CN110827932B - Medical data classification processing method and device, storage medium and electronic equipment - Google Patents
Medical data classification processing method and device, storage medium and electronic equipment Download PDFInfo
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- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
Abstract
The disclosure relates to the technical field of big data, and provides a medical data classification processing method, a medical data classification processing device, a computer storage medium and an electronic device, wherein the medical data classification processing method comprises the following steps: classifying the acquired original medical data to obtain classified medical data; removing expired data in the classified medical data to obtain various effective medical data, and determining the priority of the various effective medical data; determining the processing probability corresponding to each type of effective medical data according to the priority of each type of effective medical data; and processing the various types of effective medical data in batches according to the processing probability corresponding to the various types of effective medical data. The medical data classification processing method can avoid data congestion and improve the processing efficiency of the medical data.
Description
Technical Field
The present disclosure relates to the field of big data technologies, and in particular, to a medical data classification method, a medical data classification device, a computer storage medium, and an electronic device.
Background
With the rapid development of computer and internet technologies, people's daily life is becoming more and more undisclosed from information and data. This is especially true in the medical industry, where medical big data plays an increasingly important role in people's healthy lives. Data is the life line of the healthcare industry, ranging from blood pressure readings and surgical records to insurance claims, immunization history, patient demographics and payment receipts, each action of each member of the healthcare ecosystem relying on an endless flow of information.
Currently, common components for processing data streams are: kafka, flink, storm, spark streaming, etc., whereas congestion control schemes for data, particularly for medically large data, are lacking in the prior art. Therefore, when the amount of data is too large, a certain data flow node may be crashed, so that data congestion occurs, and the processing efficiency of medical data is low.
In view of the above, there is a need in the art to develop a new method and apparatus for classifying medical data.
It is to be noted that the information disclosed in the background section above is only used to enhance understanding of the background of the present disclosure.
Disclosure of Invention
The present disclosure is directed to a method for classifying medical data, a device for classifying medical data, a computer storage medium, and an electronic device, so as to avoid the drawback of low efficiency in processing medical data in related technologies at least to a certain extent.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the disclosure.
According to a first aspect of the present disclosure, there is provided a method for classifying medical data, including: classifying the acquired original medical data to obtain classified medical data; removing expired data in the classified medical data to obtain various types of effective medical data, and determining the priority of the various types of effective medical data; determining the processing probability corresponding to each type of effective medical data according to the priority of each type of effective medical data; and processing various types of effective medical data in batches according to the processing probability corresponding to the various types of effective medical data.
In an exemplary embodiment of the present disclosure, determining, according to priorities of various types of valid medical data, processing probabilities corresponding to the various types of valid medical data includes: determining the proportion of the priority of each type of the effective medical data to the sum of all the effective medical data; taking the ratio as a processing probability corresponding to the effective medical data; or determining data streams corresponding to various types of effective medical data according to a preset mapping principle; and determining the processing probability of the effective medical data corresponding to the data flow according to the data flow corresponding to the effective medical data and the priority of all the effective medical data corresponding to the data flow.
In an exemplary embodiment of the present disclosure, the performing batch processing on various types of valid medical data according to processing probabilities corresponding to the various types of valid medical data includes: determining a numerical value interval mapped by the processing probability of each type of effective medical data; generating a random number; determining target medical data to be processed according to the numerical value interval where the random number is located; the numerical range of the random number and the numerical range have a corresponding relationship.
In an exemplary embodiment of the present disclosure, the classifying the medical data includes at least: peak data, flat peak data, large patient data, and small patient data; the classifying the acquired original medical data to obtain classified medical data includes: classifying the original medical data according to the data variation of the original medical data in unit time to obtain the peak data and/or the flat peak data; and classifying the original medical data according to the data volume corresponding to each patient in the original medical data to obtain the big patient data and/or the small patient data.
In an exemplary embodiment of the present disclosure, the classifying the raw medical data according to a data amount corresponding to each patient in the raw medical data to obtain the large patient data and/or the small patient data includes: acquiring patient identifiers corresponding to the original medical data according to a preset patient information set; acquiring change time corresponding to each original medical data according to the patient identification; acquiring updating data corresponding to each original medical data according to the patient identification and the change time; classifying the original medical data according to the data volume of the updated data corresponding to each patient to obtain the big patient data and/or the small patient data; wherein the patient information set is used for storing the patient identification and the change time of the original medical data corresponding to the patient identification.
In an exemplary embodiment of the disclosure, before acquiring, according to a preset patient information set, a patient identifier corresponding to the changed original medical data, the method further includes: monitoring the original medical data to obtain the change time corresponding to each original medical data; and storing the change time corresponding to each original medical data and the patient identification corresponding to each original medical data into the patient information set.
In an exemplary embodiment of the present disclosure, the removing expired data in the classified medical data to obtain valid medical data includes: determining the latest medical data corresponding to each patient identifier according to the patient identifier corresponding to each classified medical data; when the timestamp corresponding to the classified medical data is earlier than the latest timestamp corresponding to the latest medical data, acquiring the discarding times corresponding to the classified medical data; if the discarding times is less than a preset threshold value, judging the classified medical data as overdue data; and removing the expired data to obtain the effective medical data.
According to a second aspect of the present disclosure, there is provided a classification processing apparatus for medical data, comprising: the classification module is used for classifying the acquired original medical data to obtain classified medical data; the removing module is used for removing the expired data in the classified medical data to obtain various types of effective medical data and determining the priority of the various types of effective medical data; the determining module is used for determining the processing probability corresponding to each type of effective medical data according to the priority of each type of effective medical data; and the processing module is used for processing various types of effective medical data in batches according to the processing probabilities corresponding to the various types of effective medical data.
According to a third aspect of the present disclosure, there is provided a computer storage medium having stored thereon a computer program which, when executed by a processor, implements the method of classification processing of medical data of the first aspect described above.
According to a fourth aspect of the present disclosure, there is provided an electronic device comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform the method of classification processing of medical data of the first aspect via execution of the executable instructions.
As can be seen from the foregoing technical solutions, the classification processing method for medical data, the classification processing apparatus for medical data, the computer storage medium, and the electronic device in the exemplary embodiment of the present disclosure have at least the following advantages and positive effects:
in the technical solutions provided by some embodiments of the present disclosure, on one hand, the obtained original medical data is classified to obtain classified medical data, so that the technical problems that data flow is blocked or even unavailable due to processing node crash caused by a single large data volume caused by the same processing on all data in the prior art can be solved, the pertinence of data processing is improved, and the safe operation of a server is ensured. The expired data in the classified medical data is removed to obtain various effective medical data, a large amount of classified medical data can be simplified, invalid expired data is removed, and data processing efficiency is improved. On the other hand, the priority of each type of effective medical data is determined, the processing probability corresponding to each type of effective medical data is determined according to the priority of each type of effective medical data, and each type of effective medical data is processed in batches according to the processing probability corresponding to each type of effective medical data.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure. It is to be understood that the drawings in the following description are merely exemplary of the disclosure, and that other drawings may be derived from those drawings by one of ordinary skill in the art without the exercise of inventive faculty.
Fig. 1 shows a flow diagram of a method for classification processing of medical data in an exemplary embodiment of the disclosure;
FIG. 2 is a sub-flow diagram illustrating a method for classification processing of medical data in an exemplary embodiment of the present disclosure;
FIG. 3 is a sub-flow diagram illustrating a method for classification processing of medical data in an exemplary embodiment of the present disclosure;
FIG. 4 is a sub-flow diagram illustrating a method for classification processing of medical data in an exemplary embodiment of the present disclosure;
FIG. 5 is a sub-flow diagram illustrating a method for classification processing of medical data in an exemplary embodiment of the present disclosure;
FIG. 6 is a sub-flow diagram illustrating a method for classification processing of medical data in an exemplary embodiment of the present disclosure;
fig. 7 is an overall flowchart illustrating a classification processing method of medical data according to an exemplary embodiment of the present disclosure;
fig. 8 is a schematic structural diagram of a classification processing apparatus for medical data in an exemplary embodiment of the present disclosure;
FIG. 9 shows a schematic diagram of a structure of a computer storage medium in an exemplary embodiment of the disclosure;
fig. 10 shows a schematic structural diagram of an electronic device in an exemplary embodiment of the present disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the subject matter of the present disclosure can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and the like. In other instances, well-known technical solutions have not been shown or described in detail to avoid obscuring aspects of the present disclosure.
The terms "a," "an," "the," and "said" are used in this specification to denote the presence of one or more elements/components/parts/etc.; the terms "comprising" and "having" are intended to be inclusive and mean that there may be additional elements/components/etc. other than the listed elements/components/etc.; the terms "first" and "second", etc. are used merely as labels, and are not limiting on the number of their objects.
Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repetitive description will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities.
Currently, common components for processing data streams are: kafka, flink, storm, spark streaming, etc., whereas congestion control schemes for data, particularly for medically large data, are lacking in the prior art. Therefore, when the amount of data is too large, a certain data flow node may be crashed, so that data congestion occurs, and the data processing efficiency is low.
In the embodiment of the present disclosure, firstly, a method for classifying and processing medical data is provided, which overcomes, at least to some extent, the defect that the method for classifying and processing medical data provided in the prior art is inefficient.
Fig. 1 is a flowchart illustrating a method for classifying medical data according to an exemplary embodiment of the present disclosure, where an execution subject of the method for classifying medical data may be a server that processes data.
Referring to fig. 1, a classification processing method of medical data according to one embodiment of the present disclosure includes the steps of:
step S110, classifying the acquired original medical data to obtain classified medical data;
step S120, removing expired data in the classified medical data to obtain various effective medical data, and determining the priority of the various effective medical data;
step S130, determining the processing probability corresponding to each type of effective medical data according to the priority of each type of effective medical data;
and step S140, performing batch processing on various types of effective medical data according to the processing probabilities corresponding to the various types of effective medical data.
In the technical scheme provided by the embodiment shown in fig. 1, on one hand, the obtained original medical data is classified to obtain classified medical data, so that the technical problem that data flow congestion or even unavailable occurs due to processing node crash caused by a single large data volume caused by the same processing on all data in the prior art can be solved, the pertinence of data processing is improved, and the safe operation of a server is ensured. The expired data in the classified medical data is removed to obtain various effective medical data, a large amount of classified medical data can be simplified, invalid expired data is removed, and data processing efficiency is improved. On the other hand, the priority of each type of effective medical data is determined, the processing probability corresponding to each type of effective medical data is determined according to the priority of each type of effective medical data, and each type of effective medical data is processed in batches according to the processing probability corresponding to each type of effective medical data.
The following describes the specific implementation of each step in fig. 1 in detail:
in step S110, the acquired original medical data is classified to obtain classified medical data.
In an exemplary embodiment of the present disclosure, the obtained original medical data may be classified to obtain classified medical data. The original medical data can be mass medical data generated by patients going to a hospital for registration, filling medical records, paying, performing operations and the like.
In an exemplary embodiment of the present disclosure, the classified medical data may include peak data, flat data, large patient data, and small patient data. The peak data refers to data whose data amount may be increased sharply at a certain time. Flat peak data, that is, data whose data amount does not increase sharply. The data corresponding to the patient in a long-term hospital can be regarded as large-patient data, and the data corresponding to the ordinary patient can be regarded as small-patient data. By classifying the original medical data, the technical problems that data flow congestion occurs or even unavailable due to processing node crash caused by overlarge single data volume caused by the fact that all data are processed identically in the prior art can be solved, the pertinence of data processing is improved, and the safe operation of a server is guaranteed.
In an exemplary embodiment of the present disclosure, by way of example, referring to fig. 2, fig. 2 shows a sub-flowchart of a classification processing method for medical data in an exemplary embodiment of the present disclosure, specifically shows a flowchart of performing classification processing on acquired original medical data to obtain classified medical data (peak data, flat data, large patient data, and small patient data), which includes steps S201 to S202, and step S110 is explained below with reference to fig. 2.
In step S201, the raw medical data is classified according to the amount of change in the raw medical data per unit time, and peak data and/or flat data are obtained.
In an exemplary embodiment of the present disclosure, a data variation of raw medical data in a unit time may be obtained, and the raw medical data may be classified according to a value of the data variation, so as to obtain peak data and/or flat peak data. Illustratively, the raw medical data may include raw medical data a, raw medical data b, and raw medical data c. The unit time can be 1 second (can be set according to the actual situation, and belongs to the protection scope of the present disclosure). The data change amount of the original medical data a within 1 second may be 10KB, the data change amount of the original medical data b within 1 second may be 20KB, and the data change amount of the original medical data c within 1 second may be 120 MB.
A first preset threshold (a preset data variation value, which may be changed according to actual conditions) may be set, and when the data variation is greater than the first preset threshold, the original medical data may be determined as peak data. For example, when the first preset threshold is 100MB, it is known that 120MB is greater than 100MB, the original medical data c may be determined as the peak data. And 10KB is less than 100MB, and 20KB is less than 100MB, the original medical data a and b less than or equal to the first preset threshold can be determined as flat peak data.
In step S202, the raw medical data is classified according to the data size corresponding to each patient in the raw medical data, and large patient data and/or small patient data are obtained.
In an exemplary embodiment of the disclosure, the original medical data may be monitored to determine whether each original medical data is changed (for example, when a patient is subjected to secondary admission, secondary registration or secondary operation, the original medical data corresponding to the patient is changed), and when the data is changed, the change time of each original medical data is obtained, and further, a patient identifier (for example, an identification number, a mobile phone number, and the like of the patient) corresponding to each original medical data and the corresponding change time are stored in a preset patient information set.
The patient information set (patient dictionary) is a set used for storing patient identifiers and change times of original medical data corresponding to the patient identifiers, and specifically, the patient identifiers can be used as keys, and the change times of the original medical data can be correspondingly stored as values. Therefore, real-time recording of data change time can be achieved, and timeliness of subsequently acquired data is guaranteed.
In an exemplary embodiment of the present disclosure, after storing the patient identifier corresponding to the changed original medical data in the patient information set, referring to fig. 3, fig. 3 shows a sub-flowchart of a classification processing method for medical data in an exemplary embodiment of the present disclosure, and specifically shows a flowchart of classifying the original medical data according to a data amount corresponding to each patient in the original medical data to obtain large patient data and/or small patient data, including steps S301 to S304, and the following explains step S110 with reference to fig. 3.
In step S301, a patient identifier corresponding to each piece of original medical data is acquired based on a preset patient information set.
In an exemplary embodiment of the present disclosure, the patient information set may be read to obtain all patient identifiers corresponding to the respective original medical data.
In step S302, a change time corresponding to each piece of original medical data is acquired based on the patient identifier.
In an exemplary embodiment of the present disclosure, after the patient identifier is obtained, the patient identifier may be used as a key, a patient information dictionary is looked up, and a change time (value) corresponding to each piece of original medical data is obtained.
In step S303, update data corresponding to each piece of original medical data is acquired based on the patient identifier and the change time.
In an exemplary embodiment of the present disclosure, after obtaining the update time, a large amount of update data corresponding to the update time may be extracted, and further, data corresponding to the patient identifier and having the same time as the change time may be used as update data corresponding to each of the acquired original medical data.
In step S304, the raw medical data is classified according to the data amount of the update data corresponding to each patient, and large patient data and/or small patient data is obtained.
In an exemplary embodiment of the present disclosure, the raw medical data may be classified according to the data amount of the update data corresponding to each patient, so as to obtain large patient data and/or small patient data. For example, a second preset threshold (a preset data amount value, which may be changed according to actual conditions) may be set, and the original medical data is classified according to the data amount of the update data corresponding to each patient, so as to obtain large patient data and small patient data. Specifically, if the data amount of the update data is greater than a second preset threshold, the update data may be determined as the large patient data. For example, when the second preset threshold is 200MB and the data amount of the update data is 300MB, 300MB is larger than 200MB, the update data may be determined as the large patient data. Similarly, the update data having the data amount less than or equal to the second preset threshold may be determined as the small patient data.
In an exemplary embodiment of the present disclosure, after obtaining the peak data, the flat peak data, the large patient data, and the small patient data, the peak data and the large patient data may be combined, for example, to obtain the peak-large patient data; the peak data and the small patient data can be combined to obtain peak-small patient data; the flat peak data and the big patient data can be merged to obtain flat peak-big patient data; the flat peak data and the small patient data can be combined to obtain flat peak-small patient data.
With continued reference to fig. 1, in step S120, the expired data in the classified medical data is removed to obtain various types of valid medical data, and the priorities of the various types of valid medical data are determined.
In an exemplary embodiment of the present disclosure, after the classified medical data is obtained, the expired data in the classified medical data may be removed to obtain various types of valid medical data. After obtaining the various types of valid medical data, the priorities of the various types of valid medical data can be determined.
For example, referring to fig. 4, fig. 4 shows a sub-flow diagram of a classification processing method of medical data in an exemplary embodiment of the present disclosure, specifically shows a flow diagram of removing expired data in classified medical data to obtain various types of valid medical data, which includes steps S401 to S404, and the following explains step S120 with reference to fig. 4.
In step S401, the latest medical data corresponding to each patient identifier is determined according to the patient identifier corresponding to each classified medical data.
In an exemplary embodiment of the present disclosure, after the classified medical data is obtained, the latest medical data corresponding to each patient identifier may be determined according to the patient identifier corresponding to each classified medical data. The latest medical data is medical data corresponding to the one update time closest to the current data processing time.
In step S402, when the timestamp corresponding to the classified medical data is earlier than the latest timestamp corresponding to the latest medical data, the number of times of discarding corresponding to the classified medical data is acquired.
In an exemplary embodiment of the present disclosure, after the latest medical data is acquired, if the timestamp corresponding to the classified medical data is earlier than the latest timestamp corresponding to the latest medical data, that is, the classified medical data is not the latest medical data of the patient, the discarding number corresponding to the classified medical data may be acquired, that is, it may be detected that the classified medical data has been discarded several times in the previous processing.
For example, a preset discarding number dictionary (similar to the patient information set, which is used for recording the discarding number of the classified medical data of each patient, and may use the patient identifier as a key, and store the discarding number of the classified medical data of the patient as a value) may be searched, and the discarding number corresponding to the classified medical data may be obtained.
In step S403, if the discarding frequency is less than the preset threshold, the classified medical data is determined as expired data.
In an exemplary embodiment of the present disclosure, when the discarding number of times of the classified medical data is smaller than a preset threshold (a third preset threshold, a preset numerical value of the discarding number of times, which may be changed according to an actual situation), the classified medical data may be determined as expired data.
In step S404, the expired data is removed to obtain valid medical data.
In an exemplary embodiment of the present disclosure, after determining expired data included in the classified medical data, the expired data may be removed, and then remaining data may be determined as valid medical data. Therefore, a large amount of classified medical data can be simplified, invalid expired data can be removed, and subsequent data processing efficiency can be improved.
In an exemplary embodiment of the present disclosure, reference may be made to fig. 5, where fig. 5 shows a sub-flow schematic diagram of a classification processing method of medical data in an exemplary embodiment of the present disclosure, and specifically shows an algorithm flow diagram for performing a removing process on the expired data, including steps S501 to S509, and a specific implementation is explained below with reference to fig. 5.
In step S501, start;
in step S502, it is determined whether the classified medical data is the latest data;
in step S503, if the data is the latest data, the classified medical data is determined to be valid medical data and stored in the database; in step S504, it is determined whether to store the data in the database; in step S505, if the data has been stored in the database, the discarding number is set to zero; skipping to step S509, and ending; if the data is not stored in the database, the process goes to step S509, and then the process is ended (meanwhile, a storage failure message or the like may be returned to remind relevant personnel to check whether the server has an operation problem).
In step S506, if the medical data is not the latest data, it is determined whether the discarding times corresponding to the classified medical data is less than a preset threshold;
in step S507, if the discarding times is less than a preset threshold, the classified medical data is rejected;
in step S508, the number of discarding times is set to be increased by one;
in step S509, the process ends.
In an exemplary embodiment of the present disclosure, after obtaining the valid medical data corresponding to the four types of classified medical data, priorities may be assigned to the types of valid medical data, and specifically, different priorities may be set for different types of valid medical data according to actual business requirements. Illustratively, when the business requirement is to know the male and female proportion of the patient, the priority of the valid medical data related to the fields of 'male, female' and the like may be set higher (for example, 1000), and the priority of the valid medical data not related to the fields of 'male, female' and the like may be set lower (for example, 30). It should be noted that, the specific value of the priority may be set according to the actual situation, and belongs to the protection scope of the present disclosure.
In the exemplary embodiment of the present disclosure, it should be noted that after the valid medical data is obtained, the interference data (for example, some batch reconciliation tasks, batch settlement tasks, etc. besides the patient examination results and the doctor diagnosis information) contained in the valid medical data can be detected. The method sets a fixed priority (for example, a lower priority, 1) for the interference data, and processes the interference data according to the fixed priority in the subsequent processing process, thereby avoiding the interference of some unimportant data to the subsequent data processing process and improving the data processing efficiency.
In an exemplary embodiment of the present disclosure, for example, a priority 4 may be assigned to the valid medical data corresponding to the peak-large patient data, a priority 3 may be assigned to the valid medical data corresponding to the peak-small patient data, a priority 2 may be assigned to the valid medical data corresponding to the flat-large patient data, and a priority 1 may be assigned to the valid medical data corresponding to the flat-small patient data, which may be specifically set according to an actual situation, and belongs to the protection scope of the present disclosure. Therefore, the technical problem that in the prior art, a server is crashed under the condition that the data volume is too large or the data is mutated can be solved, the safe operation of the server is ensured, and the data processing efficiency is improved.
With continued reference to fig. 1, in step S130, the processing probabilities corresponding to the various types of valid medical data are determined according to the priorities of the various types of valid medical data.
In an exemplary embodiment of the present disclosure, for example, the priority of each type of valid medical data may be determined as a proportion of the sum of all valid medical data; the ratio is used as the processing probability corresponding to the effective medical data.
Specifically, after setting the priority for each valid medical data, the ratio of the priorities of the various valid medical data to the sum of all valid medical data may be calculated, and the ratio is used as the processing probability corresponding to the valid medical data. For example, the processing probability of each type of valid medical data may be calculated based on the following formula:
wherein n is the total category number of the valid medical data (namely n types of valid medical data exist, and the serial number is 1-n); p (i) is the processing probability of the i-th category of valid medical data, i =1,2,3 … … n; prior (i) is the priority corresponding to the i-th type of valid medical data,
is the sum of the priorities corresponding to the n classes of valid medical data.
For example, taking the priority levels of 0 to 10 as an example, referring to the related explanation of the above steps, the valid medical data corresponding to the peak-large patient data is assigned with the priority level 4, the valid medical data corresponding to the peak-small patient data is assigned with the priority level 3, the valid medical data corresponding to the flat peak-large patient data is assigned with the priority level 2, and the valid medical data corresponding to the flat peak-small patient data is assigned with the priority level 1. The processing probability of valid medical data for the peak-large patient data category may be p (1) =4/(4+3+2+1) = 0.4. The processing probability of valid medical data of the peak-small patient data category may be p (2) =3/(4+3+2+1) = 0.3. The processing probability of valid medical data for the flat-peak large patient data class may be p (3) =2/(4+3+2+1) = 0.2. The processing probability of valid medical data of the flat-peak small patient data class may be p (4) =1/(4+3+2+1) = 0.1.
In an exemplary embodiment of the present disclosure, for example, data streams corresponding to various types of valid medical data may also be determined according to a preset mapping principle; and determining the processing probability of the effective medical data corresponding to the data flow according to the data flow corresponding to the effective medical data and the priority of all the effective medical data corresponding to the data flow.
In step S140, the various types of valid medical data are batch processed according to the processing probabilities corresponding to the various types of valid medical data.
In the exemplary embodiment of the present disclosure, after obtaining the processing probability, referring to fig. 6, fig. 6 shows a sub-flow diagram of a classification processing method for medical data in an exemplary embodiment of the present disclosure, specifically shows a flow diagram of performing batch processing on various types of valid medical data according to the processing probability corresponding to each type of valid medical data, including steps S601-S603, and the following explains step S140 with reference to fig. 6.
In step S601, a numerical range to which the processing probability of each type of valid medical data is mapped is determined.
In an exemplary embodiment of the present disclosure, a value interval to which the processing probability of each valid medical data is mapped may be determined, and the range of the value interval is the same as the range of the priority described above (0 to 10). For example, referring to the related explanation of step S130 above, the processing probability P (1) may be mapped to a value interval of 0 to 4, the probability P (2) may be mapped to a value interval of 5 to 7, the probability P (3) may be mapped to a value interval of 8 to 9, and the processing probability P (4) may be mapped to a value interval of 10.
In step S602, a random number is generated.
In an exemplary embodiment of the present disclosure, a random number may be generated, the range of the value of the random number is the same as the range of the above-described value interval (0-10), and the generated random number may be 3, for example.
In step S603, target medical data to be processed is determined according to the numerical value interval in which the random number is located; the numerical range of the random number has a corresponding relationship with the numerical range.
In an exemplary embodiment of the present disclosure, when the random number is 3, it is known that the value interval in which the random number is located is 0 to 4, and the value interval corresponds to the value interval in which the probability P (1) corresponding to the valid medical data of the peak-large patient data category is located, the valid medical data of the peak-large patient data category may be determined as the target medical data to be processed, and all valid medical data of the peak-large patient data category may be preferentially processed, for example: the gender of the patient corresponding to all valid data of the peak-to-large patient data category is preferentially counted.
In an exemplary embodiment of the present disclosure, after the first type of valid medical data needing to be processed is determined, the priority level may be adjusted in value, and the steps S601 to S603 may be executed in a loop, so as to batch process the remaining types of valid medical data.
Referring to the explanation about the above steps, for example, for the remaining 3 types of data (valid medical data of the peak-small patient data category, valid medical data of the flat-large patient data category, valid medical data of the flat-small patient data category), first, the priority corresponding to the valid medical data of the peak-small patient data category may be reset to 5, the priority corresponding to the valid medical data of the flat-large patient data category may be reset to 4, and the priority corresponding to the valid medical data of the flat-small patient data category may be reset to 1. The processing probability of valid medical data for the peak-to-small patient data category may be p (5) =5/(5+4+1) = 0.5. The processing probability of valid medical data of the flat-peak large patient data class may be p (6) =4/(5+4+1) =0.4, and the processing probability of valid medical data of the flat-peak small patient data class may be p (7) =1/(5+4+1) = 0.1.
Further, the numerical value interval to which the processing probability of each type of effective medical data is mapped can be acquired. Then illustratively, the value interval for the probability P (5) mapping may be 0-5 and the value interval for the probability P (6) mapping may be 6-9. The interval of values to which the probability p (7) maps may be 10.
Again, a random number may be generated, which may be 8, for example. Therefore, it can be seen that the numerical interval in which the random number is located is 6 to 9, and the numerical interval in which the probability P (6) corresponding to the valid medical data of the flat peak-large patient data category is located corresponds to, the valid medical data of the flat peak-large patient data category can be determined as the target medical data to be processed, and the valid medical data of the flat peak-large patient data category is preferentially processed.
Similarly, in the subsequent processing procedure, the priority level may be adjusted, the processing probability of each type of valid medical data is calculated, and the above steps S601 to S603 are executed in a loop, so as to perform batch processing on all types of valid medical data. Therefore, a new solution for determining the data processing sequence is provided, the technical problem that the timeliness is low due to disordered data processing sequence or data congestion when the data amount is overlarge in the prior art can be solved, and the timeliness of data processing is improved. Meanwhile, the technical problem that in the prior art, data are processed only according to the set priority, so that the waiting time of low-priority data is too long can be solved, and the flexibility of a data processing sequence is improved.
In an exemplary embodiment of the present disclosure, reference may be made to fig. 7, where fig. 7 shows an overall flowchart of a classification processing method for medical data in an exemplary embodiment of the present disclosure, including steps S701 to S706, and a specific implementation is explained below with reference to fig. 7.
In step S701, the data collection module obtains original medical data, and inputs the original medical data into the data discrimination module;
in step S702, the data screening module identifies and classifies the original medical data to obtain classified medical data (including peak-large patient data, peak-small number data, flat-large patient data, and flat-small patient data), and inputs the classified medical data of each category to the timeliness determination module;
in step S703, the timeliness determination module removes expired data from the classified medical data of each category to obtain valid medical data, and inputs the valid medical data to the priority adjustment module;
in step S704, the priority adjustment module determines target medical data in valid medical data to be executed, and inputs the target medical data into the data processing real-time stream;
in step S705, the data processing real-time stream performs batch processing on the input data;
in step S706, the data obtained after the processing is completed is stored in the database.
The present disclosure also provides a medical data classification processing apparatus, and fig. 8 shows a schematic structural diagram of the medical data classification processing apparatus in an exemplary embodiment of the present disclosure; as shown in fig. 8, the classification processing apparatus 800 of medical data may include a classification module 801, a culling module 802, a determination module 803, and a processing module 804. Wherein:
the classification module 801 is configured to perform classification processing on the acquired original medical data to obtain classified medical data.
In an exemplary embodiment of the present disclosure, classifying medical data includes at least: peak data, flat peak data, large patient data, and small patient data; the classification module is used for classifying the original medical data according to the data variation of the original medical data in unit time to obtain peak data and/or flat peak data; and classifying the original medical data according to the data volume corresponding to each patient in the original medical data to obtain big patient data and/or small patient data.
In an exemplary embodiment of the present disclosure, the classification module is configured to obtain a patient identifier corresponding to each piece of original medical data according to a preset patient information set; acquiring change time corresponding to each original medical data according to the patient identification; acquiring updated data corresponding to each original medical data according to the patient identification and the change time; classifying the original medical data according to the data volume of the updated data corresponding to each patient to obtain big patient data and/or small patient data; the patient information set is used for storing the patient identification and the change time of the original medical data corresponding to the patient identification.
In an exemplary embodiment of the present disclosure, the classification module is configured to monitor the original medical data to obtain a change time corresponding to each original medical data; and storing the change time corresponding to each original medical data and the patient identification corresponding to each original medical data into a patient information set.
The removing module 802 is configured to remove expired data from the classified medical data to obtain various types of valid medical data, and determine priorities of the various types of valid medical data.
In an exemplary embodiment of the present disclosure, the eliminating module is configured to determine, according to a patient identifier corresponding to each classified medical data, latest medical data corresponding to each patient identifier; when the timestamp corresponding to the classified medical data is earlier than the latest timestamp corresponding to the latest medical data, acquiring the discarding times corresponding to the classified medical data; if the discarding times is less than a third preset threshold, judging the classified medical data as expired data; and removing the expired data to obtain effective medical data.
The determining module 803 is configured to determine, according to the priority of each type of valid medical data, a processing probability corresponding to each type of valid medical data.
In an exemplary embodiment of the disclosure, the determining module is configured to determine a ratio of the priority of each type of valid medical data to the sum of all valid medical data; taking the proportion as a processing probability corresponding to the effective medical data; or determining data streams corresponding to various types of effective medical data according to a preset mapping principle; and determining the processing probability of the effective medical data corresponding to the data flow according to the data flow corresponding to the effective medical data and the priority of all the effective medical data corresponding to the data flow.
The processing module 804 is configured to perform batch processing on various types of valid medical data according to the processing probabilities corresponding to the various types of valid medical data.
In an exemplary embodiment of the disclosure, the processing module is configured to determine a numerical range to which processing probabilities of various types of valid medical data are mapped; generating a random number; determining target medical data to be processed according to the numerical value interval of the random number; the numerical range of the random number has a corresponding relationship with the numerical range.
The specific details of each module in the medical data classification processing apparatus have been described in detail in the corresponding medical data classification processing method, and therefore are not described herein again.
It should be noted that although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.
Moreover, although the steps of the methods of the present disclosure are depicted in the drawings in a particular order, this does not require or imply that the steps must be performed in this particular order, or that all of the depicted steps must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions, etc.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.
In an exemplary embodiment of the present disclosure, there is also provided a computer storage medium capable of implementing the above method. On which a program product capable of implementing the above-described method of the present specification is stored. In some possible embodiments, various aspects of the disclosure may also be implemented in the form of a program product comprising program code for causing a terminal device to perform the steps according to various exemplary embodiments of the disclosure described in the "exemplary methods" section above of this specification, when the program product is run on the terminal device.
Referring to fig. 9, a program product 900 for implementing the above method according to an embodiment of the present disclosure is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
A computer readable signal medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
In addition, in an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.
As will be appreciated by one skilled in the art, aspects of the present disclosure may be embodied as a system, method or program product. Accordingly, various aspects of the present disclosure may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" system.
An electronic device 1000 according to this embodiment of the disclosure is described below with reference to fig. 10. The electronic device 1000 shown in fig. 10 is only an example and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 10, the electronic device 1000 is embodied in the form of a general purpose computing device. The components of the electronic device 1000 may include, but are not limited to: the at least one processing unit 1010, the at least one memory unit 1020, a bus 1030 connecting different system components (including the memory unit 1020 and the processing unit 1010), and a display unit 1040.
Wherein the storage unit stores program code that is executable by the processing unit 1010 to cause the processing unit 1010 to perform steps according to various exemplary embodiments of the present disclosure described in the above section "exemplary methods" of the present specification. For example, the processing unit 1010 may perform the following as shown in fig. 1: step S110, classifying the acquired original medical data to obtain classified medical data; step S120, removing expired data in the classified medical data to obtain various types of effective medical data, and determining the priority of the various types of effective medical data; step S130, determining the processing probability corresponding to each type of effective medical data according to the priority of each type of effective medical data; step S140, according to the processing probability corresponding to each type of effective medical data, batch processing is carried out on each type of effective medical data.
The storage unit 1020 may include readable media in the form of volatile memory units, such as a random access memory unit (RAM) 10201 and/or a cache memory unit 10202, and may further include a read-only memory unit (ROM) 10203.
The memory unit 1020 may also include a program/utility 10204 having a set (at least one) of program modules 10205, such program modules 10205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
The electronic device 1000 may also communicate with one or more external devices 1100 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 1000, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 1000 to communicate with one or more other computing devices. Such communication may occur through input/output (I/O) interfaces 1050. Also, the electronic device 1000 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the internet) via the network adapter 1060. As shown, the network adapter 1060 communicates with the other modules of the electronic device 1000 over the bus 1030. It should be appreciated that although not shown, other hardware and/or software modules may be used in conjunction with the electronic device 1000, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.
Furthermore, the above-described figures are merely schematic illustrations of processes included in methods according to exemplary embodiments of the present disclosure, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
Claims (9)
1. A method for classifying medical data, comprising:
classifying the acquired original medical data to obtain classified medical data; the classifying the medical data includes at least: peak data, flat peak data, large patient data, and small patient data; the classifying the acquired original medical data to obtain classified medical data includes: classifying the original medical data according to the data variation of the original medical data in unit time to obtain the peak data and/or the flat peak data; classifying the original medical data according to the data volume of the updated data corresponding to each original medical data to obtain the big patient data and/or the small patient data;
removing expired data in the classified medical data to obtain various types of effective medical data, and determining the priority of the various types of effective medical data; the method for eliminating the expired data in the classified medical data comprises the following steps: removing expired data in the classified medical data according to the time stamp and the discarding times corresponding to the classified medical data;
determining the processing probability corresponding to each type of effective medical data according to the priority of each type of effective medical data;
and processing various types of effective medical data in batches according to the processing probability corresponding to the various types of effective medical data.
2. The method of claim 1, wherein determining the processing probability corresponding to each type of valid medical data according to the priority of each type of valid medical data comprises:
determining the proportion of the priority of each type of the effective medical data to the sum of all the effective medical data; taking the ratio as a processing probability corresponding to the effective medical data;
alternatively, the first and second electrodes may be,
determining data streams corresponding to various types of effective medical data according to a preset mapping principle; and determining the processing probability of the effective medical data corresponding to the data flow according to the data flow corresponding to the effective medical data and the priority of all the effective medical data corresponding to the data flow.
3. The method according to claim 1, wherein the batch processing of each type of valid medical data according to the processing probability corresponding to each type of valid medical data comprises:
determining a numerical value interval mapped by the processing probability of each type of effective medical data;
generating a random number;
determining target medical data to be processed according to the numerical value interval where the random number is located; the numerical range of the random number and the numerical range have a corresponding relationship.
4. The method of claim 1, wherein before classifying the raw medical data according to the data volume of the update data corresponding to each raw medical data, the method further comprises:
acquiring patient identifiers corresponding to the original medical data according to a preset patient information set;
acquiring change time corresponding to each original medical data according to the patient identification;
acquiring updating data corresponding to each original medical data according to the patient identification and the change time;
wherein the patient information set is used for storing the patient identification and the change time of the original medical data corresponding to the patient identification.
5. The method according to claim 4, wherein before acquiring the patient identifier corresponding to each of the original medical data according to a preset patient information set, the method further comprises:
monitoring the original medical data to obtain the change time corresponding to each original medical data;
and storing the change time corresponding to each original medical data and the patient identification corresponding to each original medical data into the patient information set.
6. The method according to claim 1, wherein the removing the expired data in the classified medical data according to the timestamp and the discarding number corresponding to the classified medical data comprises:
determining the latest medical data corresponding to each patient identifier according to the patient identifier corresponding to each classified medical data;
when the timestamp corresponding to the classified medical data is earlier than the latest timestamp corresponding to the latest medical data, acquiring the discarding times corresponding to the classified medical data;
if the discarding times is less than a preset threshold value, judging the classified medical data as overdue data;
and removing the expired data.
7. A medical data classification processing apparatus, comprising:
the classification module is used for classifying the acquired original medical data to obtain classified medical data; the classifying the medical data includes at least: peak data, flat peak data, large patient data, and small patient data; the classifying the acquired original medical data to obtain classified medical data includes: classifying the original medical data according to the data variation of the original medical data in unit time to obtain the peak data and/or the flat peak data; classifying the original medical data according to the data volume of the updated data corresponding to each original medical data to obtain the big patient data and/or the small patient data;
the removing module is used for removing the expired data in the classified medical data to obtain various types of effective medical data and determining the priority of the various types of effective medical data; removing expired data in the classified medical data, wherein the removing process comprises the following steps: removing expired data in the classified medical data according to the time stamp and the discarding times corresponding to the classified medical data;
the determining module is used for determining the processing probability corresponding to each type of effective medical data according to the priority of each type of effective medical data;
and the processing module is used for processing various types of effective medical data in batches according to the processing probabilities corresponding to the various types of effective medical data.
8. A computer storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method of classifying medical data according to any one of claims 1 to 6.
9. An electronic device, comprising:
a processor; and
a memory for storing executable instructions of the processor;
wherein the processor is configured to execute the method for classifying medical data according to any one of claims 1 to 6 via execution of the executable instructions.
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