CN111986036A - Medical wind control rule generation method, device, equipment and storage medium - Google Patents
Medical wind control rule generation method, device, equipment and storage medium Download PDFInfo
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Abstract
The invention relates to the field of artificial intelligence, and discloses a medical wind control rule generation method, a device, equipment and a storage medium, which are applied to the field of intelligent medical treatment and used for reducing the calculation time for generating medical wind control rules under the scene of inputting massive medical data. The method comprises the following steps: acquiring initial medical data, wherein the initial medical data is used for indicating various types of medical data; respectively sampling the initial medical data through a plurality of samplers to obtain a plurality of sampled medical data, wherein the plurality of samplers are deployed in a distributed manner; the method comprises the steps that a plurality of sampling medical data are identified through a plurality of approximate rule identifiers to obtain a plurality of candidate medical wind control rules of each approximate rule identifier, and the approximate rule identifiers are used for identifying the sampling medical data transmitted by connected samplers; and combining and screening the candidate medical wind control rules of each approximate rule recognizer through the integrated rule recognizer to obtain a plurality of target medical wind control rules.
Description
Technical Field
The invention relates to the field of platform safety, in particular to a medical wind control rule generation method, a medical wind control rule generation device, medical wind control rule generation equipment and a storage medium.
Background
The increase of fund control strength by all levels of medical insurance management units hastens the emergence of various wind control products, but most of the model products realize a rule engine by a single machine and do not effectively utilize a big data AI algorithm.
An abnormity identification function is arranged in the current wind control product, the condition of rule violation can be identified, and the identification and the definition of the rule are completed in advance. The rules may be automatically identified using data mining, and the process of identifying the rules is typically run on a stand-alone machine.
The medical data has the characteristics of multiple types and large data volume, when massive medical data is input, the computation amount of the existing wind control model is exponentially increased, and the calculation time consumed by the existing wind control model when the medical wind control rule is generated is too long.
Disclosure of Invention
The invention provides a medical wind control rule generation method, a medical wind control rule generation device, medical wind control rule generation equipment and a storage medium, which are used for reducing the calculation time for generating medical wind control rules under the scene of inputting massive medical data.
A first aspect of an embodiment of the present invention provides a medical wind control rule generating method, including: acquiring initial medical data, wherein the initial medical data is used for indicating various types of medical data; respectively sampling the initial medical data through a plurality of samplers to obtain a plurality of sampled medical data, wherein the samplers are deployed in a distributed manner; identifying the plurality of sampled medical data through a plurality of approximate rule identifiers to obtain a plurality of candidate medical wind control rules of each approximate rule identifier, wherein the approximate rule identifiers are used for identifying the sampled medical data transmitted by the connected samplers; and combining and screening the candidate medical wind control rules of each approximate rule recognizer through the integrated rule recognizer to obtain a plurality of target medical wind control rules.
Optionally, in a first implementation manner of the first aspect of the embodiment of the present invention, the identifying, by a plurality of approximation rule identifiers, the plurality of sampled medical data to obtain a plurality of candidate medical pneumatic control rules of each approximation rule identifier, where the approximation rule identifiers are used to identify sampled medical data transmitted by connected samplers, includes: calling a plurality of approximate rule identifiers to perform rule identification on the sampled medical data transmitted by the connected samplers according to an Apriori algorithm to obtain a plurality of initial association rules of each approximate rule identifier; and calling the approximate rule identifiers to respectively carry out iterative calculation on the corresponding initial association rules according to an Eclat algorithm to obtain a plurality of candidate medical wind control rules of each approximate rule identifier.
Optionally, in a second implementation manner of the first aspect of the embodiment of the present invention, the invoking multiple approximation rule identifiers to perform rule identification on the sampled medical data transmitted by the connected samplers according to Apriori algorithm to obtain multiple initial association rules of each approximation rule identifier includes: inputting a plurality of sampling medical data into a plurality of approximate rule identifiers which are correspondingly connected; and calling each approximation rule identifier and a preset Apriori algorithm to identify the received sampled medical data, and generating a plurality of initial association rules of each approximation rule identifier.
Optionally, in a third implementation manner of the first aspect of the embodiment of the present invention, the invoking each approximation rule identifier and a preset Apriori algorithm to identify the received sampled medical data and generate a plurality of initial association rules of each approximation rule identifier includes: invoking a target approximation rule identifier to search a plurality of data item sets in the received sampled medical data; acquiring a preset minimum support degree; screening the plurality of data item sets according to the minimum support degree to obtain a plurality of frequent item sets; generating a plurality of target association rules identified by a target approximation rule identifier according to the plurality of frequent item sets and a preset minimum confidence coefficient; and acquiring a plurality of other association rules identified by other approximate rule identifiers, and generating a plurality of initial association rules of each approximate rule identifier according to the plurality of target association rules and the plurality of other association rules.
Optionally, in a fourth implementation manner of the first aspect of the embodiment of the present invention, the invoking of the multiple approximation rule identifiers to perform iterative computation on the multiple initial association rules respectively according to the Eclat algorithm to obtain multiple candidate medical wind control rules of each approximation rule identifier includes: calling each approximate rule identifier to sequence the corresponding initial association rules according to an Eclat algorithm to obtain a sequenced association rule set corresponding to each approximate rule identifier; pruning the rules with the same prefix in the sorted association rule set corresponding to each approximate rule identifier to obtain a pruned association rule set corresponding to each approximate rule identifier, wherein the pruned association rule set comprises a plurality of candidate rules; stopping rule recognition of the candidate rules in the pruned association rule set corresponding to each approximate rule recognizer under a single pre-index, and generating a plurality of candidate medical wind control rules corresponding to each approximate rule recognizer, wherein the pre-index is used for indicating the source of the candidate rules.
Optionally, in a fifth implementation manner of the first aspect of the embodiment of the present invention, the merging and screening, by the integrated rule identifier, the multiple candidate medical wind control rules of each approximate rule identifier to obtain multiple target medical wind control rules includes: calling an integrated rule recognizer to collect a plurality of candidate medical wind control rules corresponding to each approximate rule recognizer to obtain a candidate medical wind control rule set; carrying out duplication removal operation on the candidate medical wind control rule set to obtain a duplicated candidate medical wind control rule set; combining the candidate medical wind control rule sets after the duplication removal to obtain a combined candidate medical wind control rule set; and screening the combined candidate medical wind control rule set according to process parameters to obtain a plurality of target medical wind control rules, wherein the process parameters comprise the total number of items and the support degree of each approximate rule identifier.
Optionally, in a sixth implementation manner of the first aspect of the embodiment of the present invention, after the integrating rule identifier merges and filters the multiple candidate medical wind control rules of each approximate rule identifier to obtain multiple target medical wind control rules, the method for generating the medical wind control rules further includes: and acquiring data to be identified, and calling the target medical wind control rules to identify the data to be identified to obtain abnormal data, wherein the abnormal data is data which does not accord with the target rules.
A second aspect of an embodiment of the present invention provides a medical wind control rule generation apparatus, including: a data acquisition module for acquiring initial medical data indicative of respective types of medical data; the data sampling module is used for respectively sampling the initial medical data through a plurality of samplers to obtain a plurality of sampled medical data, and the samplers are deployed in a distributed mode; the rule identification module is used for identifying the plurality of sampled medical data through a plurality of approximate rule identifiers to obtain a plurality of candidate medical wind control rules of each approximate rule identifier, and the approximate rule identifiers are used for identifying the sampled medical data transmitted by the connected samplers; and the rule screening module is used for combining and screening the candidate medical wind control rules of each approximate rule recognizer through the integrated rule recognizer to obtain a plurality of target medical wind control rules.
Optionally, in a first implementation manner of the second aspect of the embodiment of the present invention, the rule identifying module includes: the rule identification unit is used for calling a plurality of approximate rule identifiers to carry out rule identification on the sampled medical data transmitted by the connected samplers according to an Apriori algorithm to obtain a plurality of initial association rules of each approximate rule identifier; and the iterative calculation unit is used for calling the approximate rule identifiers to respectively carry out iterative calculation on the corresponding initial association rules according to the Eclat algorithm to obtain a plurality of candidate medical wind control rules of each approximate rule identifier.
Optionally, in a second implementation manner of the second aspect of the embodiment of the present invention, the rule identifying unit includes: the input subunit is used for respectively inputting the plurality of sampling medical data into a plurality of approximate rule identifiers which are correspondingly connected; and the generating subunit is used for calling each approximation rule identifier and a preset Apriori algorithm to identify the received sampled medical data and generating a plurality of initial association rules of each approximation rule identifier.
Optionally, in a third implementation manner of the second aspect of the embodiment of the present invention, the generating subunit is specifically configured to: invoking a target approximation rule identifier to search a plurality of data item sets in the received sampled medical data; acquiring a preset minimum support degree; screening the plurality of data item sets according to the minimum support degree to obtain a plurality of frequent item sets; generating a plurality of target association rules identified by a target approximation rule identifier according to the plurality of frequent item sets and a preset minimum confidence coefficient; and acquiring a plurality of other association rules identified by other approximate rule identifiers, and generating a plurality of initial association rules of each approximate rule identifier according to the plurality of target association rules and the plurality of other association rules.
Optionally, in a fourth implementation manner of the second aspect of the embodiment of the present invention, the iterative computation unit is specifically configured to: calling each approximate rule identifier to sequence the corresponding initial association rules according to an Eclat algorithm to obtain a sequenced association rule set corresponding to each approximate rule identifier; pruning the rules with the same prefix in the sorted association rule set corresponding to each approximate rule identifier to obtain a pruned association rule set corresponding to each approximate rule identifier, wherein the pruned association rule set comprises a plurality of candidate rules; stopping rule recognition of the candidate rules in the pruned association rule set corresponding to each approximate rule recognizer under a single pre-index, and generating a plurality of candidate medical wind control rules corresponding to each approximate rule recognizer, wherein the pre-index is used for indicating the source of the candidate rules.
Optionally, in a fifth implementation manner of the second aspect of the embodiment of the present invention, the rule screening module is specifically configured to: calling an integrated rule recognizer to collect a plurality of candidate medical wind control rules corresponding to each approximate rule recognizer to obtain a candidate medical wind control rule set; carrying out duplication removal operation on the candidate medical wind control rule set to obtain a duplicated candidate medical wind control rule set; combining the candidate medical wind control rule sets after the duplication removal to obtain a combined candidate medical wind control rule set; and screening the combined candidate medical wind control rule set according to process parameters to obtain a plurality of target medical wind control rules, wherein the process parameters comprise the total number of items and the support degree of each approximate rule identifier.
Optionally, in a sixth implementation manner of the second aspect of the embodiment of the present invention, the medical wind control rule generating device further includes: and the abnormal identification module is used for acquiring data to be identified and calling the target medical wind control rules to identify the data to be identified to obtain abnormal data, wherein the abnormal data is data which does not accord with the target rules.
A third aspect of an embodiment of the present invention provides a medical wind control rule generating device, a memory and at least one processor, where the memory stores instructions, and the memory and the at least one processor are interconnected by a line; the at least one processor invokes the instructions in the memory to cause the medical wind control rule generation device to execute the medical wind control rule generation method described above.
A fourth aspect of the embodiments of the present invention provides a computer-readable storage medium, which stores instructions that, when executed by a processor, implement the steps of the medical wind control rule generating method according to any one of the above embodiments.
According to the technical scheme provided by the embodiment of the invention, initial medical data are obtained and used for indicating various types of medical data; respectively sampling the initial medical data through a plurality of samplers to obtain a plurality of sampled medical data, wherein the plurality of samplers are deployed in a distributed manner; the method comprises the steps that a plurality of sampling medical data are identified through a plurality of approximate rule identifiers to obtain a plurality of candidate medical wind control rules of each approximate rule identifier, and the approximate rule identifiers are used for identifying the sampling medical data transmitted by connected samplers; and combining and screening the candidate medical wind control rules of each approximate rule recognizer through the integrated rule recognizer to obtain a plurality of target medical wind control rules. According to the embodiment of the invention, the high approximation rule is obtained in a short time by combining sampling and distributed computing, and the computing time for generating the medical wind control rule is shortened under the scene of inputting massive medical data.
Drawings
Fig. 1 is a schematic diagram of an embodiment of a medical wind control rule generation method in an embodiment of the present invention;
fig. 2 is a schematic diagram of another embodiment of a medical wind control rule generation method in the embodiment of the invention;
fig. 3 is a schematic diagram of an embodiment of a medical wind control rule generation device in an embodiment of the present invention;
fig. 4 is a schematic diagram of another embodiment of the medical wind control rule generation device in the embodiment of the invention;
fig. 5 is a schematic diagram of an embodiment of a medical wind control rule generating device in the embodiment of the present invention.
Detailed Description
The invention provides a medical wind control rule generation method, a medical wind control rule generation device, medical wind control rule generation equipment and a storage medium, which are used for reducing the calculation time for generating medical wind control rules under the scene of inputting massive medical data.
In order to make the technical field of the invention better understand the scheme of the invention, the embodiment of the invention will be described in conjunction with the attached drawings in the embodiment of the invention.
The terms "first," "second," "third," "fourth," and the like in the description and in the claims, as well as in the drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It will be appreciated that the data so used may be interchanged under appropriate circumstances such that the embodiments described herein may be practiced otherwise than as specifically illustrated or described herein. Furthermore, the terms "comprises," "comprising," or "having," and any variations thereof, are intended to cover non-exclusive inclusions, 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.
Referring to fig. 1, a flowchart of a medical wind control rule generating method according to an embodiment of the present invention specifically includes:
101. initial medical data is acquired, the initial medical data being indicative of respective types of medical data.
The server acquires initial medical data indicating various types of medical data.
The initial medical data may be classified into two categories, i.e., medical-related data and non-medical-related data, wherein the medical-related data includes standard medical data and special medical data, and the standard medical data includes: the medical treatment system comprises a medical treatment system, a medical treatment system and a medical treatment system, wherein the medical treatment system comprises a disease state, a disease part, an organ name, a surgery name, a medicine name, a treatment scheme, a notice and the like, special medical data comprise a user name, a user age, a user's past medical history, user's physical sign data and the like, and non-medical related data comprise: the medical insurance account number of the user, the medical insurance balance of the user, the medical insurance payment grade of the user, the medical insurance payment age limit of the user and the like, and the details are not limited herein.
It is to be understood that the executing subject of the present invention may be a medical wind control rule generating device, and may also be a server, which is not limited herein. The embodiment of the present invention is described by taking a server as an execution subject.
102. The initial medical data is sampled by a plurality of samplers to obtain a plurality of sampled medical data, and the samplers are deployed in a distributed manner.
The server respectively samples the initial medical data through a plurality of samplers to obtain a plurality of sampled medical data, and the plurality of samplers are deployed in a distributed mode.
Wherein the sampler is deployed on different computing nodes, wherein the medical data computed on each computing node is different. It should be noted that the sampler adopts a non-put-back random sampling algorithm.
103. And identifying the plurality of sampled medical data through a plurality of approximate rule identifiers to obtain a plurality of candidate medical wind control rules of each approximate rule identifier, wherein the approximate rule identifiers are used for identifying the sampled medical data transmitted by the connected samplers.
Specifically, the server calls a plurality of approximate rule identifiers to perform rule identification on the sampled medical data transmitted by the connected samplers according to an Apriori algorithm to obtain a plurality of initial association rules of each approximate rule identifier; and the server calls a plurality of approximate rule identifiers to respectively carry out iterative calculation on a plurality of corresponding initial association rules according to an Eclat algorithm to obtain a plurality of candidate medical wind control rules of each approximate rule identifier.
104. And combining and screening the candidate medical wind control rules of each approximate rule recognizer through the integrated rule recognizer to obtain a plurality of target medical wind control rules.
Specifically, the server calls an integrated rule recognizer to collect a plurality of candidate medical wind control rules corresponding to each approximate rule recognizer to obtain a candidate medical wind control rule set; the server performs duplication elimination operation on the candidate medical wind control rule set to obtain a duplicated candidate medical wind control rule set; the server carries out merging operation on the candidate medical wind control rule set after the duplication is removed to obtain a merged candidate medical wind control rule set; and the server screens the combined candidate medical wind control rule set according to the process parameters to obtain a plurality of target medical wind control rules, wherein the process parameters comprise the total number of items and the support degree of each approximate rule identifier.
According to the embodiment of the invention, the high approximation rule is obtained in a short time by combining sampling and distributed computing, and the computing time for generating the medical wind control rule is shortened under the scene of inputting massive medical data. And this scheme can be applied to in the wisdom medical treatment field to promote the construction in wisdom city.
Referring to fig. 2, another flowchart of the method for generating a medical wind control rule according to the embodiment of the present invention specifically includes:
201. initial medical data is acquired, the initial medical data being indicative of respective types of medical data.
The server acquires initial medical data indicating various types of medical data. The initial medical data may be classified into two categories, i.e., medical-related data and non-medical-related data, wherein the medical-related data includes standard medical data and special medical data, and the standard medical data includes: the medical treatment system comprises a medical treatment system, a medical treatment system and a medical treatment system, wherein the medical treatment system comprises a disease state, a disease part, an organ name, a surgery name, a medicine name, a treatment scheme, a notice and the like, special medical data comprise a user name, a user age, a user's past medical history, user's physical sign data and the like, and non-medical related data comprise: the medical insurance account number of the user, the medical insurance balance of the user, the medical insurance payment grade of the user, the medical insurance payment age limit of the user and the like, and the details are not limited herein.
It is to be understood that the executing subject of the present invention may be a medical wind control rule generating device, and may also be a server, which is not limited herein. The embodiment of the present invention is described by taking a server as an execution subject.
202. The initial medical data is sampled by a plurality of samplers to obtain a plurality of sampled medical data, and the samplers are deployed in a distributed manner.
The server respectively samples the initial medical data through a plurality of samplers to obtain a plurality of sampled medical data, and the plurality of samplers are deployed in a distributed mode.
Wherein the sampler is deployed on different computing nodes, wherein the medical data computed on each computing node is different. It should be noted that the sampler adopts a non-put-back random sampling algorithm.
203. And calling a plurality of approximate rule identifiers to perform rule identification on the sampled medical data transmitted by the connected samplers according to an Apriori algorithm to obtain a plurality of initial association rules of each approximate rule identifier.
Specifically, the server respectively inputs a plurality of sampling medical data into a plurality of approximate rule identifiers which are correspondingly connected; the server calls each approximate rule identifier and a preset Apriori algorithm to identify the received sampled medical data, and a plurality of initial association rules of each approximate rule identifier are generated.
Optionally, the server invokes each approximation rule identifier and a preset Apriori algorithm to identify the received sampled medical data, and generates a plurality of initial association rules for each approximation rule identifier, which specifically includes:
(1) invoking a target approximation rule identifier to search a plurality of data item sets in the received sampled medical data;
(2) acquiring a preset minimum support degree;
(3) screening a plurality of data item sets according to the minimum support degree to obtain a plurality of frequent item sets;
(4) generating a plurality of target association rules identified by a target approximation rule identifier according to the plurality of frequent item sets and a preset minimum confidence coefficient;
(5) and acquiring a plurality of other association rules identified by other approximate rule identifiers, and generating a plurality of initial association rules of each approximate rule identifier according to the plurality of target association rules and the plurality of other association rules.
It should be noted that all data item sets greater than or equal to the preset minimum support degree are determined as frequent item sets, and the initial association rule is a strong association rule.
For example, sampling the medical data D includes:
| TID | item set |
| 1 | Liver cirrhosis, hepatitis B, and hepatitis A |
| 2 | Liver cirrhosis, hepatitis, and hepatitis B |
| 3 | Hepatitis B and hepatitis A |
| 4 | Liver cirrhosis, hepatitis, and liver cancer |
Assuming that the minimum support degree is 50% and the minimum confidence degree is 70%, the specific identification process of the rule is as follows:
(1) a candidate frequent 1-item set C1 is generated { { liver cirrhosis }, { hepatitis }, { b liver }, { liver cancer }, and { a liver } }. (2) The medical data D is scanned and the support of each item set in C1 in D is calculated. The support number of each item set is 3,3,3,1,2, and the total number of the item sets of the sampled medical data D is 4, so that the support degree of each item set in C1 is 75%, 75%, 75%, 25%, 50%, and the frequent 1-item set L1 { { liver cirrhosis }, { hepatitis }, { a } } can be obtained according to the minimum support degree of 50%. (3) A candidate frequent 2-item set C2 { { liver cirrhosis, hepatitis }, { liver cirrhosis, hepatitis b }, { liver cirrhosis, hepatitis a }, { hepatitis, hepatitis b }, { hepatitis, hepatitis a }, { hepatitis b, liver a } } was generated from L1. (4) The medical data D is scanned and the support of each item set in C2 in D is calculated. The support of each item set is 3,2,1,2,1,2, and the total number of item sets of the sampled medical data D is 4, so that the support of each item set in C2 is 75%, 50%, 25%, 50%, 25%, and 50%, respectively. From the minimum support of 50%, the frequent 2-item set L2 { { liver cirrhosis, hepatitis }, { liver cirrhosis, hepatitis b }, { hepatitis b, hepatitis a } } can be derived. (5) A candidate frequent 3-item set C3 { { cirrhosis, hepatitis, b }, { cirrhosis, hepatitis, a }, { cirrhosis, b, a }, { hepatitis, b, a } } was generated according to L2, and can be removed because a subset { hepatitis, a } of the item set { cirrhosis, hepatitis, b } in C3 is absent from L2; the similar items set { liver cirrhosis, hepatitis B, hepatitis A }, { hepatitis, hepatitis B, hepatitis A } can also be removed. Therefore, C3 ═ cirrhosis, hepatitis b }. (6) The medical data D is scanned and the support of each item set in C3 in D is calculated. The support of each item set is 2 and the total number of the item sets of the sampled medical data D is 4, so that the support of each item set in C2 is 50%; from the minimum support of 50%, the frequent 3-item set L3 { { liver cirrhosis, hepatitis } } can be obtained. (7) L1 ═ L2 { { liver cirrhosis }, { hepatitis }, { b hepatitis }, { a liver }, { liver cirrhosis, hepatitis b }, { hepatitis, b }, { b hepatitis, a }, { liver cirrhosis, b }, { liver cirrhosis, hepatitis, b } }. (8) Only sets of items with a length of more than 1 are considered, such as { cirrhosis, hepatitis b }, all non-proper subsets of which { cirrhosis }, { hepatitis b }, { cirrhosis, hepatitis }, { hepatitis, b }, and { hepatitis b }, the association rules { cirrhosis } - > { hepatitis, b }, { hepatitis } - > { hepatitis } -, hepatitis }, hepatitis b }, { hepatitis b } - > { hepatitis, and } - > { hepatitis b }, { cirrhosis, hepatitis b } - > { hepatitis b }, { hepatitis b } - >, and { hepatitis b } - > { hepatitis }, { confidence in the association rules, 67%, 67%, 67%, 100%, and 100%, respectively. Since the minimum confidence is 70%, it is available that { liver cirrhosis, hepatitis b } - > { hepatitis }, { hepatitis, hepatitis b } - > { liver cirrhosis } is a frequent association rule. That is, hepatitis is caused by the occurrence of cirrhosis and hepatitis B, and cirrhosis is caused by the occurrence of hepatitis and hepatitis B.
204. And calling a plurality of approximate rule identifiers to respectively carry out iterative calculation on a plurality of corresponding initial association rules according to an Eclat algorithm to obtain a plurality of candidate medical wind control rules of each approximate rule identifier.
Specifically, the server calls each approximate rule identifier to sort the corresponding initial association rules according to an Eclat algorithm to obtain a sorted association rule set corresponding to each approximate rule identifier; the server prunes the rules with the same prefix in the sorted association rule set corresponding to each approximate rule identifier to obtain a pruned association rule set corresponding to each approximate rule identifier, wherein the pruned association rule set comprises a plurality of candidate rules; and the server stops rule identification of the candidate rules in the pruned association rule set corresponding to each approximate rule identifier under a single pre-index, and generates a plurality of candidate medical wind control rules corresponding to each approximate rule identifier, wherein the pre-index is used for indicating the source of the candidate rules.
According to the embodiment, through pruning operation, the number of generated rule candidate sets is reduced, and the output speed of the recognizer is increased.
It should be noted that the source generally refers to a document number, serial number, etc., and represents the identified case number. For the identified rules, in the same case, the constraint is further extended. For example, for rule expansion, if the expanded items are all under the same document, the expansion is stopped. By defining the extension direction, the amount of computation is further reduced.
205. And combining and screening the candidate medical wind control rules of each approximate rule recognizer through the integrated rule recognizer to obtain a plurality of target medical wind control rules.
Specifically, the server calls an integrated rule recognizer to collect a plurality of candidate medical wind control rules corresponding to each approximate rule recognizer to obtain a candidate medical wind control rule set; the server performs duplication elimination operation on the candidate medical wind control rule set to obtain a duplicated candidate medical wind control rule set; the server carries out merging operation on the candidate medical wind control rule set after the duplication is removed to obtain a merged candidate medical wind control rule set; and the server screens the combined candidate medical wind control rule set according to the process parameters to obtain a plurality of target medical wind control rules, wherein the process parameters comprise the total number of items and the support degree of each approximate rule identifier.
206. And acquiring data to be identified, and calling a plurality of target medical wind control rules to identify the data to be identified to obtain abnormal data, wherein the abnormal data is data which does not accord with the plurality of target rules.
The server acquires data to be identified and calls a plurality of target medical wind control rules to identify the data to be identified to obtain abnormal data, wherein the abnormal data are data which do not accord with the plurality of target rules.
According to the embodiment of the invention, the high approximation rule is obtained in a short time by combining sampling and distributed computing, and the computing time for generating the medical wind control rule is shortened under the scene of inputting massive medical data. And this scheme can be applied to in the wisdom medical treatment field to promote the construction in wisdom city.
In the above description of the method for generating medical wind control rules according to the embodiment of the present invention, referring to fig. 3, a device for generating medical wind control rules according to the embodiment of the present invention is described below, where one embodiment of the device for generating medical wind control rules according to the embodiment of the present invention includes:
a data acquisition module 301, configured to acquire initial medical data, where the initial medical data is used to indicate various types of medical data;
a data sampling module 302, configured to sample the initial medical data by using a plurality of samplers, respectively, to obtain a plurality of sampled medical data, where the plurality of samplers are deployed in a distributed manner;
the rule identification module 303 is configured to identify the plurality of sampled medical data through a plurality of approximate rule identifiers to obtain a plurality of candidate medical pneumatic control rules of each approximate rule identifier, where the approximate rule identifiers are used to identify sampled medical data transmitted by connected samplers;
and the rule screening module 304 is configured to merge and screen the multiple candidate medical wind control rules of each approximate rule identifier through the integrated rule identifier to obtain multiple target medical wind control rules.
According to the embodiment of the invention, the high approximation rule is obtained in a short time by combining sampling and distributed computing, and the computing time for generating the medical wind control rule is shortened under the scene of inputting massive medical data. And this scheme can be applied to in the wisdom medical treatment field to promote the construction in wisdom city.
Referring to fig. 4, another embodiment of the medical wind control rule generating device according to the embodiment of the present invention includes:
a data acquisition module 301, configured to acquire initial medical data, where the initial medical data is used to indicate various types of medical data;
a data sampling module 302, configured to sample the initial medical data by using a plurality of samplers, respectively, to obtain a plurality of sampled medical data, where the plurality of samplers are deployed in a distributed manner;
the rule identification module 303 is configured to identify the plurality of sampled medical data through a plurality of approximate rule identifiers to obtain a plurality of candidate medical pneumatic control rules of each approximate rule identifier, where the approximate rule identifiers are used to identify sampled medical data transmitted by connected samplers;
and the rule screening module 304 is configured to merge and screen the multiple candidate medical wind control rules of each approximate rule identifier through the integrated rule identifier to obtain multiple target medical wind control rules.
Optionally, the rule identifying module 303 includes:
a rule identification unit 3031, configured to invoke multiple approximation rule identifiers to perform rule identification on the sampled medical data transmitted by the connected sampler according to Apriori algorithm, so as to obtain multiple initial association rules of each approximation rule identifier;
an iterative calculation unit 3032, configured to invoke the multiple approximation rule identifiers to perform iterative calculation on the multiple initial association rules respectively according to an Eclat algorithm, so as to obtain multiple candidate medical wind control rules of each approximation rule identifier.
Optionally, the rule identifying unit 3031 includes:
an input subunit 30311, configured to input the plurality of sampled medical data into the plurality of approximation rule identifiers connected correspondingly, respectively;
a generating subunit 30312, configured to invoke each approximation rule identifier and a preset Apriori algorithm to identify the received sampled medical data, and generate a plurality of initial association rules for each approximation rule identifier.
Optionally, the generating subunit 30312 is specifically configured to:
invoking a target approximation rule identifier to search a plurality of data item sets in the received sampled medical data; acquiring a preset minimum support degree; screening the plurality of data item sets according to the minimum support degree to obtain a plurality of frequent item sets; generating a plurality of target association rules identified by a target approximation rule identifier according to the plurality of frequent item sets and a preset minimum confidence coefficient; and acquiring a plurality of other association rules identified by other approximate rule identifiers, and generating a plurality of initial association rules of each approximate rule identifier according to the plurality of target association rules and the plurality of other association rules.
Optionally, the iterative calculation unit 3032 is specifically configured to:
calling each approximate rule identifier to sequence the corresponding initial association rules according to an Eclat algorithm to obtain a sequenced association rule set corresponding to each approximate rule identifier; pruning the rules with the same prefix in the sorted association rule set corresponding to each approximate rule identifier to obtain a pruned association rule set corresponding to each approximate rule identifier, wherein the pruned association rule set comprises a plurality of candidate rules; stopping rule recognition of the candidate rules in the pruned association rule set corresponding to each approximate rule recognizer under a single pre-index, and generating a plurality of candidate medical wind control rules corresponding to each approximate rule recognizer, wherein the pre-index is used for indicating the source of the candidate rules.
Optionally, the rule screening module 304 is specifically configured to:
calling an integrated rule recognizer to collect a plurality of candidate medical wind control rules corresponding to each approximate rule recognizer to obtain a candidate medical wind control rule set; carrying out duplication removal operation on the candidate medical wind control rule set to obtain a duplicated candidate medical wind control rule set; combining the candidate medical wind control rule sets after the duplication removal to obtain a combined candidate medical wind control rule set; and screening the combined candidate medical wind control rule set according to process parameters to obtain a plurality of target medical wind control rules, wherein the process parameters comprise the total number of items and the support degree of each approximate rule identifier.
Optionally, the medical wind control rule generating device further includes:
the abnormal identification module 305 is configured to obtain data to be identified, and call the multiple target medical wind control rules to identify the data to be identified, so as to obtain abnormal data, where the abnormal data is data that does not meet the multiple target rules.
According to the embodiment of the invention, the high approximation rule is obtained in a short time by combining sampling and distributed computing, and the computing time for generating the medical wind control rule is shortened under the scene of inputting massive medical data. And this scheme can be applied to in the wisdom medical treatment field to promote the construction in wisdom city.
Fig. 3 to 4 describe the medical wind control rule generating device in the embodiment of the present invention in detail from the perspective of the modular functional entity, and the medical wind control rule generating device in the embodiment of the present invention is described in detail from the perspective of hardware processing.
Fig. 5 is a schematic structural diagram of a medical wind control rule generating device according to an embodiment of the present invention, where the medical wind control rule generating device 500 may generate a relatively large difference due to different configurations or performances, and may include one or more processors (CPUs) 510 (e.g., one or more processors) and a memory 520, and one or more storage media 530 (e.g., one or more mass storage devices) storing applications 533 or data 532. Memory 520 and storage media 530 may be, among other things, transient or persistent storage. The program stored on the storage medium 530 may include one or more modules (not shown), each of which may include a sequence of instructions operating on the medical wind control rule generating device 500. Still further, the processor 510 may be configured to communicate with the storage medium 530 to execute a series of instruction operations in the storage medium 530 on the medical wind control rule generating device 500.
The medical wind rules generation device 500 may also include one or more power supplies 540, one or more wired or wireless network interfaces 550, one or more input-output interfaces 560, and/or one or more operating systems 531, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, and the like. Those skilled in the art will appreciate that the configuration of the medical wind control rule generating device shown in fig. 5 does not constitute a limitation of the medical wind control rule generating device and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.
The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium, and which may also be a volatile computer-readable storage medium, having stored therein instructions, which, when run on a computer, cause the computer to perform the steps of the medical wind control rule generation method.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A medical wind control rule generation method is characterized by comprising the following steps:
acquiring initial medical data, wherein the initial medical data is used for indicating various types of medical data;
respectively sampling the initial medical data through a plurality of samplers to obtain a plurality of sampled medical data, wherein the samplers are deployed in a distributed manner;
identifying the plurality of sampled medical data through a plurality of approximate rule identifiers to obtain a plurality of candidate medical wind control rules of each approximate rule identifier, wherein the approximate rule identifiers are used for identifying the sampled medical data transmitted by the connected samplers;
and combining and screening the candidate medical wind control rules of each approximate rule recognizer through the integrated rule recognizer to obtain a plurality of target medical wind control rules.
2. The method according to claim 1, wherein the identifying the plurality of sampled medical data by a plurality of approximation rule identifiers to obtain a plurality of candidate medical wind control rules for each approximation rule identifier, the approximation rule identifiers being used to identify the sampled medical data transmitted by the connected samplers comprises:
calling a plurality of approximate rule identifiers to perform rule identification on the sampled medical data transmitted by the connected samplers according to an Apriori algorithm to obtain a plurality of initial association rules of each approximate rule identifier;
and calling the approximate rule identifiers to respectively carry out iterative calculation on the corresponding initial association rules according to an Eclat algorithm to obtain a plurality of candidate medical wind control rules of each approximate rule identifier.
3. The method for generating medical wind-control rules according to claim 2, wherein the invoking multiple approximation rule identifiers to perform rule identification on the sampled medical data transmitted by the connected samplers according to Apriori algorithm to obtain multiple initial association rules of each approximation rule identifier includes:
inputting a plurality of sampling medical data into a plurality of approximate rule identifiers which are correspondingly connected;
and calling each approximation rule identifier and a preset Apriori algorithm to identify the received sampled medical data, and generating a plurality of initial association rules of each approximation rule identifier.
4. The method according to claim 3, wherein the step of calling each approximation rule identifier and a preset Apriori algorithm to identify the received sampled medical data and generating a plurality of initial association rules for each approximation rule identifier comprises:
invoking a target approximation rule identifier to search a plurality of data item sets in the received sampled medical data;
acquiring a preset minimum support degree;
screening the plurality of data item sets according to the minimum support degree to obtain a plurality of frequent item sets;
generating a plurality of target association rules identified by a target approximation rule identifier according to the plurality of frequent item sets and a preset minimum confidence coefficient;
and acquiring a plurality of other association rules identified by other approximate rule identifiers, and generating a plurality of initial association rules of each approximate rule identifier according to the plurality of target association rules and the plurality of other association rules.
5. The method of claim 2, wherein the invoking the plurality of approximate rule identifiers to iteratively calculate a plurality of initial association rules according to an Eclat algorithm to obtain a plurality of candidate medical wind control rules for each approximate rule identifier comprises:
calling each approximate rule identifier to sequence the corresponding initial association rules according to an Eclat algorithm to obtain a sequenced association rule set corresponding to each approximate rule identifier;
pruning the rules with the same prefix in the sorted association rule set corresponding to each approximate rule identifier to obtain a pruned association rule set corresponding to each approximate rule identifier, wherein the pruned association rule set comprises a plurality of candidate rules;
stopping rule recognition of the candidate rules in the pruned association rule set corresponding to each approximate rule recognizer under a single pre-index, and generating a plurality of candidate medical wind control rules corresponding to each approximate rule recognizer, wherein the pre-index is used for indicating the source of the candidate rules.
6. The method for generating medical wind control rules according to claim 1, wherein the merging and screening the candidate medical wind control rules of each approximate rule identifier through the integrated rule identifier to obtain a plurality of target medical wind control rules comprises:
calling an integrated rule recognizer to collect a plurality of candidate medical wind control rules corresponding to each approximate rule recognizer to obtain a candidate medical wind control rule set;
carrying out duplication removal operation on the candidate medical wind control rule set to obtain a duplicated candidate medical wind control rule set;
combining the candidate medical wind control rule sets after the duplication removal to obtain a combined candidate medical wind control rule set;
and screening the combined candidate medical wind control rule set according to process parameters to obtain a plurality of target medical wind control rules, wherein the process parameters comprise the total number of items and the support degree of each approximate rule identifier.
7. The method according to any one of claims 1 to 6, wherein after the integrating rule identifier merges and filters the candidate medical wind control rules of each approximate rule identifier to obtain a plurality of target medical wind control rules, the method further comprises:
and acquiring data to be identified, and calling the target medical wind control rules to identify the data to be identified to obtain abnormal data, wherein the abnormal data is data which does not accord with the target rules.
8. A medical wind control rule generation device, comprising:
a data acquisition module for acquiring initial medical data indicative of respective types of medical data;
the data sampling module is used for respectively sampling the initial medical data through a plurality of samplers to obtain a plurality of sampled medical data, and the samplers are deployed in a distributed mode;
the rule identification module is used for identifying the plurality of sampled medical data through a plurality of approximate rule identifiers to obtain a plurality of candidate medical wind control rules of each approximate rule identifier, and the approximate rule identifiers are used for identifying the sampled medical data transmitted by the connected samplers;
and the rule screening module is used for combining and screening the candidate medical wind control rules of each approximate rule recognizer through the integrated rule recognizer to obtain a plurality of target medical wind control rules.
9. A medical wind control rule generating device, characterized by comprising: a memory having instructions stored therein and at least one processor, the memory and the at least one processor interconnected by a line;
the at least one processor invokes the instructions in the memory to cause the medical wind rules generation device to perform the medical wind rules generation method of any of claims 1-7.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores instructions that, when executed by a processor, implement the medical wind control rule generation method according to any one of claims 1-7.
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