CN117253623A - Intelligent management method and system for chronic disease data - Google Patents
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Abstract
The invention belongs to the technical field of chronic disease data management, and aims to provide an intelligent chronic disease data management method and system. The invention discloses an intelligent management method for chronic disease data, which comprises the following steps: acquiring chronic disease data of a specified disease type; dividing the chronic disease data according to disease development stages, and summarizing the preprocessed data of different disease development stages in a time sequence form to obtain arranged chronic disease data; screening the arranged chronic disease data to obtain screened chronic disease data; filling missing data of the screened chronic disease data to obtain pretreated chronic disease data; and carrying out data mining treatment on the pretreated chronic disease data to obtain a chronic disease association result. The invention can improve the accuracy of the chronic disease data processing result, and is beneficial to obtaining the disease law of the chronic disease, the treatment effect and other reference results.
Description
Technical Field
The invention belongs to the technical field of chronic disease data management, and particularly relates to an intelligent chronic disease data management method and system.
Background
Chronic diseases refer to diseases which exist for a long time and are difficult to cure, such as diabetes, hyperlipidemia, hypertension, cerebral apoplexy, coronary heart disease and the like. Along with the continuous development of medical information datamation, medical institutions such as hospitals or clinics accumulate a large amount of medical data comprising chronic disease data for realizing the treatment management of patients, and the analysis processing of massive disease data can be convenient for providing reference basis for the health management of patients. The chronic disease data generally includes physical examination information, medication record information, and diagnosis information of a patient, and in the process of managing chronic disease data, data mining and other processes are generally required to be performed based on a large number of types of chronic disease data in the prior art.
However, in using the prior art, the inventors found that there are at least the following problems in the prior art:
because of the pathogenesis of chronic diseases, most patients usually go to medical institutions for treatment when the disease is serious, or the situations that a certain physical examination item is not checked or a worker is not input in the treatment process of the patients exist, so that the situation that the chronic disease data collected by the medical institutions have missing data exists, the chronic disease data of the same patient cannot fully show the development trend corresponding to the chronic disease, and the problem that the follow-up analysis and treatment of the chronic disease data has poor precision exists. In addition, due to the standard differences of data acquisition and the like of different medical institutions, the medical data forms are diversified, and factors such as error rate and the like exist in the data records of medical workers, the overall complexity of the chronic disease data is high, and the accuracy of the subsequent analysis processing result of the chronic disease data is low.
Disclosure of Invention
The invention aims to solve the technical problems at least to a certain extent, and provides an intelligent chronic disease data management method and system.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
in a first aspect, the present invention provides an intelligent chronic disease data management method, including:
acquiring chronic disease data of a specified disease type;
dividing the chronic disease data according to disease development stages, and summarizing the preprocessed data of different disease development stages in a time sequence form to obtain arranged chronic disease data;
screening the arranged chronic disease data to obtain screened chronic disease data;
filling missing data of the screened chronic disease data to obtain pretreated chronic disease data;
and carrying out data mining treatment on the pretreated chronic disease data to obtain a chronic disease association result.
The invention screens the chronic disease data and fills the missing data in the pretreatment, and then realizes the excavation treatment of the pretreated chronic disease data to obtain the chronic disease association result, thereby improving the accuracy of the chronic disease data processing result, and being beneficial to obtaining the reference results such as the incidence rule and the treatment effect of the chronic disease.
In one possible design, obtaining chronic disease data for a specified disease type includes:
collecting multi-source initial chronic disease data, and extracting all initial chronic disease data matched with the same patient identification from the multi-source initial chronic disease data;
extracting initial chronic disease data of a designated disease type from all initial chronic disease data matched with the same patient identification;
and carrying out fusion treatment on the initial chronic disease data of the appointed disease type corresponding to the same patient to obtain the initial chronic disease data of the appointed disease type.
In the invention, the initial chronic disease data of the same patient acquired by different medical institutions is fused to obtain the initial chronic disease data, and the subsequent processing is carried out, so that the information summary of the current patient hidden in different data can be conveniently realized, and the overall health condition of the current patient can be conveniently mastered.
In one possible design, the patient identification includes a patient identification number.
In one possible design, extracting initial chronic disease data of a specified disease type from all initial chronic disease data matching the same patient identification includes:
acquiring any one of all initial chronic disease data matched with the same patient identification;
acquiring a disease type corresponding to current initial chronic disease data;
and acquiring another initial chronic disease data in all initial chronic disease data matched with the same patient identification until the initial chronic disease data of the designated disease type in all initial chronic disease data matched with the same patient identification is acquired.
It should be noted that, for chronic disease data of the same disease type, it is usually characterized by a combination of physical examination data of different detection indexes, where there is a low correlation between the detection indexes, so the present invention can consider that the correlation degree of physical examination data of different detection indexes is low. Based on the method, the similarity of the initial chronic disease data belonging to different disease types is determined through the likelihood function, so that the disease type corresponding to the initial chronic disease data is determined, the problem that the chronic disease data is not matched with the disease type due to error recording or error diagnosis of the disease type can be avoided, and further acquisition of all initial chronic disease data of the same disease type of a patient is realized.
In one possible design, obtaining the disease type corresponding to the current initial chronic disease data includes:
obtaining likelihood functions corresponding to different disease types;
respectively taking the current initial chronic disease data as independent variables of likelihood functions corresponding to different disease types to obtain likelihood function values corresponding to different disease types;
and obtaining the maximum likelihood function value in the likelihood function values corresponding to different disease types, and taking the disease type corresponding to the maximum likelihood function value as the disease type corresponding to the current initial chronic disease data.
In one possible design, the likelihood function for a type a disease type is:
wherein x is i For chronic disease data of type a disease, i e {1,2, … …, n }, n being a natural number greater than 1; p (P) Ai (x i ) To indicate when the ith chronic disease data is x i At the time of discriminating x i Probability density function for type a disease; wherein,
in sigma Ai Variance, μ of the ith chronic disease data for the type A disease Ai E is a natural constant, which is a mathematical expectation of the i-th sample health data of the type a disease.
In one possible design, the fusion of the initial chronic disease data of the specified disease type corresponding to the same patient is performed in a time series.
In one possible design, the filling of missing data for the post-screening chronic disease data to obtain pre-processed chronic disease data includes:
acquiring the window size of a sliding window according to the data density of the screened chronic disease data;
and traversing the screened chronic disease data according to time sequence by using the sliding window with the window size, and filling the data window when the data in any data window of the screened chronic disease data is empty, until the screened chronic disease data is traversed.
In one possible design, the data mining process is performed on the preprocessed chronic disease data to obtain a chronic disease association result, including:
acquiring a frequent item data set in the preprocessed chronic disease data;
and obtaining a strong association rule according to the frequent item data set, and carrying out visualization processing on the strong association rule as a chronic disease association result.
In a second aspect, the present invention provides an intelligent chronic disease data management system, configured to implement the intelligent chronic disease data management method according to any one of the above-mentioned aspects; the chronic disease data intelligent management system comprises:
the data acquisition module is used for acquiring chronic disease data of a designated disease type;
the data segmentation module is in communication connection with the data acquisition module and is used for dividing the chronic disease data according to disease development stages, and summarizing the preprocessed data of different disease development stages in a time sequence form to obtain arranged chronic disease data;
the data screening module is in communication connection with the data segmentation module and is used for screening the arranged chronic disease data to obtain screened chronic disease data;
the data filling module is in communication connection with the data screening module and is used for filling missing data of the screened chronic disease data to obtain pretreated chronic disease data;
and the data mining module is in communication connection with the data filling module and is used for carrying out data mining processing on the preprocessed chronic disease data to obtain a chronic disease association result.
In a third aspect, the present invention provides an electronic device, comprising:
a memory for storing computer program instructions; the method comprises the steps of,
a processor for executing the computer program instructions to perform the operations of the intelligent chronic disease data management method as set forth in any one of the preceding claims.
In a fourth aspect, the present invention provides a computer readable storage medium storing computer program instructions readable by a computer, the computer program instructions being configured to perform operations of the method for intelligent management of chronic disease data as described in any of the preceding claims when run.
Drawings
FIG. 1 is a flow chart of a method for intelligent management of chronic disease data in an embodiment;
FIG. 2 is a block diagram of a chronic disease data intelligent management system in an embodiment;
fig. 3 is a block diagram of an electronic device in an embodiment.
Detailed Description
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the present invention will be briefly described below with reference to the accompanying drawings and the description of the embodiments or the prior art, and it is obvious that the following description of the structure of the drawings is only some embodiments of the present invention, and other drawings can be obtained according to these drawings without inventive effort to a person skilled in the art. It should be noted that the description of these examples is for aiding in understanding the present invention, but is not intended to limit the present invention.
Example 1:
the embodiment discloses a chronic disease data intelligent management method, which can be executed by computer equipment or virtual machines with certain computing resources, such as personal computers, smart phones, personal digital assistants or electronic equipment such as wearable equipment, or virtual machines.
As shown in FIG. 1, a method for intelligently managing chronic disease data may include, but is not limited to, the following steps:
s1, obtaining chronic disease data of a designated disease type, and establishing a chronic disease database based on the chronic disease data; in this embodiment, the initial chronic disease data, chronic disease data and other data are all in the form of data sets, and specifically, in this embodiment, each initial chronic disease data or chronic disease data corresponds to all data in one diagnosis and treatment process, and mainly includes data represented by numbers, such as physical examination data, test data and the like, which can represent the development condition of a chronic disease and is convenient for direct calculation.
In the prior art, during the treatment of a patient, there may be situations that the same patient is treated in different periods to different medical institutions, based on which the following improvements are further made in this embodiment: acquiring chronic disease data of a specified disease type, comprising:
s101, acquiring multi-source initial chronic disease data, and extracting all initial chronic disease data matched with the same patient identifier from the multi-source initial chronic disease data; it should be noted that the multi-source initial chronic disease data includes initial chronic disease data of a specified disease type called from a plurality of medical institutions, which constitute the most primitive data sources of the chronic disease database.
Specifically, in this embodiment, the patient identification includes a patient identification number. It should be noted that, because the patient numbers of different diagnosis and treatment institutions are generally only unique in the diagnosis and treatment institutions and different in numbering rules, for this purpose, the patient identifier is set to be a patient identification card number capable of uniquely characterizing the patient identity, and it should be understood that the patient identifier may also be formed by combining information such as a name, a birth date, a sex, and the like, and be used as a substitute identifier of the patient identification card number to perform initial chronic disease data acquisition.
S102, extracting initial chronic disease data of a designated disease type from all initial chronic disease data matched with the same patient identification.
In the prior art, there are cases of error records of disease types, or error judgment of disease types due to poor diagnosis and treatment levels of medical staff in medical institutions, and the like, so that the disease types corresponding to a certain group of initial chronic disease data are different from the actual conditions, in order to realize acquisition of all initial chronic disease data of the same disease type of patients, the embodiment further makes the following improvements: extracting initial chronic disease data of a specified disease type from all initial chronic disease data matched with the same patient identification, wherein the initial chronic disease data comprises:
s1021, acquiring any one of all initial chronic disease data matched with the same patient identification, and extracting physical examination data from the initial chronic disease data;
s1022, obtaining the disease type corresponding to the current initial chronic disease data.
Specifically, in this embodiment, obtaining the disease type corresponding to the current initial chronic disease data includes:
A1. obtaining likelihood functions corresponding to different disease types;
in this embodiment, the likelihood function corresponding to the type a disease is:
wherein x is i For chronic disease data of type a disease, i e {1,2, … …, n }, n being a natural number greater than 1; p (P) Ai (x i ) To indicate when the ith chronic disease data is x i At the time of discriminating x i Probability density function for type a disease; wherein,
in sigma Ai Variance, μ of the ith chronic disease data for the type A disease Ai E is a natural constant, which is a mathematical expectation of the i-th sample health data of the type a disease.
Mu, in the form of a powder Ai Characterizing an average, σ, of the ith chronic disease data in a plurality of chronic disease data corresponding to the type A disease Ai The variance of the ith chronic disease data in the plurality of chronic disease data corresponding to the A type disease is characterized.
A2. Respectively taking physical examination data in the current initial chronic disease data as independent variables of likelihood functions corresponding to different disease types to obtain likelihood function values corresponding to different disease types;
A3. and obtaining the maximum likelihood function value in the likelihood function values corresponding to different disease types, and taking the disease type corresponding to the maximum likelihood function value as the disease type corresponding to the current initial chronic disease data.
S1023, acquiring another initial chronic disease data in all initial chronic disease data matched with the same patient identification until the initial chronic disease data of the designated disease type in all initial chronic disease data matched with the same patient identification is obtained.
It should be noted that, for chronic disease data of the same disease type, it is generally characterized by a combination of physical examination data of different detection indexes, where there is a low correlation between the detection indexes, so the present embodiment may consider that the correlation degree of physical examination data of different detection indexes is low. Based on this, in this embodiment, the similarity that a certain initial chronic disease data belongs to an initial chronic disease of different disease types is determined through the likelihood function, so that the disease type corresponding to the initial chronic disease data is determined, the problem that the chronic disease data is not matched with the disease type due to the error in recording the disease type or the error in diagnosis can be avoided, and further, the collection of all initial chronic disease data of the same disease type of a patient is realized.
S103, carrying out fusion treatment on the initial chronic disease data of the appointed disease type corresponding to the same patient to obtain the initial chronic disease data of the appointed disease type.
In this embodiment, when the fusion processing is performed on the initial chronic disease data of the specified disease type corresponding to the same patient, the fusion processing is implemented in a time series manner. With the arrangement, the information of the development trend of the chronic disease of the patient can be conveniently obtained through the initial chronic disease data of the designated disease type.
In this embodiment, the initial chronic disease data of the same patient collected by different medical institutions is fused to obtain initial chronic disease data, and subsequent processing is performed, so that information summary of the current patient, which is hidden in different data, can be conveniently realized, and the overall health condition of the current patient can be mastered.
S2, dividing the chronic disease data according to disease development stages, and summarizing the preprocessed data of different disease development stages in a time sequence form to obtain the arranged chronic disease data.
S3, screening the chronic disease data after arrangement to obtain the chronic disease data after screening.
S4, filling missing data of the chronic disease data after screening to obtain the pretreated chronic disease data.
Specifically, the step of filling missing data of the chronic disease data after screening to obtain pretreated chronic disease data comprises the following steps:
s401, acquiring the window size of a sliding window according to the data density of the screened chronic disease data; it should be noted that, in this embodiment, the window size of the sliding window is determined according to the data density of the chronic disease data after screening, where the data density is the time distribution density arranged according to different development stages of the disease, for example, in the implementation process, the window size of the sliding window may be set to be capable of covering at least 70% of the chronic disease data after screening, that is, 30% of the missing window in the process that the sliding window traverses the chronic medical record data after screening.
S402, traversing the sliding window with the window size according to time sequence, and filling the data window when the data in any data window of the screened chronic disease data is empty, until the data of the screened chronic disease is traversed.
S5, carrying out data mining processing on the preprocessed chronic disease data based on an association rule algorithm to obtain a chronic disease association result.
In this embodiment, performing data mining processing on the preprocessed chronic disease data to obtain a chronic disease association result, including:
s501, acquiring a frequent item data set in the preprocessed chronic disease data; it should be noted that, the frequent item data set is a data set greater than or equal to a preset minimum support threshold value in the chronic disease data after pretreatment;
s502, obtaining a strong association rule according to the frequent item data set, and carrying out visualization processing on the strong association rule as a chronic disease association result. It should be noted that, in this embodiment, the strong association rule should be greater than or equal to a preset minimum support threshold and a preset minimum confidence threshold.
According to the embodiment, the chronic disease data is screened and the missing data is filled for pretreatment, then the excavation treatment of the pretreated chronic disease data is realized, and the process of obtaining the chronic disease association result is achieved.
Example 2:
the embodiment discloses an intelligent chronic disease data management system which is used for realizing the intelligent chronic disease data management method in the embodiment 1; as shown in fig. 2, the chronic disease data intelligent management system includes:
the data acquisition module is used for acquiring chronic disease data of a designated disease type;
the data segmentation module is in communication connection with the data acquisition module and is used for dividing the chronic disease data according to disease development stages, and summarizing the preprocessed data of different disease development stages in a time sequence form to obtain arranged chronic disease data;
the data screening module is in communication connection with the data segmentation module and is used for screening the arranged chronic disease data to obtain screened chronic disease data;
the data filling module is in communication connection with the data screening module and is used for filling missing data of the screened chronic disease data to obtain pretreated chronic disease data;
and the data mining module is in communication connection with the data filling module and is used for carrying out data mining processing on the preprocessed chronic disease data to obtain a chronic disease association result.
Example 3:
on the basis of embodiment 1 or 2, this embodiment discloses an electronic device, which may be a smart phone, a tablet computer, a notebook computer, a desktop computer, or the like. The electronic device may be referred to as a user terminal, a portable terminal, a desktop terminal, etc., as shown in fig. 3, the electronic device includes:
a memory for storing computer program instructions; the method comprises the steps of,
a processor configured to execute the computer program instructions to perform the operations of the intelligent chronic disease data management method according to any one of embodiment 1.
In particular, processor 301 may include one or more processing cores, such as a 4-core processor, an 8-core processor, and the like. The processor 301 may be implemented in at least one hardware form of DSP (Digital Signal Processing ), FPGA (Field-Programmable Gate Array, field programmable gate array), PLA (Programmable Logic Array ). The processor 301 may also include a main processor, which is a processor for processing data in an awake state, also called a CPU (Central Processing Unit ), and a coprocessor; a coprocessor is a low-power processor for processing data in a standby state. In some embodiments, the processor 301 may be integrated with a GPU (Graphics Processing Unit, image processor) for taking care of rendering and drawing of content that the display screen is required to display.
Memory 302 may include one or more computer-readable storage media, which may be non-transitory. Memory 302 may also include high-speed random access memory, as well as non-volatile memory, such as one or more magnetic disk storage devices, flash memory storage devices. In some embodiments, a non-transitory computer readable storage medium in memory 302 is used to store at least one instruction for execution by processor 301 to implement the chronic disease data intelligent management method provided by embodiment 1 in the present application.
In some embodiments, the terminal may further optionally include: a communication interface 303, and at least one peripheral device. The processor 301, the memory 302 and the communication interface 303 may be connected by a bus or signal lines. The respective peripheral devices may be connected to the communication interface 303 through a bus, signal line, or circuit board. Specifically, the peripheral device includes: at least one of radio frequency circuitry 304, a display screen 305, and a power supply 306.
The communication interface 303 may be used to connect at least one peripheral device associated with an I/O (Input/Output) to the processor 301 and the memory 302. In some embodiments, processor 301, memory 302, and communication interface 303 are integrated on the same chip or circuit board; in some other embodiments, either or both of the processor 301, the memory 302, and the communication interface 303 may be implemented on separate chips or circuit boards, which is not limited in this embodiment.
The Radio Frequency circuit 304 is configured to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The radio frequency circuitry 304 communicates with a communication network and other communication devices via electromagnetic signals.
The display screen 305 is used to display a UI (User Interface). The UI may include graphics, text, icons, video, and any combination thereof.
The power supply 306 is used to power the various components in the electronic device.
Example 4:
on the basis of any one of embodiments 1 to 3, this embodiment discloses a computer-readable storage medium for storing computer-readable computer program instructions configured to perform the operations of the chronic disease data intelligent management method described in embodiment 1 when run.
It will be apparent to those skilled in the art that the modules or steps of the invention described above may be implemented in a general purpose computing device, they may be concentrated on a single computing device, or distributed across a network of computing devices, or they may alternatively be implemented in program code executable by computing devices, such that they may be stored in a memory device for execution by the computing devices, or they may be separately fabricated into individual integrated circuit modules, or multiple modules or steps within them may be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
Finally, it should be noted that the above embodiments are merely illustrative of the technical solution of the present invention, and not limiting thereof; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some of the technical features thereof can be replaced by equivalents. Such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. An intelligent management method for chronic disease data is characterized in that: comprising the following steps:
acquiring chronic disease data of a specified disease type;
dividing the chronic disease data according to disease development stages, and summarizing the preprocessed data of different disease development stages in a time sequence form to obtain arranged chronic disease data;
screening the arranged chronic disease data to obtain screened chronic disease data;
filling missing data of the screened chronic disease data to obtain pretreated chronic disease data;
and carrying out data mining treatment on the pretreated chronic disease data to obtain a chronic disease association result.
2. The intelligent chronic disease data management method according to claim 1, wherein: acquiring chronic disease data of a specified disease type, comprising:
collecting multi-source initial chronic disease data, and extracting all initial chronic disease data matched with the same patient identification from the multi-source initial chronic disease data;
extracting initial chronic disease data of a designated disease type from all initial chronic disease data matched with the same patient identification;
and carrying out fusion treatment on the initial chronic disease data of the appointed disease type corresponding to the same patient to obtain the initial chronic disease data of the appointed disease type.
3. The intelligent chronic disease data management method according to claim 2, wherein: the patient identification includes a patient identification number.
4. The intelligent chronic disease data management method according to claim 2, wherein: extracting initial chronic disease data of a specified disease type from all initial chronic disease data matched with the same patient identification, wherein the initial chronic disease data comprises:
acquiring any one of all initial chronic disease data matched with the same patient identification;
acquiring a disease type corresponding to current initial chronic disease data;
and acquiring another initial chronic disease data in all initial chronic disease data matched with the same patient identification until the initial chronic disease data of the designated disease type in all initial chronic disease data matched with the same patient identification is acquired.
5. The intelligent chronic disease data management method according to claim 4, wherein: obtaining a disease type corresponding to the current initial chronic disease data, including:
obtaining likelihood functions corresponding to different disease types;
respectively taking the current initial chronic disease data as independent variables of likelihood functions corresponding to different disease types to obtain likelihood function values corresponding to different disease types;
and obtaining the maximum likelihood function value in the likelihood function values corresponding to different disease types, and taking the disease type corresponding to the maximum likelihood function value as the disease type corresponding to the current initial chronic disease data.
6. The intelligent chronic disease data management method according to claim 5, wherein: the likelihood function corresponding to the type A disease is:
wherein x is i For chronic disease data of type a disease, i e {1,2, … …, n }, n being a natural number greater than 1; p (P) Ai (x i ) To indicate when the ith chronic disease data is x i At the time of discriminating x i Probability density for type A diseaseA function; wherein,
in sigma Ai Variance, μ of the ith chronic disease data for the type A disease Ai E is a natural constant, which is a mathematical expectation of the i-th sample health data of the type a disease.
7. The intelligent chronic disease data management method according to claim 2, wherein: when the initial chronic disease data of the appointed disease type corresponding to the same patient are fused, the fusion processing is realized in a time sequence mode.
8. The intelligent chronic disease data management method according to claim 1, wherein: filling missing data of the screened chronic disease data to obtain pretreated chronic disease data, wherein the method comprises the following steps of:
acquiring the window size of a sliding window according to the data density of the screened chronic disease data;
and traversing the screened chronic disease data according to time sequence by using the sliding window with the window size, and filling the data window when the data in any data window of the screened chronic disease data is empty, until the screened chronic disease data is traversed.
9. The intelligent chronic disease data management method according to claim 1, wherein: performing data mining processing on the pretreated chronic disease data to obtain a chronic disease association result, wherein the data mining processing comprises the following steps:
acquiring a frequent item data set in the preprocessed chronic disease data;
and obtaining a strong association rule according to the frequent item data set, and carrying out visualization processing on the strong association rule as a chronic disease association result.
10. An intelligent chronic disease data management system is characterized in that: for implementing the intelligent management method of chronic disease data according to any one of claims 1 to 9; the chronic disease data intelligent management system comprises:
the data acquisition module is used for acquiring chronic disease data of a designated disease type;
the data segmentation module is in communication connection with the data acquisition module and is used for dividing the chronic disease data according to disease development stages, and summarizing the preprocessed data of different disease development stages in a time sequence form to obtain arranged chronic disease data;
the data screening module is in communication connection with the data segmentation module and is used for screening the arranged chronic disease data to obtain screened chronic disease data;
the data filling module is in communication connection with the data screening module and is used for filling missing data of the screened chronic disease data to obtain pretreated chronic disease data;
and the data mining module is in communication connection with the data filling module and is used for carrying out data mining processing on the preprocessed chronic disease data to obtain a chronic disease association result.
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