CN112233799A - Artificial intelligence of medical system and establishing method and establishing system thereof - Google Patents
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Abstract
The invention relates to the field of medical systems, in particular to artificial intelligence of a medical system, and an establishment method and an establishment system thereof. The establishing method comprises the following steps: obtaining raw data, the raw data comprising raw system data derived from at least one medical system; establishing normalization on the original data according to the data attributes to form normalized data; establishing logic association for the normalized data to form basic data; and establishing an application model as system artificial intelligence according to the medical requirements by combining the basic data so as to obtain an optimal result for realizing the corresponding medical requirements. The invention introduces various data through various ways as the original data for establishing the artificial intelligence of the medical system, establishes an application model for meeting or acquiring the result of the medical requirement, and realizes man-machine interaction through an operation interface so as to realize that a user acquires the optimal result output by the application model through operation, thereby meeting the corresponding medical requirement.
Description
Technical Field
The invention relates to the field of medical systems, in particular to artificial intelligence of a medical system, and an establishment method and an establishment system thereof.
Background
With the development of science and technology, the subjects are mutually permeated and promoted, the research range of the control field is not limited to the automation development fields of industrial process control, aerospace technology and the like, but is expanded to a wide application field, wherein the theoretical knowledge and technical means in the control field are applied to realize an intelligent medical diagnosis system, accurate medical diagnosis is completed, and the method is also a branch of control direction research.
Medical software is a software system that assists clinicians in medical tasks.
The research of the intelligent medical system relates to comprehensive research in the fields of information processing technology, artificial intelligence, medical diagnosis and other subjects. The research aims to realize the automation of the medical diagnostic instrument, provide various related information of the human body in a quantitative and visual mode and obtain a more accurate diagnostic result. Medical intelligence is part of artificial intelligence, namely, the perception capability of human beings is amplified by modern technologies and means, and the judgment and reasoning capability of the human beings is simulated. Therefore, the intelligent system can expand the diagnosis capability of doctors and improve the diagnosis efficiency, and becomes an important auxiliary tool for medical diagnosis.
However, smart medical systems tend to suffer from the following drawbacks: 1. the intelligence degree is low, the best result is difficult to obtain, or the result has errors; 2. the manual operation degree is high, a lot of information still needs to be manually input on the spot, and the efficiency and the accuracy are low; 3. each system needs to be edited and data collected again, the research and development time cost is high, the labor cost is very high, although a plurality of sets of systems are often correlated, the systems are independently developed, the actual correlation is poor, and a large amount of work is needed to realize effective correlation; 4. the basic data is not comprehensive, and the obtained result is not the best result.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a medical system artificial intelligence, an establishing method and an establishing system thereof aiming at the defects in the prior art, and solve the problems of low intelligence degree, high artificial operation degree, high research and development cost, incomplete basic data and the like of the prior system.
The technical scheme adopted by the invention for solving the technical problems is as follows: the method for establishing the artificial intelligence of the medical system comprises the following steps:
obtaining raw data, the raw data comprising raw system data derived from at least one medical system;
establishing normalization on the original data according to the data attributes to form normalized data;
establishing logic association for the normalized data to form basic data;
and establishing an application model as system artificial intelligence according to the medical requirements by combining the basic data so as to obtain an optimal result for realizing the corresponding medical requirements.
Wherein, the preferred scheme is: the raw data further includes at least one of raw non-informatization data, raw non-medical system data derived in non-medical systems, raw non-selected system data derived in non-selected medical systems.
Wherein, the preferred scheme is: the medical system comprises a software system which can realize normal operation of a medical service place, and the original system data is information data input or generated in the operation process of the software system.
Preferably, the step of establishing the normalization includes:
classifying the original data according to the data attributes;
and perfecting the classified original data to form normalized data.
Preferably, the step of classifying the raw data according to the data attribute includes:
setting a plurality of labels based on different data attributes and a standardized template;
endowing each original data with a corresponding label;
and classifying the original data according to the normalized template.
Preferably, the step of refining the classified raw data includes: and completing or modifying the information in the normalized template according to the original data or/and the third-party data.
Preferably, the step of logically associating includes:
setting an association rule;
and establishing logical association on the normalized data according to association rules to form at least one set of associated data chain or associated data set based on certain normalized data, wherein the associated data chain or associated data set is used as basic data of the input application model.
Preferably, the step of establishing an application model includes:
inputting at least one set of associated data chain or associated data set according to medical requirements;
establishing a query path in the basic data to solve the medical requirement;
and displaying the optimal result obtained according to the query path through a software interface to realize the corresponding medical requirement.
The technical scheme adopted by the invention for solving the technical problems is as follows: provided is a system for establishing artificial intelligence of a medical system, comprising:
a raw data acquisition unit that acquires raw data including raw system data derived from at least one medical system;
establishing a normalization unit, and establishing normalization on the original data according to the data attributes to form normalized data;
the logic association unit is used for establishing logic association on the normalized data to form basic data;
and the application model establishing unit is used for establishing an application model as system artificial intelligence according to the medical requirements by combining the basic data so as to obtain an optimal result for realizing the corresponding medical requirements.
The technical scheme adopted by the invention for solving the technical problems is as follows: the artificial intelligence of the medical system is established by the establishing method, the artificial intelligence of the medical system comprises at least one application model, and the application model outputs a corresponding optimal result according to medical requirements and by combining basic data.
Compared with the prior art, the method has the advantages that various data are imported in various ways to serve as original data for establishing artificial intelligence of the medical system, the application model is established for meeting or acquiring results of medical requirements, man-machine interaction can be realized through the operation interface, users can acquire preferred results output by the application model through operation, and accordingly corresponding medical requirements are met.
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The invention will be further described with reference to the accompanying drawings and examples, in which:
FIG. 1 is a schematic flow chart of a method for establishing artificial intelligence of a medical system according to the present invention;
FIG. 2 is a schematic diagram of the construction of the artificial intelligence building system of the medical system of the present invention;
FIG. 3 is a flow chart illustrating the establishment of normalization according to the present invention;
FIG. 4 is a schematic flow chart illustrating the classification of raw data according to data attributes according to the present invention;
FIG. 5 is a flow chart illustrating the logical association of the present invention;
FIG. 6 is a flow chart illustrating the process of creating an application model according to the present invention.
Detailed Description
The preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings.
As shown in FIG. 1, the present invention provides a preferred embodiment of a method for establishing artificial intelligence for a medical system.
A method for establishing artificial intelligence of a medical system comprises the following steps:
step S10, acquiring original data;
step S20, establishing normalization on the original data according to the data attributes to form normalized data;
step S30, establishing logic association for the normalized data to form basic data;
and step S40, establishing an application model as system artificial intelligence according to the medical requirements by combining the basic data to obtain a preferred result for realizing the corresponding medical requirements.
Wherein the raw data comprises raw system data derived from at least one medical system. The data information derived from at least one or more medical systems is used as original system data to be used as original data for establishing artificial intelligence of the medical systems, wherein the original system data comprises input data, data generated in a processing process and output data in the medical systems, and can be all data or part of key data; the medical system is a service system existing and operated in the existing medical service place. And the export mode can directly transfer data or transfer data through third-party software or third-party equipment.
Specifically, various data are imported through multiple ways to serve as original data for establishing artificial intelligence of the medical system, for example, the original system data exported from the selected medical system serves as the original data, the medical system collects information data capable of supporting normal operation of a medical service place, and on the basis of sufficient and detailed information data, the artificial intelligence of the support system can accurately and effectively obtain a way for optimally solving medical requirements and obtain a preferred result for realizing corresponding medical requirements; the core algorithm comprises semantic analysis and image processing, semantic recognition and analysis processing are achieved, and then high-accuracy recognition of the images is achieved to be converted into recognizable character contents. For example, in a hospital, aiming at a software system related to normal operation of a medical service place, information data input or generated in the operation process of the software system is imported to serve as original system data, the software system comprises a patient outpatient service system, a medical system, a drug management system, a staff system, a security system, an administrative system and the like, the information data in each operation link can be ensured to be collected and applied, so that the basic data of artificial intelligence of the medical system can be strengthened and enriched, effective intelligent processing can be carried out according to enough basic data, and the best and comprehensive result can be obtained. Preferably, the medical system comprises a software system involved in normal operation of the medical service place, and the original system data is information data input or generated in the operation process of the software system.
Furthermore, the original system data is imported on the basis of the original software system, the data is real and accurate and has strong real-time performance, but other types of original data can be collected for enriching the original data, and the original data further comprises at least one of original non-informatization data, original non-medical system data exported from a non-medical system and original non-selected system data exported from a non-selected medical system. The original non-information data is non-information data, data needs to be informationized through a special means to become original data, for example, paper data, and the paper data can be informationized through image shooting, scanning and inputting and other modes; for another example, important information data is not uploaded to the medical system, and needs to be input and informationized in other ways, for example, the personal information of the patient is uploaded through a WeChat platform. The original non-medical system data is information data input or generated in the using process of the non-medical system through other ways, for example, specific information of certain medical equipment needs to be acquired, and is connected with equipment information software of an equipment manufacturer through a third party channel to acquire the specific information of corresponding medical equipment, so that channel tracing and product comparison of the medical equipment are realized; and for example, access a drug inventory system provided by a governmental unit to obtain replacement information for current drugs. The original non-selected system data is obtained by sharing information data of medical systems additionally added in the follow-up process on the basis of the selected medical system, such as medical systems of other hospitals.
In this embodiment, the original data is acquired in a messy and difficult to associate, the original data needs to be initially processed, normalization is established on the original data according to data attributes to form normalized data, and substantially, the normalized data is converted into visual data through codes and is realized according to an established standard. Specifically, each data includes at least one data attribute, for example, the data attribute of the name "chen somebody" includes outpatient department patient, patient of a certain operation, person belonging to a certain test order, etc., different data attributes are set according to specific requirements to meet the division and specification of the original data to form multiple sets of normalized data; meanwhile, the data is supplemented, the data is deleted in an overlapped mode, the errors are modified, and the operations of supplementing, deleting and modifying can be carried out through a conventional query means, an artificial filling means or an intelligent filling means based on machine learning.
Further, logic association is established for the normalized data to form basic data. Because the normalization is established on the original data according to the data attributes to form the normalized data, a plurality of sets of normalized data can be formed, each set of normalized data at least comprises data information with logical relevance, for example, both sets of normalized data belong to a certain person, both sets of normalized data are treated by a certain doctor, both sets of normalized data are detected at a certain time period on a certain day, and the like. The establishment of the logical association can be performed firstly or later, for example, after certain logical association is realized according to a preset rule, an application model is established as system artificial intelligence according to medical requirements; or, the medical requirements are set firstly, and the logic association of the normalized data is carried out according to the basic data required by the medical requirements, so as to associate the basic data required to be combined and used by the medical.
In this embodiment, and referring to fig. 2, a system for establishing artificial intelligence of a medical system is provided, where the system for establishing artificial intelligence of a medical system includes an original data obtaining unit, a normalization unit, a logical association unit, and an application model establishing unit, where the original data obtaining unit obtains original data, where the original data includes original system data derived from at least one medical system, the normalization unit establishes normalization on the original data according to data attributes to form normalized data, the logical association unit establishes logical association on the normalized data to form basic data, and the application model establishing unit combines the basic data and establishes an application model according to medical requirements as system artificial intelligence to obtain a preferred result of achieving corresponding medical requirements.
The establishing system can be realized by background software or a plurality of modeling/processing software to form a total establishing system containing an original data acquiring unit, a normalizing establishing unit, a logic association unit and an application model establishing unit in a matching way.
As shown in fig. 3 and 4, the present invention provides a preferred embodiment of establishing normalization.
The step of establishing normalization comprises:
step S21, classifying the original data according to the data attributes;
and step S22, perfecting the classified original data to form normalized data.
Specifically, the original data are acquired in a messy and difficult-to-associate manner, the original data need to be firstly subjected to preliminary processing, the original data are sequentially divided and classified according to the characteristic of data attributes, the attributes needing to be classified are effectively classified according to preset rules to form various sets of classified data combinations, the disordered original data are set in a normalized manner to detail specific data information in the original data, and other data which accord with the attributes of the disordered original data are classified and summarized.
After the data collected by a plurality of medical systems or other modes are classified, the formed original data set is basically in a very full state and contains data information with different dimensions, the original data set can be effectively described from a plurality of angles, and real, accurate and strong-relevance data support is provided for subsequently obtaining a preferred result for realizing corresponding medical requirements. However, since various factors may cause problems in the classified original data set, such as missing data, data errors, and the like, the data needs to be refined according to various ways to obtain and complement or modify the data, for example, setting different similarity thresholds, searching for data with a similarity above the similarity threshold, classifying and complementing the missing data, or searching for more data, such as third-party platform data or personal information filling, to complement the missing data.
In this embodiment, and referring to fig. 4, the step of classifying the original data according to the data attribute includes:
step S211, setting a plurality of labels based on different data attributes and a normalized template;
step S212, endowing labels corresponding to the original data;
and S213, classifying the original data according to the normalized template.
Specifically, a plurality of standardized templates are formed according to medical standards or self-set standards, wherein the standardized templates are established through customization, but the establishment principle can be based on medical system specifications and is substantially an integration method according to the medical specifications without departing from the practice of modern medical hooks; and setting labels based on different data attributes according to requirements, wherein each original data can be endowed with at least one label to represent the data attributes of the original data, the labels comprise belongings, medical staff, medicine information, time, medical history, cases, detection reports and the like, a general label can be set at an upper level, and detailed labels can be set at a lower level, so that the expressed meanings of the original data are richer, and the associated chains of the original data and other data are more. And collecting the original data according to the tags, sorting and classifying the collected original data set according to the normalized templates, and filling the sorted and classified original data set into the corresponding normalized templates to form classified data.
Preferably, the step of refining the classified raw data comprises: and completing or modifying the information in the normalized template according to the original data or/and the third-party data. In the intelligent form filling process, the data accuracy and the replaceable data are judged, missing data are reminded, and more original data can be supplemented or modified conveniently in the follow-up process. Furthermore, the accuracy grade of the data can be set, suspected error data can be marked, and follow-up manual examination and confirmation can be facilitated. The third-party data comprises patient form filling, data of a third-party institution and data transferred by other hospitals.
As illustrated in FIG. 5, the present invention provides a preferred embodiment of logical association.
The step of logically associating comprises:
step S31, setting association rules;
and step S32, establishing logic association for the normalized data according to association rules to form at least one set of associated data chain or associated data set based on a certain normalized data, which is used as basic data of the input application model.
Specifically, the normalized data is preferably a data set constituting a normalized template, or other types of data sets, each normalized data that can be associated has associable data information, the data information with the associability is logically associated according to an association rule, and at least one set of associated data chain or associated data set based on a certain normalized data is formed to serve as basic data for inputting the application model. The association rule is a rule for associating according to some special application requirements, for example, associating medical information belonging to a certain person, managing detection information belonging to a certain person, and associating corresponding medical personnel.
The association rule can be effective normalized data association according to the same data type, or additional rules are added on the basis of the rule for screening, if some normalized data are not associated; the association rule may also be a normalized data association based on similar data, which refers to data of similar type or having at least one or more identical tags.
As shown in FIG. 6, the present invention provides a preferred embodiment for creating an application model.
The step of establishing an application model comprises:
step S41, inputting at least one set of associated data chain or associated data set according to medical requirements;
step S42, establishing a query path in the basic data to solve the medical requirement;
and step S43, displaying the optimal result for realizing the corresponding medical requirement according to the query path through a software interface.
Specifically, an application model is established for meeting or obtaining a result of the medical requirement, and man-machine interaction can be realized through an operation interface, so that a user can obtain a preferred result output by the application model through operation, and the corresponding medical requirement is met. The core of each application model should include at least one medical requirement, so that a required answer can be found in basic data as a result answer for solving the medical requirement, and according to the medical requirement, various associated data information is input in advance, for example, at least one set of associated data chain or associated data set is input, various data information is called through the application model, a query path for solving the medical requirement is established, and query, acquisition and pairing of data are quickly and accurately realized.
Preferably, in a comprehensive application system, a plurality of application models are combined, and by inputting various required data and even inputting all the sorted basic data into the comprehensive application system, at least one query path for solving medical requirements is established, and a preferred result based on the most basic data information is obtained. Specifically, the artificial intelligence of the medical system is formed by the establishing method, the artificial intelligence of the medical system comprises at least one application model, and the application model outputs a corresponding optimal result according to medical requirements and by combining basic data, or a comprehensive application system is formed by a plurality of application models.
In the present invention, preferred embodiments of an electronic device and a computer-readable storage medium are also provided.
An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the set-up method when executing the program. And a computer-readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, carries out the set-up method. Specifically, the establishing method, the establishing system and the artificial intelligence of the medical system can be expressed in a software form and can be set by matching with hardware equipment.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the scope of the present invention, but rather as embodying the invention in a wide variety of equivalent variations and modifications within the scope of the appended claims.
Claims (10)
1. A method for establishing artificial intelligence of a medical system is characterized by comprising the following steps:
obtaining raw data, the raw data comprising raw system data derived from at least one medical system;
establishing normalization on the original data according to the data attributes to form normalized data;
establishing logic association for the normalized data to form basic data;
and establishing an application model as system artificial intelligence according to the medical requirements by combining the basic data so as to obtain an optimal result for realizing the corresponding medical requirements.
2. The method of establishing according to claim 1, wherein: the raw data further includes at least one of raw non-informatization data, raw non-medical system data derived in non-medical systems, raw non-selected system data derived in non-selected medical systems.
3. The method of establishing according to claim 1 or 2, characterized in that: the medical system comprises a software system which can realize normal operation of a medical service place, and the original system data is information data input or generated in the operation process of the software system.
4. Method of establishing according to claim 1 or 2, characterized in that said step of establishing a normalization comprises:
classifying the original data according to the data attributes;
and perfecting the classified original data to form medical and/or non-medical normalized data.
5. The method of creating in accordance with claim 4, wherein said step of categorizing the raw data according to data attributes comprises:
setting a plurality of labels based on different data attributes and a standardized template;
endowing each original data with a corresponding label;
and classifying the original data according to the normalized template.
6. The method of creating as claimed in claim 5, wherein said step of refining the classified raw data comprises: and completing or modifying the information in the normalized template according to the original data or/and the third-party data.
7. Method for establishing a logical association according to claim 1 or 2, characterized in that said step of logical association comprises:
setting an association rule;
and establishing logical association on the normalized data according to association rules to form at least one set of associated data chain or associated data set based on certain normalized data, wherein the associated data chain or associated data set is used as basic data of the input application model.
8. The method of building of claim 7, wherein the step of building an application model comprises:
inputting at least one set of associated data chain or associated data set according to medical requirements;
establishing a query path in the basic data to solve the medical requirement;
and displaying the optimal result obtained according to the query path through a software interface to realize the corresponding medical requirement.
9. A system for establishing artificial intelligence of a medical system is characterized by comprising:
a raw data acquisition unit that acquires raw data including raw system data derived from at least one medical system;
establishing a normalization unit, and establishing normalization on the original data according to the data attributes to form normalized data;
the logic association unit is used for establishing logic association on the normalized data to form basic data;
and the application model establishing unit is used for establishing an application model as system artificial intelligence according to the medical requirements by combining the basic data so as to obtain an optimal result for realizing the corresponding medical requirements.
10. The artificial intelligence of the medical system is characterized in that: the artificial intelligence of the medical system is established by the establishing method of any one of claims 1 to 8, the artificial intelligence of the medical system comprises at least one application model, and the application model outputs the corresponding preferred result according to medical requirements and by combining basic data.
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Citations (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN105335620A (en) * | 2015-11-13 | 2016-02-17 | 冯金辉 | System and method for automatically and intelligently providing personalized medical information services |
CN105608327A (en) * | 2015-12-31 | 2016-05-25 | 复旦大学附属华山医院 | Method and equipment for realizing clinical information sharing |
CN105912846A (en) * | 2016-04-07 | 2016-08-31 | 南京小网科技有限责任公司 | Intelligent medical aid decision making system on basis of cloud computing technique and medical knowledge base technique |
CN107273698A (en) * | 2017-07-06 | 2017-10-20 | 武靖 | The processing in artificial intelligence training standard storehouse and detection method, system |
CN109346169A (en) * | 2018-10-17 | 2019-02-15 | 长沙瀚云信息科技有限公司 | A kind of artificial intelligence assisting in diagnosis and treatment system and its construction method, equipment and storage medium |
CN109643429A (en) * | 2016-08-23 | 2019-04-16 | 伊路米纳有限公司 | For sharing the association system and method for medical data |
CN109727680A (en) * | 2018-12-28 | 2019-05-07 | 上海列顿信息科技有限公司 | A kind of region clinical path management system based on big data technology |
-
2020
- 2020-10-23 CN CN202011148978.4A patent/CN112233799A/en active Pending
Patent Citations (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN105335620A (en) * | 2015-11-13 | 2016-02-17 | 冯金辉 | System and method for automatically and intelligently providing personalized medical information services |
CN105608327A (en) * | 2015-12-31 | 2016-05-25 | 复旦大学附属华山医院 | Method and equipment for realizing clinical information sharing |
CN105912846A (en) * | 2016-04-07 | 2016-08-31 | 南京小网科技有限责任公司 | Intelligent medical aid decision making system on basis of cloud computing technique and medical knowledge base technique |
CN109643429A (en) * | 2016-08-23 | 2019-04-16 | 伊路米纳有限公司 | For sharing the association system and method for medical data |
CN107273698A (en) * | 2017-07-06 | 2017-10-20 | 武靖 | The processing in artificial intelligence training standard storehouse and detection method, system |
CN109346169A (en) * | 2018-10-17 | 2019-02-15 | 长沙瀚云信息科技有限公司 | A kind of artificial intelligence assisting in diagnosis and treatment system and its construction method, equipment and storage medium |
CN109727680A (en) * | 2018-12-28 | 2019-05-07 | 上海列顿信息科技有限公司 | A kind of region clinical path management system based on big data technology |
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