CN113704555A - Feature management method based on medical direction federal learning - Google Patents
Feature management method based on medical direction federal learning Download PDFInfo
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- 238000007726 management method Methods 0.000 title claims abstract description 45
- 238000000034 method Methods 0.000 claims abstract description 18
- 238000012549 training Methods 0.000 claims abstract description 15
- 238000013507 mapping Methods 0.000 claims abstract description 12
- 238000010801 machine learning Methods 0.000 claims abstract description 9
- 238000006243 chemical reaction Methods 0.000 claims abstract description 8
- 201000010099 disease Diseases 0.000 claims abstract description 5
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/80—Information retrieval; Database structures therefor; File system structures therefor of semi-structured data, e.g. markup language structured data such as SGML, XML or HTML
- G06F16/81—Indexing, e.g. XML tags; Data structures therefor; Storage structures
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/80—Information retrieval; Database structures therefor; File system structures therefor of semi-structured data, e.g. markup language structured data such as SGML, XML or HTML
- G06F16/84—Mapping; Conversion
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
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- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Abstract
The application discloses a feature management method based on medical direction federal learning, which comprises the following steps: mapping the international value range and the local value range to establish a conversion mapping relation; converting medical data input according to a local value range into standardized data according to an international value range according to the conversion mapping relation; setting a standard feature template according to the disease name aimed at by the machine learning model, wherein the standard feature template comprises a plurality of retrieval items corresponding to international values of standardized data; retrieving and acquiring a required data index according to the retrieval items of the standard feature template; and initiating a request for the Federal learning training machine learning model to other training nodes according to the selection operation of the data index. The method has the advantages that the characteristic data can be quickly retrieved, and the characteristic management method based on the federal study of the medical directions can be set in a personalized mode.
Description
Technical Field
The application relates to the field of federal learning, in particular to a feature management method based on federal learning of medical directions.
Background
In the near future, the medical industry will incorporate more high technologies such as artificial intelligence, sensing technology and the like, so that the medical service is made to be intelligent in real sense, and the prosperity and development of the medical industry are promoted. Under the background of new Chinese medical improvement, intelligent medical treatment is going to live in the lives of common people. The data of the medical industry has the need of privacy protection, so that when artificial intelligence is applied to the research, model training and data prediction in the medical field, a plurality of medical institutions are often required to perform the research, model training and data prediction in a networking and data collaboration mode.
When the machine learning training is carried out on the data of the medical system, the feature selection is carried out on the patient data in the database, the selection of the feature often influences the training result, the feature selection and the screening of the data cannot be conveniently carried out by the conventional technical scheme, and therefore negative effects are brought to the final model training result and the federal learning result.
Disclosure of Invention
In order to overcome the defects in the prior art, the application provides a feature management method based on the federal study of medical directions, which comprises the following steps: a unified reference standard for data standardization is established and defined as an international value range; making an academy reference standard for data standardization, and defining the academy reference standard as a local value range; mapping the international value range and the local value range to establish a conversion mapping relation; converting the medical data input according to the local value range into standardized data according to the international value range according to the conversion mapping relation; setting a standard feature template according to the disease name aimed at by a machine learning model, wherein the standard feature template comprises a plurality of retrieval items corresponding to international values of the standardized data; retrieving and acquiring a required data index according to the retrieval items of the standard feature template; and initiating a request for the Federal learning training machine learning model to other training nodes according to the selection operation of the data index.
Further, the feature management method based on the medical direction federal learning further comprises the following steps: inquiring and matching a corresponding international value according to a character field input by a user; and adding the international value and the corresponding standard field thereof to the retrieval entry of the standard feature template.
Further, the feature management method based on the medical direction federal learning further comprises the following steps: setting a logical relation of a plurality of retrieval items in the standard feature template; and generating a searching formula for searching according to the logical relation.
Further, the feature management method based on the medical direction federal learning further comprises the following steps: and acquiring the data index at each federal learning node according to the international value corresponding to the retrieval formula.
Further, the feature management method based on the medical direction federal learning further comprises the following steps: modifying or adding the logical relationship of a plurality of the retrieval entries in the standard feature template; and generating a searching formula for searching according to the logical relation.
Further, the feature management method based on the medical direction federal learning further comprises the following steps: inquiring and matching a corresponding international value according to a character field input by a user; the international value and its corresponding standard field are added to a new feature template.
Further, the feature management method based on the medical direction federal learning further comprises the following steps: setting a comparison symbol for each retrieval item to determine an operation condition of the retrieved numerical relationship.
Further, the feature management method based on the medical direction federal learning further comprises the following steps: and setting a preset threshold value for each retrieval item so as to determine a threshold condition of the retrieved data relation.
Further, the feature management method based on the medical direction federal learning further comprises the following steps: and setting a connection symbol for each retrieval item so as to determine the logical relationship between the retrieval items.
Further, the feature management method based on the medical direction federal learning further comprises the following steps: generating a retrieval formula and a retrieval result aiming at the retrieval formula according to all the retrieval items; the retrieval result comprises a data source, a data amount and a data value.
The application has the advantages that: the feature management method based on the federal study of medical directions can quickly search feature data and can be set in a personalized mode.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, serve to provide a further understanding of the application and to enable other features, objects, and advantages of the application to be more apparent. The drawings and their description illustrate the embodiments of the invention and do not limit it. In the drawings:
FIG. 1 is a schematic diagram of the main steps of a method for feature management based on federal learning from medical directions according to an embodiment of the present application;
FIG. 2 is a schematic illustration of an operator interface for a method for feature management based on federal learning from medical directions according to an embodiment of the present application;
FIG. 3 is a schematic illustration of a second operator interface of a method for feature management based on federal learning from medical directions according to an embodiment of the present application;
fig. 4 is a schematic diagram of a third operation interface of a feature management method based on federal learning of medical directions according to an embodiment of the application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the data so used may be interchanged under appropriate circumstances such that embodiments of the application described herein may be used. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
Referring to fig. 1 to 4, the present application provides a feature management method based on federal learning of medical directions, including the following steps: a unified reference standard for data standardization is established and defined as an international value range; making an academy reference standard for data standardization, and defining the academy reference standard as a local value range; mapping the international value range and the local value range to establish a conversion mapping relation; converting medical data input according to a local value range into standardized data according to an international value range according to the conversion mapping relation; setting a standard feature template according to the disease name aimed at by the machine learning model, wherein the standard feature template comprises a plurality of retrieval items corresponding to international values of standardized data; retrieving and acquiring a required data index according to the retrieval items of the standard feature template; and initiating a request for the Federal learning training machine learning model to other training nodes according to the selection operation of the data index.
As a more specific scheme, as shown in fig. 2 and 3, the international value range is formulated by setting a national standard value and a corresponding description of the national standard value. Specifically, the national standard value includes at least an arabic numeral and a chinese character.
When data standardization is performed, the national standard values or the corresponding national standard value descriptions thereof can be edited or deleted, and table files with the national standard values and the national standard value descriptions can be imported.
More specifically, the local value domain is formulated by setting a local value and a corresponding local value description. The place value includes at least Arabic numerals and the place value description includes at least Chinese characters.
As shown in fig. 2, the international value range can be edited through an interface or an import mode.
As shown in fig. 3, the international value range and the local value range may be associated and mapped by chinese characters of the international value description and the local value description.
As shown in the interface of fig. 4, in order to facilitate the user operation and query, the method of the present application may query and match the corresponding international value according to the text field input by the user; the international value and its corresponding standard field are added to the search entry of the standard feature template.
In order to simplify the management workload, the logical relationship of a plurality of retrieval entries in the standard feature template can be set for general diseases and models; and generating a searching formula for searching according to the logical relation.
And when the data are acquired from each training node, acquiring a data index at each federal learning node according to the international value corresponding to the search formula. The data index referred to herein is data indicating a data location, length, and type, and not data itself.
In order to realize the retrieval of personalized feature data, the feature management method based on the medical direction federal learning further comprises the following steps: modifying or adding the logic relation of a plurality of retrieval items in the standard feature template; and generating a searching formula for searching according to the logical relation.
As an expanded technical scheme, in order to adapt to the new model training requirement, the feature management method based on the medical direction federal learning further comprises the following steps: inquiring and matching a corresponding international value according to a character field input by a user; the international value and its corresponding standard field are added to a new feature template.
As a further alternative, as shown in fig. 4, a comparison sign may be set for each search entry to determine an operation condition of the searched numerical relationship. In addition, a preset threshold value may be set for each retrieval item to determine a threshold condition of the retrieved data relationship. A connection symbol is set for each retrieval item to determine a logical relationship between the retrieval items. This allows more refined acquisition of the required characteristic data.
As a more specific scheme, the feature management method based on the medical direction federal learning further comprises the following steps: generating a retrieval formula and a retrieval result aiming at the retrieval formula according to all the retrieval items; the retrieval result comprises a data source, a data amount and a data value.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. A feature management method based on medical direction federal learning is characterized in that:
the feature management method based on the medical direction federal learning comprises the following steps:
a unified reference standard for data standardization is established and defined as an international value range;
making an academy reference standard for data standardization, and defining the academy reference standard as a local value range;
mapping the international value range and the local value range to establish a conversion mapping relation;
converting the medical data input according to the local value range into standardized data according to the international value range according to the conversion mapping relation;
setting a standard feature template according to the disease name aimed at by a machine learning model, wherein the standard feature template comprises a plurality of retrieval items corresponding to international values of the standardized data;
retrieving and acquiring a required data index according to the retrieval items of the standard feature template;
and initiating a request for the Federal learning training machine learning model to other training nodes according to the selection operation of the data index.
2. The method for feature management based on federal learning of medical directions as claimed in claim 1, wherein:
the feature management method based on the medical direction federal learning further comprises the following steps:
inquiring and matching a corresponding international value according to a character field input by a user;
and adding the international value and the corresponding standard field thereof to the retrieval entry of the standard feature template.
3. The method for feature management based on federal learning of medical directions as claimed in claim 2, wherein:
the feature management method based on the medical direction federal learning further comprises the following steps:
setting a logical relation of a plurality of retrieval items in the standard feature template;
and generating a searching formula for searching according to the logical relation.
4. The method for feature management based on federal learning of medical directions as claimed in claim 3, wherein:
the feature management method based on the medical direction federal learning further comprises the following steps:
and acquiring the data index at each federal learning node according to the international value corresponding to the retrieval formula.
5. The method for feature management based on federal learning of medical directions as in claim 4, wherein:
the feature management method based on the medical direction federal learning further comprises the following steps:
modifying or adding the logical relationship of a plurality of the retrieval entries in the standard feature template;
and generating a searching formula for searching according to the logical relation.
6. The method for feature management based on federal learning of medical directions as in claim 5, wherein:
the feature management method based on the medical direction federal learning further comprises the following steps:
inquiring and matching a corresponding international value according to a character field input by a user;
the international value and its corresponding standard field are added to a new feature template.
7. The method for feature management based on federal learning of medical directions as in claim 6, wherein:
the feature management method based on the medical direction federal learning further comprises the following steps:
setting a comparison symbol for each retrieval item to determine an operation condition of the retrieved numerical relationship.
8. The method for feature management based on federal learning of medical directions as claimed in claim 7, wherein:
the feature management method based on the medical direction federal learning further comprises the following steps:
and setting a preset threshold value for each retrieval item so as to determine a threshold condition of the retrieved data relation.
9. The method for feature management based on federal learning of medical directions as in claim 8, wherein:
the feature management method based on the medical direction federal learning further comprises the following steps:
and setting a connection symbol for each retrieval item so as to determine the logical relationship between the retrieval items.
10. The method for feature management based on federal learning of medical directions as claimed in claim 9, wherein:
the feature management method based on the medical direction federal learning further comprises the following steps:
generating a retrieval formula and a retrieval result aiming at the retrieval formula according to all the retrieval items;
the retrieval result comprises a data source, a data amount and a data value.
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| CN202110803319.8A CN113704555B (en) | 2021-07-16 | 2021-07-16 | Feature management method based on medical direction federal learning |
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| CN202110803319.8A CN113704555B (en) | 2021-07-16 | 2021-07-16 | Feature management method based on medical direction federal learning |
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