CN112001805B - 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, and discloses a medical insurance data processing method, device, equipment and medium based on a fixed time window, wherein the method comprises the steps of acquiring a triggering time point and a triggering total number of medical insurance use events within a preset time period; displaying the medical insurance use events and the triggering 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 the preset time axis into a plurality of sub time axes according to a preset fixed time window and a time interval; recording a sub-time axis containing the largest total number of medical insurance use events as a first sub-time axis; according to a preset fixed time window and a preset frequency determining method, determining a first most dense frequency corresponding to a first sub-time axis; and prompting medical insurance use event abnormality in a preset time period when the first most dense frequency meets a preset frequency standard. The invention improves the processing efficiency of the abnormal data.
Description
Technical Field
The present invention relates to the field of data processing, and in particular, to a method, apparatus, device, and medium for processing medical insurance data based on a fixed time window.
Background
With the development of science and 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 medical fields, application fields, etc.
In the medical field, abnormal data query is often required for medical insurance data, for example, query about the number of times of visits to the same department in a week on the premise of medical insurance; or on the premise of medical insurance, the patient takes medicine for a week, so as to judge the abnormality of the medical insurance data of the patient according to the data. The prior art generally adopts a sliding time window method. The sliding time window method refers to sliding a unit of a time window from left to right on a time axis constructed based on a time point of occurrence of an event for a preset time window of interest, so as to obtain the number of events contained in the current time window. The method has the following defects: the method needs to exhaust the whole time axis to obtain the corresponding result of each time window, and has longer detection time, thereby leading to lower detection efficiency.
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, which are used for solving the problems of long detection time and low detection efficiency.
A method for processing medical insurance data based on a fixed time window, comprising:
acquiring a trigger time point of a medical insurance use event in a preset time period and the total number of times of triggering the medical insurance use event;
according to a preset display rule, displaying the medical insurance use events and the triggering time points thereof on a preset time axis, and acquiring time intervals between all two adjacent medical insurance use events on the preset time axis;
cutting the preset time axis into a plurality of sub time axes 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 largest total number of the medical insurance use events as a first sub-time axis;
according to the preset fixed time window and a preset frequency determining method, determining a first most dense frequency corresponding to the first sub-time axis; the first most dense frequency representation corresponds to the preset fixed time window, and the maximum frequency of triggering the medical insurance using event exists in the first sub-time axis;
And prompting medical insurance use event abnormality in the preset time period when the first most dense frequency meets a preset frequency standard.
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 use event and the total triggering times of the medical insurance use event in a preset time period;
the data display module is used for displaying the medical insurance use events and the triggering time points thereof on a preset time axis according to preset display rules, and acquiring the time intervals between all two adjacent medical insurance use 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-time axis determining module is used for obtaining the total number of the medical insurance use events contained in each sub-time axis and recording the sub-time axis containing the largest total number of the medical insurance use events as a first sub-time axis;
the first frequency determining module is used for determining a first most dense frequency corresponding to the first sub-time axis according to the preset fixed time window and a preset frequency determining method; the first most dense frequency representation corresponds to the preset fixed time window, and the maximum frequency of triggering the medical insurance using event exists in the first sub-time axis;
The first abnormality prompting module is used for prompting abnormality of medical insurance use events in the preset time period when the first most dense frequency accords with a preset frequency standard.
A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the above-described fixed time window based medical insurance data processing method when executing the computer program.
A computer readable storage medium storing a computer program which when executed by a processor implements the above-described fixed time window based medical insurance data processing method.
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 the preset time period are obtained; according to a preset display rule, displaying the medical insurance use events and the triggering time points thereof on a preset time axis, and acquiring time intervals between all two adjacent medical insurance use events on the preset time axis; cutting the preset time axis into a plurality of sub time axes 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 largest total number of the medical insurance use events as a first sub-time axis; according to the preset fixed time window and a preset frequency determining method, determining a first most dense frequency corresponding to the first sub-time axis; the first most dense frequency representation corresponds to the preset fixed time window, and the maximum frequency of triggering the medical insurance using event exists in the first sub-time axis; and prompting medical insurance use event abnormality in the preset time period when the first most dense frequency meets a preset frequency standard.
Firstly, cutting a preset time axis by means of a time interval on the time axis and a preset fixed time window; the preset time axis is effectively pruned, exhaustion of the time axis is not needed, and therefore abnormal data processing time is saved; secondly, first detection is carried out on a first sub-time axis containing the most medical insurance use events, when the first most dense frequency corresponding to the first sub-time axis meets the preset frequency standard, the abnormal phenomenon of the medical insurance use events in the preset time period can be judged, the rest sub-time axis can be deleted, the detection time is shortened, meanwhile, the calculated amount of a system is reduced, and therefore whether the abnormal phenomenon exists in the preset time period can be checked more rapidly; by the method, in medical insurance using events with large data quantity, whether abnormal phenomena occur can be detected more quickly, so that construction of a smart city is promoted.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments of the present invention will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic view of an application environment of a medical insurance data processing method based on a fixed time window according to an embodiment of the present invention;
FIG. 2 is a flow chart of a method of processing medical insurance data based on a fixed time window in accordance with an embodiment of the present invention;
FIG. 3 is a flowchart of S30 in a method for processing medical insurance data based on a fixed time window according to an embodiment of the present invention;
FIG. 4 is a flowchart of S40 in a method for processing medical insurance data based on a fixed time window according to an embodiment of the present invention;
FIG. 5 is a flowchart of S50 in a method for processing medical insurance data based on a fixed time window in accordance with an 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 in an embodiment of the present invention;
FIG. 7 is a schematic block diagram of a timeline cutting module in a medical insurance data processing device based on a fixed time window in accordance with an embodiment of the present invention;
FIG. 8 is a functional block diagram of a first sub-timeline determination module in a medical insurance data processing device based on a fixed time window in accordance with an embodiment of the present invention;
FIG. 9 is a functional block diagram of a first frequency determination module in a medical insurance data processing apparatus based on a fixed time window in accordance with an embodiment of the present invention;
FIG. 10 is a schematic diagram of a computer device in accordance with an embodiment of the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The medical insurance data processing method based on the fixed time window provided by the embodiment of the invention can be applied to an 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, and the medical insurance data processing system based on the fixed time window comprises a client and a server as shown in fig. 1, wherein the client and the server communicate through a network, so that the problems of long detection time and low detection efficiency are solved. The client is also called a client, and refers to a program corresponding to the server for providing local service for the client. The client may be installed on, but is not limited to, various personal computers, notebook computers, smartphones, tablet computers, and portable wearable devices. The server may be implemented as a stand-alone server or as a server cluster composed of a plurality of servers.
In one embodiment, as shown in fig. 2, a method for processing medical insurance data based on a fixed time window is provided and applied to the server shown in fig. 1, and the method includes the following steps:
s10: and acquiring a trigger time point of the medical insurance use event in a preset time period and the total trigger times of the medical insurance use event.
The preset time period may be set according to actual scene requirements, and for example, the preset time period may be one week or one month. Illustratively, the medical insurance usage event may be a record of a patient's visit to the same department; or the number of times the patient takes the drug. The triggering time point refers to a time point corresponding to the occurrence of the medical insurance use event monitored within a preset time period. The total number of triggers refers to the total number of medical insurance usage event triggers within a preset period of time.
S20: and displaying the medical insurance use events and the triggering time points thereof on a preset time axis according to preset display rules, and acquiring the time intervals between all two adjacent medical insurance use events on the preset time axis.
The preset display rule may be that each medical insurance use event is ordered 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 corresponding time point of each medical insurance usage event.
Specifically, after the trigger time points of the medical insurance use events and the total number of times of the triggering of the medical insurance use events in the preset time period are obtained, each medical insurance use event is displayed on a preset time axis according to the sequence of the corresponding trigger time points, so that the corresponding time point of each medical insurance use event and the total number of times of the triggering of the medical insurance use events (namely, the number of coordinate points on the preset time axis) can be clearly known on the preset time axis, and the time interval between all the two adjacent medical insurance use events on the preset time axis is obtained.
S30: and cutting the preset time axis into a plurality of sub time axes according to a preset fixed time window and the time interval.
The preset fixed time window refers to a fixed monitoring window, that is, the triggering 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 a preset time axis and dividing the 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 time interval 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 or not; the first time interval refers to a time interval located at the first position on the preset time axis.
S303: and deleting the first time interval and the first medical insurance using event positioned before the first time interval from the preset time axis when the time interval is the first time interval.
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 use event positioned after the time interval as a starting point event of a next sub time axis, and recording the medical insurance use event positioned before the time interval as an ending point event of a previous sub time axis.
Specifically, after the medical insurance use events and the trigger time points thereof are displayed on a preset time axis according to a preset display rule, and time intervals between all two adjacent medical insurance use events on the preset time axis are acquired, a preset fixed time window is compared with each time interval, so as to determine whether the time intervals are larger than the preset fixed time window.
Because the preset fixed time window is set according to a specific application scene, when the time interval is larger than the preset fixed time window, the trigger time points of the two adjacent medical insurance using events corresponding to the time interval are long in time interval and exceed the monitoring time of the preset fixed time window, namely, the two adjacent medical insurance using events corresponding to the time interval are characterized as having no abnormality, namely, normal data, so that the time for searching the abnormal data is saved by deleting the time interval, and the abnormal data processing efficiency is improved.
Further, 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 use events corresponding to the first time interval is the first medical insurance use event located before the first time interval on the preset time axis, and when the first medical insurance use event is independently used as a sub-time axis after the first time interval is cut, the sub-time axis is not abnormal, so 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 use event located before the first time interval are deleted from the preset time axis, and the 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 use event positioned after the time interval as a starting point event of a next sub time axis, and recording the medical insurance use event positioned before the time interval as an ending point event of a previous sub time axis.
Further, when the time interval is the end time interval (i.e., the last time interval on the preset time axis) and the end time interval is greater than the preset fixed time window, after the end time interval is cut, the previous medical insurance use event corresponding to the end time interval is taken as the last event of the previous sub time axis, and the rest is the last medical insurance use event, and when the last medical insurance use event is taken as a sub time axis alone, the sub time axis is not abnormal, so that the end time interval and the last medical insurance use event 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 as conforming to the monitoring range of the predetermined fixed time window, so that the time interval is preserved, 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 largest 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 medical insurance use events contained in each sub time axis is obtained, and the sub time axis containing the largest total number of medical insurance use events is recorded as a first sub time axis.
Further, as shown in fig. 4, in step S40, the following steps are further included:
s401: and deleting a qualified time axis, wherein the qualified time axis refers to the sub-time axis with the total number of included medical insurance using events being less than the preset number.
Wherein the preset number refers to the number of times that the medical insurance use event is allowed to trigger; illustratively, in a medical scenario, the patient is allowed to visit the same department at a maximum of three times within a week on the premise of using medical insurance; or the medicine taking times in a week are the most allowed to occur four times.
S402: and recording the sub-time axis containing the largest total number of medical insurance use events as a first sub-time axis after deleting the qualified time axis and the number of the sub-time axes is greater than or equal to one.
S403: and when the number of the sub time axes after deleting the qualified time axis is equal to zero, prompting that the medical insurance use event in the preset time period is not abnormal.
Specifically, after cutting the preset time axis into a plurality of sub time axes according to a preset fixed time window and the time interval, acquiring the total number of medical insurance use events contained in each sub time axis; deleting any sub-time axis, namely a qualified time axis when the total number of medical insurance use events on the sub-time axis is less than the preset number; after deleting all qualified time axes, detecting the number of remaining sub time axes, if the number of remaining sub time axes is greater than or equal to one, namely, characterizing that an unqualified time axis still exists at the moment, recording the sub time axis containing the largest total number of medical insurance use events as a first sub time axis, and performing first detection on the first sub time axis in a subsequent step.
Further, if the number of the remaining sub-time axes is equal to zero, it is indicated that no unqualified time axis exists at the moment, that is, no abnormality occurs in the medical insurance use event on each sub-time axis, so that the medical insurance use event can be prompted to be abnormal in a preset time period.
S50: according to the preset fixed time window and a preset frequency determining method, determining a first most dense frequency corresponding to the first sub-time axis; the first most dense frequency representation corresponds to the preset fixed time window, and the maximum frequency of triggering the medical insurance use event exists in the first sub-time axis.
The preset frequency determining method is used for determining the most dense frequency on any one sub-time axis.
Specifically, after the total number of the medical insurance use events contained in each sub-time axis is obtained, the sub-time axis containing the largest total number of the medical insurance use events is recorded as a first sub-time axis, the maximum frequency for triggering the medical insurance use events in the first sub-time axis in the preset fixed time window is determined according to the preset fixed time window and the preset frequency determining method, and the maximum frequency is recorded as a first denser frequency corresponding to the first sub-time axis.
Further, as shown in fig. 5, the step S50 specifically includes the following steps:
s501: and selecting a preset number of medical insurance use events on the first sub-time axis as an initialization element group according to a time sequence, and recording the total duration of the time intervals between adjacent medical insurance use events in the initialization element group as a first accumulated time interval.
The time sequence refers to the sequence of triggering time points corresponding to medical insurance use events. The preset number refers to the number of times that the medical insurance use event is allowed to trigger; illustratively, in a medical scenario, the patient is allowed to visit the same department at a maximum of three times within a week on the premise of using medical insurance; or the medicine taking times in a week are the most allowed to occur four times. The initialization element set is a combination comprising a predetermined number of medical insurance usage events on the first sub-timeline.
Specifically, after the total number of the medical insurance use events contained in each sub-time axis is obtained, the sub-time axis containing the largest total number of the medical insurance use events is recorded as a first sub-time axis, and then the preset number of the medical insurance use events are selected as an initialization element group according to the sequence of the triggering time points corresponding to the medical insurance use events on the first sub-time axis. It will be appreciated that the first and second medical insurance usage events on the first sub-timeline may be selected as the initialization element set. The first medical insurance use event and the third medical insurance use event on the first sub-time axis should not be selected as the initialization element group, because the maximum frequency to be determined in this embodiment is related to each medical insurance use event, if the medical insurance use event is not continuously selected, the maximum frequency obtained finally may be wrong, and the accuracy of abnormal data processing is reduced. In this embodiment, the first medical insurance use event of the first sub-time axis is taken as a starting point, that is, a preset number of medical insurance use events are selected from the first medical insurance use 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 mode as first frequency data.
Specifically, after a preset number of medical insurance use events on the first sub-time axis are selected as an initialization element group according to a time sequence, recording the total duration of time intervals between adjacent medical insurance use 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 smaller than or equal to the preset fixed time window, characterizing that the first accumulated time interval meets the monitoring requirement of the preset fixed time window, and storing the first accumulated time interval and the initialization element group in an associated mode as first frequency data.
S503: detecting whether there is a medical insurance usage event after the last medical insurance usage event in the initialization element group.
Specifically, when the first accumulated time interval is smaller than or equal to the preset fixed time window, after the first accumulated time interval and the initialization element group are stored in a correlated mode as first frequency data, whether a medical insurance use event exists after the last medical insurance use event in the initialization element group is detected. Illustratively, assuming that the first sub-timeline contains five warranty use events arranged in a time-sequential manner, and assuming that the initialization element group contains a first warranty use event, a second warranty use event, and a third warranty use event, there is still a warranty use event after the last warranty use event (i.e., the third warranty use event) in the initialization element group.
S504: and recording the first frequency data as a first most dense frequency corresponding to the first sub-time axis when no medical insurance use event exists after the last medical insurance use event in the initialization element group.
Specifically, after detecting whether a medical insurance use event exists after the last medical insurance use event in the initialization element group, when the medical insurance use event does not exist after the last medical insurance use event in the initialization element group, the first sub-time axis is characterized to be completely verified, and then the first frequency data is recorded as a first most dense frequency corresponding to the first sub-time axis.
Further, after step S503, the method further includes:
s505: and when a medical insurance use event exists after the last medical insurance use event in the initialization element group, adding the medical insurance use event after the last medical insurance use event in the initialization element group into the initialization element group to form a second element group, and recording the total duration of the time interval between the adjacent medical insurance use events in the second element group as a second accumulated time interval.
Specifically, after detecting whether a medical insurance use event exists after the last medical insurance use event in the initialization element group, if the medical insurance use event exists after the last medical insurance use event in the initialization element group, the first sub-time axis is characterized to be not verified, the medical insurance use event after the last medical insurance use 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 the adjacent medical insurance use 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 use event exists after the last medical insurance use event in the second element group, storing the second accumulated time interval and the second element group in association as a first most dense frequency corresponding to the first sub-time axis.
Specifically, after adding the last medical insurance use event in the initialization element group to form a second element group, recording the total duration of the time intervals between the adjacent medical insurance use events in the second element group as a second accumulated time interval, comparing the second accumulated time interval with a preset fixed time window, detecting whether the medical insurance use event exists after the last medical insurance use event in the second element group when the second accumulated time interval is smaller than or equal to the preset fixed time window, and storing the second accumulated time interval and the second element group in association as a first dense frequency corresponding to the first sub-time axis if the medical insurance use event does not exist after the last medical insurance use event in the second element group. It will be appreciated that if, after the addition of a new medical insurance use event, the second cumulative time interval corresponding to the second element group is still less than or equal to the preset fixed time window, the time interval between the other medical insurance use event and the second medical insurance use event is characterized as short, and therefore the second cumulative time interval is associated with the second element group and stored as the first dense frequency corresponding to the first sub-time axis.
Further, if there is still a medical insurance usage event after the last medical insurance usage event in the second element group, repeating steps S505 to S506 to obtain a fourth element group, a fifth element group, and the like, until all medical insurance usage events in the first sub-time axis are traversed.
In a specific embodiment, after step S501, that is, after selecting, according to a time sequence, a preset number of medical insurance usage events on the first sub-time axis as an initialization element group, 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: and when the first accumulated time interval is greater than the preset fixed time window, if a medical insurance use event still exists after the last medical insurance use event in the initialization element group, adding the medical insurance use event after the last medical insurance use event in the initialization element group into the initialization element group, deleting the first medical insurance use 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 the adjacent medical insurance use events in the third element group as a third accumulated time interval.
Specifically, after recording the total duration of the time intervals between adjacent medical insurance use 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 detecting whether a medical insurance use event exists after the last medical insurance use event in the initialization element group when the first accumulated time interval is larger than the preset fixed time window; and when the first accumulated time interval is greater than the preset fixed time window, if a medical insurance use event still exists after the last medical insurance use event in the initialization element group, adding the medical insurance use event after the last medical insurance use event in the initialization element group into the initialization element group, deleting the first medical insurance use 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 the adjacent medical insurance use events in the third element group as a third accumulated time interval.
S508: and when the third accumulation time interval is smaller than or equal to the preset fixed time window, storing the third accumulation time interval and the third element group in an associated mode as third frequency data.
S509: and recording the largest frequency density of the first frequency data and the third frequency data as a first most dense frequency corresponding to the first sub-time axis when the medical insurance use event does not exist after the last medical insurance use event in the third element group.
The frequency density refers to the ratio of the number of 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 accumulation time interval is smaller than or equal to a preset fixed time window, storing the third accumulation time interval and the third element in association as third frequency data; detecting whether a medical insurance use event exists after the last medical insurance use event in the third element group, and if the medical insurance use event does not exist after the last medical insurance use 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 use events in the initialized element group to the first accumulated time interval in the first frequency data; acquiring the frequency density of third frequency data, namely the ratio of the number of medical insurance use events in a third element group in the third frequency data to a third accumulated time interval; and further recording the highest frequency density of the first frequency data and the third frequency data as a first most dense 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 prompting that the medical insurance use event in the preset time period is not abnormal when the medical insurance use event does not exist after the last medical insurance use event in the initialization element group.
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 meets the requirement, so that after the first accumulated time interval is greater than the preset fixed time window, when there is no medical insurance usage event after the last medical insurance usage time in the initialization element group, the medical insurance usage time on the first sub-time axis is characterized as abnormal, and therefore, the medical insurance usage event within the preset time period can be counted without abnormal phenomenon.
S60: and prompting medical insurance use event abnormality in the preset time period when the first most dense frequency meets a preset frequency standard.
The preset frequency standard can be set according to different scenes; assuming that in a medical scenario, a patient should acquire the same prescription no more than five times in a month, if the patient is more than five times, the patient is characterized by suspicion of fraudulent insurance, and the like, and therefore the preset frequency standard is set to be that the patient acquires the same prescription no more than five times in a month.
Specifically, after determining the first most dense frequency corresponding to the first sub-time axis according to the preset fixed time window and the preset frequency determining method, comparing the first most dense frequency with a preset frequency standard to determine whether the first most dense frequency meets the preset frequency standard, and if the first most dense frequency meets the preset frequency standard, prompting that an abnormal phenomenon exists in medical insurance use events within a preset time period for a verifier to verify the medical insurance use events.
Further, when the first most dense frequency meets a preset frequency standard, the fact that the medical insurance use event has an abnormal phenomenon in a preset time period is confirmed on the first sub-time axis is characterized, the rest sub-time axis can be deleted, the calculated amount of the system is reduced, and meanwhile, the abnormal data processing time is shortened.
In a specific embodiment, after step S50, that is, after determining the first most dense 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 more than the total number of medical insurance use events as a second sub-time axis when the first most dense frequency does not meet a preset frequency standard.
Specifically, after determining the first most dense frequency corresponding to the first sub-time axis according to the preset fixed time window and the preset frequency determining method, comparing the first most dense frequency with a preset frequency standard to determine whether the first most dense frequency meets the preset frequency standard, if the first most dense frequency does not meet the preset frequency standard, characterizing that the medical insurance event on the first sub-time axis is not abnormal, so that it is necessary to continuously check whether the medical insurance event in the remaining sub-time axis is abnormal, and further recording the sub-time axis containing the total number of medical insurance events as the second sub-time axis.
S80: determining a second most dense frequency corresponding to the second sub-time axis according to the preset fixed time window and a preset frequency determining method; the second most dense frequency representation corresponds to the preset fixed time window, and the maximum frequency of triggering the medical insurance usage event exists in the second sub-time axis.
Specifically, when the first most dense frequency does not meet a preset frequency standard, recording a sub-time axis containing more total medical insurance use events as a second sub-time axis, determining that the second sub-time axis has the maximum frequency for triggering medical insurance use events in the preset fixed time window according to the preset fixed time window and a preset frequency determining method, and recording the maximum frequency as a second most dense frequency corresponding to the second sub-time axis.
S90: and prompting medical insurance use event abnormality in the preset time period when the second most dense frequency meets a preset frequency standard.
Specifically, after determining the second most dense frequency corresponding to the second sub-time axis according to the preset fixed time window and the preset frequency determining method, comparing the second most dense frequency with a preset frequency standard to determine whether the second most dense frequency meets the preset frequency standard, and if the second most dense frequency meets the preset frequency standard, prompting that an abnormal phenomenon exists in medical insurance use events within a preset time period for a verifier to verify the medical insurance use events.
Further, when the second most dense frequency meets the preset frequency standard, the abnormal phenomenon of the medical insurance use event in the preset time period is determined on the second sub-time axis, the rest sub-time axis can be deleted, and the system calculated amount is reduced, and meanwhile, the abnormal data processing time is shortened.
Further, when the second most dense frequency does not meet the preset frequency standard, checking a sub-time axis containing the third most dense frequency of the total number of target time, if the second most dense frequency still does not meet the preset frequency standard, traversing all remaining sub-time axes in sequence, stopping the checking process when the most dense frequency corresponding to any sub-time axis meets the preset frequency standard, prompting that an abnormal phenomenon exists in medical insurance use events in a preset time period, or stopping checking when the remaining sub-time axis is zero and the most dense frequency corresponding to all sub-time axes does not meet the preset frequency standard, and prompting that the medical insurance use events temporarily do not exist in the preset time period.
In this embodiment, firstly, a preset time axis is cut by comparing a time interval on the time axis with a preset fixed time window; effectively pruning a preset time axis, so that the time for processing abnormal data is saved; secondly, first detection is carried out on a first sub-time axis containing the most medical insurance use events, when the first most dense frequency corresponding to the first sub-time axis meets the preset frequency standard, the abnormal phenomenon of the medical insurance use events in the preset time period can be judged, the rest sub-time axis can be deleted, the detection time is shortened, meanwhile, the calculated amount of a system is reduced, and therefore whether the abnormal phenomenon exists in the preset time period can be checked more rapidly; by the method, whether the abnormal phenomenon occurs or not can be detected more rapidly under the scene that a large amount of data exists in the preset time period.
In another particular embodiment, to ensure privacy and security of the medical insurance usage events in the above embodiments, the medical insurance usage events may be stored in a blockchain. Among them, blockchain (Blockchain) is an encrypted, chained transaction memory structure formed by blocks (blocks).
For example, the header of each chunk may include both the hash values of all transactions in the chunk and the hash values of all transactions in the previous chunk, thereby enabling tamper-and anti-counterfeiting of transactions in the chunk based on the hash values; the newly generated transactions, after being filled into the block and passing through the consensus of the nodes in the blockchain network, are appended to the tail of the blockchain to form a chain growth.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present invention.
In an embodiment, a medical insurance data processing device based on a fixed time window is provided, where 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 by 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 a trigger time point of a medical insurance use event and the total number of times of triggering the medical insurance use event in a preset time period;
the data display module 20 is configured to display the medical insurance use event and the trigger time point thereof on a preset time axis according to a preset display rule, and obtain time intervals between all two adjacent medical insurance use 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;
A first sub-time axis determining module 40, configured to obtain a total number of the medical insurance use events included in each sub-time axis, and record, as a first sub-time axis, a sub-time axis including the maximum total number of medical insurance use events;
a first frequency determining module 50, configured to determine a first most dense frequency corresponding to the first sub-time axis according to the preset fixed time window and a preset frequency determining method; the first most dense frequency representation corresponds to the preset fixed time window, and the maximum frequency of triggering the medical insurance using event exists in the first sub-time axis;
the first abnormality prompting module 60 is configured to prompt an abnormality of the medical insurance usage event in the preset time period 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 of the time intervals with the preset fixed time window;
a position detection 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 refers to a time interval located at the first position on the preset time axis;
A first data deleting unit 303, configured to delete a 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;
and the second data deleting unit 304 is 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 use event located after the time interval as a start event of a next sub-time axis, and record the medical insurance use event located 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 determination module 70, configured to record a sub-timeline including a total number of medical insurance usage events as a second sub-timeline when the first most dense frequency does not meet a preset frequency criterion;
a second frequency determining module 80, configured to determine a second most dense frequency corresponding to the second sub-time axis according to the preset fixed time window and a preset frequency determining method; the second most dense frequency representation corresponds to the preset fixed time window, and the maximum frequency of triggering the medical insurance using event exists in the second sub-time axis;
A second abnormality prompting module 90, configured to prompt an abnormality of the medical insurance usage event in the preset time period when the second most dense frequency meets a preset frequency standard.
Preferably, as shown in fig. 8, the first sub-time axis determining module 40 includes the following units:
a timeline deleting unit 401, configured to delete a qualified timeline, where the qualified timeline refers to the sub-timeline that contains less than a preset number of total medical insurance usage events.
A sub-timeline determining unit 402, configured to record, as a first sub-timeline, a sub-timeline containing the largest total number of medical insurance usage events when the number of sub-timelines after deleting the qualified timeline is greater than or equal to one.
A first abnormality prompting unit 403, configured to prompt that no abnormality occurs in the medical insurance usage event in the preset time period when the number of sub-time axes after deleting the qualified time axis is equal to zero.
Preferably, as shown in fig. 9, the first frequency determining module 50 includes the following units:
the element group determining unit 501 is configured to select, as an initialization element group, a preset number of medical insurance use events on the first sub-time axis according to a time sequence, and record a total duration of a time interval between adjacent medical insurance use events in the initialization element group as a first accumulated time interval.
A first frequency data determining unit 502, configured to store the first accumulation time interval and the initialization element group as first frequency data in association when the first accumulation time interval is less than or equal to the preset fixed time window;
an event detecting unit 503, configured to detect whether a medical insurance usage event exists 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 denser frequency corresponding to the first sub-time axis when there is no medical insurance usage event after the 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 adjustment unit 505, configured to add, when there is a medical insurance usage event after a last medical insurance usage event in the initialization element group, the medical insurance usage event after the last medical insurance usage event in the initialization element group to the initialization element group, so as 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 store the second accumulated time interval and the second element group in association as a first most dense frequency corresponding to the first sub-time axis if there is no medical insurance usage event after the last medical insurance usage event in the second element group when the second accumulated time interval is less than or equal to the preset fixed time window.
Preferably, the first frequency determining module 50 further includes the following units:
a second element group adjustment unit 507, configured to, when the first accumulated time interval is greater than the preset fixed time window, add a medical insurance use event after the last medical insurance use event in the initialization element group to the initialization element group if the medical insurance use event still exists after the last medical insurance use event in the initialization element group, delete the first medical insurance use event in the initialization element group according to a time sequence, so as to form a third element group, and record a total duration of a time interval between adjacent medical insurance use events in the third element group as a third accumulated time interval;
a third frequency determining unit 508, configured to store the third accumulation time interval and the third tuple in association as third frequency data when the third accumulation time interval is less than or equal to the preset fixed time window;
A fourth frequency determining unit 509, configured to record, when there is no medical insurance usage event after the last medical insurance usage event in the third element group, the most dense one of the first frequency data and the third frequency data as the first most dense frequency corresponding to the first sub-time axis.
Preferably, the first frequency determining module 50 further includes the following units:
and the second abnormality prompting unit is used for prompting that the medical insurance use event in the preset time period is not abnormal when the medical insurance use event does not exist after the last medical insurance use event in the initialization element group.
For specific limitations on the medical insurance data processing apparatus based on the fixed time window, reference may be made to the above limitation on the medical insurance data processing method based on the fixed time window, and the description thereof will not be repeated here. The various modules in the above-described fixed time window-based medical insurance data processing apparatus may be implemented in whole or in part in software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a server, and the internal structure of which 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 includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used for the data used in the medical insurance data processing method based on the fixed time window in the above embodiment. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by a processor, implements a method of processing medical insurance data based on a fixed time window.
In one embodiment, a computer device is provided that includes a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the fixed time window based medical insurance data processing method of the above embodiments when the computer program is executed by the processor.
In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, which when executed by a processor implements the medical insurance data processing method based on the fixed time window in the above embodiment.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile 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), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the 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 scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention.
Claims (9)
1. A method for processing medical insurance data based on a fixed time window, comprising:
acquiring a trigger time point of a medical insurance use event in a preset time period and the total number of times of triggering the medical insurance use event;
According to a preset display rule, displaying the medical insurance use events and the triggering time points thereof on a preset time axis, and acquiring time intervals between all two adjacent medical insurance use events on the preset time axis;
cutting the preset time axis into a plurality of sub time axes 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 largest total number of the medical insurance use events as a first sub-time axis;
according to the preset fixed time window and a preset frequency determining method, determining a first most dense frequency corresponding to the first sub-time axis; the first most dense frequency representation corresponds to the preset fixed time window, and the maximum frequency of triggering the medical insurance using event exists in the first sub-time axis;
prompting medical insurance use event abnormality in the preset time period when the first most dense frequency meets a preset frequency standard;
the determining, according to the preset fixed time window and the preset frequency determining method, a first denser frequency corresponding to the first sub-time axis includes:
Selecting a preset number of medical insurance use events on the first sub-time axis as an initialization element group according to a time sequence, and recording the total duration of the time intervals between adjacent medical insurance use 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;
and recording the first frequency data as a first most dense frequency corresponding to the first sub-time axis when no medical insurance use event exists after the last medical insurance use event in the initialization element group.
2. The method for processing medical insurance data based on a fixed time window according to claim 1, wherein said cutting the preset time axis into a plurality of sub time axes according to a preset fixed time window and the time interval includes:
comparing each time interval 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 or not; the first time interval refers to a time interval located at the first position on the preset time axis;
Deleting the first time interval and a first medical insurance use event positioned before the first time interval from the preset time axis when the time interval is the first time interval;
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 use event positioned after the time interval as a starting point event of a next sub time axis, and recording the medical insurance use event positioned before the time interval as an ending point event of a previous sub time axis.
3. The method for processing medical insurance data based on a fixed time window according to claim 1, wherein after determining the first most dense frequency corresponding to the first sub-time axis, further comprising:
recording a sub-time axis containing more than the total number of medical insurance use events as a second sub-time axis when the first most dense frequency does not meet a preset frequency standard;
determining a second most dense frequency corresponding to the second sub-time axis according to the preset fixed time window and a preset frequency determining method; the second most dense frequency representation corresponds to the preset fixed time window, and the maximum frequency of triggering the medical insurance using event exists in the second sub-time axis;
And prompting medical insurance use event abnormality in the preset time period when the second most dense frequency meets a preset frequency standard.
4. The method for processing medical insurance data based on a fixed time window according to claim 1, wherein the steps of obtaining the total number of medical insurance usage events included in each sub-time axis, and recording the sub-time axis containing the largest total number of medical insurance usage events as the first sub-time axis, further include:
deleting a qualified time axis, wherein the qualified time axis refers to the sub-time axis with the total number of included medical insurance using events less than the preset number;
recording the sub-time axis containing the largest total number of medical insurance use events as a first sub-time axis when the number of the sub-time axes after deleting the qualified time axis is greater than or equal to one;
and when the number of the sub time axes after deleting the qualified time axis is equal to zero, prompting that the medical insurance use event in the preset time period is not abnormal.
5. The fixed time window based medical insurance data processing method according to claim 1, wherein after said detecting whether there is a medical insurance usage event after the last medical insurance usage event in said initializing element group, further comprising:
When a medical insurance use event exists after the last medical insurance use event in the initialization element group, adding the medical insurance use event after the last medical insurance use event in the initialization element group into the initialization element group to form a second element group, and recording the total duration of the time interval between the adjacent medical insurance use 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 use event exists after the last medical insurance use event in the second element group, storing the second accumulated time interval and the second element group in association as a first most dense frequency corresponding to the first sub-time axis.
6. The fixed time window based medical insurance data processing method according to claim 1, wherein after recording the total duration of the time interval between adjacent medical insurance use events in said initializing element group as the first accumulated time interval, further comprising:
when the first accumulated time interval is greater than the preset fixed time window, if a medical insurance use event still exists after the last medical insurance use event in the initialization element group, adding the medical insurance use event after the last medical insurance use event in the initialization element group into the initialization element group, deleting the first medical insurance use 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 the adjacent medical insurance use 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, storing the third accumulated time interval and the third element group in an associated mode as third frequency data;
and recording the largest frequency density of the first frequency data and the third frequency data as a first most dense frequency corresponding to the first sub-time axis when the medical insurance use event does not exist after the last medical insurance use event in the third element group.
7. A medical insurance data processing device based on a fixed time window, comprising:
the data acquisition module is used for acquiring 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;
the data display module is used for displaying the medical insurance use events and the triggering time points thereof on a preset time axis according to preset display rules, and acquiring the time intervals between all two adjacent medical insurance use 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-time axis determining module is used for obtaining the total number of the medical insurance use events contained in each sub-time axis and recording the sub-time axis containing the largest total number of the medical insurance use events as a first sub-time axis;
The first frequency determining module is used for determining a first most dense frequency corresponding to the first sub-time axis according to the preset fixed time window and a preset frequency determining method; the first most dense frequency representation corresponds to the preset fixed time window, and the maximum frequency of triggering the medical insurance using event exists in the first sub-time axis;
the first abnormality prompting module is used for prompting abnormality of medical insurance use events in the preset time period when the first most dense frequency accords with a preset frequency standard;
the first frequency determining module comprises the following units:
the element group determining unit is used for selecting a preset number of medical insurance using events on the first sub-time axis as an initializing element group according to a time sequence, and recording the total duration of the time intervals between adjacent medical insurance using events in the initializing element group as a first accumulated time interval;
the first frequency data determining unit is used for storing the first accumulation time interval and the initialization element group in an associated mode as first frequency data when the first accumulation time interval is smaller than or equal to the preset fixed time window;
the event detection unit is used for detecting whether a medical insurance use event exists after the last medical insurance use event in the initialization element group;
And the first frequency determining unit is used for recording the first frequency data as a first most dense frequency corresponding to the first sub-time axis when no medical insurance use event exists after the last medical insurance use event in the initialization element group.
8. 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 of claims 1 to 6 when the computer program is executed.
9. A computer readable storage medium storing a computer program, wherein the computer program when executed by a processor implements a method of processing medical insurance data based on a fixed time window as claimed in any of claims 1 to 6.
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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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| CN202010929662.2A CN112001805B (en) | 2020-09-07 | 2020-09-07 | Medical insurance data processing method, device, equipment and medium based on fixed time window |
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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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