CN110993114A - Medical data analysis method and device, storage device and electronic equipment - Google Patents
Medical data analysis method and device, storage device and electronic equipment Download PDFInfo
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Abstract
The embodiment of the invention relates to a medical data analysis method and device, a storage medium and electronic equipment; relates to the technical field of medical big data processing, and the method comprises the following steps: acquiring medical data to be analyzed in a first preset time period, and comparing the first medical data to be analyzed by using a first preset medical data dictionary to obtain a first comparison result; when it is determined that target data information exists in the first comparison result, calculating a first data list and a first number of the target data information included in the first comparison result; when the first quantity of the target data information is determined to be larger than a preset quantity threshold value, comparing the target data information by using a second preset medical data dictionary to obtain a second comparison result; and obtaining an analysis result of the medical data to be analyzed according to the second comparison result, and displaying the analysis result. The embodiment of the invention improves the accuracy of the analysis result.
Description
Technical Field
The embodiment of the invention relates to the technical field of medical big data processing, in particular to a medical data analysis method, a medical data analysis device, a computer-readable storage medium and electronic equipment.
Background
Epidemics continue to be a major threat to human health and life safety. For the infectious disease emergency, only by early detection and timely early warning, time can be won for implementing various response measures, the event is controlled in a sprouting state, and the damage degree of the event is reduced to the maximum extent.
In the previous epidemic analysis scheme, the analysis can be performed only after manual declaration by hospitals in various places and manual aggregation.
However, the above solution has the following drawbacks: on one hand, a large number of manual links are involved, and hospital operators are required to be familiar with and comply with regulations, otherwise, omission and delay are caused, and further, the accuracy of an analysis result is low and the timeliness is poor; on the other hand, the patient also needs to rely on a disease control center or a health care committee-related institution for regular training and supervision, and a large amount of time and labor cost are wasted.
Therefore, it is desirable to provide a new medical data analysis method and apparatus.
It is to be noted that the information invented in the above background section is only for enhancing the understanding of the background of the present invention, and therefore, may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
An object of the present invention is to provide a medical data analysis method, a medical data analysis apparatus, a computer-readable storage medium, and an electronic device, which overcome, at least to some extent, the problem of low accuracy of analysis results due to limitations and disadvantages of the related art.
According to an aspect of the present disclosure, there is provided a medical data analysis method including:
acquiring medical data to be analyzed in a first preset time period, comparing the first medical data to be analyzed by using a first preset medical data dictionary to obtain a first comparison result, and correspondingly storing the target data information into a first data list according to the attribute information of the target data information in the first comparison result;
when it is determined that target data information exists in the first comparison result, calculating a first data list and a first number of the target data information included in the first comparison result;
when the first quantity of the target data information is determined to be larger than a preset quantity threshold value, comparing the target data information by using a second preset medical data dictionary to obtain a second comparison result;
and obtaining an analysis result of the medical data to be analyzed according to the second comparison result, and displaying the analysis result.
In an exemplary embodiment of the present disclosure, before calculating the first data list and the first amount of the target data information included in the first comparison result, the medical data analysis method further includes:
and correspondingly storing the target data information into the first preset list according to the attribute information of the target data information in the first comparison result.
In an exemplary embodiment of the present disclosure, calculating the first number of the target data information included in the first data list and the first comparison result includes:
acquiring the storage time of the target data information in the first data list;
generating a second data list according to the target data information of which the storage time is less than a second preset time period;
a first quantity of target data information included in the second data list is calculated.
In an exemplary embodiment of the disclosure, before comparing the target data information with a second preset medical data dictionary to obtain a second comparison result, the medical data analysis method further includes:
calculating a second quantity of the target data information included in the historical medical data in a third preset time period in the first data list;
and calculating the increase proportion between the first quantity and the second quantity, and sorting the target data information which is larger than a preset proportion threshold value in the second data list according to the size of the increase proportion when the increase proportion is confirmed to be larger than the preset proportion threshold value.
In an exemplary embodiment of the present disclosure, comparing the target data information with a second preset medical data dictionary to obtain a second comparison result includes:
and determining the target data information with the largest growth ratio according to the sequencing result, and comparing the target data information with the largest growth ratio by using a second preset medical data dictionary to obtain a second comparison result.
In an exemplary embodiment of the present disclosure, the medical data analysis method further includes:
assigning the second comparison result, and generating a third data list according to the second comparison result after the assignment processing;
normalizing the second comparison result with the same attribute information in the third data list;
and sorting the second comparison results according to the assignment size of each second comparison result after normalization processing, and obtaining a second comparison result with the largest assignment according to the sorting result.
In an exemplary embodiment of the present disclosure, obtaining an analysis result of the medical data to be analyzed according to the second comparison result includes:
comparing the second comparison result with the largest assignment by using a second preset medical data dictionary to obtain a plurality of third comparison results;
and taking the third comparison result with the highest matching degree as the analysis result.
In an exemplary embodiment of the present disclosure, the first data list includes a plurality of hospital codes, hospital names, patient codes, patient names, medicine codes, medicine standard names, medicine manufacturer names, medicine prescription quantities, medicine prescription units, prescription doctor codes, prescription doctor names, prescription times, and storage times of the target data information.
According to an aspect of the present disclosure, there is provided a medical data analysis apparatus including:
the first comparison module is used for acquiring medical data to be analyzed within a first preset time period, comparing the first medical data to be analyzed by using a first preset medical data dictionary to obtain a first comparison result, and correspondingly storing the target data information into the first data list according to the attribute information of the target data information in the first comparison result;
the first calculation module is used for calculating a first data list and a first quantity of the target data information included in the first comparison result when the target data information exists in the first comparison result;
the second comparison module is used for comparing the target data information by using a second preset medical data dictionary to obtain a second comparison result when the first quantity of the target data information is determined to be greater than a preset quantity threshold;
and the analysis result display module is used for obtaining the analysis result of the medical data to be analyzed according to the second comparison result and displaying the analysis result.
According to an aspect of the present disclosure, there is provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the medical data analysis method according to any one of the above-described exemplary embodiments.
According to an aspect of the present disclosure, there is provided an electronic device including:
a processor; and
a memory for storing executable instructions of the processor;
wherein the processor is configured to perform the medical data analysis method of any of the above exemplary embodiments via execution of the executable instructions.
On one hand, medical data to be analyzed in a first preset time period are obtained, and a first preset medical data dictionary is used for comparing the first medical data to be analyzed to obtain a first comparison result; when the target data information exists in the first comparison result, calculating a first data list and a first quantity of the target data information included in the first comparison result; then when the first quantity of the target data information is determined to be larger than a preset quantity threshold value, comparing the target data information by using a second preset medical data dictionary to obtain a second comparison result; finally, an analysis result of the medical data to be analyzed is obtained according to the second comparison result, and the analysis result is displayed, so that the problems that in the prior art, due to the fact that a large number of manual links are involved, hospital operators are required to be familiar with and comply with regulations, otherwise omission and delay are caused, the accuracy of the analysis result is low, and timeliness is poor are solved, the accuracy of the analysis result is improved, and meanwhile timeliness of the analysis result is improved; on the other hand, the problem that a large amount of time and labor cost are wasted due to the fact that regular training and supervision are required to be carried out by depending on a disease control center or a health committee related organization in the prior art is solved, and the analysis efficiency of medical data to be analyzed is improved; on the other hand, a first comparison result is obtained by comparing the first medical data to be analyzed by using the first preset medical data dictionary, and when the target data information exists in the first comparison result, the first data list and the first quantity of the target data information included in the first comparison result are calculated; and comparing the target data information by using a second preset medical data dictionary to obtain a second comparison result when the first quantity of the target data information is determined to be larger than the preset quantity threshold, so that the accuracy of the second comparison result is improved, and the accuracy of the analysis result is further improved.
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 invention, as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention. It is obvious that the drawings in the following description are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.
Fig. 1 schematically shows a flow chart of a method of medical data analysis according to an exemplary embodiment of the invention;
FIG. 2 schematically illustrates a flowchart of a method of calculating a first data list and a first amount of target data information included in a first comparison result, according to an exemplary embodiment of the present invention;
FIG. 3 schematically illustrates a flow chart of another method of medical data analysis according to an exemplary embodiment of the invention;
FIG. 4 schematically illustrates a flow chart of another method of medical data analysis according to an exemplary embodiment of the invention;
FIG. 5 schematically illustrates a flow chart of another method of medical data analysis according to an exemplary embodiment of the invention;
fig. 6 schematically shows a block diagram of a medical data analysis apparatus according to an exemplary embodiment of the present invention;
fig. 7 schematically shows an electronic device for implementing the above-described medical data analysis method according to an exemplary embodiment of the present invention.
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 provide a thorough understanding of embodiments of the invention. One skilled in the relevant art will recognize, however, that the invention may be practiced without one or more of the specific details, or with other methods, components, devices, steps, and so forth. In other instances, well-known technical solutions have not been shown or described in detail to avoid obscuring aspects of the invention.
Furthermore, the drawings are merely schematic illustrations of the invention 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. These functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
Epidemic diseases refer to infectious diseases that can infect a large number of people, and can be spread widely in a short time, such as influenza, meningitis, cholera, and the like. Epidemics can occur only somewhere, or can be a global pandemic. If a large area of infectious diseases occur in a certain place, the infectious diseases need to be reported to a health department as soon as possible and corresponding measures are taken.
The current epidemic analysis process is as follows:
first, everywhere infectious disease hospital or infectious department of hospital or health hospital in villages and towns designates a special person. Specifically, after a suspected infectious disease patient is received, diagnosis is performed; when the infectious disease is confirmed, the information is registered, and finally, the information is reported according to the requirements of the local health committee or the disease control center.
Secondly, after receiving the reported infectious disease information, the local disease control center or the health committee related organization firstly collects the information, then judges the information according to the standard by an expert, and finally carries out early warning when confirming that the observed value of a certain disease exceeds the expected value.
However, the epidemic analysis process involves a lot of manual steps, and requires the hospital operator to be familiar with and follow the regulations, otherwise it will cause omission and delay. The smooth development of the process requires a disease control center or a health care committee related organization to carry out regular training and supervision, and is time-consuming and labor-consuming.
In the present exemplary embodiment, a medical data analysis method is first provided, and the method may be operated in a server, a server cluster, a cloud server, or the like, or may be operated in an equipment terminal; of course, those skilled in the art may also operate the method of the present invention on other platforms as needed, and this is not particularly limited in this exemplary embodiment. Referring to fig. 1, the medical data analysis method may include the steps of:
step 110, medical data to be analyzed in a first preset time period are obtained, a first preset medical data dictionary is used for comparing the first medical data to be analyzed to obtain a first comparison result, and the target data information is correspondingly stored in a first data list according to attribute information of the target data information in the first comparison result.
Step S120, when the target data information exists in the first comparison result, calculating a first data list and a first quantity of the target data information included in the first comparison result.
Step S130, when the first quantity of the target data information is determined to be larger than a preset quantity threshold value, comparing the target data information by using a second preset medical data dictionary to obtain a second comparison result.
And S140, obtaining an analysis result of the medical data to be analyzed according to the second comparison result, and displaying the analysis result.
In the medical data analysis method, on one hand, a first comparison result is obtained by acquiring medical data to be analyzed in a first preset time period and comparing the first medical data to be analyzed by using a first preset medical data dictionary; when the target data information exists in the first comparison result, calculating a first data list and a first quantity of the target data information included in the first comparison result; then when the first quantity of the target data information is determined to be larger than a preset quantity threshold value, comparing the target data information by using a second preset medical data dictionary to obtain a second comparison result; finally, an analysis result of the medical data to be analyzed is obtained according to the second comparison result, and the analysis result is displayed, so that the problems that in the prior art, due to the fact that a large number of manual links are involved, hospital operators are required to be familiar with and comply with regulations, otherwise omission and delay are caused, the accuracy of the analysis result is low, and timeliness is poor are solved, the accuracy of the analysis result is improved, and meanwhile timeliness of the analysis result is improved; on the other hand, the problem that a large amount of time and labor cost are wasted due to the fact that regular training and supervision are required to be carried out by depending on a disease control center or a health committee related organization in the prior art is solved, and the analysis efficiency of medical data to be analyzed is improved; on the other hand, a first comparison result is obtained by comparing the first medical data to be analyzed by using the first preset medical data dictionary, and when the target data information exists in the first comparison result, the first data list and the first quantity of the target data information included in the first comparison result are calculated; and comparing the target data information by using a second preset medical data dictionary to obtain a second comparison result when the first quantity of the target data information is determined to be larger than the preset quantity threshold, so that the accuracy of the second comparison result is improved, and the accuracy of the analysis result is further improved.
Hereinafter, each step involved in a medical data analysis method according to an exemplary embodiment of the present invention will be explained and explained in detail with reference to the drawings.
First, the first preset medical data dictionary and the second preset medical data dictionary according to the exemplary embodiment of the present invention are explained and explained.
The first preset medical data dictionary may be, for example, a dictionary of infectious diseases and their corresponding drugs, which may include disease codes, disease names, infectious disease markers, drug codes, drug standard names, etc., and may also include other information, disease symptoms, etc., which is not limited in this example.
The second preset medical data dictionary may be, for example, a medicine indication dictionary, which may include, for example, medicine codes, medicine standard names, indication codes, indication names, etc., and may also include other information, such as symptoms and their corresponding dosages, etc., which is not limited in this example.
In step S110, medical data to be analyzed in a first preset time period is obtained, a first preset medical data dictionary is used to compare the first medical data to be analyzed to obtain a first comparison result, and the target data information is correspondingly stored in the first data list according to the attribute information of the target data information in the first comparison result.
In the present exemplary embodiment, first, medical data to be analyzed within a first preset time period is acquired; in order to ensure real-time performance and accuracy of the analysis result, the first preset time period may be 1 day, or may be other time periods, which is not limited in this example. In particular, the analysis system may acquire the medical data to be analyzed at a fixed time period each day. For example, the system is connected to the medical insurance database of a city medical insurance office 1 point in the morning every day, and medicine prescription information (medical data to be analyzed) reported by each hospital in the medical insurance data system of the previous day is read.
Further, after the medical data to be analyzed is obtained, the first medical data to be analyzed may be compared by using the first preset medical data dictionary to obtain a first comparison result. Specifically, according to the infectious diseases and the corresponding medicine dictionary thereof, the medicine prescription information of the previous day is comprehensively compared to obtain a first comparison result; the first comparison result can be used to determine whether the first medical data to be analyzed has infectious diseases and corresponding drugs in the corresponding drug dictionary.
In step S120, when it is determined that the target data information exists in the first comparison result, a first data list and a first number of the target data information included in the first comparison result are calculated.
In the present exemplary embodiment, first, the first data list is explained and explained. Specifically, the data stored in the first data list may include a hospital code, a hospital name, a patient code, a patient name, a drug code, a drug standard name, a drug manufacturer name, a drug prescription quantity, a drug prescription unit, a prescription doctor code, a prescription doctor name, a prescription time, a storage time of the target data information, and the like. The first data list may be used to store the first medical data to be analyzed when it is determined that the first medical data to be analyzed includes an infectious disease and a corresponding drug in the drug dictionary corresponding to the infectious disease. It should be noted that, the medical data to be analyzed stored in the first data list all relate to the infectious disease and the corresponding medicine in the corresponding medicine dictionary, so the first data list may be used as a prescription data table for the suspected infectious disease in the medical data to be analyzed, where the first data list may include prescription data for the infectious disease in all time periods.
Specifically, when it is determined that there is an infectious disease in the first comparison result and a corresponding drug in the drug dictionary corresponding to the infectious disease, the first data list and the first number of the target data information included in the first comparison result may be calculated. Specifically, referring to fig. 2, calculating the first data list and the first quantity of the target data information included in the first comparison result may include steps S210 to S230, which will be described in detail below.
In step S210, a storage time of the target data information in the first data list is acquired.
In step S220, a second data list is generated according to the target data information whose storage time is less than a second preset time period.
In step S230, a first number of target data information included in the second data list is calculated.
Hereinafter, steps S210 to S230 will be explained and explained. Firstly, the storage time of each target data information (medicine in a suspected infectious disease prescription) in a first data list can be acquired, and then a second data list is generated according to the target data information of which the storage time is less than a second preset time period; the second preset time period may be, for example, 3 days, or other time periods, which is not limited in this example; then, the first amount of each drug is counted. For example, the data of the suspected infectious disease prescription of the last 3 days can be added with the prescription amount and then merged into a data table (second data list) of the accumulated suspected infectious disease prescription of the last 3 days; the second data list may include drug codes, drug standard names, prescription quantity statistics, statistics times, etc. It should be noted that, in consideration of the specificity of spreading of the epidemic, in order to improve the timeliness and accuracy of the analysis result as much as possible, the optimal time period for selecting the second preset time period is 3 days, and may be 5 days, or may be selected separately according to the need, which is not limited in this example.
Further, when the first comparison result is judged to contain the infectious disease and the corresponding medicine in the corresponding medicine dictionary; the target data information may be correspondingly stored in the first preset list according to the attribute information of the target data information in the first comparison result. Specifically, in order to facilitate the subsequent statistical increase rate, the target data information may be stored to a location corresponding to the attribute information according to the attribute information (such as a drug code or a drug standard name, etc.) of the target data information.
In step S130, when it is determined that the first quantity of the target data information is greater than the preset quantity threshold, a second preset medical data dictionary is used to compare the target data information, so as to obtain a second comparison result.
In the present exemplary embodiment, in order to avoid missing a medicine prescribed for a suspected infectious disease due to a mistake of medical staff, a first amount of target data information may be judged first; for example, when the first number is smaller than a predetermined number threshold, which may be 1 or 2, for example, and cannot exceed 3, the subsequent analysis step may not be performed, and the first number may be filtered out directly. By the method, the number of comparison with the second preset dictionary data structure required in the later period can be reduced, and the analysis efficiency is improved.
Further, when it is determined that the first quantity of the target data information is greater than the preset quantity threshold, in order to further improve the accuracy of the second comparison result, as shown in fig. 3, the medical data analysis method may further include step S310 and step S320, which will be described in detail below.
In step S310, a second amount of the target data information included in the historical medical data within a third preset time period in the first data list is calculated.
In step S320, an increase ratio between the first quantity and the second quantity is calculated, and when it is determined that the increase ratio is greater than a preset ratio threshold, target data information in the second data list that is greater than the preset ratio threshold is sorted according to the size of the increase ratio.
Hereinafter, step S310 and step S320 will be explained and explained. First, according to the storage time of each target data information, calculating a second number of target data information included in the historical medical data in a third preset time period in the first data list; the third predetermined time period may include a plurality of time periods, such as: 90 days, 60 days, 30 days, 20 days, 10 days, 5 days, and so on; then, calculating an increase ratio between the first quantity and the second quantity of the target data information, and sorting the target data information which is larger than a preset ratio threshold value in the second data list according to the size of the increase ratio when the increase ratio is confirmed to be larger than the preset ratio threshold value.
For example, first, the number (second number) of suspected infectious diseases in the last 3 days of the same drug at 6 points before 90 days, 60 days, 30 days, 20 days, 10 days, and 5 days (third preset time period) is calculated;
secondly, using the data of the data table (second data list) of the suspected infectious disease prescription of the last 3 days, comparing the suspected infectious disease prescription number of the last 3 days of the same kind of medicine at 6 points before 90 days, 60 days, 30 days, 20 days, 10 days and 5 days (third preset time period), calculating the increase percentage of each time point, and after adding (positive percentage and negative percentage are added according to 0), storing the increase percentage to the cumulative suspected infectious disease prescription increase addition data table (including but not limited to medicine code, medicine standard name, statistical date, increase percentage addition, increase percentage before 90 days, increase percentage before 60 days, increase percentage before 30 days, increase percentage before 20 days, increase percentage before 10 days and increase percentage before 5 days);
further, from the cumulative suspected infectious disease prescription addition data table of the last 3 days, firstly, the medicine prescriptions with the growth percentage exceeding 20% (preset proportion threshold) are found, then the medicine prescriptions are sorted from high to low according to the growth percentage addition, and then the medicine prescriptions with the most recent growth are statistically sorted according to the order of the growth percentage before 5 days, the growth percentage before 10 days, the growth percentage before 20 days, the growth percentage before 30 days, the growth percentage before 60 days and the growth percentage before 90 days (a threshold value X can be set according to the operation result in the future, and the X medicine prescriptions with the most recent growth are found).
Further, after the target data information is sorted, the target data information may be compared by using a second preset medical data dictionary to obtain a second comparison result. Specifically, the target data information with the largest growth ratio can be determined according to the sorting result, and the second preset medical data dictionary is used for comparing the target data information with the largest growth ratio to obtain a second comparison result. For example, first, the data of the medicine prescription with the largest growth ratio is determined according to the above sorting result, and then the data of the medicine prescription with the largest growth ratio is compared by using the medicine indication dictionary to obtain the corresponding indication (second comparison result).
In step S140, an analysis result of the medical data to be analyzed is obtained according to the second comparison result, and the analysis result is displayed.
In the present exemplary embodiment, in order to further improve the accuracy of the analysis result, as shown in fig. 4, the medical data analysis method may further include steps S410 to S430, which will be described in detail below.
In step S410, an assignment process is performed on the second comparison result, and a third data list is generated according to the second comparison result after the assignment process.
In step S420, a normalization process is performed on the second comparison results with the same attribute information in the third data list.
In step S430, the second comparison results are sorted according to the assignment size of each second comparison result after the normalization processing, and the second comparison result with the largest assignment is obtained according to the sorting result.
Hereinafter, steps S410 to S430 will be explained and explained. Specifically, first, an assignment is made for each indication (percentage increase 100) and stored in an indication analysis table (third data list); wherein, the third data list may include a symptom code, a symptom name, a drug code, a drug standard name, an indication assignment, and the like; the same symptom code is then merged in the indication analysis table, the indication assignments for each prescription are added, and the indication with the highest (most likely) assignment is listed from high to low.
Further, after a second comparison result with the largest assignment is obtained, an analysis result of the medical data to be analyzed can be obtained according to the second comparison result, and the analysis result is displayed. Specifically, firstly, a second preset medical data dictionary is used for comparing a second comparison result with the largest assignment to obtain a plurality of third comparison results; and secondly, taking the third comparison result with the highest matching degree as the analysis result. For example, the most probable infectious diseases are found out from the most probable indications and compared with the infectious disease medicine dictionary table, the most probable infectious diseases are found out from the indications with the highest assignment in the indication analysis table and displayed to the disease control center or health committee related organization, so that the disease control center or health committee related organization can check in time and prevent and treat in time to avoid causing larger infectious diseases.
A method for analyzing medical data according to an exemplary embodiment of the present invention will be further explained and explained with reference to fig. 5. Referring to fig. 5, the medical data analysis method may include the steps of:
step S510, configuring infectious diseases, corresponding medicine dictionaries and medicine indication dictionaries;
step S520, reading all medicine prescription information uploaded by all hospitals in the previous day every morning, comparing all the medicine prescription information according to the infectious diseases and the corresponding medicine dictionaries, and storing the suspected infectious disease prescription data obtained by comparison;
step S530, counting the prescription amount of the infectious disease prescription in the last 3 days according to each medicine type, and comparing the cumulative prescription data of three days before 90 days, 60 days, 30 days, 20 days, 10 days and 5 days;
step S540, adding the growth percentages of the six points to generate suspected infectious disease prescription growth data, and listing the suspected infectious disease prescription with the fastest growth according to the growth percentages and the latest priority;
step S550, according to the medicine indication dictionary, finding out the corresponding indication from the suspected infectious disease prescription growing the fastest, and performing possibility assignment without the indication;
step S560, after calculating the possibility assignment of the same indication, sorting from high to low; and according to the medicine dictionary corresponding to the infectious diseases, finding out the infectious diseases with the highest possibility from the indications with the highest possibility assignment as analysis results, and displaying the analysis results.
In the medical data analysis method according to the exemplary embodiment of the present invention, on one hand, after the mechanism of the present invention is adopted, the disease control center or the health care committee-related institution can visually and real-timely see the latest suspected infectious disease related data automatically analyzed according to the medicine prescription information of the current day, in addition to the existing infectious disease reporting mechanism. All the processes are based on intelligent data analysis, manual reporting is not needed, real-time and visual effects are achieved, further epidemic prevention mode intelligent learning can be conducted based on data, and hospitals are guided to store relevant medicines according to time.
On the other hand, the old mode that the existing epidemic analysis needs to be manually declared by a hospital in a place and then manually aggregated to analyze the current high-incidence epidemic in the place and lacks certain real-time performance is changed, and the newly invented automatic analysis system is used for automatically analyzing related diseases based on medicine prescription information in a medical insurance data system in the place, analyzing the current possible high-incidence epidemic diseases in real time through an algorithm and submitting the epidemic diseases to related departments as reference.
On the other hand, the new automatic analysis mechanism improves the analysis efficiency, reduces the manual process, improves the analysis real-time performance and can well support the work of relevant health departments.
Furthermore, prescription data in the medical insurance data system can be used for epidemic analysis research, so that the medical insurance system and the public health system are fused; moreover, an analysis and early warning mechanism except for manual reporting can be provided for a disease control center or a health committee related mechanism, and double guarantee is achieved; meanwhile, a real-time and visual analysis method can be provided for a disease control center or a health care committee related organization, and big data which can be subsequently expanded and analyzed can be provided.
The present disclosure also provides a medical data analysis device. Referring to fig. 6, the medical data analysis apparatus may include a first comparison module 610, a first calculation module 620, a second comparison module 630, and an analysis result presentation module 640. Wherein:
the first comparison module 610 may be configured to obtain medical data to be analyzed within a first preset time period, compare the first medical data to be analyzed by using a first preset medical data dictionary to obtain a first comparison result, and store the target data information into the first data list correspondingly according to attribute information of the target data information in the first comparison result.
The first calculating module 620 may be configured to calculate a first data list and a first amount of the target data information included in the first comparison result when it is determined that the target data information exists in the first comparison result.
The second comparing module 630 may be configured to, when it is determined that the first quantity of the target data information is greater than the preset quantity threshold, compare the target data information with a second preset medical data dictionary to obtain a second comparison result.
The analysis result display module 640 may be configured to obtain an analysis result of the medical data to be analyzed according to the second comparison result, and display the analysis result.
In an exemplary embodiment of the present disclosure, the medical data analysis apparatus further includes:
the storage module may be configured to store the target data information into the first preset list correspondingly according to the attribute information of the target data information in the first comparison result.
In an exemplary embodiment of the present disclosure, calculating the first number of the target data information included in the first data list and the first comparison result includes:
acquiring the storage time of the target data information in the first data list; generating a second data list according to the target data information of which the storage time is less than a second preset time period; a first quantity of target data information included in the second data list is calculated.
In an exemplary embodiment of the present disclosure, the medical data analysis apparatus further includes:
the second calculation module may be configured to calculate a second amount of the target data information included in the historical medical data within a third preset time period in the first data list.
The first sorting module may be configured to calculate an increase ratio between the first number and the second number, and sort, according to a size of the increase ratio, target data information in the second data list that is greater than a preset ratio threshold when it is determined that the increase ratio is greater than the preset ratio threshold.
In an exemplary embodiment of the present disclosure, comparing the target data information with a second preset medical data dictionary to obtain a second comparison result includes:
and determining the target data information with the largest growth ratio according to the sequencing result, and comparing the target data information with the largest growth ratio by using a second preset medical data dictionary to obtain a second comparison result.
In an exemplary embodiment of the present disclosure, the medical data analysis apparatus further includes:
the first assignment module may be configured to perform assignment processing on the second comparison result, and generate a third data list according to the second comparison result after the assignment processing.
The normalization processing module may be configured to perform normalization processing on the second comparison results with the same attribute information in the third data list.
The second sorting module may be configured to sort the second comparison results according to the assignment sizes of the second comparison results after the normalization processing, and obtain a second comparison result with a largest assignment according to the sorting result.
In an exemplary embodiment of the present disclosure, obtaining an analysis result of the medical data to be analyzed according to the second comparison result includes:
comparing the second comparison result with the largest assignment by using a second preset medical data dictionary to obtain a plurality of third comparison results; and taking the third comparison result with the highest matching degree as the analysis result.
In an exemplary embodiment of the present disclosure, the first data list includes a plurality of hospital codes, hospital names, patient codes, patient names, medicine codes, medicine standard names, medicine manufacturer names, medicine prescription quantities, medicine prescription units, prescription doctor codes, prescription doctor names, prescription times, and storage times of the target data information.
The specific details of each module in the medical data analysis apparatus have been described in detail in the corresponding medical data analysis 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 invention. 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 invention 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.
In an exemplary embodiment of the present invention, there is also provided an electronic device capable of implementing the above method.
As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method or program product. Thus, various aspects of the invention 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 700 according to this embodiment of the invention is described below with reference to fig. 7. The electronic device 700 shown in fig. 7 is only an example and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 7, electronic device 700 is embodied in the form of a general purpose computing device. The components of the electronic device 700 may include, but are not limited to: the at least one processing unit 710, the at least one memory unit 720, and a bus 730 that couples various system components including the memory unit 720 and the processing unit 710.
Wherein the storage unit stores program code that is executable by the processing unit 710 such that the processing unit 710 performs the steps according to various exemplary embodiments of the present invention as described in the above section "exemplary method" of the present specification. For example, the processing unit 710 may perform step S110 as shown in fig. 1: acquiring medical data to be analyzed in a first preset time period, comparing the first medical data to be analyzed by using a first preset medical data dictionary to obtain a first comparison result, and correspondingly storing the target data information into a first data list according to the attribute information of the target data information in the first comparison result; step S120: when it is determined that target data information exists in the first comparison result, calculating a first data list and a first number of the target data information included in the first comparison result; step S130: when the first quantity of the target data information is determined to be larger than a preset quantity threshold value, comparing the target data information by using a second preset medical data dictionary to obtain a second comparison result; step S140: and obtaining an analysis result of the medical data to be analyzed according to the second comparison result, and displaying the analysis result.
The storage unit 720 may include readable media in the form of volatile memory units, such as a random access memory unit (RAM)7201 and/or a cache memory unit 7202, and may further include a read only memory unit (ROM) 7203.
The storage unit 720 may also include a program/utility 7204 having a set (at least one) of program modules 7205, such program modules 7205 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 700 may also communicate with one or more external devices 800 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 700, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 700 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 750. Also, the electronic device 700 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 760. As shown, the network adapter 760 communicates with the other modules of the electronic device 700 via the bus 730. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 700, 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 embodiment of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to make a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) execute the method according to the embodiment of the present invention.
In an exemplary embodiment of the present invention, there is also provided a computer-readable storage medium having stored thereon a program product capable of implementing the above-described method of the present specification. In some possible embodiments, aspects of the invention may also be implemented in the form of a program product comprising program code means for causing a terminal device to carry out the steps according to various exemplary embodiments of the invention described in the above section "exemplary methods" of the present description, when said program product is run on the terminal device.
According to the program product for realizing the method, the portable compact disc read only memory (CD-ROM) can be adopted, the program code is included, and the program product can be operated on terminal equipment, such as a personal computer. However, the program product of the present invention is not limited in this regard and, in the present 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 aspects of the present invention 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).
Furthermore, the above-described figures are merely schematic illustrations of processes involved in methods according to exemplary embodiments of the invention, 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 invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.
Claims (10)
1. A method of medical data analysis, comprising:
acquiring medical data to be analyzed in a first preset time period, comparing the first medical data to be analyzed by using a first preset medical data dictionary to obtain a first comparison result, and correspondingly storing the target data information into a first data list according to the attribute information of the target data information in the first comparison result;
when it is determined that target data information exists in the first comparison result, calculating a first data list and a first number of the target data information included in the first comparison result;
when the first quantity of the target data information is determined to be larger than a preset quantity threshold value, comparing the target data information by using a second preset medical data dictionary to obtain a second comparison result;
and obtaining an analysis result of the medical data to be analyzed according to the second comparison result, and displaying the analysis result.
2. The medical data analysis method according to claim 1, wherein calculating the first data list and the first number of the target data information included in the first comparison result comprises:
acquiring the storage time of the target data information in the first data list;
generating a second data list according to the target data information of which the storage time is less than a second preset time period;
a first quantity of target data information included in the second data list is calculated.
3. The medical data analysis method according to claim 2, wherein before the target data information is compared by using a second preset medical data dictionary to obtain a second comparison result, the medical data analysis method further comprises:
calculating a second quantity of the target data information included in the historical medical data in a third preset time period in the first data list;
and calculating the increase proportion between the first quantity and the second quantity, and sorting the target data information which is larger than a preset proportion threshold value in the second data list according to the size of the increase proportion when the increase proportion is confirmed to be larger than the preset proportion threshold value.
4. The medical data analysis method according to claim 3, wherein the comparing the target data information with a second preset medical data dictionary to obtain a second comparison result comprises:
and determining the target data information with the largest growth ratio according to the sequencing result, and comparing the target data information with the largest growth ratio by using a second preset medical data dictionary to obtain a second comparison result.
5. The medical data analysis method according to claim 4, further comprising:
assigning the second comparison result, and generating a third data list according to the second comparison result after the assignment processing;
normalizing the second comparison result with the same attribute information in the third data list;
and sorting the second comparison results according to the assignment size of each second comparison result after normalization processing, and obtaining a second comparison result with the largest assignment according to the sorting result.
6. The medical data analysis method according to claim 5, wherein obtaining the analysis result of the medical data to be analyzed according to the second comparison result comprises:
comparing the second comparison result with the largest assignment by using a second preset medical data dictionary to obtain a plurality of third comparison results;
and taking the third comparison result with the highest matching degree as the analysis result.
7. The medical data analysis method according to any one of claims 1 to 6, wherein the first data list includes a plurality of hospital codes, hospital names, patient codes, patient names, medicine codes, medicine standard names, medicine manufacturer names, medicine prescription quantities, medicine prescription units, prescription doctor codes, prescription doctor names, prescription times, and storage times of target data information.
8. A medical data analysis apparatus, comprising:
the first comparison module is used for acquiring medical data to be analyzed in a first preset time period and comparing the first medical data to be analyzed by using a first preset medical data dictionary to obtain a first comparison result;
the first calculation module is used for calculating a first data list and a first quantity of the target data information included in the first comparison result when the target data information exists in the first comparison result;
the second comparison module is used for comparing the target data information by using a second preset medical data dictionary to obtain a second comparison result when the first quantity of the target data information is determined to be greater than a preset quantity threshold;
and the analysis result display module is used for obtaining the analysis result of the medical data to be analyzed according to the second comparison result and displaying the analysis result.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the medical data analysis method according to any one of claims 1 to 7.
10. An electronic device, comprising:
a processor; and
a memory for storing executable instructions of the processor;
wherein the processor is configured to perform the medical data analysis method of any one of claims 1-7 via execution of the executable instructions.
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