CN112927810B - Smart medical response method based on big data and smart medical cloud computing system - Google Patents

Smart medical response method based on big data and smart medical cloud computing system Download PDF

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CN112927810B
CN112927810B CN202110309028.3A CN202110309028A CN112927810B CN 112927810 B CN112927810 B CN 112927810B CN 202110309028 A CN202110309028 A CN 202110309028A CN 112927810 B CN112927810 B CN 112927810B
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CN112927810A (en
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崔剑虹
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Ningbo Ningfan Information Technology Co ltd
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/28—Databases characterised by their database models, e.g. relational or object models
    • G06F16/284—Relational databases
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • 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
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • 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 disclosed embodiment provides a smart medical response method based on big data and a smart medical cloud computing system, wherein a medical service entry network is constructed according to an interpretation relationship and an extended association attribute, and then a correlation relationship between medical services and entries is learned based on the medical service entry network, so that medical information response is more accurately performed for the medical services. The method comprises the steps of performing service learning according to a medical service entry network, iteratively learning the association characteristics between the medical service and the entries, wherein the association characteristics not only comprise the service flow direction relation of the medical service, but also comprise the jump relation between the medical service and the medical service or the theme level attribute relation between the entries and the entries, and outputting the confidence that the medical service may be associated with a certain entry according to the association characteristics, so that the service response precision and reliability of medical service response are improved.

Description

Smart medical response method based on big data and smart medical cloud computing system
Technical Field
The disclosure relates to the technical field of smart medical treatment based on big data technology, and exemplarily relates to a smart medical treatment response method based on big data and a smart medical treatment cloud computing system.
Background
The smart medical service is an important component of a smart city, and is a medical system which comprehensively applies technologies such as a medical internet of things, data fusion transmission and exchange, cloud computing, a metropolitan area network and the like, fuses medical infrastructure and IT infrastructure through an information technology, takes a medical cloud data center as a core, spans space-time limitation of an original medical system, and carries out intelligent decision on the basis, so that the optimization of medical service is realized.
The intelligent medical treatment integrates an individual, an apparatus and a mechanism into a whole, closely associates a patient, a medical staff, an insurance company, a researcher and the like, realizes business cooperation, and increases the triple benefits of society, mechanism and individuals. Meanwhile, medical services such as remote registration, online consultation and online payment are pushed to hands of each person through technologies such as mobile communication and mobile internet, and the problem of difficulty in seeing a doctor is relieved.
Based on this, in the related art, for different intelligent medical terminals (for example, terminals disposed in medical service institutions) disposed all over, for the online consultation process, multiple times of interaction with the user are required, and medical response information of the medical service can be accurately pushed for the user according to the latest medical service entry, so that the user can continue to perform consultation navigation according to the required information. However, in the related art, the accuracy and reliability of the medical service response are low.
Disclosure of Invention
In order to overcome at least the above disadvantages in the prior art, an object of the present disclosure is to provide a smart medical response method and a smart medical cloud computing system based on big data.
In a first aspect, the present disclosure provides a smart medical response method based on big data, which is applied to a smart medical cloud computing system, where the smart medical cloud computing system is in communication connection with a plurality of smart medical mobile terminals, and the method includes:
constructing a medical service entry network, wherein the medical service entry network comprises elements and association attributes, the elements comprise medical service elements corresponding to medical services and entry elements corresponding to entries, the two elements with interpretation relations or extended association attributes are subjected to association labeling by using the association attributes, the interpretation relations comprise attribute relations between the medical service elements which have initiated an interpretation process and the entry elements, and the extended association attributes comprise at least one of jump relations between the medical service elements and topic hierarchy relations between the entry elements;
performing service learning based on the medical service entry network to obtain medical service learning characteristics of medical service elements and entry learning characteristics of the entry elements, wherein the service learning is used for performing deep learning according to the association attributes among the elements and extracting network contact characteristics in the medical service entry network;
and outputting medical response information of the target medical service according to at least two confidence degrees of medical service elements of the target medical service and at least two entry elements, wherein the confidence degrees are determined according to the medical service learning characteristics and the entry learning characteristics.
In a second aspect, the disclosed embodiment further provides a big data based smart medical response system, which includes a smart medical cloud computing system and a plurality of smart medical mobile terminals in communication connection with the smart medical cloud computing system;
the smart medical cloud computing system is used for:
constructing a medical service entry network, wherein the medical service entry network comprises elements and association attributes, the elements comprise medical service elements corresponding to medical services and entry elements corresponding to entries, the two elements with interpretation relations or extended association attributes are subjected to association labeling by using the association attributes, the interpretation relations comprise attribute relations between the medical service elements which have initiated an interpretation process and the entry elements, and the extended association attributes comprise at least one of jump relations between the medical service elements and topic hierarchy relations between the entry elements;
performing service learning based on the medical service entry network to obtain medical service learning characteristics of medical service elements and entry learning characteristics of the entry elements, wherein the service learning is used for performing deep learning according to the association attributes among the elements and extracting network contact characteristics in the medical service entry network;
and outputting medical response information of the target medical service according to at least two confidence degrees of medical service elements of the target medical service and at least two entry elements, wherein the confidence degrees are determined according to the medical service learning characteristics and the entry learning characteristics.
According to any one of the aspects, in the embodiment provided by the present disclosure, a medical service entry network is constructed according to the interpretation relationship and the extended association attribute, and then the correlation relationship between the medical service and the entry is learned based on the medical service entry network, so that the medical information response is performed more accurately for the medical service. The method comprises the steps of performing service learning according to a medical service entry network, iteratively learning association characteristics between medical services and entries, wherein the association characteristics comprise not only service flow direction relations of the medical services, but also jump relations between the medical services and the medical services, or topic level attribute relations between the entries and the entries, outputting confidence degrees that the medical services may be associated with one entry according to the association characteristics, if the confidence degrees are high, it is indicated that the medical services have high relevance to the entries, the medical services tend to be associated with the entries more, the entries are associated with the medical services, and the service response accuracy and reliability of medical service response are improved.
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In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings that need to be called in the embodiments are briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present disclosure, and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a schematic view of an application scenario of a big data-based smart medical response system according to an embodiment of the present disclosure;
FIG. 2 is a schematic flow chart illustrating a method for providing big data based intelligent medical response according to an embodiment of the present disclosure;
FIG. 3 is a block diagram illustrating the functional blocks of a big data based smart medical response device according to an embodiment of the present disclosure;
fig. 4 is a schematic block diagram of a smart medical cloud computing system for implementing the big-data-based smart medical response method according to the embodiment of the present disclosure.
Detailed Description
The present disclosure is described in detail below with reference to the drawings, and the specific operation methods in the method embodiments can also be applied to the device embodiments or the system embodiments.
Fig. 1 is an explanatory diagram of a big data-based smart medical response system 10 according to an embodiment of the disclosure. The big data based smart medical response system 10 may include a smart medical cloud computing system 100 and a smart medical mobile terminal 200 communicatively connected to the smart medical cloud computing system 100. The big data based smart medical response system 10 shown in fig. 1 is only one possible example, and in other possible embodiments, the big data based smart medical response system 10 may include only at least some of the components shown in fig. 1 or may include other components.
In a possible design approach, the smart medical cloud computing system 100 and the smart medical mobile terminal 200 in the big-data based smart medical response system 10 may cooperatively perform the big-data based smart medical response method described in the following method embodiments, and the detailed description of the method embodiments may be referred to in the following steps of the smart medical cloud computing system 100 and the smart medical mobile terminal 200.
To solve the technical problems in the background art, fig. 2 is a flowchart illustrating a big data based smart medical response method according to an embodiment of the present disclosure, which may be executed by the smart medical cloud computing system 100 shown in fig. 1, and the big data based smart medical response method will be described in detail below.
And step S110, constructing a medical service entry network.
In this embodiment, the medical service entry network includes elements and association attributes, the elements include medical service elements corresponding to the medical services and entry elements corresponding to the entries, the two elements having an interpretation relationship or an extended association attribute use the association attributes for association tagging, the interpretation relationship includes an attribute relationship between the medical service elements and the entry elements that have initiated an interpretation procedure, and the extended association attribute includes at least one of a jump relationship between the medical service elements and a topic hierarchy relationship between the entry elements.
For example, the smart medical cloud computing system may build a network of medical business terms based on medical business big data stored in a database. The medical service entry network is a large-scale network structure formed by medical service big data of a large number of medical services. Two types of elements can be included in the medical service entry network: medical business elements and entry elements. For example, at least two medical service elements or at least two entry elements exist in the medical service entry network, for example, since the medical service entry network is constructed using a large amount of medical service data stored in the smart medical cloud computing system 100, a plurality of medical service elements and a plurality of entry elements exist in the medical service entry network. For example, the attribute relationship between the elements is represented by an association attribute, for example, when there is an interpretation relationship between the medical service and the entry, the medical service element and the entry element are labeled by the association attribute, and the association attribute represents the interpretation relationship between the two elements; for another example, when a jump relationship exists between the medical service and the medical service, two medical service elements are labeled by using an associated attribute, and the associated attribute represents the jump relationship between the two elements; for another example, for a medical service, different association degrees may be assigned to different terms associated with the medical service, and then an association degree may be assigned to an association attribute between a medical service element and a term element, where the association attribute indicates that there are an interpretation relationship between the two medical service elements and the association degree of the interpretation relationship. For example, in one embodiment, the association attribute may be a directed association attribute or an undirected association attribute. The directional association attribute indicates that the attribute relationship between the two elements may have directionality, for example, in the jump relationship between the medical service and the medical service, the medical service a refers to the medical service B, and the association attribute between the medical service element a of the medical service a and the medical service element B of the medical service B may be an active reference that points from the medical service a to the medical service B to emphasize the medical service a, or of course, the association attribute may also be a referenced relationship that points from the medical service B to the medical service a to emphasize the medical service B.
For example, assume that a medical service element a, a term element B, and an association attribute C between the elements are included in a medical service term network, and the association attribute C represents an attribute relationship between the elements. For example, a medical service occupies one medical service element in the medical service entry network, and a term occupies one entry element in the medical service entry network, so that when ten thousand medical services and one thousand terms are included in the data for constructing the medical service entry network, ten thousand medical service elements and one thousand entry elements are correspondingly included in the medical service entry network.
Three relationships between elements are mentioned above: explaining the relationship, jumping relationship and topic hierarchy relationship, and the three relationships are introduced below.
The interpretation relationship is an attribute relationship between the medical service (medical service element) and the entry (entry element). The interpretation relationship represents that a direct association relationship exists between the medical service and the entry, that is, an interpretation process occurs between the medical service and the entry, for example, the entry is added to the medical service, the entry is quoted by the medical service, a keyword of the entry is generated during construction of the medical service, related information of the entry is quoted by the medical service, and the like. For example, the interpretation relationship may be acquired by the smart medical mobile terminal 200, the smart medical mobile terminal 200 sends the acquired interpretation relationship to the smart medical cloud computing system 100 for storage, and the smart medical cloud computing system 100 constructs a medical service entry network according to the stored interpretation relationship of each medical service reported by each smart medical mobile terminal 200. For example, since the medical service has an interpretation relationship with the entry, the medical service element corresponding to the medical service has an interpretation relationship with the entry element corresponding to the entry.
A jump relationship is an attribute relationship between two medical services (medical service elements). The skip relation represents that a direct association relation exists between two medical services, for example, a service relation exists between the two medical services, a reference and referenced relation exists between the two medical services, a subscription and subscribed relation exists between the two medical services, a sharing and shared relation exists between the two medical services, the two medical services (accounts) belong to the same service provider, and the like. For example, the skip relation may also be an indirect association relation, for example, two medical services belong to the same service category, two medical services join the same correlation label together, two medical services have a service relation with another medical service, and the like. For example, the medical service mentioned in the present disclosure refers to a medical service that can be identified by the smart medical cloud computing system 100, that is, a medical service account, and if a plurality of medical service nodes exist in the same medical service, the same medical service is identified as a plurality of medical services by the smart medical cloud computing system. For example, since there is a jump relationship between two medical services, there is a jump relationship between the medical service element corresponding to the medical service and the medical service element corresponding to the other medical service.
The topic hierarchy is a weighted interpretation relationship. For example, the topic matching degree of the medical service to the entry at the topic level can be calculated according to the topic level of the entry, and the association degree of the medical service to the entry can be obtained according to the topic matching degree. This embodiment refers to the interpretation relationship weighted by the degree of association as the topic hierarchy relationship. That is, the topic hierarchy relationship is a topic matching degree relationship between the term and the frequent pattern item, for example, the term element having an interpretation relationship with the first medical service element includes a first term element, a second term element and a third term element, when the first term element is the center of the first medical service element, the topic matching degree of the first term element and the second term element is 3 units, and the topic matching degree of the first term element and the third term element is 1 unit, then the association degree of the first medical service element and the association attribute of the first term element is the greatest, the association degree of the first medical service element and the association attribute of the second term element is the next, and the association degree of the first medical service element and the association attribute of the third term element is the smallest. For example, the association attribute representing the topic hierarchical relationship is also a medical service element and a term element which are connected and have an interpretation relationship, but the association attribute may have an association degree, and the association degree represents the topic matching degree of the term element and the frequent pattern item of the medical service element, so the topic hierarchical relationship between the term elements can be represented according to the association degree on the association attribute.
For example, the smart medical cloud computing system 100 obtains topic associated data and extension data of a medical service entry, where the topic associated data of the medical service entry includes at least two medical services and entries related to the medical services, and the extension data includes at least one of medical service skip data and entry topic data; and constructing a medical service entry network according to the theme related data and the expansion data of the medical service entries.
For example, the above-mentioned medical service data is used to construct the medical service entry network, where the medical service data includes the subject related data and the extension data of the medical service entry. The topic association data of the medical service entry is used for recording an interpretation relationship between the medical service and the entry, for example, the topic association data of the medical service entry includes: medical service (medical service Identification (ID)), vocabulary entry (vocabulary entry Identification (ID)), and correspondence (interpretation relationship) between medical service and vocabulary entry. The extension data contains three possibilities: skipping data of medical services; entry topic data; medical service skip data and entry topic data. The medical service skipping data is used for recording the skipping relation between the medical service and the medical service, and the medical service skipping data comprises: the first medical service, the second medical service, and a correspondence relationship (jump relationship) between the first medical service and the second medical service. The term topic data is used for topic hierarchy information of the airborne term, for example, the term topic hierarchy data includes: the subject hierarchy of the entries and the entries, or the subject nodes of the entries and the entries.
For example, the data contained in the three data listed above includes data content that is only the basic data type required for implementing the method provided by the present disclosure, and based on the data types listed above, the subject associated data of the medical service entry may further include: the medical service updates the subject frequency of the entry, the medical service information and the like; the medical service jump data may further include: the type of the jump relationship (active jump, passive jump, etc.), the confidentiality of the service, the duration of the service relationship, the interaction frequency of the service, whether to refer specifically, the number of common service, etc.; the term topic data may further include: entry type, attributes, etc. of the entry.
For example, a medical service entry jumping network can be constructed based on the interpretation relationship and the jumping relationship, a medical service entry topic network can be constructed based on the interpretation relationship and the topic hierarchy relationship, and a medical service entry jumping network and a medical service entry topic network can be constructed based on the interpretation relationship, the jumping relationship and the topic hierarchy relationship. Different network contact characteristics between the medical service and the entry can be learned by learning based on different networks, and further medical service medical response information is provided from different layers.
Step S120, performing service learning based on the medical service entry network to obtain medical service learning characteristics of the medical service elements and entry learning characteristics of the entry elements.
And the service learning is used for deep learning and extracting network contact characteristics in the medical service entry network according to the association attributes among the elements.
For example, the embodiment may employ CNN to extract features in the medical service entry network, specifically feature propagation among elements, and for each element, may absorb and fuse features propagated by its associated element, and generate a new vector whose dimensions are unchanged by combining with its existing features. The characteristic propagation can be iterated for multiple layers, so that the connection information of one layer or even a high layer in a network structure is extracted; meanwhile, the initial attribute feature of the element can be used as the 0 th learning feature, so that the graph convolution network can effectively utilize the element attribute and the structure information of the network at the same time.
For example, the smart medical cloud computing system first generates 0 th learning feature (initial learning feature) of each element according to the attribute of each element itself, for example, generates an initial learning feature of a medical service element according to a medical service ID, and generates an initial learning feature of a term element according to a term ID. Or the intelligent medical cloud computing system can also generate initial learning features of the medical service elements according to the medical service ID and the medical service gender and generate initial learning features of the entry elements according to the entry ID and the entry type.
Then, the intelligent medical cloud computing system inputs the initial learning features of the element to the associated elements according to the connection relation between the element and the associated elements, and meanwhile, the element can obtain the initial learning features of the associated elements input by the associated elements. The element can learn the learning characteristics of the element according to the obtained initial learning characteristics of the associated element and the initial learning characteristics of the element to obtain the 1 st learning characteristic of the element. Thus, the 1 st learning feature of the element can learn the feature of the associated element of the element. Repeating the step of business learning, the 2 nd learning feature of the element can be obtained according to the 1 st learning feature of the element and the 1 st learning feature of the associated element, and since the 1 st learning feature of the associated element has learned the feature of the associated element, the 2 nd learning feature of the element includes the feature of the associated element. Therefore, the element can continuously learn the characteristics of the remote element by iterative learning.
For example, as shown in fig. 4, taking three elements in the medical service entry network as an example, wherein element 1 and element 2 are labeled by the association attribute, element 2 and element 3 are labeled by the association attribute, element 1 has the initial learning feature 1, element 2 has the initial learning feature 2, and element 3 has the initial learning feature 3. First, feature propagation is performed, with element 1 sending initial learning features 1 to element 2, element 2 sending initial learning features 2 to element 1 and element 3, and element 3 sending initial learning features 3 to element 2. Then, feature learning is performed, the element 1 obtains a 1 st learning feature 1 according to the obtained initial learning feature 2 and the initial learning feature 1 of itself, the element 2 obtains a 1 st learning feature 2 according to the obtained initial learning feature 1 and the initial learning feature 3 and the initial learning feature 2 of itself, and the element 3 obtains a 1 st learning feature 3 according to the obtained initial learning feature 2 and the initial learning feature 3 of itself. Then, feature propagation is performed again, element 1 inputs the 1 st learning feature 1 to element 2, element 2 inputs the 1 st learning feature 2 to element 1 and element 3, and element 3 inputs the 1 st learning feature 3 to element 2. Then, feature learning is performed again, the element 1 obtains a 2 nd learning feature 1 according to the obtained 1 st learning feature 2 and the 1 st learning feature 1 of itself, the element 2 obtains a 2 nd learning feature 2 according to the obtained 1 st learning feature 1 and the 1 st learning feature 3 and the 1 st learning feature 2 of itself, and the element 3 obtains a 2 nd learning feature 3 according to the obtained 1 st learning feature 2 and the 1 st learning feature 3 of itself. Thus, the element 1 can learn the feature of the element 3 after two business learning, and similarly, the element 3 can also learn the feature of the element 1.
The feature propagation refers to a process in which each element inputs the learning feature of the element to the associated element, and the feature learning refers to a process in which each element updates the learning feature of the element according to the obtained learning feature. And the deeper learning characteristics can be obtained by carrying out the business learning in an iteration mode.
For example, when the association attribute in the medical service entry network is a directed association attribute, the feature propagation is propagated in one direction according to the direction indicated by the association attribute, for example, the association attribute of element 1 and element 2 points from element 1 to element 2, then during the feature propagation, element 1 will send the learning feature of this element to element 2, and element 2 will not send the learning feature of this element to element 1.
For example, the initial learning features of the healthcare business elements and the entry elements are generated from the element information of the elements. For example, the element information of the medical service element includes attribute information of the medical service, and for example, the element information includes at least one of a medical service ID, a medical service category, a medical service time, a medical service name, a medical service portrait, and a medical service unique code. The element information of the entry element includes attribute information of the entry, for example, the element information includes at least one of an entry ID and an entry type.
Taking the example that the element information includes the medical service ID or the entry ID, the initial learning feature of the medical service element and the initial learning feature of the entry element may be generated according to the following calculation formula.
Figure DEST_PATH_IMAGE002
Wherein p isuFor the initial learning feature of the u-th medical service element, PTA transformation matrix for medical services;v U u is a unique hot coding characteristic of medical services. qx is the initial learning characteristic of the xth entry element, QTFor the transformation matrix of the medical service,v U i as entriesThe one-hot coding feature of (1). T is the transpose of the matrix, U is used for representing medical services, and I is used for representing entries.
The one-hot encoding feature is to encode K states with an N-bit state register, each state having a separate register bit, and only one of the register bits being valid. Taking the medical service ID as an example, if there are 10 medical services in total, the unique hot coded feature of the medical service ID is a ten-dimensional vector, the 5 th dimension value of the unique hot coded feature of the 5 th medical service is 1, the other dimension values are 0, the 10 th dimension value of the unique hot coded feature of the 10 th medical service is 1, and the other dimension values are 0. The one-hot coding characteristics of the entry ID are the same.
Because the dimension of the unique hot code feature is usually very high, the data density is sparse, and the calculation is not facilitated, the conversion matrix is adopted for conversion to obtain the initial learning feature, for example, the dimension of the unique hot code feature is ten thousand, if the unique hot code feature is to be converted into 64 dimensions, the size of the conversion matrix is 10000 × 64, and the unique hot code feature is multiplied by the conversion matrix to obtain the 64-dimensional initial learning feature.
Similarly, other element information can also obtain the initial learning characteristic by multiplying the conversion matrix by the one-hot coding characteristic. For example, for the type of the entry, if the type of the entry is divided into 10 categories and the total number of the entries is 1 ten thousand, the unique hot coding feature may be a vector of 10 × 10000, and if the 100 th element belongs to the 3 rd category, the value at the position of (3,100) is 1, and the values at other positions are 0. For example, for one element, there may be a plurality of initial learning features corresponding to a plurality of element information, for example, the initial learning features of the entry element may include an ID initial learning feature and a type initial learning feature.
For example, in the following embodiments, the service learning for the medical service entry jumping network and the service learning for the medical service entry topic network may be introduced separately, and no matter in the medical service entry jumping network or the medical service entry topic network, the initial learning features of the same elements are the same, that is, the 0 th learning feature of the same element in the two networks is the same, and the bottom layer representation thereof is shared.
Step S130, outputting medical response information of the target medical service according to the medical service element of the target medical service and at least two confidences of at least two entry elements.
In this embodiment, the confidence is determined according to the medical service learning features and the entry learning features.
For example, the confidence level is a combination of a medical service element and a term element corresponding to a confidence level. When the intelligent medical cloud computing system needs to determine medical response information of a medical service, the medical service elements are fixed as a target medical service, the term elements comprise all terms on a medical service term network, the confidence of each term (the rest terms except the term having an explanation relationship with the target medical service) in the target medical service and medical service term network is calculated, and the first terms with the highest confidence are used as the medical response information of the target medical service.
For example, after iterative business learning, each element may get multiple learning features. And calculating a confidence coefficient according to the learning characteristics of one medical service element and the learning characteristics of one entry element, wherein the confidence coefficient is used for representing the degree of relevance of the medical service to the entry. For example, a higher confidence level indicates a higher likelihood that the healthcare service will associate the term in the future.
Therefore, to predict the medical response information that may be related to the target medical service, the confidence level of the medical service element of the target medical service and all the entry elements in the medical service entry network that do not have an interpretation relationship with the target medical service is calculated. And then, sequencing the terms which are not accessed by the medical services from high confidence to low confidence, and outputting the first few terms with the highest confidence as medical response information of the target medical services.
For example, in the case of medical response information, instead of calculating the confidence level between the term having an interpretation relationship with the medical service and the medical service, only the confidence level between the term having no interpretation relationship with the medical service and the medical service may be calculated, and then several terms are selected from the terms having no interpretation relationship and are associated with the medical service as medical response information.
In conclusion, the medical service entry network is constructed according to the interpretation relationship and the extended association attribute, and then the correlation relationship between the medical service and the entries is learned based on the medical service entry network, so that the medical information response is more accurately performed for the medical service. The method comprises the steps of performing service learning according to a medical service entry network, iteratively learning the association characteristics between the medical service and the entries, wherein the association characteristics not only comprise the service flow direction relation of the medical service, but also comprise the jump relation between the medical service and the medical service or the theme level attribute relation between the entries and the entries, and outputting the confidence that the medical service may be associated with a certain entry according to the association characteristics, so that the service response precision and reliability of medical service response are improved.
For example, an exemplary embodiment of constructing a medical service entry hop network based on an interpretation relationship and a hop relationship and performing feature propagation based on the medical service entry hop network is provided. Namely, the medical service entry network comprises a medical service entry skip network, and the correlation attributes in the medical service entry skip network are used for the interpretation relation and the skip relation among the connection elements.
In a separate exemplary embodiment, on the basis of the foregoing exemplary embodiment, step S120 further includes step S1201, and step S130 further includes step S1301.
Step S1201, performing service learning based on the medical service entry jump network to obtain medical service jump learning characteristics of the medical service elements and entry jump learning characteristics of the entry elements.
For example, the extension data includes medical service jump data, and the medical service jump data includes jump relationships between medical services; the medical service entry network comprises a medical service entry jump network constructed according to the subject associated data of the medical service entry and the medical service jump data, and two elements with an interpretation relation or a jump relation in the medical service entry jump network are subjected to associated labeling by using associated attributes.
For example, the smart medical cloud computing system uses the association attribute to connect the medical service elements and the entry elements according to the interpretation relationship between the medical service elements and the entry elements, and uses the association attribute to connect two medical service elements with a jump relationship according to the jump relationship between the medical service elements, so as to obtain a medical service entry jump network.
For example, feature propagation includes two processes, feature input and feature acquisition.
First, feature propagation is performed. And calling the element to input the xth jump learning feature of the element to the associated element based on the medical service entry jump network, wherein the associated element is an element labeled by the associated attribute and the element, and the 0 th jump learning feature is an initial learning feature generated according to the element information of the element.
For example, the associated element is an element that inputs a learning feature to the local element, for example, if the associated attribute is a directional associated attribute, and element a points to element B through the directional associated attribute, element a is an associated element of element B, but element B is not an associated element of element a, because element B does not send the learning feature to element a.
Then, feature acquisition is performed. And the calling element obtains the x-th associated jump learning characteristic of the associated element input by the associated element.
For example, in the medical service entry skipping network, for the medical service elements, the association elements include an associated medical service element and an associated entry element, and the xth associated skipping learning feature obtained by the medical service element includes an xth associated medical service skipping learning feature and an xth associated entry skipping learning feature; for the entry element, the association element comprises an association medical service element, and the xth association skip learning feature obtained by the entry element comprises an xth association medical service skip learning feature;
for example, a feature learning update mode is given.
Performing feature learning according to the xth jump learning feature of the element and the xth associated jump learning feature of the associated element to obtain the xth +1 jump learning feature of the element; the medical service elements correspond to the (x +1) th medical service jump learning feature, and the entry elements correspond to the (x +1) th entry jump learning feature.
For example, in response to the element being a medical service association element, determining the sum of the xth jump learning feature, the xth associated medical service jump learning feature and the xth associated entry jump learning feature as the xth +1 jump feature vector; calculating the inner product of the (x +1) th jump feature vector and the (x +1) th jump weight; and calculating the (x +1) th jump sum of the (x +1) th jump vector inner product and the (x +1) th jump service feature, and substituting the (x +1) th jump sum into an activation function to obtain the (x +1) th medical service jump learning feature of the medical service element.
Determining the sum of the xth jump learning feature and the xth associated medical service jump learning feature as an xth +1 jump feature vector in response to the element being a vocabulary entry element; calculating the inner product of the 2(i +1) th jump feature vector of the (x +1) th jump feature vector and the (x +1) th jump weight; and calculating the 2(i +1) th jump sum of the 2(i +1) th jump vector inner product and the x +1 th jump service characteristic, and substituting the 2(i +1) th jump sum into an activation function to obtain the x +1 th entry jump learning characteristic of the entry element.
And repeating the steps to carry out service learning in an iteration mode, so that the Wth medical service skipping learning characteristic of the medical service element in the medical service entry skipping network is obtained, and the Wth entry skipping learning characteristic of the entry element in the medical service entry skipping network is obtained.
For example, the following calculation formula for feature learning in the medical service entry jump network is given according to the method.
Figure DEST_PATH_IMAGE004
Wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE006
the jump learning characteristic of the E +1 medical service of the u-th medical service element, sigma is an activation function, Ws is the jump weight of the u-th medical service element at the E +1 layer,
Figure DEST_PATH_IMAGE008
skipping learning features for the E medical service of the u medical service element,
Figure DEST_PATH_IMAGE010
is the sum of the E term jump learning features of the term element associated with the u term medical service element,
Figure DEST_PATH_IMAGE012
learning features for the E-th entry jump of the entry element associated with the u-th healthcare element,
Figure DEST_PATH_IMAGE014
is the sum of E medical service jump learning characteristics of the medical service element associated with the u medical service element,
Figure DEST_PATH_IMAGE016
and the bs is the skip service characteristic of the u medical service element at the E +1 layer.
Wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE018
is the E +1 entry jump learning characteristic of the ith entry element, sigma is an activation function, Ws is the jump weight of the ith entry element at the E +1 layer,
Figure DEST_PATH_IMAGE020
learning features for the E-th entry jump of the ith entry element,
Figure DEST_PATH_IMAGE022
is the sum of the E medical service jump learning features of the medical service element associated with the i-th entry element,
Figure DEST_PATH_IMAGE024
for the E medical service jump learning characteristic of the medical service element associated with the ith entry element, bs is the jump service of the ith entry element at the E +1 layerAnd (5) characterizing.
Wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE026
jump learning feature for the 0 th medical service of the u-th medical service element, puThe initial learning characteristic of the u-th medical service element.
Figure DEST_PATH_IMAGE028
For the 0 th entry jump learning feature of the ith entry element, qxIs the initial learning feature of the ith entry element.
For example, the E +1 st jump weight Ws of the medical service element is different from the E +1 st jump weight Ws of the entry element, and each element has independent jump weight and jump service characteristics at each layer.
Step S1301, medical response information of the target medical service is output according to the medical service element of the target medical service and at least two jump confidence coefficients of at least two vocabulary entry elements, and the jump confidence coefficients are determined according to the jump learning feature of the medical service and the jump learning feature of the vocabulary entry.
For example, a method of calculating hop confidence is presented.
The intelligent medical cloud computing system sequentially fuses W medical service skipping learning features of medical service elements to obtain medical service skipping fusion features; sequentially fusing the W entry jump learning characteristics of the entry elements to obtain entry jump fusion characteristics; obtaining a skipping confidence coefficient according to a vector inner product of the medical service skipping fusion feature and the entry skipping fusion feature; and determining the entries corresponding to the first K entry elements with the maximum skipping confidence coefficient of the medical service element of the target medical service in the at least two entry elements as the medical response information of the target medical service.
For example, a calculation formula for sequentially fusing W medical service jump learning features of medical service elements is as follows.
Figure DEST_PATH_IMAGE030
Wherein, the first and the second end of the pipe are connected with each other,
Figure DEST_PATH_IMAGE032
for the medical service skip fusion feature of the u-th medical service element,
Figure DEST_PATH_IMAGE034
is the 0 th medical service jump learning characteristic of the u-th medical service element, | | represents fusion,
Figure DEST_PATH_IMAGE036
skipping learning features for the E medical service of the u medical service element.
Wherein, the first and the second end of the pipe are connected with each other,
Figure DEST_PATH_IMAGE038
for the entry jump fusion feature of the ith entry element,
Figure DEST_PATH_IMAGE040
a skip learning feature for the 0 th entry of the ith entry element,
Figure DEST_PATH_IMAGE042
and skipping learning features for the E-th entry of the ith entry element.
For example, the equation for calculating the hop confidence is as follows.
Figure DEST_PATH_IMAGE044
Wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE046
the jump confidence of the u medical service element and the i entry element,
Figure DEST_PATH_IMAGE048
a medical service skip fusion feature for the u-th medical service element,
Figure DEST_PATH_IMAGE050
and the entry jump fusion characteristic of the ith entry element.
In conclusion, the correlation relationship between the medical service and the entry is further learned by adopting a graph convolution method according to the theme related data of the medical service entry and the medical service jump data, so that the medical information response is more accurately performed for the medical service. The medical service entry jump network is constructed by using the subject associated data of the medical service entry and the medical service jump data, the medical service entry jump network not only comprises the data of the medical service historical update entry, but also comprises the jump relation of the medical service, then, the business learning is carried out according to the medical business entry jumping network, the association characteristics between the medical business and the entries are iteratively learned, the related characteristics not only comprise the service flow relation of the medical service, but also comprise the jump relation between the medical service and the medical service, outputting a skipping confidence coefficient that the medical service may be associated with a certain entry according to the association characteristics, and if the skipping confidence coefficient is higher, it indicates that the medical service has a greater relevance to the entry, the more the medical service tends to associate the entry, the entry is associated to the medical service, so that the service response precision and reliability of the medical service response are improved.
For example, an exemplary embodiment is provided for constructing a medical service entry topic network based on an interpretation relationship and a topic hierarchy relationship, and performing feature propagation based on the medical service entry topic network. Namely, the medical service entry network includes a medical service entry topic network, and two elements in the medical service entry topic network, which have an interpretation relationship or a topic hierarchical relationship, are associated and labeled by using an association attribute.
Another exemplary embodiment of the present disclosure is given below. On the basis of the foregoing exemplary embodiment, step S110 further includes step S1101 to step S1104, step S120 further includes step S1202, and step S130 further includes step S1302.
Step S1101, obtaining at least one frequent pattern item of the medical service by using a frequent pattern item mining algorithm, where the frequent pattern item is a frequent pattern item obtained according to entry topic occurrence information of an entry having an interpretation relationship with the medical service.
For example, a network of medical services and associated terms may be constructed according to the interpretation relationship between the medical services and the terms, and the association attribute between elements in the network represents the interpretation relationship between the medical services and the terms. Based on this, the association attribute of the interpretation relation can be weighted according to the topic hierarchy relation of the vocabulary entry.
For example, each entry that is highly popular with medical services typically surrounds several frequent pattern terms. Therefore, according to the terms associated with the medical service in the topic association data of the medical service terms, the frequent pattern term with higher degree of the medical service is found out, and then the topic matching degree of each term and the frequent pattern term is respectively calculated, so that the association degree of the medical service on the terms in the topic matching degree can be obtained, for example, the greater the difference from the frequent pattern term, the smaller the association degree of the medical service on the terms.
Therefore, the smart medical cloud computing system needs to find out the frequent pattern items with high popularity in the medical service from the plurality of entries having the interpretation relation with the medical service. For example, the topic association data of the medical service entries further includes the times or frequency of the medical service update entries, the smart medical cloud computing system sorts the entries according to the times or frequency of the medical service update entries from high to low, then takes the entry with the highest time or frequency as a first frequent pattern item, respectively computes the direct topic matching degrees of other entries and the frequent pattern item, and divides the entries with the direct topic matching degrees larger than the topic matching degree threshold value into the frequent pattern item partitions corresponding to the frequent pattern items. And finding out the entry with the highest frequency or frequency from the entries with the direct subject matching degree larger than the threshold value of the subject matching degree as a second frequent pattern item, respectively calculating the direct subject matching degrees of the remaining entries and the second frequent pattern item, and dividing the entry with the direct subject matching degree larger than the threshold value of the subject matching degree into the frequent pattern item partition corresponding to the second frequent pattern item. Therefore, the process of obtaining the frequent mode items is completed until all the entries are divided into the frequent mode item partition of one frequent mode item, and at least one frequent mode item with high medical service popularity can be obtained by using the method.
For example, the frequent pattern item with a high medical service popularity may be found according to other manners, for example, after the multiple entries are divided into multiple frequent pattern item regions according to the above method, the topic level average value of all entries in the frequent pattern item partition is obtained, and the entry corresponding to the topic level average value is used as the frequent pattern item of the frequent pattern item partition. For another example, the first few terms with the highest frequency degree of the medical service are taken as frequent pattern items, and then other terms are divided into frequent pattern item partitions of the frequent pattern items with the closest topic matching degree.
For example, a frequent pattern item partition refers to a partition interval in which the topic matching degree with the frequent pattern item is greater than a topic matching degree threshold. And the theme matching degree threshold is used for dividing the entries into frequent pattern item partitions corresponding to the frequent pattern items. For example, the topic matching degree thresholds corresponding to different frequent pattern items may be the same or different. For example, an entry having an explanation relationship with the medical service is drawn into an entry distribution network of entries according to subject level information (subject level) of the entries, then the entry having the largest number of medical service visits on the entry distribution network is used as a first frequent pattern item, a frequent pattern item partition of the first frequent pattern item is found by taking the first frequent pattern item as a reference item and taking a subject matching degree threshold as a range, and the entries located in the frequent pattern item partition belong to the frequent pattern item partition of the first frequent pattern item. And then, taking the entry with the highest medical service access frequency in the entries outside the frequent pattern entry partition of the first frequent pattern item as a second frequent pattern item, and finding out the frequent pattern entry partition of the second frequent pattern item by taking the second frequent pattern item as a reference item and taking a topic matching degree threshold as a range, wherein the entries positioned in the frequent pattern entry partition belong to the frequent pattern entry partition of the second frequent pattern item. Therefore, a plurality of frequent pattern items of the medical service can be found out on the entry distribution network according to the subject level information of the entries.
Step S1102, the entries whose topic matching degrees with the frequent pattern items are greater than the threshold are divided into frequent pattern item partitions corresponding to the frequent pattern items.
For example, according to the explanation of step S1101, a plurality of entries corresponding to medical services may be recently divided into frequent pattern item divisions.
For example, when the topic matching degree of one entry topic is smaller than the threshold (topic matching degree threshold), the entry is divided into the frequent pattern item partition of the frequent pattern item with the closest topic matching degree, i.e. the division is performed according to the principle of closeness.
Step S1103, calculating the topic matching degree of the entries in the frequent pattern item partition and the frequent pattern item, and determining the association degree of the medical service to the entries according to the topic matching degree.
And respectively calculating the direct subject matching degree of each entry and the frequent pattern item in the frequent pattern item partition according to the subject level information (subject level) of the entry, and then determining the association degree of the entry according to the direct subject matching degree, so that the entry with longer direct subject matching degree is associated with smaller degree, and the entry with shorter direct subject matching degree is associated with larger degree.
For example, the weight of the entry corresponding to the frequent pattern item in each frequent pattern item region is determined as 1, and the weights of the other entries are 1 divided by the direct topic matching degree, so that the farther the direct topic matching degree is, the smaller the correlation degree is, and the closer the direct topic matching degree is, the larger the correlation degree is.
And step S1104, constructing a medical service entry topic network according to the interpretation relation between the medical service and the entry and the association degree of the medical service to the entry.
The association degree of the terms according to the medical service can be weighted for the interpretation relationship, that is, in the medical service term topic network, the association attributes between the medical service and the terms have weighted values, the weighted values of different association attributes are different, and the weighted values are the association degree of the medical service to the terms.
In the feature learning, the vector of the element may be updated according to the weight value of the associated attribute. For example, when the element obtains a first learning feature of a first related element and a second learning feature of a second related element during feature propagation, where the degree of association between the element and the first related element related attribute is 0.5 and the degree of association between the element and the second related element related attribute is 0.3, the first learning feature and the second learning feature are weighted by the degree of association, and feature learning is performed based on the learning feature of the element, the weighted first learning feature, and the weighted second learning feature. For example, the first learning feature after weighting is 0.5 × first learning feature, and the second learning feature after weighting is 0.3 × second learning feature.
Therefore, the topic hierarchy information of the entry can be introduced into the medical service entry network, and the characteristics of the medical service correlation on the topic hierarchy level can be extracted according to the topic hierarchy information of the entry, so that the medical service response information can be better sent to the medical service.
Step S1202, performing service learning based on the medical service entry topic network to obtain medical service topic learning characteristics of the medical service elements and entry topic learning characteristics of the entry elements.
For example, the extension data includes entry topic data, which at least includes entry topic occurrence information of an entry; the medical service entry network comprises a medical service entry topic network, and the association attribute in the medical service entry topic network is used for connecting the access relation between the medical service element and the entry element and the topic hierarchy relation between the medical service element and the entry element.
For example, feature propagation includes two processes, feature input and feature acquisition.
First, feature propagation is performed. And calling the element to input the xth theme learning feature of the element to the associated element based on the medical service entry theme network, wherein the associated element is an element labeled by the associated attribute and the element, and the 0 th theme learning feature is an initial learning feature generated according to the element information of the element.
Then, feature acquisition is performed. And the calling element obtains the x-th associated topic learning characteristic of the associated element input by the associated element.
For example, in the medical service entry topic network, for a medical service element, the association element includes an association entry element, and the xth association topic learning feature obtained by the medical service element includes an xth association entry topic learning feature; for the entry element, the associated element comprises an associated medical service element, and the xth associated subject learning feature obtained by the entry element comprises an xth associated medical service subject learning feature;
for example, a feature learning update mode is given.
Performing feature learning according to the xth theme learning feature of the element and the xth associated theme learning feature of the associated element to obtain the xth +1 theme learning feature of the element; the medical service elements correspond to the (x +1) th medical service theme learning feature, and the entry elements correspond to the (x +1) th entry theme learning feature.
For example, in response to the element being a medical service association element, calculating a first vector inner product of the association degree of the xth associated entry topic learning feature and the association element, and determining the sum of the xth topic learning feature and the first vector inner product as the xth topic feature vector; calculating the (x +1) th theme feature vector inner product of the (x +1) th theme feature vector and the (x +1) th theme weight; and calculating the x +1 th theme sum of the x +1 th theme vector inner product and the x +1 th theme bias, and substituting the x +1 th theme sum into the activation function to obtain the x +1 th medical service theme learning feature of the medical service element.
In response to the fact that the element is a vocabulary entry element, calculating a second vector inner product of the association degree of the xth associated medical service theme learning feature and the associated element, and determining the sum of the xth theme learning feature and the second vector inner product as an xth +1 theme feature vector; calculating the 2(i +1) th theme vector inner product of the (x +1) th theme feature vector and the (x +1) th theme weight; and calculating the 2(i +1) th theme sum of the 2(i +1) th theme vector inner product and the x +1 th theme offset, and substituting the 2(i +1) th theme sum into the activation function to obtain the x +1 th entry theme learning characteristic of the entry element.
And repeating the steps to carry out service learning in an iteration mode, so that the W-th medical service theme learning characteristic of the medical service element in the medical service vocabulary entry theme network is obtained, and the W-th vocabulary entry theme learning characteristic of the vocabulary entry element in the medical service vocabulary entry theme network is obtained.
For example, a calculation formula for feature learning in a medical service entry topic network is given according to the method.
Figure DEST_PATH_IMAGE052
Wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE054
learning features for the E +1 th medical service theme of the u-th medical service element, wherein sigma is an activation function, Wg is the theme weight of the u-th medical service element at the E +1 th layer,
Figure DEST_PATH_IMAGE056
learning features for the E-th healthcare topic of the u-th healthcare element,
Figure DEST_PATH_IMAGE058
learning a weighted confidence for the E-th entry topic of the entry element associated with the u-th healthcare element,
Figure DEST_PATH_IMAGE060
learning features for the E-th entry topic of the entry element associated with the u-th healthcare element,
Figure DEST_PATH_IMAGE062
and bg is the association degree of the u-th medical service element and the association attribute of the associated entry element, and bg is the theme bias of the u-th medical service element at the E +1 th layer.
Wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE064
learning characteristics of an E +1 th entry theme of the ith entry element, wherein sigma is an activation function, Wg is the theme weight of the ith entry element at the E +1 th layer,
Figure DEST_PATH_IMAGE066
for the ith entryThe E-th entry topic learning feature of an element,
Figure DEST_PATH_IMAGE068
learning a weighted confidence for the E medical business topic of the medical business element associated with the ith entry element,
Figure DEST_PATH_IMAGE070
learning features for an E-th healthcare topic of a healthcare element associated with an i-th entry element,
Figure DEST_PATH_IMAGE072
and bg is the association degree of the ith entry element and the association attribute of the associated medical service element, and bg is the theme bias of the ith entry element at the E +1 layer.
Wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE074
and (4) learning characteristics of the 0 th medical service theme of the u-th medical service element, and pu is the initial learning characteristics of the u-th medical service element.
Figure DEST_PATH_IMAGE076
Learning characteristics of the 0 th entry theme of the ith entry element, and qx is initial learning characteristics of the ith entry element.
For example, the E +1 th topic weight Wg of a healthcare business element is a different weight than the E +1 th topic weight Wg of a term element, each element having a unique topic weight and topic bias at each level.
Step S1302, outputting medical response information of the target medical service according to the medical service element of the target medical service and at least two subject confidence degrees of at least two entry elements, wherein the subject confidence degree is determined according to the medical service subject learning feature and the entry subject learning feature.
For example, a method of calculating hop confidence is presented.
The intelligent medical cloud computing system sequentially fuses the W medical service theme learning features of the medical service elements to obtain medical service theme fusion features; sequentially fusing the W entry topic learning characteristics of the entry elements to obtain entry topic fusion characteristics; obtaining a topic confidence coefficient according to the vector inner product of the medical service topic fusion characteristics and the entry topic fusion characteristics; and determining the entries corresponding to the first K entry elements with the maximum topic confidence of the medical service element of the target medical service in the at least two entry elements as the medical response information of the target medical service.
For example, a calculation formula for sequentially fusing W medical business topic learning features of medical business elements is as follows.
Figure DEST_PATH_IMAGE078
Wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE080
the medical service theme fusion feature of the u-th medical service element,
Figure DEST_PATH_IMAGE082
learning features for the 0 th medical service theme of the u-th medical service element, | | represents fusion,
Figure DEST_PATH_IMAGE084
and learning the characteristics for the E medical service theme of the u medical service element.
Wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE086
for the entry topic fusion feature of the ith entry element,
Figure DEST_PATH_IMAGE088
learning features for the 0 th entry topic for the ith entry element,
Figure DEST_PATH_IMAGE090
learning features for the E-th entry topic of the ith entry element.
For example, the formula for calculating the confidence of a topic is as follows.
Figure DEST_PATH_IMAGE092
Wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE094
the subject confidence of the u medical service element and the i entry element,
Figure DEST_PATH_IMAGE096
the fusion characteristics of the medical service theme of the u-th medical service element,
Figure DEST_PATH_IMAGE098
and the entry topic fusion characteristics of the ith entry element.
In summary, the correlation relationship between the medical service and the entry is further learned by adopting a graph convolution method according to the topic associated data and the entry topic data of the medical service entry, so that the medical information response is more accurately performed for the medical service. The medical service entry topic network is constructed by using topic associated data and entry topic data of medical service entries, the medical service entry topic network not only contains data of medical service history updating entries, but also contains topic hierarchical relations of the entries and frequent pattern items of the medical service, then service learning is carried out according to the medical service entry topic network, association characteristics between the medical service and the entries are iteratively learned, the association characteristics not only contain service flow direction relations of the medical service, but also contain topic hierarchical relations of the entries and frequent pattern items, a topic confidence coefficient that the medical service may be associated with one entry is output according to the association characteristics, if the topic confidence coefficient is high, the relevance of the medical service to the entry is large, the medical service tends to associate the entry, the entry is associated with the medical service, the service response precision and reliability of medical service response are improved.
For example, an exemplary embodiment is provided for constructing a medical service term skip network and a medical service term topic network based on an interpretation relationship, a skip relationship and a topic hierarchy relationship, and performing feature propagation based on the medical service term skip network and the medical service term topic network. Namely, the medical service entry network comprises a medical service entry skip network and a medical service entry topic network; two elements with interpretation relation or jump relation in the medical service entry jump network are associated and labeled by using the association attributes; and performing association labeling on two elements with interpretation relation or topic hierarchical relation in the medical service entry topic network by using the association attribute.
Another exemplary embodiment of the present disclosure is given below. For example, step S120 further includes step S1201 to step S1202, and step S130 further includes step S1303 to step S1306.
Step S1201, performing service learning based on the medical service entry jump network to obtain medical service jump learning characteristics of the medical service elements and entry jump learning characteristics of the entry elements.
For example, the explanation of step S1201 may refer to the aforementioned step S1201.
Step S1202, performing service learning based on the medical service entry topic network to obtain medical service topic learning characteristics of the medical service elements and entry topic learning characteristics of the entry elements.
For example, the explanation of step S1202 may refer to the aforementioned step S1202.
For example, in this embodiment, the sequence executed in step S1201 and step S1202 is not limited, and the two steps may be executed simultaneously; the two steps can also be executed in sequence, and the sequence can also be any.
Step S1303, sequentially fusing the W medical service skipping learning features of the medical service elements to obtain a medical service skipping fusion feature; sequentially fusing W entry skipping learning characteristics of the entry elements to obtain entry skipping fusion characteristics; and obtaining the skipping confidence coefficient according to the vector inner product of the medical service skipping fusion characteristic and the entry skipping fusion characteristic.
Step S1304, sequentially fusing the W medical service theme learning features of the medical service elements to obtain medical service theme fusion features; sequentially fusing the W entry topic learning characteristics of the entry elements to obtain entry topic fusion characteristics; and obtaining the topic confidence coefficient according to the vector inner product of the medical service topic fusion feature and the entry topic fusion feature.
For example, business learning can be performed in a medical business entry jumping network and a medical business entry topic network, respectively. That is, the same medical service element has different learning features in the two networks (the 0 th learning feature is the same, and the other learning features are different). Therefore, the skipping confidence coefficient and the topic confidence coefficient can be respectively calculated according to the learning features in two different networks, and then the final confidence coefficient is obtained according to the skipping confidence coefficient and the topic confidence coefficient.
Step S1305, determining the weighted confidence of the jump and the confidence of the topic as the confidence.
For example, the calculation formula of the confidence is as follows.
Figure DEST_PATH_IMAGE100
Wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE102
the confidence of the u medical service element and the i entry element, thetag is the weight of the subject confidence,
Figure DEST_PATH_IMAGE104
is the topic prediction weight, thetas is the weight of the hop confidence,
Figure DEST_PATH_IMAGE106
is the hop confidence.
Step S1306, determining, as the medical response information of the target medical service, a vocabulary entry corresponding to the first K vocabulary entry elements with the maximum confidence of the medical service element of the target medical service, from among the at least two vocabulary entry elements.
The intelligent medical cloud computing system sequences the entries corresponding to the entry elements according to the entry elements which are not associated with the target medical services and a plurality of confidence degrees between the entry elements of the target medical services, and outputs the first entries with the highest confidence degrees as medical response information of the target medical services.
In summary, the medical service entry jump network is constructed according to the interpretation relationship between the medical service and the entry and the jump relationship between the medical service and the medical service, the jump relationship between the medical service is introduced into the medical service entry network, and the medical service correlation is extracted according to the interpretation relationship and the jump relationship. The medical service entry topic network is constructed according to the interpretation relation between the medical service and the entries and the topic hierarchy relation among the entries, the topic hierarchy information of the entries is introduced into the medical service entry network, and the medical service correlation is extracted according to the interpretation relation and the topic hierarchy of the entries. Then, the confidence coefficient is calculated according to the skipping confidence coefficient of the skipping perception level and the theme confidence coefficient of the theme perception level, which are extracted and determined by the features of the two networks, wherein the confidence coefficient comprises perception features on the skipping level, the theme level and the explanation level, and the relevance vocabulary entry of the medical service is predicted according to the three levels, so that the accuracy of medical response information can be improved. Moreover, when the medical service has fewer interpretation relations, the other two relations can be used for carrying out intelligent medical response on the medical service, the correlation acquisition capacity of the model on the cold-start medical service is improved, and the medical response information of the medical service is better.
For example, the method provided by the present disclosure is performed by a smart medical response model on a smart medical cloud computing system, and the present embodiment provides a training method for the smart medical response model.
Another exemplary embodiment of the present disclosure is given below, for example, on the basis of the foregoing exemplary embodiment, step S130 is followed by step S140 to step S170.
Step S140, a first annotation object confidence of the first annotation object is obtained, where the first annotation object includes the sample medical service and the first annotation object entry having the interpretation relationship, and the first annotation object confidence is a confidence of the sample medical service and the first annotation object entry.
Step S150, a second annotation object confidence of a second annotation object is obtained, the second annotation object comprises a sample medical service without an interpretation relation and a second annotation object entry, and the second annotation object confidence is the confidence of the sample medical service and the second annotation object entry.
For example, after a network of medical business terms is constructed and business learning is performed on elements on the network, the smart medical response model may be trained based on the confidence level output by the smart medical response model.
For example, based on the method of partial order learning, the medical service and the vocabulary entry having an interpretation relationship are used as a first labeled object, the medical service and the vocabulary entry not having an interpretation relationship are used as a second labeled object, a loss function is used to calculate a difference parameter value between the first labeled object and the second labeled object, and a model parameter is adjusted according to the difference parameter value, so that the confidence coefficient theme matching degree of the first labeled object and the second labeled object is increased, and the larger the confidence coefficient difference value between the first labeled object and the second labeled object is, the stronger the distinguishing capability of the intelligent medical response model for the first labeled object and the second labeled object is.
For example, taking an example of calculating a difference parameter value by using a first annotation object and a second annotation object, the smart medical cloud computing system selects a sample medical service from a plurality of medical services, and then obtains a first annotation object and a second annotation object of the sample medical service, that is, obtains a confidence of the sample medical service and an entry having an interpretation relationship, and obtains a confidence of the sample medical service and an entry having no interpretation relationship, and then calls a loss function to calculate a difference parameter value of the two confidences. For example, a set of first annotation object and second annotation object must correspond to the same medical service, i.e. the triple data forming < medical service, first annotation object, second annotation object >, and the difference parameter value is calculated according to the confidence of the first annotation object and the second annotation object in the triple data.
For example, for a medical service, the number of terms having an interpretation relationship is small, and the number of terms having no interpretation relationship is large, so for each first annotation object, a part of the terms having no interpretation relationship is randomly extracted from a plurality of terms having no interpretation relationship according to a certain proportion as a second annotation object to form triple data, and each group of triple data is substituted into a loss function to calculate a difference parameter value.
Step S160, a loss function is called to calculate a difference parameter value between the confidence of the first annotation object and the confidence of the second annotation object.
For example, a loss function is given:
Figure DEST_PATH_IMAGE108
wherein, Loss is a difference parameter value, O represents the constructed ternary group data, u is the u-th medical service element, x is the first label object (the x-th vocabulary entry element), j is the second label object (the j-th vocabulary entry element), and sigma is an activation function,
Figure DEST_PATH_IMAGE110
the confidence degrees of the first labeling object of the u medical service element and the i entry element,
Figure DEST_PATH_IMAGE112
and the confidence degrees of the second labeling object of the u medical service element and the j entry element.
Step S170, training the intelligent medical response model according to the difference parameter values.
For example, the smart medical cloud computing system adjusts the smart medical response model according to the discrepancy parameter value, so as to minimize the discrepancy parameter value of the confidence coefficient output by the adjusted smart medical response model. For example, the smart medical cloud computing system may minimize the discrepancy parameter value of the confidence of the output by adjusting each jump weight, jump service feature, topic weight, and topic bias for each element.
In summary, in the method provided in this embodiment, the intelligent medical response model is trained by using a method based on partial order learning, so that a difference between the confidence level output by the model to the first labeled object and the confidence level output by the model to the second labeled object is as large as possible, thereby improving the distinguishing capability of the model to the first labeled object and the second labeled object, and improving the capability of accurately recommending the model.
For example, the present embodiment further provides a smart medical response model, which mainly includes a four-layer structure.
A first layer: and the data input layer is used for inputting medical service skipping network data (medical service skipping data), medical service entry access history (topic associated data of medical service entries) and entry topic attribute data (entry topic data).
A second layer: and the construction layer is used for constructing the medical service entry network. The medical service entry skipping network data are used for constructing a medical service entry skipping map, a medical service entry explaining network is constructed according to medical service entry access history, and a medical service entry topic network is constructed according to entry topic attribute data, wherein the medical service entry skipping map and the medical service entry explaining network are added to form the medical service entry skipping network mentioned in the embodiment, and the medical service entry explaining network and the medical service entry topic network are added to form the medical service entry topic network mentioned in the embodiment. For example, the three networks may be the same network, that is, an interpretation relationship, a jump relationship, and a topic hierarchy relationship are simultaneously constructed on one network, and only when feature propagation is performed, jump propagation is performed based on the interpretation relationship and the jump relationship in the network, and topic preference propagation is performed based on the interpretation relationship and the topic hierarchy relationship in the network, that is, two propagation layers are constructed according to different attribute relationships and are respectively used for propagating the jump relationship and the topic hierarchy relationship.
And a third layer: a feature propagation layer based on graph structure. The intelligent medical response model provided by the embodiment is constructed with two feature propagation layers: a hop-aware spontaneous relevance feature propagation layer and a topic preference feature propagation layer. The jump-perception spontaneous correlation characteristic propagation layer is a characteristic propagation layer constructed based on a medical service entry jump network, and the theme preference characteristic propagation layer is a characteristic propagation layer constructed based on a medical service entry theme network. The two propagation layers share the initial learning features of the bottom layer, that is, in the two propagation layers, the medical service initial learning features of the medical service elements and the entry initial learning features of the entry elements are the same.
A fourth layer: a joint prediction layer. And calculating to obtain a jump confidence coefficient according to the entry jump perception feature (entry jump learning feature) and the medical service jump perception feature (medical service jump learning feature) in the joint prediction layer, calculating to obtain a topic confidence coefficient according to the entry topic perception feature (entry topic learning feature) and the medical service topic perception feature (medical service topic learning feature), and calculating to obtain a joint scoring output (confidence coefficient) according to the jump confidence coefficient and the topic confidence coefficient. Outputting the medical response information according to the confidence level.
For example, the present embodiment also provides an exemplary embodiment of applying the intelligent medical response method provided by the present disclosure in practice.
The intelligent medical cloud computing system acquires entry attribute data of entries to construct entry topic data, acquires access records generated by medical service updated entries to construct topic associated data of medical service entries, and acquires jump network relation data of medical services to construct medical service jump data. And inputting the subject data of the entries, the subject associated data of the medical service entries and the medical service jump data into the intelligent medical response model, sequencing the entries which are not updated according to the confidence coefficient from high to low to obtain a sequencing result, and returning the sequencing result to the medical service (terminal).
For example, the intelligent medical response method provided by the present disclosure may be used in an online consultation service of intelligent medical, and provide response information for medical business, which may be responded to the user input information, for the medical business, so that the term mentioned in the above method embodiment is a response result. The smart medical cloud computing system 100 stores the recorded data of the medical service in each response result, and the interpretation relationship between the medical service and the response result can be determined according to the data. The medical business can also be used as business service by introducing other medical businesses, adding other medical businesses and the like in the application program, and the intelligent medical cloud computing system 100 can determine the jump relationship among the medical businesses according to the data. The smart medical cloud computing system 100 may further store topic hierarchy information of each response result, may obtain a frequent pattern item with a high medical service popularity according to historical data of medical service access response results, and may obtain an association degree, i.e., a topic hierarchy relationship, of the medical service to the entry according to a topic hierarchy of each response result and a topic matching degree of the frequent pattern item. Based on the data, the smart medical cloud computing system 100 can construct a medical service entry skip network and a medical service entry topic network according to medical service data and response result data of response results of a large number of medical services stored in the database, and then can issue a response result with relevance for the medical services based on the smart medical response method.
Fig. 3 is a schematic diagram of functional modules of a big data based smart medical response device 300 according to an embodiment of the disclosure, and the functions of the functional modules of the big data based smart medical response device 300 are described in detail below.
The construction module 310 is configured to construct a medical service entry network, where the medical service entry network includes elements and association attributes, the elements include medical service elements corresponding to medical services and entry elements corresponding to entries, the two elements having an interpretation relationship or an extended association attribute use the association attributes to perform association tagging, the interpretation relationship includes an attribute relationship between the medical service elements and the entry elements that have initiated an interpretation procedure, and the extended association attribute includes at least one of a jump relationship between the medical service elements and a topic hierarchy relationship between the entry elements.
The learning module 320 is configured to perform service learning based on the medical service entry network to obtain medical service learning features of the medical service elements and entry learning features of the entry elements, where the service learning is configured to perform deep learning according to association attributes between the elements and extract network contact features in the medical service entry network.
The output module 330 is configured to output medical response information of the target medical service according to at least two confidences of the medical service element of the target medical service and the at least two entry elements, where the confidences are determined according to the medical service learning feature and the entry learning feature.
Fig. 4 illustrates a hardware structure of the smart medical cloud computing system 100 for implementing the big-data based smart medical response method according to the embodiment of the present disclosure, and as shown in fig. 4, the smart medical cloud computing system 100 may include a processor 110, a machine-readable storage medium 120, a bus 130, and a transceiver 140.
In a specific implementation process, the at least one processor 110 executes the smart medical cloud computing system stored in the machine-readable storage medium 120 to execute instructions, so that the processor 110 may execute the smart medical response method based on big data according to the above method embodiment, the processor 110, the machine-readable storage medium 120, and the transceiver 140 are connected through the bus 130, and the processor 110 may be configured to control the transceiving action of the transceiver 140, so as to perform data transceiving with the smart medical mobile terminal 200.
For a specific implementation process of the processor 110, reference may be made to the above-mentioned various method embodiments executed by the smart medical cloud computing system 100, which implement similar principles and technical effects, and the detailed description of the embodiments is omitted here.
In addition, the readable storage medium is preset with a smart medical cloud computing system execution instruction, and when the processor executes the smart medical cloud computing system execution instruction, the smart medical response method based on big data is realized.
Finally, it should be understood that the examples in this specification are only intended to illustrate the principles of the examples in this specification. Other variations are also possible within the scope of this description. Accordingly, by way of example, and not limitation, alternative configurations of the embodiments of the specification can be seen as matching the teachings of the specification. Accordingly, the embodiments of the present description are not limited to only those embodiments explicitly described and depicted herein.

Claims (10)

1. A smart medical response method based on big data is applied to a smart medical cloud computing system, the smart medical cloud computing system is in communication connection with a plurality of smart medical mobile terminals, and the method comprises the following steps:
constructing a medical service entry network, wherein the medical service entry network comprises elements and association attributes, the elements comprise medical service elements corresponding to medical services and entry elements corresponding to entries, the two elements with interpretation relations or extended association attributes are subjected to association labeling by using the association attributes, the interpretation relations comprise attribute relations between the medical service elements which have initiated an interpretation process and the entry elements, the extended association attributes comprise at least one of jump relations between the medical service elements and topic hierarchy relations between the entry elements, and the medical service entry network is constructed based on big data generated by historical medical services;
performing service learning based on the medical service entry network to obtain medical service learning characteristics of medical service elements and entry learning characteristics of the entry elements, wherein the service learning is used for performing deep learning according to the association attributes among the elements and extracting network contact characteristics in the medical service entry network;
and outputting medical response information of the target medical service according to at least two confidence degrees of medical service elements of the target medical service and at least two entry elements, wherein the confidence degrees are determined according to the medical service learning characteristics and the entry learning characteristics.
2. The intelligent medical response method based on big data as claimed in claim 1, wherein the medical service entry network comprises a medical service entry jump network, and two elements in the interpretation relationship or the jump relationship are associated and labeled by using the association attribute;
the medical service learning characteristics of the medical service elements and the entry learning characteristics of the entry elements are obtained by performing service learning based on the medical service entry network, and the method comprises the following steps:
performing the service learning based on the medical service entry jump network to obtain medical service jump learning characteristics of medical service elements and entry jump learning characteristics of the entry elements;
the outputting the medical response information of the target medical service according to the medical service element of the target medical service and at least two confidences of at least two entry elements comprises:
and outputting medical response information of the target medical service according to the medical service element of the target medical service and at least two jump confidence coefficients of at least two vocabulary entry elements, wherein the jump confidence coefficients are determined according to the jump learning characteristics of the medical service and the jump learning characteristics of the vocabulary entries.
3. The intelligent medical response method based on big data as claimed in claim 2, wherein the step of performing the business learning based on the medical business term skip network to obtain the medical business skip learning feature of the medical business element and the term skip learning feature of the term element comprises:
calling the element to input the xth jump learning feature of the element to an associated element based on the medical service entry jump network, wherein the associated element is an element labeled by the associated attribute and the element, and the 0 th jump learning feature is an initial learning feature generated according to element information of the element;
calling the element to obtain the xth associated jump learning characteristic of the associated element input by the associated element;
performing feature learning according to the xth jump learning feature of the element and the xth associated jump learning feature of the associated element to obtain the xth +1 jump learning feature of the element;
the medical service elements correspond to the (x +1) th medical service jump learning feature, and the entry elements correspond to the (x +1) th entry jump learning feature;
and repeating the steps to carry out the service learning in an iterative manner, so as to obtain the Wth medical service skip learning characteristic of the medical service element in the medical service vocabulary entry skip network and obtain the Wth vocabulary entry skip learning characteristic of the vocabulary entry element in the medical service vocabulary entry skip network.
4. The intelligent medical response method based on big data as claimed in claim 3, wherein the outputting the medical response information of the target medical service according to at least two skip confidences of the medical service element of the target medical service and at least two entry elements comprises:
sequentially fusing the W medical service skipping learning features of the medical service elements to obtain medical service skipping fusion features;
sequentially fusing the W entry jump learning features of the entry elements to obtain entry jump fusion features;
obtaining a skipping confidence coefficient according to the vector inner product of the medical service skipping fusion feature and the entry skipping fusion feature;
and determining the entries corresponding to the first K entry elements with the maximum skipping confidence coefficient of the medical service element of the target medical service in the at least two entry elements as the medical response information of the target medical service.
5. The intelligent medical response method based on big data according to any one of claims 2-4, wherein the constructing of the medical service entry network comprises:
and connecting the medical service elements and the entry elements by using the association attributes according to the interpretation relationship between the medical service elements and the entry elements, and connecting two medical service elements with the jump relationship by using the association attributes according to the jump relationship between the medical service elements to obtain a medical service entry jump network.
6. The intelligent medical response method based on big data as claimed in any one of claims 1-4, wherein the medical service entry network comprises a medical service entry topic network, and two elements with the interpretation relationship or the topic hierarchy relationship are associated and labeled by using the association attribute;
the construction of the medical service entry network comprises the following steps:
acquiring at least one frequent pattern item of the medical service by using a frequent pattern item mining algorithm, wherein the frequent pattern item is obtained according to entry topic occurrence information of an entry having an interpretation relation with the medical service;
dividing the entries of which the theme matching degrees with the frequent pattern items are larger than a threshold value into frequent pattern item partitions corresponding to the frequent pattern items;
calculating the subject matching degree of the entries in the frequent pattern item partition and the frequent pattern item, and determining the association degree of the medical service to the entries according to the subject matching degree;
and constructing the medical service entry topic network according to the interpretation relation between the medical service and the entry and the association degree of the medical service to the entry.
7. The intelligent medical response method based on big data as claimed in claim 6, wherein said performing the business learning based on the medical business term network to obtain the medical business learning features of the medical business elements and the term learning features of the term elements comprises:
performing the service learning based on the medical service entry topic network to obtain medical service topic learning characteristics of medical service elements and entry topic learning characteristics of the entry elements;
the outputting the medical response information of the target medical service according to the medical service element of the target medical service and at least two confidences of at least two entry elements comprises:
outputting medical response information of the target medical service according to medical service elements of the target medical service and at least two subject confidence degrees of at least two entry elements, wherein the subject confidence degree is determined according to the medical service subject learning features and the entry subject learning features;
the step of performing the service learning based on the medical service entry topic network to obtain the medical service topic learning characteristics of the medical service elements and the entry topic learning characteristics of the entry elements includes:
calling the element to input the xth theme learning feature of the element to an associated element based on the medical service entry theme network, wherein the associated element is an element labeled with the element through the associated attribute, and the 0 th theme learning feature is an initial learning feature generated according to element information of the element;
calling the element to obtain the xth associated topic learning characteristic of the associated element input by the associated element;
performing feature learning according to the xth theme learning feature of the element and the xth associated theme learning feature of the associated element to obtain the xth +1 theme learning feature of the element; the medical service elements correspond to the (x +1) th medical service theme learning features, and the entry elements correspond to the (x +1) th entry theme learning features;
and repeating the steps to carry out the service learning in an iteration mode to obtain the W-th medical service theme learning characteristic of the medical service element in the medical service vocabulary entry theme network and obtain the W-th vocabulary entry theme learning characteristic of the vocabulary entry element in the medical service vocabulary entry theme network.
8. The intelligent medical response method based on big data as claimed in claim 7, wherein the outputting the medical response information of the target medical service according to at least two confidences of the medical service element of the target medical service and at least two of the term elements comprises:
sequentially fusing the W medical service theme learning features of the medical service elements to obtain medical service theme fusion features;
sequentially fusing the W entry topic learning features of the entry elements to obtain entry topic fusion features;
obtaining a topic confidence coefficient according to the vector inner product of the medical service topic fusion feature and the entry topic fusion feature;
and determining the entries corresponding to the first K entry elements with the maximum subject confidence of the medical service element of the target medical service in the at least two entry elements as the medical response information of the target medical service.
9. The intelligent medical response method based on big data according to any of claims 1-4, wherein the medical service term network comprises a medical service term skip network and a medical service term subject network;
the two elements with the explanation relationship or the jump relationship in the medical service entry jump network are subjected to associated labeling by using the associated attributes, and the two elements with the explanation relationship or the topic hierarchy relationship in the medical service entry topic network are subjected to associated labeling by using the associated attributes;
the medical service learning characteristics of the medical service elements and the entry learning characteristics of the entry elements are obtained by performing service learning based on the medical service entry network, and the method comprises the following steps:
performing the service learning based on the medical service entry jump network to obtain medical service jump learning characteristics of medical service elements and entry jump learning characteristics of the entry elements;
performing the service learning based on the medical service entry topic network to obtain medical service topic learning characteristics of medical service elements and entry topic learning characteristics of the entry elements;
the outputting the medical response information of the target medical service according to the medical service element of the target medical service and at least two confidences of at least two entry elements comprises:
sequentially fusing the W medical service skipping learning features of the medical service elements to obtain medical service skipping fusion features;
sequentially fusing the W entry skipping learning features of the entry elements to obtain entry skipping fusion features, and obtaining skipping confidence degrees according to the vector inner product of the medical service skipping fusion features and the entry skipping fusion features;
sequentially fusing the W medical service theme learning features of the medical service elements to obtain medical service theme fusion features;
sequentially fusing the W entry theme learning characteristics of the entry elements to obtain entry theme fusion characteristics, and obtaining a theme confidence coefficient according to the vector inner product of the medical service theme fusion characteristics and the entry theme fusion characteristics;
and determining the weighted confidence of the jump confidence and the topic confidence as the confidence, and determining the entry corresponding to the first K entry elements with the maximum confidence of the medical service elements of the target medical service in the at least two entry elements as the medical response information of the target medical service.
10. A smart medical cloud computing system, comprising a processor, a machine-readable storage medium, and a network interface, wherein the machine-readable storage medium, the network interface, and the processor are associated with each other through a bus system, the network interface is configured to be communicatively connected to at least one smart medical mobile terminal, the machine-readable storage medium is configured to store smart medical cloud computing system instructions, and the processor is configured to execute the smart medical cloud computing system instructions in the machine-readable storage medium to perform the big data based smart medical response method according to any one of claims 1 to 9.
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