accumulated time interval is smaller than or equal to the preset fixed time window, the third accumulated time interval and the third element group are stored in a correlated mode as third frequency data;
and when no medical insurance using event exists after the last medical insurance using event in the third element group, recording the maximum frequency density in the first frequency data and the third frequency data as the first most dense frequency corresponding to the first sub time axis.
8. A medical insurance data processing apparatus based on a fixed time window, comprising:
the data acquisition module is used for acquiring the triggering time point of the medical insurance usage event and the total triggering times of the medical insurance usage event within a preset time period;
the data display module is used for displaying the medical insurance usage events and the trigger time points thereof on a preset time axis according to preset display rules and acquiring time intervals between all two adjacent medical insurance usage events on the preset time axis;
the time axis cutting module is used for cutting the preset time axis into a plurality of sub time axes according to a preset fixed time window and the time interval;
the first sub-timeline determining module is used for acquiring the total number of the medical insurance use events contained in each sub-timeline, and recording the sub-timeline containing the maximum total number of the medical insurance use events as the first sub-timeline;
the first frequency determining module is used for determining a first densest frequency corresponding to the first sub time axis according to the preset fixed time window and a preset frequency determining method; the first most frequent representation corresponds to the preset fixed time window, and the maximum frequency of the medical insurance usage event in the first sub-time axis is triggered;
and the first abnormity prompting module is used for prompting that the medical insurance use event in the preset time period is abnormal when the first most dense frequency meets a preset frequency standard.
9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the fixed time window-based medical insurance data processing method according to any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, implements the fixed time window-based medical insurance data processing method according to any one of claims 1 to 7.
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| CN202010929662.2A CN112001805B (en) | 2020-09-07 | 2020-09-07 | Medical insurance data processing method, device, equipment and medium based on fixed time window |
| PCT/CN2020/124408 WO2021151332A1 (en) | 2020-09-07 | 2020-10-28 | Medical insurance data processing method and apparatus based on fixed time window, and device and medium |
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| CN109615546A (en) * | 2018-12-13 | 2019-04-12 | 平安医疗健康管理股份有限公司 | Extremely medical recognition methods, device, terminal and computer readable storage medium |
| CN109658109A (en) * | 2018-10-29 | 2019-04-19 | 平安医疗健康管理股份有限公司 | Detection method, device, terminal and the storage medium that medical insurance is swiped the card extremely |
| CN111210356A (en) * | 2020-01-14 | 2020-05-29 | 平安医疗健康管理股份有限公司 | Medical insurance data analysis method and device, computer equipment and storage medium |
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| CN109598628B (en) * | 2018-11-30 | 2022-09-20 | 平安医疗健康管理股份有限公司 | Method, device and equipment for identifying medical insurance fraud behaviors and readable storage medium |
| CN110322356B (en) * | 2019-04-22 | 2020-08-07 | 山东大学 | Medical insurance abnormity detection method and system based on HIN mining dynamic multi-mode |
| CN111028089B (en) * | 2019-11-25 | 2023-05-02 | 泰康保险集团股份有限公司 | Abnormal operation identification method and device, computer storage medium and electronic equipment |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109658109A (en) * | 2018-10-29 | 2019-04-19 | 平安医疗健康管理股份有限公司 | Detection method, device, terminal and the storage medium that medical insurance is swiped the card extremely |
| CN109615546A (en) * | 2018-12-13 | 2019-04-12 | 平安医疗健康管理股份有限公司 | Extremely medical recognition methods, device, terminal and computer readable storage medium |
| CN111210356A (en) * | 2020-01-14 | 2020-05-29 | 平安医疗健康管理股份有限公司 | Medical insurance data analysis method and device, computer equipment and storage medium |
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