CN117936011A - Intelligent medical service management system based on big data - Google Patents
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Abstract
The invention discloses a big data-based intelligent medical service management system, which comprises an intelligent medical service management system design module, a medical data collection module, a medical data preprocessing module, a medical data management module, a patient privacy protection module and an intelligent medical service management system optimization module. The invention belongs to the technical field of medical information, in particular to an intelligent medical service management system based on big data, which adopts data hierarchical storage and data index, performs hierarchical storage on the data according to access frequency and importance, and establishes a good data index and metadata management mechanism; processing the data by adopting a differential privacy protection model, introducing a noise change data set, and auditing the access authority of the user by using a key management service; and calculating the minimum convergence rate of the system by adopting a distributed optimization algorithm of the Markov switching topology and adjusting the influence factors and states among the nodes.
Description
Technical Field
The invention belongs to the technical field of medical information, and particularly relates to an intelligent medical service management system based on big data.
Background
The intelligent medical service management system is a system integrated by utilizing artificial intelligence and information processing technology, and aims to provide more efficient, accurate and convenient medical service management, realize resource optimal configuration and refined patient management, improve medical service quality and efficiency, reduce medical accidents and misdiagnosis and improve medical experience. However, the existing intelligent medical service management system has the technical problems of high storage cost and reduced data access efficiency due to long-term data management and storage; the intelligent medical service management system has the technical problems that the expandability is low, the data processing delay is high, the page loading is slow, and the normal operation of medical services is affected; there is a large amount of medical data storage that is difficult to guarantee data security, leads to patient privacy to reveal, influences patient's technical problem to the trust of hospital.
Disclosure of Invention
Aiming at the technical problems of high storage cost and low data access efficiency caused by long-term data management and storage, the intelligent medical service management system based on big data adopts data hierarchical storage and data index to store data in a hierarchical manner according to the access frequency and the importance, establishes a good data index and metadata management mechanism, reduces the storage cost and improves the data access rate; aiming at the technical problems that a large amount of medical data are stored and the data security is difficult to ensure, so that the privacy of a patient is revealed and the trust of the patient to a hospital is influenced, a differential privacy protection model is adopted to process the data, a noise change data set is introduced, a key management service is used for checking the access authority of a user, and the trust of the patient to the hospital is increased; aiming at the technical problems that the intelligent medical service management system has low expandability, high data processing delay and slow page loading, and influences the normal operation of medical services, a distributed optimization algorithm of a Markov switching topology is adopted, the minimum convergence rate of the system is calculated through influence factors and state adjustment among nodes, the system optimization efficiency is evaluated, and the system expandability is improved.
The invention provides a big data-based intelligent medical service management system, which comprises an intelligent medical service management system design module, a medical data collection module, a medical data preprocessing module, a medical data management module, a patient privacy protection module and an intelligent medical service management system optimization module;
The intelligent medical service management system design module is used for carrying out database design, interface design and function development;
The medical data collection module is used for collecting medical data;
the medical data preprocessing module is used for cleaning data, integrating data and converting data;
The medical data management module is used for carrying out data hierarchical storage and data indexing on the preprocessed medical data, carrying out hierarchical storage on the data according to the access frequency and the importance, and establishing a good data indexing and metadata management mechanism;
The patient privacy protection module processes data by using a differential privacy protection model, introduces a noise change data set and uses a key management service to check the access authority of a user;
The intelligent medical service management system optimization module is specifically a distributed optimization algorithm using a Markov switching topology, system optimization is realized through influence factors and state adjustment among nodes, the minimum convergence rate of the system is calculated, and the system optimization efficiency is estimated.
Further, in the intelligent medical service management system design module, the hospital needs are known, the objective and the scope of intelligent medical service management are determined, and database design, interface design and function development are performed.
Further, in the medical data collection module, medical data is collected, including patient information, doctor diagnosis and treatment data, and drug inventory data.
Further, in the medical data preprocessing module, a data cleaning unit, a data integration unit and a data conversion unit are provided, and the medical data preprocessing module comprises the following contents:
the data cleaning unit is used for removing noise, abnormal values and missing values in the data and improving the quality and accuracy of the data;
The data integration unit is used for integrating scattered data into a consistent data set, so that effective information can be better analyzed and mined;
and the data conversion unit is used for carrying out text preprocessing on medical text data in medical data analysis, including word segmentation, stop word removal and stem extraction, and carrying out feature scaling and data standardization on continuous data.
Further, in the medical data management module, a data hierarchical storage unit and a data index unit are provided, and the medical data management module includes the following contents:
The data hierarchical storage unit is used for storing medical data in a hierarchical manner according to the access frequency and the importance of the medical data, storing commonly used medical data in high-performance storage equipment to realize quick retrieval and access, and storing cold data in a storage medium with lower cost, wherein the cold data refers to the medical data which is not commonly used, so that cost benefit and long-term storage are provided;
the data index unit establishes a good medical data index and metadata management mechanism, wherein in medical data, the metadata comprises patient information, medical record type, doctor information and data acquisition time, and establishes a good metadata management mechanism, so that consistency, traceability and credibility of the medical data can be ensured, and the medical data is easier to be rapidly positioned and retrieved through accurate metadata, thereby improving the quality and reliability of the data, and the metadata management mechanism comprises the following contents:
metadata collection, collecting and recording relevant attribute and description information of data, including data sources, data types, data formats and data update time;
Metadata storage, storing the collected metadata in a metadata management system, considering the searchability, query performance and storage capacity of the metadata;
Metadata maintenance, which is to perform daily maintenance work on metadata, including updating metadata information, repairing errors and deleting expired metadata, and maintaining the accuracy and availability of the metadata;
Metadata retrieval, providing convenient metadata query and retrieval functions, wherein the retrieval functions are based on keywords, attribute filtering and data relationships, and meet different query requirements of users;
metadata sharing, namely sharing and exchanging metadata through a publish-subscribe mode, interface call and data export import, so that different systems can use and manage metadata together.
Further, in the patient privacy protection module, a differential privacy protection unit and an access right management unit are provided, and the patient privacy protection module includes the following contents:
The differential privacy protection unit processes the medical data by adopting a differential privacy protection model, and the query result of the medical data can not reveal specific information of a patient by introducing noise and changing a data set, and comprises the following contents:
centralized differential privacy is used for controlling the allowable individual privacy revealing degree by using the privacy budget, and the following formula is used:
；
where Pr [ ] represents the probability that the result generated by algorithm A falls within set S, D represents the input dataset, The method comprises the steps that the method is a peer data set of D, the peer data sets differ by one record at most, S represents a set and is used for limiting the range of a result generated by an algorithm A, epsilon is privacy budget and represents the privacy protection degree of the algorithm, and the smaller the value of epsilon is, the higher the privacy protection degree of data is;
differential privacy definition, in which individual privacy is protected by introducing control noise into query results, the following formula is used:
；
；
where Y (D) is the result of the query performed on the data set D, f (D) is the result of the calculation of the function f on the data set D, Representing the Laplace density function, adding noise components in each dimension by Laplace noise,/>, andQuery sensitivity, which is a function f (D), D is the query dimension, D1 and D2 are the neighbor datasets,/>Representing the maximum difference of the function f on the adjacent data sets D1 and D2, p representing the measurement mode adopted;
And the noise attenuation function is to enable the noise meter to be updated periodically by taking training rounds as units, and exponential noise attenuation is adopted, wherein the formula is as follows:
；
Wherein σ t1 represents the noise scale in the t1 st training round, σ 0 is the initial noise scale, t1 represents the index of the training round, and k is the decay rate;
And the access right management unit uses the key management server to check the access right of the user, grants different data access rights according to the role of the user, and ensures that only legal users can access and use the data.
Further, in the intelligent medical service management system optimization module, a system optimization target determining unit, a markov chain establishing unit, an initial state allocation unit, a distributed optimization unit and a system optimization evaluation unit are arranged, and the intelligent medical service management system optimization module comprises the following contents:
determining a system optimization target unit, and in an intelligent medical service management system, determining a system optimization target, wherein the optimization target comprises the steps of improving the patient treatment efficiency, reducing the hospital resource waste and improving the medical service quality;
Establishing a Markov chain unit, defining the state of the system as different states of a treatment process, including waiting of a patient, doctor treatment and examination, establishing a Markov chain model, helping understand transition rules among different states in the system, and modeling and analyzing transition probability;
An initial state allocation unit that allocates patients to different visit states while the system is running, the initial state allocation requiring consideration of characteristics of the patients, severity of illness, and doctor resources;
The distributed optimization unit uses a distributed optimization algorithm under a Markov switching topology, and achieves the cooperative optimization effect of the system by calculating control input and weight through a formula, wherein the formula is as follows:
；
；
wherein u i (T) represents a control input of the node i at a time T, wherein T is a total time, w i (T) represents a weight of the node i at the time T, a represents an influence factor, a ij represents an influence factor of the node j on the node i, z t represents a state of the node i at the time T, z t-1 represents a state of the node i at the time T-1, b represents an amplification factor of the control input, F i (T) represents a local cost function of the node i at the time T;
The system optimization evaluation unit calculates the minimum convergence rate of the system, and evaluates the efficiency in the system optimization process, wherein the formula is as follows:
；
Wherein phi is the minimum convergence rate of the system, the larger the minimum convergence rate is, the faster the convergence point of the system in the distributed optimization process is represented, the efficiency and the speed in the system optimization process are evaluated by calculating the minimum convergence rate, and a1, a2 and a3 respectively represent influence factors among different nodes.
By adopting the scheme, the beneficial effects obtained by the invention are as follows:
(1) Aiming at the technical problems of high storage cost and low data access efficiency caused by long-term data management and storage, data is stored in a grading manner according to the access frequency and importance by adopting data hierarchical storage and data indexing, a good data indexing and metadata management mechanism is established, the storage cost is reduced, and the data access rate is improved;
(2) Aiming at the technical problems that a large amount of medical data are stored and the data security is difficult to ensure, so that the privacy of a patient is revealed and the trust of the patient to a hospital is influenced, a differential privacy protection model is adopted to process the data, a noise change data set is introduced, a key management service is used for checking the access authority of a user, and the trust of the patient to the hospital is increased;
(3) Aiming at the technical problems that the intelligent medical service management system has low expandability, high data processing delay and slow page loading, and influences the normal operation of medical services, a distributed optimization algorithm of a Markov switching topology is adopted, the minimum convergence rate of the system is calculated through influence factors and state adjustment among nodes, the system optimization efficiency is evaluated, and the system expandability is improved.
Drawings
FIG. 1 is a schematic diagram of an intelligent medical service management system based on big data according to the present invention;
FIG. 2 is a schematic diagram of a medical data management module;
FIG. 3 is a schematic diagram of a patient privacy protection module;
FIG. 4 is a schematic diagram of an intelligent healthcare management system optimization module.
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the invention; all other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the description of the present invention, it should be understood that the terms "upper," "lower," "front," "rear," "left," "right," "top," "bottom," "inner," "outer," and the like indicate orientation or positional relationships based on those shown in the drawings, merely to facilitate description of the invention and simplify the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus should not be construed as limiting the invention.
Referring to fig. 1, the smart health care system based on big data provided by the invention comprises a smart health care system design module, a medical data collection module, a medical data preprocessing module, a medical data management module, a patient privacy protection module and a smart health care system optimization module;
The intelligent medical service management system design module is used for carrying out database design, interface design and function development;
The medical data collection module is used for collecting medical data;
the medical data preprocessing module is used for cleaning data, integrating data and converting data;
The medical data management module is used for carrying out data hierarchical storage and data indexing, carrying out hierarchical storage on data according to the access frequency and the importance, and establishing a good data indexing and metadata management mechanism;
The patient privacy protection module processes data by using a differential privacy protection model, introduces a noise change data set and uses a key management service to check the access authority of a user;
The intelligent medical service management system optimization module is specifically a distributed optimization algorithm using a Markov switching topology, system optimization is realized through influence factors and state adjustment among nodes, the minimum convergence rate of the system is calculated, and the system optimization efficiency is estimated.
In the second embodiment, referring to fig. 1, the embodiment is based on the above embodiment, and in the smart health care system design module, the hospital needs are known, the goal and scope of smart health care management are determined, and database design, interface design and function development are performed.
Embodiment III referring to FIG. 1, the embodiment is based on the above embodiment in which medical data is collected in a medical data collection module, the medical data including patient information, doctor diagnosis data, and drug inventory data.
In a fourth embodiment, referring to fig. 1, the embodiment is based on the above embodiment, and a data cleaning unit, a data integration unit and a data conversion unit are provided in a medical data preprocessing module, where the medical data preprocessing module includes the following contents:
the data cleaning unit is used for removing noise, abnormal values and missing values in the data and improving the quality and accuracy of the data;
The data integration unit is used for integrating scattered data into a consistent data set, so that effective information can be better analyzed and mined;
and the data conversion unit is used for carrying out text preprocessing on medical text data in medical data analysis, including word segmentation, stop word removal and stem extraction, and carrying out feature scaling and data standardization on continuous data.
An embodiment five, referring to fig. 1 and 2, is based on the above embodiment, in a medical data management module, which is provided with a data hierarchical storage unit and a data index unit, and includes the following contents:
The data hierarchical storage unit is used for storing medical data in a hierarchical manner according to the access frequency and the importance of the medical data, storing commonly used medical data in high-performance storage equipment to realize quick retrieval and access, and storing cold data in a storage medium with lower cost, wherein the cold data refers to the medical data which is not commonly used, so that cost benefit and long-term storage are provided;
the data index unit establishes a good medical data index and metadata management mechanism, wherein in medical data, the metadata comprises patient information, medical record type, doctor information and data acquisition time, and establishes a good metadata management mechanism, so that consistency, traceability and credibility of the medical data can be ensured, and the medical data is easier to be rapidly positioned and retrieved through accurate metadata, thereby improving the quality and reliability of the data, and the metadata management mechanism comprises the following contents:
metadata collection, collecting and recording relevant attribute and description information of data, including data sources, data types, data formats and data update time;
Metadata storage, storing the collected metadata in a metadata management system, considering the searchability, query performance and storage capacity of the metadata;
Metadata maintenance, which is to perform daily maintenance work on metadata, including updating metadata information, repairing errors and deleting expired metadata, and maintaining the accuracy and availability of the metadata;
Metadata retrieval, providing convenient metadata query and retrieval functions, wherein the retrieval functions are based on keywords, attribute filtering and data relationships, and meet different query requirements of users;
metadata sharing, namely sharing and exchanging metadata through a publish-subscribe mode, interface call and data export import, so that different systems can use and manage metadata together.
By executing the operations, the data is stored in a grading manner according to the access frequency and the importance by adopting the data hierarchical storage and the data index, a good data index and metadata management mechanism is established, the storage cost is reduced, the data access rate is improved, and the technical problems of higher storage cost and reduced data access efficiency caused by long-term data management and storage are solved.
An embodiment six, referring to fig. 1 and 3, is based on the above embodiment, and in a patient privacy protection module, there is provided a differential privacy protection unit and an access right management unit, where the patient privacy protection module includes the following contents:
The differential privacy protection unit processes the medical data by adopting a differential privacy protection model, and the query result of the medical data can not reveal specific information of a patient by introducing noise and changing a data set, and comprises the following contents:
centralized differential privacy is used for controlling the allowable individual privacy revealing degree by using the privacy budget, and the following formula is used:
；
where Pr [ ] represents the probability that the result generated by algorithm A falls within set S, D represents the input dataset, The method comprises the steps that the method is a peer data set of D, the peer data sets differ by one record at most, S represents a set and is used for limiting the range of a result generated by an algorithm A, epsilon is privacy budget and represents the privacy protection degree of the algorithm, and the smaller the value of epsilon is, the higher the privacy protection degree of data is;
differential privacy definition, in which individual privacy is protected by introducing control noise into query results, the following formula is used:
；
；
where Y (D) is the result of the query performed on the data set D, f (D) is the result of the calculation of the function f on the data set D, Representing the Laplace density function, adding noise components in each dimension by Laplace noise,/>, andQuery sensitivity, which is a function f (D), D is the query dimension, D1 and D2 are the neighbor datasets,/>Representing the maximum difference of the function f on the adjacent data sets D1 and D2, p representing the measurement mode adopted;
And the noise attenuation function is to enable the noise meter to be updated periodically by taking training rounds as units, and exponential noise attenuation is adopted, wherein the formula is as follows:
；
Wherein σ t1 represents the noise scale in the t1 st training round, σ 0 is the initial noise scale, t1 represents the index of the training round, and k is the decay rate;
And the access right management unit uses the key management server to check the access right of the user, grants different data access rights according to the role of the user, and ensures that only legal users can access and use the data.
By executing the operations, the differential privacy protection model is adopted to process the data, the noise change data set is introduced, the access authority of the user is checked by using the key management service, the trust degree of the patient to the hospital is increased, and the technical problems that the data security is difficult to ensure for a large amount of medical data storage, the privacy of the patient is revealed, and the trust of the patient to the hospital is influenced are solved.
An embodiment six, referring to fig. 1 and fig. 4, is based on the above embodiment, and in the smart health care management system optimization module, there is provided a system optimization determining target unit, a markov chain establishing unit, an initial state allocation unit, a distributed optimization unit, and a system optimization evaluation unit, where the smart health care management system optimization module includes the following contents:
determining a system optimization target unit, and in an intelligent medical service management system, determining a system optimization target, wherein the optimization target comprises the steps of improving the patient treatment efficiency, reducing the hospital resource waste and improving the medical service quality;
Establishing a Markov chain unit, defining the state of the system as different states of a treatment process, including waiting of a patient, doctor treatment and examination, establishing a Markov chain model, helping understand transition rules among different states in the system, and modeling and analyzing transition probability;
An initial state allocation unit that allocates patients to different visit states while the system is running, the initial state allocation requiring consideration of characteristics of the patients, severity of illness, and doctor resources;
The distributed optimization unit uses a distributed optimization algorithm under a Markov switching topology, and achieves the cooperative optimization effect of the system by calculating control input and weight through a formula, wherein the formula is as follows:
；
；
wherein u i (T) represents a control input of the node i at a time T, wherein T is a total time, w i (T) represents a weight of the node i at the time T, a represents an influence factor, a ij represents an influence factor of the node j on the node i, z t represents a state of the node i at the time T, z t-1 represents a state of the node i at the time T-1, b represents an amplification factor of the control input, F i (T) represents a local cost function of the node i at the time T;
The system optimization evaluation unit calculates the minimum convergence rate of the system, and evaluates the efficiency in the system optimization process, wherein the formula is as follows:
；
Wherein phi is the minimum convergence rate of the system, the larger the minimum convergence rate is, the faster the convergence point of the system in the distributed optimization process is represented, the efficiency and the speed in the system optimization process are evaluated by calculating the minimum convergence rate, and a1, a2 and a3 respectively represent influence factors among different nodes.
By executing the operation, the distributed optimization algorithm of the Markov switching topology is adopted, the minimum convergence rate of the system is calculated through influence factors and state adjustment among the nodes, the system optimization efficiency is evaluated, the system expandability is improved, and the technical problems that the intelligent medical service management system is low in expandability, high in data processing delay and slow in page loading and affects the normal operation of medical services are solved.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made hereto without departing from the spirit and principles of the present invention.
The invention and its embodiments have been described above with no limitation, and the actual construction is not limited to the embodiments of the invention as shown in the drawings. In summary, if one of ordinary skill in the art is informed by this disclosure, a structural manner and an embodiment similar to the technical solution should not be creatively devised without departing from the gist of the present invention.
Claims (7)
1. An intelligent medical service management system based on big data is characterized in that: the system comprises an intelligent medical service management system design module, a medical data collection module, a medical data preprocessing module, a medical data management module, a patient privacy protection module and an intelligent medical service management system optimization module;
The intelligent medical service management system design module is used for carrying out database design, interface design and function development;
The medical data collection module is used for collecting medical data;
The medical data preprocessing module is used for cleaning, integrating and converting medical data;
The medical data management module is used for carrying out data hierarchical storage and data indexing, carrying out hierarchical storage on data according to the access frequency and the importance, and establishing a good medical data indexing and metadata management mechanism;
The patient privacy protection module processes data by using a differential privacy protection model, introduces a noise change data set and uses a key management service to check the access authority of a user;
The intelligent medical service management system optimization module is specifically a distributed optimization algorithm using a Markov switching topology, and the minimum convergence rate of the system is calculated through influence factors and state adjustment among nodes to realize system optimization.
2. The intelligent medical service management system based on big data according to claim 1, wherein: the intelligent medical service management system optimization module is provided with a system optimization determining target unit, a Markov chain establishing unit, an initial state allocation unit, a distributed optimization unit and a system optimization evaluation unit, and comprises the following contents:
determining a system optimization target unit, and in the intelligent medical service management system, determining a system optimization target;
Establishing a Markov chain unit, defining the state of the system as different states of a treatment process, including patient waiting, doctor treatment and examination, establishing a Markov chain model, and modeling and analyzing transition probability;
An initial state allocation unit that allocates the patient to different visit states while the system is running;
The distributed optimization unit uses a distributed optimization algorithm under a Markov switching topology, and achieves the cooperative optimization effect of the system by calculating control input and weight through a formula, wherein the formula is as follows:
；
；
wherein u i (T) represents a control input of the node i at a time T, wherein T is a total time, w i (T) represents a weight of the node i at the time T, a represents an influence factor, a ij represents an influence factor of the node j on the node i, z t represents a state of the node i at the time T, z t-1 represents a state of the node i at the time T-1, b represents an amplification factor of the control input, F i (T) represents a local cost function of the node i at the time T;
The system optimization evaluation unit calculates the minimum convergence rate of the system, and the formula is as follows:
；
Wherein phi is the minimum convergence rate of the system, the larger the minimum convergence rate is, the faster the convergence point of the system in the distributed optimization process is represented, the efficiency and the speed in the system optimization process are evaluated by calculating the minimum convergence rate, and a1, a2 and a3 respectively represent influence factors among different nodes.
3. The intelligent medical service management system based on big data according to claim 1, wherein: in patient privacy protection module, be equipped with differential privacy protection unit and access rights management unit, patient privacy protection module includes following content:
The differential privacy protection unit processes the medical data by adopting a differential privacy protection model, and the query result of the medical data can not reveal specific information of a patient by introducing noise and changing a data set, and comprises the following contents:
centralized differential privacy is used for controlling the allowable individual privacy revealing degree by using the privacy budget, and the following formula is used:
；
where Pr [ ] represents the probability that the result generated by algorithm A falls within set S, D represents the input dataset, The method comprises the steps that the method is a peer data set of D, the peer data sets differ by one record at most, S represents a set and is used for limiting the range of a result generated by an algorithm A, epsilon is privacy budget and represents the privacy protection degree of the algorithm, and the smaller the value of epsilon is, the higher the privacy protection degree of data is;
differential privacy definition, in which individual privacy is protected by introducing control noise into query results, the following formula is used:
；
；
where Y (D) is the result of the query performed on the data set D, f (D) is the result of the calculation of the function f on the data set D, Representing a laplace density function, adding a noise component in each dimension by laplace noise,Query sensitivity, which is a function f (D), D is the query dimension, D1 and D2 are the neighbor datasets,/>Representing the maximum difference of the function f on the adjacent data sets D1 and D2, p representing the measurement mode adopted;
And the noise attenuation function is to enable the noise meter to be updated periodically by taking training rounds as units, and exponential noise attenuation is adopted, wherein the formula is as follows:
；
Wherein σ t1 represents the noise scale in the t1 st training round, σ 0 is the initial noise scale, t1 represents the index of the training round, and k is the decay rate;
And the access right management unit uses the key management server to check the access right of the user, grants different data access rights according to the role of the user, and ensures that only legal users can access and use the data.
4. The intelligent medical service management system based on big data according to claim 1, wherein: the medical data management module is provided with a data hierarchical storage unit and a data index unit, and comprises the following contents:
The data grading storage unit is used for grading and storing the medical data according to the access frequency and importance of the medical data, storing the commonly used medical data in high-performance storage equipment, realizing quick retrieval and access, and storing cold data in a storage medium with lower cost;
A data index unit for establishing a good medical data index and a metadata management mechanism, wherein the metadata management mechanism comprises the following contents:
metadata collection, collecting and recording relevant attribute and description information of data, including data sources, data types, data formats and data update time;
Metadata storage, storing the collected metadata in a metadata management system, considering the searchability, query performance and storage capacity of the metadata;
Metadata maintenance, which is to perform daily maintenance work on metadata, including updating metadata information, repairing errors and deleting expired metadata;
Metadata retrieval, providing convenient metadata query and retrieval functions, wherein the retrieval functions are based on keywords, attribute filtering and data relationships, and meet different query requirements of users;
Metadata sharing, namely sharing and exchanging metadata through a publishing and subscribing mode, interface calling and data export and import.
5. The intelligent medical service management system based on big data according to claim 1, wherein: in the intelligent medical service management system design module, the hospital requirements are known, the goal and the scope of intelligent medical service management are determined, and database design, interface design and function development are performed.
6. The intelligent medical service management system based on big data according to claim 1, wherein: in a medical data collection module, medical data is collected, including patient information, doctor diagnostic data, and drug inventory data.
7. The intelligent medical service management system based on big data according to claim 1, wherein: the medical data preprocessing module is provided with a data cleaning unit, a data integration unit and a data conversion unit, and comprises the following contents:
The data cleaning unit is used for removing noise, abnormal values and missing values in the data;
a data integration unit for integrating medical data from a plurality of sources, including a patient management system, an electronic medical records, and a hospital ERP system, into a consistent data set;
and the data conversion unit is used for carrying out text preprocessing on medical text data in medical data analysis, including word segmentation, stop word removal and stem extraction, and carrying out feature scaling and data standardization on continuous data.
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