CN112309524A

CN112309524A - Intelligent medical information pushing method and system based on artificial intelligence and cloud platform

Info

Publication number: CN112309524A
Application number: CN202011221644.5A
Authority: CN
Inventors: 吴九云
Original assignee: Individual
Current assignee: Individual
Priority date: 2020-04-26
Filing date: 2020-04-26
Publication date: 2021-02-02
Also published as: CN112309523A; CN111540425A; CN111540425B

Abstract

The embodiment of the disclosure provides an intelligent medical information pushing method, system and cloud platform based on artificial intelligence, wherein first electronic medical record information of a first electronic medical record user in a target visit semantic interval is obtained, and after a medical record pushing reference unit of the first electronic medical record information is determined, second electronic medical record information which is in an association relation with the medical record pushing reference unit in the target visit semantic interval is determined, so that a pushed knowledge point thermodynamic diagram is generated according to the first electronic medical record information and the second electronic medical record information, and then when the fact that the electronic medical record information of the first electronic medical record user is updated is detected, the pushed knowledge point thermodynamic diagram is updated correspondingly, and therefore knowledge point pushing is carried out. So, can carry out the relevant knowledge point propelling movement of wisdom medical treatment with electronic medical record information matching to each patient of being convenient for carries out effectual knowledge point study and promotes the consciousness of seeing a doctor and the daily consciousness of paying close attention to of self.

Description

Intelligent medical information pushing method and system based on artificial intelligence and cloud platform

Technical Field

The disclosure relates to the technical field of intelligent medical treatment and artificial intelligence, in particular to an intelligent medical treatment information pushing method and system based on artificial intelligence and a cloud platform.

Background

The wisdom medical treatment utilizes advanced internet of things technology through making the regional medical information platform of healthy archives, can realize the interdynamic between patient and medical staff, medical institution, the wisdom medical service terminal, reaches electronic information ization and intellectuality gradually.

The current patient medical records are stored in an intelligent medical service platform in an electronic medical record form and are uploaded in real time through an intelligent medical service terminal, however, in the traditional scheme, the pushing of knowledge points related to intelligent medical treatment matched with electronic medical record information is lacked, so that the effective learning of the knowledge points of each patient is influenced, and the self diagnosis consciousness and the daily attention consciousness are improved.

Disclosure of Invention

In order to overcome at least the above-mentioned deficiencies in the prior art, the present disclosure aims to provide a smart medical information pushing method, system and cloud platform based on artificial intelligence, which can push knowledge points related to smart medical treatment matched with electronic medical record information, thereby facilitating effective knowledge point learning of each patient and improving self-diagnosis consciousness and daily attention consciousness.

In a first aspect, the present disclosure provides an intelligent medical information pushing method based on artificial intelligence, which is applied to an electronic medical record cloud platform in communication connection with a plurality of intelligent medical service terminals, and the method includes:

acquiring first electronic medical record information of a first electronic medical record user in a target visit semantic interval, performing artificial intelligence analysis on electronic medical record data of each corresponding electronic medical record unit in an electronic medical record database of an associated patient user according to each electronic medical record uploading node associated with the first electronic medical record information to obtain medical record matching data matched with each electronic medical record uploading node associated with the first electronic medical record information, and determining a medical record pushing reference unit of the first electronic medical record information according to the medical record matching data of each electronic medical record uploading node associated with the first electronic medical record information;

determining second electronic medical record information corresponding to a second electronic medical record user having an association relation with the medical record push reference unit in the target visit semantic interval, and establishing an association relation between the second electronic medical record user corresponding to the second electronic medical record information and the first electronic medical record user;

generating a knowledge point pushing thermodynamic diagram according to the first electronic medical record information and the second electronic medical record information, and storing the knowledge point pushing thermodynamic diagram into a preset knowledge point pushing queue;

when it is detected that the first electronic medical record user has electronic medical record information updating, determining second updating data of each second electronic medical record user according to first updating data of the first electronic medical record user and a correlation object of second electronic medical record information corresponding to each second electronic medical record user and the medical record push reference unit;

and updating the first updating data and the second updating data into a knowledge point pushing thermodynamic diagram in the knowledge point pushing queue according to the associated object, and pushing knowledge points to the intelligent medical service terminals of the first electronic medical record user and each second electronic medical record user according to the knowledge point pushing thermodynamic diagram in the knowledge point pushing queue.

In a possible implementation manner of the first aspect, the step of performing artificial intelligence analysis on the electronic medical record data of each corresponding electronic medical record unit in the electronic medical record database of the associated patient user according to each electronic medical record uploading node associated with the first electronic medical record information to obtain medical record matching data matched with each electronic medical record uploading node associated with the first electronic medical record information includes:

acquiring a first medical record structured label of electronic medical record data of an electronic medical record unit aiming at the electronic medical record data of each corresponding electronic medical record unit in an electronic medical record database of an associated patient user, wherein the first medical record structured label is used for representing a diagnosis behavior label and a diagnosis process label of the electronic medical record diagnosis process of the electronic medical record unit;

respectively carrying out artificial intelligent identification on the first medical record structured label according to each electronic medical record uploading node associated with the electronic medical record information to obtain a first visit behavior label classification vector sequence and a visit process label classification vector sequence corresponding to the first visit behavior label classification vector sequence;

acquiring a first diagnosis item vector sequence and diagnosis item associated information of electronic medical record data of the electronic medical record unit, and extracting a content vector unit of the first diagnosis item vector sequence, wherein the content vector unit of the first diagnosis item vector sequence comprises a set format vector unit;

acquiring a set format vector unit of historical electronic medical record data associated with the associated patient user, and adjusting the set format vector unit of the first diagnostic item vector sequence according to the set format vector unit to enable the matching relationship between the set format vector units in the first diagnostic item vector sequence to be matched with the matching relationship between the set format vector units in the preset historical electronic medical record data;

after the adjustment is finished, obtaining a content vector unit of a second diagnosis project vector sequence, and generating the second diagnosis project vector sequence according to the content vector unit of the second diagnosis project vector sequence;

according to the diagnosis item correlation information and the content vector unit of the second diagnosis item vector sequence, searching to obtain a diagnosis process labeling classification vector sequence matched with the diagnosis item correlation information and a first diagnosis behavior labeling classification vector sequence corresponding to the diagnosis process labeling classification vector sequence, and according to the content vector unit of the second diagnosis item vector sequence, adjusting the first diagnosis behavior labeling classification vector sequence corresponding to the diagnosis process labeling classification vector sequence to obtain a second diagnosis behavior labeling classification vector sequence;

merging the second visit behavior labeling classification vector sequence and the second diagnosis item vector sequence to obtain a merged vector sequence of which the electronic medical record data of the electronic medical record unit is matched with each electronic medical record uploading node associated with the electronic medical record information;

and carrying out artificial intelligence analysis on the merged vector sequence of the electronic medical record unit to obtain an artificial intelligence analysis result of the electronic medical record unit.

In a possible implementation manner of the first aspect, the step of performing artificial intelligence analysis on the merged vector sequence of the electronic medical record unit to obtain an artificial intelligence analysis result of the electronic medical record unit includes:

extracting corresponding examination item index vectors and examination item associated disease range vectors from the merged vector sequence of the electronic medical record unit;

performing regression analysis on the inspection item index vector and the inspection item associated disease range vector respectively to obtain corresponding first regression analysis vector information and second regression analysis vector information;

determining first to-be-determined regression analysis vector information corresponding to first last regression analysis vector information corresponding to the first to-be-determined regression analysis vector information in the first regression analysis vector information; wherein, the first corresponding last regression analysis vector information is the last regression analysis vector information corresponding to the previous check item index vector;

determining second undetermined regression analysis vector information corresponding to second corresponding last regression analysis vector information in the second regression analysis vector information; wherein the second corresponding last regression analysis vector information is the corresponding last regression analysis vector information corresponding to the previous examination item associated disease range vector;

and carrying out artificial intelligence analysis according to the first undetermined regression analysis vector information and the second undetermined regression analysis vector information to obtain an artificial intelligence analysis result of the electronic medical record unit.

In a possible implementation manner of the first aspect, the step of acquiring first electronic medical record information of the first electronic medical record user in the target visit semantic interval includes:

acquiring an analysis conversion script corresponding to the first electronic medical record user and used for analyzing and converting the medical information of the first electronic medical record user;

extracting a plurality of analysis script instructions and an access script instruction of the analysis conversion script, wherein the analysis script instructions are used for instructing a first electronic medical record user to convert different medical record information into medical record behavior data, and the access script instruction is used for instructing the first electronic medical record user to access the external open medical record information;

mapping the analysis script instruction and the access script instruction to a preset instruction execution process to obtain an analysis conversion parameter for performing analysis conversion on the first electronic medical record user, wherein the analysis conversion parameter is used for representing a conversion logic of the first electronic medical record user when the first electronic medical record user converts the information of the medical diagnosis into medical record matching data;

according to the visit information which is accepted and accessed by the first electronic medical record user and comprises all initial visit medical record information of the first electronic medical record user in the target visit semantic interval, fitting all the initial visit medical record information according to the time sequence to obtain initial electronic medical record information;

and converting the initial electronic medical record information based on the analysis conversion parameters to obtain the first electronic medical record information.

In a possible implementation manner of the first aspect, the step of determining, according to medical record matching data of each electronic medical record uploading node associated with the first electronic medical record information, a medical record pushing reference unit of the first electronic medical record information includes:

acquiring a push reference feature vector sequence corresponding to each electronic medical record uploading node matched with medical record matching data from the first electronic medical record information;

calculating a first push node sequence of push nodes corresponding to the push reference feature vector sequence, denoising the first push node sequence to remove push nodes lower than a set confidence coefficient in the first push node sequence, and acquiring a second push node sequence of push nodes corresponding to the push reference feature vector sequence;

taking each push node in the second push node sequence as a push unit object, and acquiring push track information of each push unit object in the plurality of push unit objects;

acquiring pushing key knowledge points of each pushing unit object according to the pushing track information of each pushing unit object and the pushing range of each pushing unit object in the simulated pushing process, wherein the pushing key knowledge points comprise the pushing range and the corresponding uploading times and total times of each electronic medical record uploading node;

calculating to obtain a push node range of each push unit object according to the push node of each push unit object and the push range of each push unit object, and inquiring a preset label information table to obtain target push label information of the plurality of push unit objects according to the push node range of each push unit object and the upload times and total times distributed by the corresponding upload nodes of each electronic medical record;

determining that target push tag information of the plurality of push unit objects associates the plurality of push unit objects to obtain association intervals of associated objects corresponding to the plurality of push unit objects;

and matching the association intervals of the associated objects corresponding to the plurality of pushing unit objects with the corresponding pushing node ranges, and determining the medical record pushing reference unit of the first electronic medical record information according to the matched pushing node ranges.

In a possible implementation manner of the first aspect, the step of determining that second electronic medical record information of an associated object exists with the medical record push reference unit in the target visit semantic interval includes:

and determining the target electronic medical record information which is overlapped with the medical record pushing reference unit in the target diagnosis semantic interval or is partially covered on the medical record pushing reference unit in the target diagnosis semantic interval as second electronic medical record information which is associated with the medical record pushing reference unit.

In a possible implementation manner of the first aspect, the step of establishing an association relationship between a second electronic medical record user corresponding to the second electronic medical record information and the first electronic medical record user includes:

setting a main association identifier for a second electronic medical record user corresponding to the second electronic medical record information;

and setting a slave association identifier corresponding to the master association identifier for the first electronic medical record user, wherein the master association identifier and the slave association identifier are in one-to-one correspondence.

In a possible implementation manner of the first aspect, the step of generating a push knowledge point thermodynamic diagram according to the first electronic medical record information and the second electronic medical record information includes:

respectively extracting electronic medical record knowledge points corresponding to the first electronic medical record information and the second electronic medical record information and the occurrence frequency of each electronic medical record knowledge point;

generating a pushing knowledge point thermal unit of each electronic medical record knowledge point according to each electronic medical record knowledge point and the occurrence frequency of each electronic medical record knowledge point;

and summarizing the knowledge pushing point thermodynamic units of each electronic medical record knowledge point to generate the knowledge pushing point thermodynamic diagram.

In a possible implementation manner of the first aspect, the determining, according to the first update data of the first electronic medical record user and the associated object of the second electronic medical record information corresponding to each second electronic medical record user and the medical record push reference unit, the second update data of each second electronic medical record user includes:

determining second electronic medical record information corresponding to each second electronic medical record user and a target reference medical record item corresponding to the associated object of the medical record pushing reference unit, wherein the target reference medical record item is an auxiliary reference unit item of the medical record pushing reference unit;

determining the medical record inquiry frequency corresponding to the target reference medical record item according to the item label of the target reference medical record item, wherein the item label is used for representing the item knowledge point hierarchy and the knowledge point frequency of the target reference medical record item;

updating the first updating data based on the medical record inquiry frequency of the target reference medical record item corresponding to each second electronic medical record user to obtain target updating data;

mapping the target updating data to a pre-stored data mapping table corresponding to each second electronic medical record user to obtain second updating data of each second electronic medical record user;

the target update data is used for representing medical record inquiry data between a second target person corresponding to the second electronic medical record user and a first target person corresponding to the first electronic medical record user in a target reference medical record item corresponding to the second electronic medical record user, the medical record inquiry data comprises limb medical record inquiry data and language medical record inquiry data, the data mapping table is used for establishing a mapping relation between the medical record inquiry data and medical record inquiry frequency, and the second update data is used for representing the medical record inquiry frequency of the second target person corresponding to the second electronic medical record user by the first target person.

In a possible implementation manner of the first aspect, the step of updating, according to the association object, the first update data and the second update data to a knowledge point pushing thermodynamic diagram in the knowledge point pushing queue includes:

setting a first queue pushing mark point for first electronic medical record information of the first updating data in a knowledge point pushing thermodynamic diagram in the knowledge point pushing queue;

setting a second queue pushing mark point for second electronic medical record information of each second updating data in a knowledge point pushing thermodynamic diagram in the knowledge point pushing queue according to the associated object;

and updating the first updating data and the second updating data into a knowledge point pushing thermodynamic diagram in the knowledge point pushing queue according to the first queue pushing mark point and the second queue pushing mark point respectively.

In a possible implementation manner of the first aspect, the step of pushing the knowledge points to the intelligent medical service terminals of the first electronic medical record user and each second electronic medical record user according to the push knowledge point thermodynamic diagrams in the knowledge point push queue includes:

generating prompt information according to the first updating data and each second updating data;

and in the process of pushing the knowledge points to the intelligent medical service terminals of the first electronic medical record user and each second electronic medical record user, issuing the prompt information to the intelligent medical service terminals of the first electronic medical record user and each second electronic medical record user.

In a second aspect, the disclosed embodiment further provides an intelligent medical information pushing device based on artificial intelligence, which is applied to an electronic medical record cloud platform in communication connection with a plurality of intelligent medical service terminals, and the device includes:

the acquisition module is used for acquiring first electronic medical record information of a first electronic medical record user in a target visit semantic interval, carrying out artificial intelligence analysis on electronic medical record data of each corresponding electronic medical record unit in an electronic medical record database of an associated patient user according to each electronic medical record uploading node associated with the first electronic medical record information to obtain medical record matching data matched with each electronic medical record uploading node associated with the first electronic medical record information, and determining a medical record pushing reference unit of the first electronic medical record information according to medical record matching data of each electronic medical record uploading node associated with the first electronic medical record information;

the first determining module is used for determining second electronic medical record information corresponding to a second electronic medical record user having an association relation with the medical record pushing reference unit in the target visit semantic interval and establishing an association relation between the second electronic medical record user corresponding to the second electronic medical record information and the first electronic medical record user;

the generating module is used for generating a knowledge point pushing thermodynamic diagram according to the first electronic medical record information and the second electronic medical record information and storing the knowledge point pushing thermodynamic diagram into a preset knowledge point pushing queue;

the second determining module is used for determining second updating data of each second electronic medical record user according to the first updating data of the first electronic medical record user and the second electronic medical record information corresponding to each second electronic medical record user and the associated object of the medical record pushing reference unit when the fact that the first electronic medical record user has electronic medical record information updating is detected;

and the pushing module is used for updating the first updating data and the second updating data into the knowledge point pushing thermodynamic diagrams in the knowledge point pushing queue according to the associated object, and pushing the knowledge points of the intelligent medical service terminals of the first electronic medical record user and each second electronic medical record user according to the knowledge point pushing thermodynamic diagrams in the knowledge point pushing queue.

In a third aspect, the disclosed embodiment further provides an artificial intelligence based smart medical information pushing system, which is characterized in that the artificial intelligence based smart medical information pushing system comprises an electronic medical record cloud platform and a plurality of smart medical service terminals in communication connection with the electronic medical record cloud platform;

the electronic medical record cloud platform is used for acquiring first electronic medical record information of a first electronic medical record user in a target visit semantic interval, carrying out artificial intelligence analysis on electronic medical record data of each corresponding electronic medical record unit in an electronic medical record database of an associated patient user according to each electronic medical record uploading node associated with the first electronic medical record information to obtain medical record matching data matched with each electronic medical record uploading node associated with the first electronic medical record information, and determining a medical record pushing reference unit of the first electronic medical record information according to medical record matching data of each electronic medical record uploading node associated with the first electronic medical record information;

the electronic medical record cloud platform is used for determining second electronic medical record information corresponding to a second electronic medical record user having an association relation with the medical record push reference unit in the target visit semantic interval, and establishing an association relation between the second electronic medical record user corresponding to the second electronic medical record information and the first electronic medical record user;

the electronic medical record cloud platform is used for generating a pushing knowledge point thermodynamic diagram according to the first electronic medical record information and the second electronic medical record information, and storing the pushing knowledge point thermodynamic diagram into a preset knowledge point pushing queue;

the electronic medical record cloud platform is used for determining second updating data of each second electronic medical record user according to first updating data of the first electronic medical record user and associated objects of second electronic medical record information corresponding to each second electronic medical record user and the medical record push reference unit when the fact that the first electronic medical record user has electronic medical record information updating is detected;

the electronic medical record cloud platform is used for updating the first updating data and the second updating data into the knowledge point pushing thermodynamic diagrams in the knowledge point pushing queue according to the associated object, and pushing the knowledge points of the intelligent medical service terminals of the first electronic medical record user and each second electronic medical record user according to the knowledge point pushing thermodynamic diagrams in the knowledge point pushing queue.

In a fourth aspect, an embodiment of the present disclosure further provides an electronic medical record cloud platform, where the electronic medical record cloud platform includes a processor, a machine-readable storage medium, and a network interface, the machine-readable storage medium, the network interface, and the processor are connected by a bus system, the network interface is used for being in communication connection with at least one smart medical service terminal, the machine-readable storage medium is used for storing a program, an instruction, or a code, and the processor is used for executing the program, the instruction, or the code in the machine-readable storage medium to execute the smart medical information pushing method based on artificial intelligence in any one of the first aspect or the possible design of the first aspect.

In a fifth aspect, an embodiment of the present disclosure provides a computer-readable storage medium, in which instructions are stored, and when executed, cause a computer to perform the method for pushing intelligent medical information based on artificial intelligence in the first aspect or any one of the possible designs of the first aspect.

Based on any one of the above aspects, the method and the device for pushing the knowledge point generate a pushed knowledge point thermodynamic diagram according to the first electronic medical record information and the second electronic medical record information by acquiring the first electronic medical record information of the first electronic medical record user in the target visit semantic interval, determining the medical record pushing reference unit of the first electronic medical record information, and then determining the second electronic medical record information which is in an association relationship with the medical record pushing reference unit in the target visit semantic interval, and then correspondingly updating the pushed knowledge point thermodynamic diagram when detecting that the electronic medical record information of the first electronic medical record user is updated, and thus performing knowledge point pushing. So, can carry out the relevant knowledge point propelling movement of wisdom medical treatment with electronic medical record information matching to each patient of being convenient for carries out effectual knowledge point study and promotes the consciousness of seeing a doctor and the daily consciousness of paying close attention to of self.

Drawings

To more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings that are required to be used in the embodiments will be 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 may be obtained from the drawings without inventive effort.

Fig. 1 is a schematic view of an application scenario of an intelligent medical information pushing system based on artificial intelligence according to an embodiment of the present disclosure;

fig. 2 is a schematic flowchart illustrating an intelligent medical information pushing method based on artificial intelligence according to an embodiment of the disclosure;

fig. 3 is a functional block diagram of an intelligent medical information pushing device based on artificial intelligence according to an embodiment of the disclosure;

fig. 4 is a block diagram illustrating a structure of an electronic medical record cloud platform for implementing the above intelligent medical information pushing method based on artificial intelligence 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 interactive schematic diagram of an intelligent medical information pushing system 10 based on artificial intelligence according to an embodiment of the present disclosure. The intelligent medical information pushing system 10 based on artificial intelligence can comprise an electronic medical record cloud platform 100 and an intelligent medical service terminal 200 which is in communication connection with the electronic medical record cloud platform 100. The intelligent medical information pushing system 10 based on artificial intelligence shown in fig. 1 is only one possible example, and in other possible embodiments, the intelligent medical information pushing system 10 based on artificial intelligence may also include only one of the components shown in fig. 1 or may also include other components.

In this embodiment, the smart medical services terminal 200 may include a mobile device, a tablet computer, a laptop computer, or any combination thereof. In some embodiments, the mobile device may include a smart home device, a wearable device, a smart mobile device, a virtual reality device, an augmented reality device, or the like, or any combination thereof. In some embodiments, the smart home devices may include control devices of smart electrical devices, smart monitoring devices, smart televisions, smart cameras, and the like, or any combination thereof. In some embodiments, the wearable device may include a smart bracelet, a smart lace, smart glass, a smart helmet, a smart watch, a smart garment, a smart backpack, a smart accessory, or the like, or any combination thereof. In some embodiments, the smart mobile device may include a smartphone, a personal digital assistant, a gaming device, and the like, or any combination thereof. In some embodiments, the virtual reality device and/or the augmented reality device may include a virtual reality helmet, virtual reality glass, a virtual reality patch, an augmented reality helmet, augmented reality glass, an augmented reality patch, or the like, or any combination thereof. For example, the virtual reality device and/or augmented reality device may include various virtual reality products and the like.

In this embodiment, the internet of things cloud electronic medical record cloud platform 100 and the smart medical service terminal 200 in the smart medical information pushing system 10 based on artificial intelligence can cooperate to execute the smart medical information pushing method based on artificial intelligence described in the following method embodiment, and the detailed description of the following method embodiment can be referred to in the specific steps of executing the electronic medical record cloud platform 100 and the smart medical service terminal 200.

To solve the technical problem in the foregoing background, fig. 2 is a schematic flowchart of a method for pushing intelligent medical information based on artificial intelligence according to an embodiment of the present disclosure, which can be executed by the electronic medical record cloud platform 100 shown in fig. 1, and the method for pushing intelligent medical information based on artificial intelligence is described in detail below.

Step S110, first electronic medical record information of a first electronic medical record user in a target visit semantic interval is obtained, artificial intelligence analysis is conducted on electronic medical record data of each corresponding electronic medical record unit in an electronic medical record database of an associated patient user according to each electronic medical record uploading node associated with the first electronic medical record information, medical record matching data matched with each electronic medical record uploading node associated with the first electronic medical record information are obtained, and a medical record pushing reference unit of the first electronic medical record information is determined according to medical record matching data of each electronic medical record uploading node associated with the first electronic medical record information.

Step S120, determining second electronic medical record information corresponding to a second electronic medical record user having an association relation with the medical record push reference unit in the target visit semantic interval, and establishing an association relation between the second electronic medical record user corresponding to the second electronic medical record information and the first electronic medical record user.

Step S130, generating a pushing knowledge point thermodynamic diagram according to the first electronic medical record information and the second electronic medical record information, and storing the pushing knowledge point thermodynamic diagram into a preset knowledge point pushing queue.

Step S140, when it is detected that the first electronic medical record user has the electronic medical record information update, determining second update data of each second electronic medical record user according to the first update data of the first electronic medical record user and the associated object of the second electronic medical record information and the medical record push reference unit corresponding to each second electronic medical record user.

Step S150, updating the first update data and the second update data into the knowledge point pushing thermodynamic diagrams in the knowledge point pushing queue according to the association object, and performing knowledge point pushing on the intelligent medical service terminal 200 of the first electronic medical record user and each second electronic medical record user according to the knowledge point pushing thermodynamic diagrams in the knowledge point pushing queue.

In this embodiment, the target visit semantic interval may refer to a preset visit semantic interval meeting the knowledge point pushing requirement, and the visit semantic interval may refer to an interval composed of semantic field sequences composed of semantic analysis results of a plurality of visit keywords.

In this embodiment, the electronic medical record uploading node may refer to a medical treatment node in the uploading process of the electronic medical record, for example, a medical treatment department, a medical treatment area, and the like, which is not specifically limited herein. The electronic medical record unit can refer to different units of electronic medical record information which are divided according to different medical record plates (such as past disease history, family disease history and the like).

Based on the above steps, in this embodiment, by acquiring first electronic medical record information of a first electronic medical record user in a target visit semantic interval, and after determining a medical record push reference unit of the first electronic medical record information, determining second electronic medical record information having an association relationship with the medical record push reference unit in the target visit semantic interval, a pushed knowledge point thermodynamic diagram is generated according to the first electronic medical record information and the second electronic medical record information, and then when it is detected that the electronic medical record information of the first electronic medical record user is updated, the pushed knowledge point thermodynamic diagram is updated correspondingly, and thus knowledge point pushing is performed. So, can carry out the relevant knowledge point propelling movement of wisdom medical treatment with electronic medical record information matching to each patient of being convenient for carries out effectual knowledge point study and promotes the consciousness of seeing a doctor and the daily consciousness of paying close attention to of self.

In one possible implementation, for step S110, the following exemplary sub-steps will be given next, and described in detail below.

And the substep S111, acquiring an analysis conversion script corresponding to the first electronic medical record user and used for analyzing and converting the medical information of the first electronic medical record user.

The substep S112 extracts a plurality of parsing script commands and an access script command for parsing the conversion script.

For example, the parsing script instructions can be used to instruct the first electronic medical record user to convert different medical record information into medical record behavior data, and the access script instructions can be used to instruct the first electronic medical record user to access the external open medical record information.

And a substep S113, mapping the analysis script instruction and the access script instruction to a preset instruction execution process to obtain an analysis conversion parameter for performing analysis conversion on the first electronic medical record user, wherein the analysis conversion parameter is used for representing a conversion logic of the first electronic medical record user when the information about diagnosis is converted into medical record matching data.

And a substep S114, according to the visit information received and accessed by the first electronic medical record user, wherein the visit information comprises all initial visit medical record information of the first electronic medical record user in the target visit semantic interval, and all the initial visit medical record information is fitted according to the time sequence to obtain the initial electronic medical record information.

And a substep S115, converting the initial electronic medical record information based on the analysis conversion parameters to obtain first electronic medical record information.

Further, in one possible implementation manner, for step S110, the following another exemplary sub-step will be given next, and the detailed description is given below.

In the substep S116, for the electronic medical record data of each corresponding electronic medical record unit in the electronic medical record database of the associated patient user, a first medical record structured label of the electronic medical record data of the electronic medical record unit is obtained.

For example, the first medical record structured label is used for characterizing the visit behavior label and the visit process label of the electronic medical record visit process of the electronic medical record unit.

Illustratively, the visit behavior label may refer to a reason of the present visit behavior, and the visit process label may refer to process information of the present visit.

And the substep S117 is used for respectively carrying out artificial intelligent identification on the first medical record structured label according to each electronic medical record uploading node associated with the electronic medical record information to obtain a first visit behavior label classification vector sequence and a visit process label classification vector sequence corresponding to the first visit behavior label classification vector sequence.

In the substep S118, a first diagnostic item vector sequence and diagnostic item association information of the electronic medical record data of the electronic medical record unit are obtained, and a content vector unit of the first diagnostic item vector sequence is extracted, where the content vector unit of the first diagnostic item vector sequence includes a set format vector unit.

And a substep S119, obtaining a set format vector unit of the historical electronic medical record data associated with the patient user, and adjusting the set format vector unit of the first diagnostic item vector sequence according to the set format vector unit to match the matching relationship between the set format vector units in the first diagnostic item vector sequence with the matching relationship between the set format vector units in the preset historical electronic medical record data.

And a substep S1191, after the adjustment is finished, obtaining a content vector unit of the second diagnostic item vector sequence, and generating the second diagnostic item vector sequence according to the content vector unit of the second diagnostic item vector sequence.

And a substep S1192 of finding out a diagnosis process labeling classification vector sequence matched with the diagnosis project associated information and a first diagnosis behavior labeling classification vector sequence corresponding to the diagnosis process labeling classification vector sequence according to the diagnosis project associated information and the content vector unit of the second diagnosis project vector sequence, and adjusting the first diagnosis behavior labeling classification vector sequence corresponding to the diagnosis process labeling classification vector sequence according to the content vector unit of the second diagnosis project vector sequence to obtain a second diagnosis behavior labeling classification vector sequence.

And a substep S1193 of merging the second visit behavior labeling classification vector sequence and the second diagnosis item vector sequence to obtain a merged vector sequence matching the electronic medical record data of the electronic medical record unit and each electronic medical record uploading node associated with the electronic medical record information.

And a substep S1194 of performing artificial intelligence analysis on the merged vector sequence of the electronic medical record unit to obtain an artificial intelligence analysis result of the electronic medical record unit.

In sub-step S1194, as an alternative example, the sub-step may be embodied by the following further sub-steps, which are described in detail below.

(1) And extracting corresponding examination item index vectors and examination item associated disease range vectors from the merged vector sequence of the electronic medical record unit.

(2) And performing regression analysis on the inspection item index vector and the inspection item associated disease range vector to obtain corresponding first regression analysis vector information and second regression analysis vector information.

(3) And determining first to-be-determined regression analysis vector information corresponding to the first last regression analysis vector information corresponding to the first regression analysis vector information in the first regression analysis vector information. Wherein, the first corresponding last regression analysis vector information is the last regression analysis vector information corresponding to the previous check item index vector.

(4) And determining second undetermined regression analysis vector information corresponding to second corresponding last regression analysis vector information in the second regression analysis vector information. Wherein, the second corresponding last regression analysis vector information is the last regression analysis vector information corresponding to the previous examination item related disease range vector.

(5) And carrying out artificial intelligence analysis according to the first undetermined regression analysis vector information and the second undetermined regression analysis vector information to obtain an artificial intelligence analysis result of the electronic medical record unit.

Exemplarily, in (5), as an alternative example, the following embodiments may be specifically implemented, which are specifically described below.

(A) And obtaining the determined regression analysis target regions and regression analysis vector information included in each regression analysis target region according to the first undetermined regression analysis vector information and the second undetermined regression analysis vector information.

(B) And identifying artificial intelligence analysis information corresponding to the regression analysis vector information in each regression analysis target region, and clustering the artificial intelligence analysis information corresponding to the regression analysis vector information in each regression analysis target region according to the respective corresponding preset medical record matching weight to obtain the artificial intelligence analysis result of the electronic medical record unit.

For example, in part (a) of part (5), the present embodiment may calculate a hamming distance between each of the first to-be-determined regression analysis vector information and the first corresponding last regression analysis vector information, if the hamming distance between the first of the first to-be-determined regression analysis vector information and the first corresponding last regression analysis vector information is the smallest and within a set hamming distance interval, select the first of the first to-be-determined regression analysis vector information as the first selected regression analysis vector information, and calculate each of the second to-be-determined regression analysis vector information and the hamming distance between each of the second to-be-determined regression analysis vector information and the second corresponding last regression analysis vector information, if the hamming distance between the second of the second to-be-determined regression analysis vector information and the second corresponding last regression analysis vector information is the smallest, and within the set hamming distance interval, selecting the second regression analysis vector information as the second selected regression analysis vector information.

On the basis, the first medical record matching unit vectors corresponding to the first selected regression analysis vector information and the second selected regression analysis vector information respectively can be determined according to the first selected regression analysis vector information and the corresponding first corresponding last regression analysis vector information, the second selected regression analysis vector information and the corresponding second corresponding last regression analysis vector information.

The first medical record matching unit vector can include medical record matching type information and a medical record matching content vector unit based on electronic medical record unit medical record matching, and the last regression analysis vector information corresponding to the first medical record matching unit vector can be regression analysis vector information corresponding to a current previous examination item index vector and a previous examination item associated disease range vector.

Then, third to-be-determined regression analysis vector information corresponding to the first regression analysis vector information may be determined in the second regression analysis vector information, third selected regression analysis vector information corresponding to the first regression analysis vector information may be selected from the third to-be-determined regression analysis vector information, and content vector units corresponding to the third selected regression analysis vector information and the first regression analysis vector information, respectively, may be determined according to the third selected regression analysis vector information and the corresponding first regression analysis vector information.

It is worth exemplarily explaining that the content vector unit is a content vector unit in which each regression analysis vector information is corresponding to the merged vector sequence, the first regression analysis vector information is the regression analysis vector information corresponding to the inspection item index vector, and the second regression analysis vector information is the regression analysis vector information corresponding to the inspection item associated disease range vector.

Therefore, according to the first medical record matching unit vector and the content vector unit which correspond to the regression analysis vector information respectively, the second medical record matching unit vector which corresponds to the regression analysis vector information respectively is determined, the second medical record matching unit vector comprises medical record matching type information and a medical record matching content vector unit, if the identification value difference degree of the medical record matching type information between any two regression analysis vector information is within the set Hamming distance interval and the vector difference parameter between the medical record matching content vector units is within the set Hamming distance interval, and the state difference between the content vector units of any two regression analysis vector information is within a set Hamming distance interval, and any two regression analysis vector information are combined into the same regression analysis target area to obtain the regression analysis vector information included in each regression analysis target area.

In a possible implementation manner, still referring to step S120, in the step of determining a medical record push reference unit of the first electronic medical record information according to medical record matching data of each electronic medical record uploading node associated with the first electronic medical record information, this embodiment may obtain, from the first electronic medical record information, a push reference feature vector sequence corresponding to each electronic medical record uploading node matched with the medical record matching data.

On the basis, a first push node sequence of the push nodes corresponding to the push reference feature vector sequence can be calculated, denoising is carried out on the first push node sequence to remove the push nodes lower than the set confidence coefficient in the first push node sequence, and a second push node sequence of the push nodes corresponding to the push reference feature vector sequence is obtained.

Then, each push node in the second push node sequence is used as a push unit object, and push track information of each push unit object in the plurality of push unit objects is obtained.

And then, acquiring a pushing key knowledge point of each pushing unit object according to the pushing track information of each pushing unit object and the pushing range of each pushing unit object in the simulated pushing process.

The pushing key knowledge points can include pushing ranges and corresponding uploading times and total times corresponding to the uploading nodes of the electronic medical records.

Therefore, the push node range of each push unit object can be obtained through calculation according to the push node of each push unit object and the push range of each push unit object, and the preset label information table is inquired according to the push node range of each push unit object and the uploading times and the total times distributed by the corresponding uploading nodes of the electronic medical records to obtain the target push label information of the plurality of push unit objects. Then, determining target push tag information of the plurality of push unit objects to associate the plurality of push unit objects to obtain associated intervals of associated objects corresponding to the plurality of push unit objects, matching the associated intervals of the associated objects corresponding to the plurality of push unit objects with corresponding push node ranges, and determining medical record push reference units of the first electronic medical record information according to the matched push node ranges.

In a possible implementation manner, for step S120, in the process of determining that the second electronic medical record information of the associated object exists with the medical record pushing reference unit in the target visit semantic interval, the embodiment may determine that the second electronic medical record information of the associated object exists with the medical record pushing reference unit in the target visit semantic interval, or the target electronic medical record information partially covered with the medical record pushing reference unit in the target visit semantic interval.

In a possible implementation manner, in step S120, in the process of establishing an association relationship between a second electronic medical record user and a first electronic medical record user corresponding to second electronic medical record information, the embodiment may set a master association identifier for the second electronic medical record user corresponding to the second electronic medical record information, and then set a slave association identifier corresponding to the master association identifier for the first electronic medical record user, where the master association identifier and the slave association identifier correspond to each other one to one.

In one possible implementation, for step S130, the following exemplary sub-steps will be given next, and described in detail below.

And the substep S131, respectively extracting the electronic medical record knowledge points corresponding to the first electronic medical record information and the second electronic medical record information and the occurrence times of each electronic medical record knowledge point.

And a substep S132, generating a pushing knowledge point thermal unit of each electronic medical record knowledge point according to each electronic medical record knowledge point and the occurrence frequency of each electronic medical record knowledge point.

For example, assuming that an electronic medical record knowledge point a, an electronic medical record knowledge point B, an electronic medical record knowledge point C, and an electronic medical record knowledge point D exist, and the respective occurrence times of the electronic medical record knowledge point a, the electronic medical record knowledge point B, the electronic medical record knowledge point C, and the electronic medical record knowledge point D are Q1, Q2, Q3, and Q4, the thermal rendering values W1, W2, W3, and W4 of the pushed knowledge point thermal unit corresponding to the occurrence times Q1, Q2, Q3, and Q4, respectively, and then the pushed knowledge point thermal unit of each electronic medical record knowledge point is generated according to the rendering modes corresponding to the thermal rendering values W1, W2, W3, and W4, respectively.

And a substep S133, summarizing the knowledge pushing point thermodynamic units of each electronic medical record knowledge point to generate a knowledge pushing point thermodynamic diagram.

In one possible implementation, for step S140, the following exemplary sub-steps will be given next, and described in detail below.

And the substep S141 is to determine a target referenced medical record item corresponding to the second electronic medical record information corresponding to each second electronic medical record user and the associated object of the medical record pushing reference unit, wherein the target referenced medical record item is an attached reference unit item of the medical record pushing reference unit.

And the substep S142, determining the case history inquiry frequency corresponding to the target reference case history item according to the item label of the target reference case history item, wherein the item label is used for representing the item knowledge point hierarchy and the knowledge point frequency of the target reference case history item.

And the substep S143 is to update the first updating data based on the medical record query frequency of the target reference medical record item corresponding to each second electronic medical record user to obtain target updating data.

And a substep S144, mapping the target updating data to a pre-stored data mapping table corresponding to each second electronic medical record user to obtain second updating data of each second electronic medical record user.

It is worth to be noted that the target update data may be used to represent medical record query data between a second target person corresponding to the second electronic medical record user and a first target person corresponding to the first electronic medical record user in a target reference medical record item corresponding to the second electronic medical record user, where the medical record query data includes limb medical record query data and language medical record query data, the data mapping table is used to establish a mapping relationship between the medical record query data and medical record query frequency, and the second update data is used to represent the frequency of the second target person corresponding to the second electronic medical record user in medical record query by the first target person.

In one possible implementation manner, for step S150, in the process of updating the first update data and the second update data into the push knowledge point thermodynamic diagram in the knowledge point push queue according to the associated object, the following exemplary sub-steps will be given next, and are described in detail below.

And a substep S151, setting a first queue pushing mark point for the first electronic medical record information of the first updating data in the knowledge point pushing thermodynamic diagram in the knowledge point pushing queue.

And a substep S152, setting a second queue pushing mark point for the second electronic medical record information of each second updating data in the knowledge point thermodynamic diagram in the knowledge point pushing queue according to the associated object.

And a substep S153, updating the first updating data and the second updating data into the knowledge point pushing thermodynamic diagram in the knowledge point pushing queue according to the first queue pushing mark point and the second queue pushing mark point respectively.

In a possible implementation manner, in order to facilitate timely update prompting, in step S150, in the process of pushing knowledge points to the intelligent medical service terminal 200 of the first electronic medical record user and each second electronic medical record user according to the pushed knowledge point thermodynamic diagrams in the knowledge point pushing queue, the following exemplary sub-steps will be given, which are described in detail below.

And a substep S154 of generating a prompt message according to the first update data and each second update data.

In the sub-step S155, in the process of pushing the knowledge points to the intelligent medical service terminals 200 of the first electronic medical record user and each second electronic medical record user, the prompt information is sent to the intelligent medical service terminals 200 of the first electronic medical record user and each second electronic medical record user.

Fig. 3 is a schematic diagram of functional modules of an intelligent medical information pushing apparatus 300 based on artificial intelligence according to an embodiment of the present disclosure, in this embodiment, the intelligent medical information pushing apparatus 300 based on artificial intelligence may be divided into the functional modules according to an embodiment of a method executed by the electronic medical record cloud platform 100, that is, the following functional modules corresponding to the intelligent medical information pushing apparatus 300 based on artificial intelligence may be used to execute each embodiment of the method executed by the electronic medical record cloud platform 100. The smart medical information pushing apparatus 300 may include an obtaining module 310, a first determining module 320, a generating module 330, a second determining module 340, and a pushing module 350, wherein the functions of the functional modules of the smart medical information pushing apparatus 300 are described in detail below.

The acquisition module 310 is configured to acquire first electronic medical record information of a first electronic medical record user in a target visit semantic interval, perform artificial intelligence analysis on electronic medical record data of each corresponding electronic medical record unit in an electronic medical record database of an associated patient user according to each electronic medical record uploading node associated with the first electronic medical record information, obtain medical record matching data matched with each electronic medical record uploading node associated with the first electronic medical record information, and determine a medical record pushing reference unit of the first electronic medical record information according to medical record matching data of each electronic medical record uploading node associated with the first electronic medical record information. The obtaining module 310 may be configured to perform the step S110, and the detailed implementation of the obtaining module 310 may refer to the detailed description of the step S110.

The first determining module 320 is configured to determine second electronic medical record information corresponding to a second electronic medical record user having an association relationship with the medical record push reference unit in the target visit semantic interval, and establish an association relationship between the second electronic medical record user corresponding to the second electronic medical record information and the first electronic medical record user. The first determining module 320 may be configured to perform the step S120, and for a detailed implementation of the first determining module 320, reference may be made to the detailed description of the step S120.

The generating module 330 is configured to generate a knowledge point pushing thermodynamic diagram according to the first electronic medical record information and the second electronic medical record information, and store the knowledge point pushing thermodynamic diagram in a preset knowledge point pushing queue. The generating module 330 may be configured to execute the step S130, and the detailed implementation of the generating module 330 may refer to the detailed description of the step S130.

The second determining module 340 is configured to determine, when it is detected that the electronic medical record information update exists in the first electronic medical record user, second update data of each second electronic medical record user according to the first update data of the first electronic medical record user and an associated object of a second electronic medical record information and a medical record push reference unit corresponding to each second electronic medical record user. The second determining module 340 may be configured to perform the step S140, and for a detailed implementation of the second determining module 340, reference may be made to the detailed description of the step S140.

The pushing module 350 is configured to update the first update data and the second update data to the pushed knowledge point thermodynamic diagrams in the knowledge point pushing queue according to the associated object, and perform knowledge point pushing on the intelligent medical service terminal 200 of the first electronic medical record user and each second electronic medical record user according to the pushed knowledge point thermodynamic diagrams in the knowledge point pushing queue. The pushing module 350 may be configured to perform the step S150 of pushing the intelligent medical information based on artificial intelligence, and the detailed implementation of the pushing module 350 may refer to the detailed description of the step S150.

It should be noted that the division of the modules of the above apparatus is only a logical division, and the actual implementation may be wholly or partially integrated into one physical entity, or may be physically separated. And these modules can be realized in the form of software called by processing element; or may be implemented entirely in hardware; and part of the modules can be realized in the form of calling software by the processing element, and part of the modules can be realized in the form of hardware. For example, the obtaining module 310 may be a processing element separately set up, or may be implemented by being integrated into a chip of the apparatus, or may be stored in a memory of the apparatus in the form of program code, and the processing element of the apparatus calls and executes the functions of the obtaining module 310. Other modules are implemented similarly. In addition, all or part of the modules can be integrated together or can be independently realized. The processing element described herein may be an integrated circuit having signal processing capabilities. In implementation, each step of the above method or each module above may be implemented by an integrated logic circuit of hardware in a processor element or an instruction in the form of software.

For example, the above modules may be one or more integrated circuits configured to implement the above methods, such as: one or more Application Specific Integrated Circuits (ASICs), or one or more microprocessors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs), among others. For another example, when some of the above modules are implemented in the form of a processing element scheduler code, the processing element may be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor that can call program code. As another example, these modules may be integrated together, implemented in the form of a system-on-a-chip (SOC).

Fig. 4 shows a hardware structure diagram of an electronic medical record cloud platform 100 for implementing the control device provided by the embodiment of the present disclosure, and as shown in fig. 4, the electronic medical record cloud platform 100 may include a processor 110, a machine-readable storage medium 120, a bus 130, and a transceiver 140.

In a specific implementation process, at least one processor 110 executes computer-executable instructions stored in the machine-readable storage medium 120 (for example, the obtaining module 310, the first determining module 320, the generating module 330, the second determining module 340, and the pushing module 350 included in the smart medical information pushing apparatus 300 based on artificial intelligence shown in fig. 3), so that the processor 110 may execute the smart medical information pushing method based on artificial intelligence according to the above method embodiment, where 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 transceiving actions of the transceiver 140, so as to transceive data with the aforementioned smart medical service terminal 200.

For a specific implementation process of the processor 110, reference may be made to the above-mentioned method embodiments executed by the electronic medical record cloud platform 100, and implementation principles and technical effects are similar, which are not described herein again.

In the embodiment shown in fig. 4, it should be understood that the Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present invention may be embodied directly in a hardware processor, or in a combination of the hardware and software modules within the processor.

The machine-readable storage medium 120 may comprise high-speed RAM memory and may also include non-volatile storage NVM, such as at least one disk memory.

The bus 130 may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. The bus 130 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, the buses in the figures of the present application are not limited to only one bus or one type of bus.

In addition, the embodiment of the disclosure also provides a readable storage medium, in which computer execution instructions are stored, and when a processor executes the computer execution instructions, the intelligent medical information pushing method based on artificial intelligence is implemented.

The readable storage medium described above may be implemented by any type of volatile or non-volatile memory device or combination thereof, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk. Readable storage media can be any available media that can be accessed by a general purpose or special purpose computer.

The foregoing description, for purpose of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or to limit the disclosure to the precise forms disclosed. Many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the disclosure and its practical applications, to thereby enable others skilled in the art to best utilize the disclosure and various embodiments with various modifications as are suited to the particular use contemplated. The foregoing description, for purpose of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or to limit the disclosure to the precise forms disclosed. Many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the disclosure and its practical applications, to thereby enable others skilled in the art to best utilize the disclosure and various embodiments with various modifications as are suited to the particular use contemplated.

Claims

1. A smart medical information pushing method based on artificial intelligence is characterized by being applied to an electronic medical record cloud platform in communication connection with a plurality of smart medical service terminals, and the method comprises the following steps:

updating the first updating data and the second updating data into a knowledge point pushing thermodynamic diagram in a knowledge point pushing queue according to the associated object, and pushing knowledge points to the intelligent medical service terminals of the first electronic medical record user and each second electronic medical record user according to the knowledge point pushing thermodynamic diagram in the knowledge point pushing queue;

the step of performing artificial intelligence analysis on the electronic medical record data of each corresponding electronic medical record unit in the electronic medical record database of the associated patient user according to each electronic medical record uploading node associated with the first electronic medical record information to obtain medical record matching data matched with each electronic medical record uploading node associated with the first electronic medical record information comprises the following steps:

2. The intelligent medical information pushing method based on artificial intelligence of claim 1, wherein the step of obtaining the first electronic medical record information of the first electronic medical record user in the target visit semantic interval comprises:

3. The intelligent medical information pushing method based on artificial intelligence of claim 1, wherein the step of determining the medical record pushing reference unit of the first electronic medical record information according to the medical record matching data of each electronic medical record uploading node associated with the first electronic medical record information comprises:

4. The intelligent medical information pushing method based on artificial intelligence of claim 1, wherein the step of determining the second electronic medical record information of the object associated with the medical record pushing reference unit in the target visit semantic interval comprises:

5. The intelligent medical information pushing method based on artificial intelligence of claim 1, wherein the step of establishing the association relationship between the second electronic medical record user corresponding to the second electronic medical record information and the first electronic medical record user comprises:

6. The intelligent medical information pushing method based on artificial intelligence of any one of claims 1-5, wherein the step of generating a pushed knowledge point thermodynamic diagram according to the first electronic medical record information and the second electronic medical record information comprises:

7. The intelligent medical information pushing method based on artificial intelligence as claimed in claim 1, wherein the step of determining the second update data of each second electronic medical record user according to the first update data of the first electronic medical record user and the association object of the second electronic medical record information corresponding to each second electronic medical record user and the medical record pushing reference unit comprises:

8. The intelligent medical information pushing method based on artificial intelligence of claim 1, wherein the step of updating the first update data and the second update data into the knowledge point pushing thermodynamic diagram in the knowledge point pushing queue according to the association object comprises:

9. The intelligent medical information pushing system based on artificial intelligence is characterized by comprising an electronic medical record cloud platform and a plurality of intelligent medical service terminals in communication connection with the electronic medical record cloud platform;

the electronic medical record cloud platform is used for updating the first updating data and the second updating data into a knowledge point pushing thermodynamic diagram in a knowledge point pushing queue according to the associated object, and pushing knowledge points of the intelligent medical service terminals of the first electronic medical record user and each second electronic medical record user according to the knowledge point pushing thermodynamic diagram in the knowledge point pushing queue;

the method for the electronic medical record cloud platform to perform artificial intelligence analysis on the electronic medical record data of each corresponding electronic medical record unit in the electronic medical record database of the associated patient user according to each electronic medical record uploading node associated with the first electronic medical record information to obtain medical record matching data matched with each electronic medical record uploading node associated with the first electronic medical record information includes the following steps:

10. An electronic medical record cloud platform, which comprises a processor, a machine-readable storage medium and a network interface, wherein the machine-readable storage medium, the network interface and the processor are connected through a bus system, the network interface is used for being in communication connection with at least one intelligent medical service terminal, the machine-readable storage medium is used for storing programs, instructions or codes, and the processor is used for executing the programs, the instructions or the codes in the machine-readable storage medium to execute the intelligent medical information pushing method based on artificial intelligence according to any one of claims 1 to 8.