CN114020926A - Data processing method and device and electronic equipment - Google Patents
Data processing method and device and electronic equipment Download PDFInfo
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/36—Creation of semantic tools, e.g. ontology or thesauri
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
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- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H15/00—ICT specially adapted for medical reports, e.g. generation or transmission thereof
Abstract
The application discloses a data processing method and device and electronic equipment. Wherein, the method comprises the following steps: acquiring physiological data of a target object; determining a target node corresponding to the physiological data and a derivative node associated with the target node in a target knowledge graph; under the condition that the derived node is of a specified type, acquiring supplementary data input by the target object, wherein the supplementary data is used for verifying the validity of the derived node of the specified type; generating a tag set corresponding to the target object at least according to the supplementary data and the data corresponding to the target node, wherein tags in the tag set are used for representing state data of the target object; and generating report information corresponding to the target object according to the labels in the label set, and outputting the report information. The method and the device solve the technical problem that currently obtained medical health data lack correlation and guiding significance.
Description
Technical Field
The application relates to the field of knowledge graphs, in particular to a data processing method and device and electronic equipment.
Background
With the progress of medical health science and technology, people have invented a lot of medical instruments and biochemical tests, which can help to diagnose diseases, monitor illness states and manage chronic diseases, and can obtain a lot of other data in life through the internet and internet of things technology, which together form a medical health big data portrait in human life.
However, many medical health data cannot be fully utilized at present, and there are the following reasons: 1. the inspection reports are usually static numbers or words, or simply interpretations; 2. these data usually need to be read by professional medical staff, but the medical science popularization is difficult to implement for the medical institutions in medium and small cities or at the primary level; 3. in most cases, many medical knowledge points have many preconditions or contexts. In other words, many inspection results have great differences in data obtained from different people, different diseases, and different detection environments, so that in most cases, doctors can make corresponding interpretation or countermeasures only on the premise of making certain details, especially when other possibilities are eliminated by other means, so that most inspection reports lack other data and cannot obtain greater guiding significance; 4. the general examination and examination reports are single data or text results, only one time point in the doctor diagnosis and treatment process is taken as a reference, and no long-term and continuous executable scheme or plan is formed subsequently.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the application provides a data processing method, a data processing device and electronic equipment, and aims to at least solve the technical problem that currently obtained medical health data lack correlation and guiding significance.
According to an aspect of an embodiment of the present application, there is provided a data processing method, including: acquiring physiological data of a target object; determining a target node corresponding to the physiological data and a derivative node associated with the target node in a target knowledge graph; under the condition that the derived node is of the specified type, acquiring supplementary data input by the target object, wherein the supplementary data is used for verifying the validity of the derived node of the specified type; generating a tag set corresponding to the target object at least according to the supplementary data and the data corresponding to the target node, wherein tags in the tag set are used for representing state data of the target object; and generating report information corresponding to the target object according to the labels in the label set, and outputting the report information.
Optionally, the nodes in the target knowledge-graph include: entity information in the medical health data, and edges in the target knowledge graph represent the incidence relation between two nodes connected by the edges.
Optionally, acquiring supplementary data input by the target object includes: generating prompt information under the condition that the derived node is of a specified type, wherein the prompt information is used for prompting a target object to input the associated information of the derived node; and receiving the associated information input by the target object according to the prompt information.
Optionally, when there are a plurality of prompt messages, receiving corresponding data corresponding to at least one of the plurality of prompt messages input by the target object.
Optionally, after receiving the association information input by the target object according to the prompt information, the method includes: and judging whether the received associated information meets the condition corresponding to the derivative node of the specified type, and if so, taking the associated information as supplementary data.
Optionally, generating a tag set corresponding to the target object at least according to the supplementary data and the data corresponding to the target node, including: determining a timestamp of the supplemental data and a timestamp of data corresponding to the target node, wherein the timestamp comprises at least one of: collecting time, relative time information between the current collecting time and historical collecting time; and generating a label according to the supplementary data, the time stamp of the supplementary data, the data corresponding to the target node and the time stamp of the data corresponding to the target node.
Optionally, after generating the tag set corresponding to the target object at least according to the supplementary data and the data corresponding to the target node, the method further includes: encoding the tags in the tag set to obtain data indexes corresponding to the tags; and searching a database corresponding to the tag according to the data index, wherein the database stores corresponding information for processing the tag.
According to another aspect of the embodiments of the present application, there is also provided a data processing apparatus, including: the first acquisition module is used for acquiring physiological data of a target object; a determination module for determining a target node corresponding to the physiological data and a derivative node associated with the target node in a target knowledge-graph; the second acquisition module is used for acquiring supplementary data input by the target object under the condition that the derived node is of the specified type, and the supplementary data is used for verifying the validity of the derived node of the specified type; the generating module is used for generating a label set corresponding to the target object at least according to the supplementary data and the data corresponding to the target node, wherein labels in the label set are used for representing state data of the target object; and generating report information corresponding to the target object according to the labels in the label set, and outputting the report information.
According to still another aspect of the embodiments of the present application, there is also provided an electronic device including: a memory for storing program instructions; a processor, coupled to the memory, for performing the following functions when executing the program instructions: acquiring physiological data of a target object; determining a target node corresponding to the physiological data and a derivative node associated with the target node in a target knowledge graph; under the condition that the derived node is of the specified type, acquiring supplementary data input by the target object, wherein the supplementary data is used for verifying the validity of the derived node of the specified type; generating a tag set corresponding to the target object at least according to the supplementary data and the data corresponding to the target node, wherein tags in the tag set are used for representing state data of the target object; and generating report information corresponding to the target object according to the labels in the label set, and outputting the report information.
According to still another aspect of the embodiments of the present application, there is also provided a nonvolatile storage medium including a stored program, wherein the apparatus in which the nonvolatile storage medium is controlled to execute the above data processing method when the program runs.
In the embodiment of the application, the physiological data of the target object is acquired; determining a target node corresponding to the physiological data and a derivative node associated with the target node in a target knowledge graph; under the condition that the derived node is of the specified type, acquiring supplementary data input by the target object, wherein the supplementary data are used for verifying the validity of the derived node of the specified type and comprise time information; generating a tag set corresponding to the target object at least according to the supplementary data and the data corresponding to the target node, wherein tags in the tag set are used for representing state data of the target object; report information corresponding to the target object is generated according to the tags in the tag set, and the purpose of generating different dynamic tag sets according to the health conditions of different target objects is achieved, so that the physiological data of the target object at different time stamps are associated with the current physiological data of the target object, and the technical problem that the currently obtained medical health data is lack of correlation and guiding significance is solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a block diagram of a hardware structure of an electronic device for implementing a data processing method according to an embodiment of the present application;
FIG. 2 is a flow chart of a method of data processing according to an embodiment of the present application;
FIG. 3 is a flow chart of obtaining supplemental data entered by a target object according to an embodiment of the present application;
FIG. 4 is a flow diagram of tag generation from a timestamp in accordance with an embodiment of the present application;
FIG. 5 is a flow chart of encoding a tag according to an embodiment of the present application;
fig. 6 is a block diagram of a data processing apparatus according to an embodiment of the present application;
FIG. 7 is a flow chart of a method of data processing based on medical health data according to an embodiment of the present application;
FIG. 8 is a schematic diagram of a target knowledge-graph according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
First, some terms or terms appearing in the description of the embodiments of the present application are applicable to the following explanations:
knowledge graph: the knowledge domain visualization or knowledge domain mapping map is a series of different graphs for displaying the relationship between the knowledge development process and the structure, and the visualization technology is used for describing knowledge resources and carriers thereof, mining, analyzing, constructing, drawing and displaying knowledge and the mutual relation between the knowledge resources and the carriers. The modern theory of the multidisciplinary fusion purpose is achieved by combining the theory and method of applying mathematics, graphics, information visualization technology, information science and other disciplines with the method of metrology introduction analysis, co-occurrence analysis and the like and utilizing a visual map to vividly display the core structure, development history, frontier field and overall knowledge framework of the disciplines.
The fact graph is a graph network which takes events as nodes and relations among the events as edges, and is different from the knowledge graph in that most of the entities and relations are stable, most of the relations in the fact graph are uncertain, and the transfer is carried out with a certain probability.
The event knowledge base is a knowledge base formed by combining events as a core through logical relations between the events, and is divided into two routes of an event knowledge base and an abstract event map through different event research objects.
Fig. 1 shows a hardware configuration block diagram of an electronic device (or mobile device) for implementing a data processing method. As shown in fig. 1, an electronic device 10 (e.g., a mobile device) may include one or more (shown as 102a, 102b, … …, 102 n) processors 102 (processor 102 may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 104 for storing data, and a transmission module 106 for communication functions. Besides, the method can also comprise the following steps: a display, an input/output interface (I/O interface), a Universal Serial Bus (USB) port (which may be included as one of the ports of the I/O interface), a network interface, a power source, and/or a camera. It will be understood by those skilled in the art that the structure shown in fig. 1 is only an illustration and is not intended to limit the structure of the electronic device. For example, electronic device 10 may also include more or fewer components than shown in FIG. 1, or have a different configuration than shown in FIG. 1.
It should be noted that the one or more processors 102 and/or other data processing circuitry described above may be referred to generally herein as "data processing circuitry". The data processing circuitry may be embodied in whole or in part in software, hardware, firmware, or any combination thereof. Further, the data processing circuitry may be a single, stand-alone processing module, or incorporated in whole or in part into any of the other elements in the electronic device 10 (or mobile device). As referred to in the embodiments of the application, the data processing circuit acts as a processor control (e.g. selection of a variable resistance termination path connected to the interface).
The memory 104 may be used to store software programs and modules of application software, such as program instructions/data storage devices corresponding to the data processing method in the embodiment of the present application, and the processor 102 executes various functional applications and data processing by executing the software programs and modules stored in the memory 104, and the processor 102 in the embodiment of the present application is used to implement the following functions when executing the program instructions: acquiring physiological data of a target object; determining a target node corresponding to the physiological data and a derivative node associated with the target node in a target knowledge graph; under the condition that the derived node is of the specified type, acquiring supplementary data input by the target object, wherein the supplementary data is used for verifying the validity of the derived node of the specified type; generating a tag set corresponding to the target object at least according to the supplementary data and the data corresponding to the target node, wherein tags in the tag set are used for representing state data of the target object; and generating report information corresponding to the target object according to the labels in the label set, and outputting the report information. The memory 104 may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory located remotely from the processor 102, which may be connected to the electronic device 10 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission module 106 is used to receive or transmit data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the electronic device 10. In one example, the transmission device 106 includes a Network adapter (NIC) that can be connected to other Network devices through a base station to communicate with the internet. In one example, the transmission device 106 can be a Radio Frequency (RF) module, which is used to communicate with the internet in a wireless manner.
The display may be, for example, a touch screen type Liquid Crystal Display (LCD) that may enable a user to interact with a user interface of the electronic device 10 (or mobile device).
It should be noted here that in some alternative embodiments, the computer device (or mobile device) shown in fig. 1 described above may include hardware elements (including circuitry), software elements (including computer code stored on a computer-readable medium), or a combination of both hardware and software elements. It should be noted that fig. 1 is only one example of a particular specific example and is intended to illustrate the types of components that may be present in the computer device (or mobile device) described above.
In accordance with an embodiment of the present application, there is provided a method embodiment of data processing, it should be noted that the steps illustrated in the flowchart of the figure may be performed in a computer system such as a set of computer executable instructions, and that while a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than here.
Under the operating environment, the embodiment of the present application provides a data processing method as shown in fig. 2. Fig. 2 is a flowchart of a data processing method according to an embodiment of the present application, and as shown in fig. 2, the method includes the following steps:
step S202, acquiring physiological data of a target object, wherein the physiological data comprises health data of the current target object acquired through medical detection equipment or portable equipment;
step S204, determining a target node corresponding to the physiological data and a derivative node associated with the target node in a target knowledge graph;
step S206, under the condition that the derived node is in the designated type, acquiring supplementary data input by the target object, wherein the supplementary data is used for verifying the validity of the derived node in the designated type, and the designated type is a node needing to interact with the target object;
step S208, generating a label set corresponding to the target object at least according to the supplementary data and the data corresponding to the target node, wherein labels in the label set are used for representing state data of the target object;
step S210, generating report information corresponding to the target object according to the tags in the tag set, and outputting the report information.
Through the steps, the physiological data of the target object can be acquired; determining a target node corresponding to the physiological data and a derivative node associated with the target node in a target knowledge graph; under the condition that the derived node is of the specified type, acquiring supplementary data input by the target object, wherein the supplementary data are used for verifying the validity of the derived node of the specified type and comprise time information; generating a tag set corresponding to the target object at least according to the supplementary data and the data corresponding to the target node, wherein tags in the tag set are used for representing state data of the target object; report information corresponding to the target object is generated according to the tags in the tag set, and the purpose of generating different dynamic tag sets according to the health conditions of different target objects is achieved, so that the physiological data of the target object at different time stamps are associated with the current physiological data of the target object, and the technical problem that the currently obtained medical health data is lack of correlation and guiding significance is solved.
In step S204, the nodes in the target knowledge-graph include: entity information or concept information in the medical health data, such as condition, diagnosis, medication, instruments, indicators, monitoring data, patient, etc.; an edge in the target knowledge graph represents an associative relationship, such as cause, relevance, risk, contraindication, recommendation, dependency, etc., between two nodes to which the edge connects. According to different association relations between two nodes, it is possible to divide the direction into one or two directions, whether there is a weight, 1 to 1 or 1 to many, whether there is a condition, whether it is continuous (0% to 100%) or binary (0 or 1), and so on. In this step, the target knowledge graph may also be replaced by other computer data formats, such as a relational database, a data list, a data file, a distributed database, and the like.
Two nodes and one edge form a knowledge point, and the knowledge point is related to medical health. The target knowledge-graph may be multi-dimensional, or heterogeneous, in overall vision. According to the target knowledge graph, data or images related to the health condition can be intelligently analyzed, that is, the physiological data of the target object acquired in step S202 may be text data or video data.
In step S204, determining target nodes corresponding to the physiological data and derivative nodes associated with the target nodes in the target knowledge-graph, and if the target nodes corresponding to the physiological data can be directly searched in the target knowledge-graph, directly adding the target nodes into the analysis dataset; when other nodes related to the physiological data are indirectly found in the target knowledge graph through the edges of the graph, namely the association relationship between two nodes, the nodes are called as derived nodes, and meanwhile, the derived nodes are also added into the analysis data set, and the current node and the derived nodes can be in a one-hop relationship or a multi-hop relationship.
In step S206, supplementary data input by the target object is obtained, where the supplementary data is to expand the derived node, and as shown in the flowchart of fig. 3, the method specifically includes the following steps:
step S302, generating prompt information under the condition that the derived node is of a specified type, wherein the prompt information is used for prompting a target object to input the associated information of the derived node;
step S304, receiving the relevant information input by the target object according to the prompt information.
In step S304, after receiving the association information input by the target object according to the prompt information, the method specifically includes the following steps: and judging whether the received associated information meets the condition corresponding to the derivative node of the specified type, and if so, taking the associated information as supplementary data.
In the process of expanding the derivative nodes, under the condition that the derivative nodes are of the specified type, the specified type is the nodes needing to interact with the target object, if more supplementary information is needed, the target object can be required to input the relevant information by generating prompt information in an interaction mode with the target object, when the relevant information input by the target object meets the sufficient condition corresponding to the derivative nodes of the specified type, the relevant data is added into the analysis data set as supplementary data, if the relevant information input by the target object does not meet the sufficient condition corresponding to the derivative nodes of the specified type, the relevant data is excluded, namely, the relevant data is not added into the analysis data set, and the analysis data set can be dynamically updated through the interaction process.
It should be noted that, the process of generating the prompt information may be: different prompt messages are generated based on the types of the health data indicated by the derivative nodes, the data type corresponding to each derivative node corresponds to a prompt message set, one or more prompt messages are selected from the prompt message set according to a preset rule, and the preset rule can be determined according to historical health data or diagnosis and treatment data of the target object. When a plurality of prompt messages exist, receiving corresponding data input by a target object according to at least one of the plurality of prompt messages, specifically, displaying the plurality of prompt messages selected from a set according to a preset rule in an interactive interface of a terminal, wherein a user can select one or more prompt messages from the interactive interface to respond, namely receiving a selection instruction of the target object for the plurality of prompt messages, because the plurality of prompt messages are not necessarily consistent with the actual situation; and inputting corresponding associated information in a dialog interface corresponding to the prompt information selected by the selection instruction.
In another alternative embodiment, all the prompt messages of the prompt message set corresponding to the derived node may also be directly presented in the interactive interface.
The above process is explained by the following example, if the derived node is "hypoglycemia", in order to know the current health status of the target object, the target object is asked whether to use insulin, whether to use an secretagogue (insulin secretagogue), and the like, and these questions correspond to the prompt information of the above process, and the target object inputs the answers to these questions according to its own actual situation.
When judging whether the associated information input by the target object meets the sufficient condition corresponding to the derivative node of the specified type, explaining by taking the derivative node as a hypoglycemic example, if the associated information input by the target object is the current blood sugar of 3.6 and the blood sugar is lower than 3.9, but the target object is a non-pregnant woman, the sufficient condition of hypoglycemia is formed; if the target object is a female in gestation period and the blood sugar is lower than 3.3, the condition of hypoglycemia in gestation period is formed; if it is 'hypoglycemia', the target object 'uses insulin', and the hypoglycemia time is 2-4 hours after insulin injection, which constitutes a sufficient condition of 'insulin overdose'. And according to the actual conditions of different target objects, corresponding to the sufficient conditions of different derivative nodes. If the derived node is not hypoglycemic, no prompt information is generated and the target object is not required to enter the associated information.
In step S208, a tag set corresponding to the target object is generated at least according to the supplementary data and the data corresponding to the target node, as shown in the flowchart of fig. 4, which specifically includes the following steps:
step S402, determining the time stamp of the supplementary data and the time stamp of the data corresponding to the target node, wherein the time stamp comprises at least one of the following: collecting time, relative time information between the current collecting time and historical collecting time;
step S404, generating labels according to the supplementary data and the time stamps of the supplementary data, and the data corresponding to the target node and the time stamps of the data corresponding to the target node.
Since the supplementary data and the data corresponding to the target node are in the analysis dataset, and the analysis dataset also includes the data corresponding to the derivative node, the relevant contents from step S402 to step S404 are also applicable to the derivative node in the analysis dataset. Through analyzing node, label in the data set to and the corresponding timestamp, formed the continuous little data map of target object long-term developments, this little data map and the knowledge map based on a large amount of crowds compare, have as follows different:
1. knowledge-graphs are a common theoretical large data, while small data-graphs are a relatively small subset, but at the same time are concrete examples. For example, a person may be classified into "too light, normal, overweight, obese, very obese" and the like according to the body mass index BMI of the person; but, a specific patient, Zhang three, has a body mass index of 26 and has only one specific "obesity" status label.
2. A small data-graph is an unambiguous data set subject to or subject to known personal information constraints. For example, a normal person has hypoglycemia at a blood sugar level of 3.9 or less, but if it is a pregnant woman in gestation, it is not. Therefore, in a simple report, a large data set of the knowledge graph may not be automatically analyzed and directly subjected to a conclusion set. However, for a known male individual, the small data set can definitely exclude the premise of ' whether the pregnant woman is pregnant ' or not ', and the conclusion of hypoglycemia is automatically obtained.
In step S208, after generating a tag set corresponding to the target object at least according to the supplementary data and the data corresponding to the target node, as shown in the flowchart of fig. 5, the method specifically includes the following steps:
step S502, encoding the labels in the label set to obtain data indexes corresponding to the labels;
step S504, a database corresponding to the tag is searched according to the data index, wherein the database stores corresponding information for processing the tag. If the label is "hypoglycemia", the corresponding database stores, for example, doctor adjustment medication (doctor must have prescription), patient education, intervention means, etc. in terms of patient education, including: methods of dose control to inform the patient of insulin, timing of insulin administration, determination of insulin overdose, and the like, among interventions, include: and immediately eating a food containing 15 g of carbohydrate, etc.
By searching the corresponding database, algorithms and data query results such as corresponding intervention schemes, report tactical templates, corresponding graphs, control targets, follow-up plans and the like can be generated from the small data maps corresponding to the target objects, so that the purposes of science popularization knowledge points, decision judgment following medical science and the like are achieved. Meanwhile, according to the data and the label in the small data map of the target object, intelligent reports such as a webpage version, a voice version, a text version and a video version aiming at the target object and family members are generated respectively through customizing the template, and tools for serving professional persons such as doctors, nurses, health managers and dieticians can be formed. On the other hand, the small data map contains the label, so that the small data map can be used as a data set for machine learning training and verification.
Taking the analysis of dynamic blood glucose data as an example, the generated report should include the summary of the test results, the basic data analysis, the dynamic blood glucose trend map and key index dashboard, and the interpretation of the clinical pathway of glycometabolism kinetics in six steps: the first step, data sufficiency: close-up of temporal coverage; second, prevention of hypoglycemia: a hypoglycemic event close-up; step three, a diurnal fluctuation type: performing MODD map feature; step four, daily volatility: MAGE close-up; step five, blood sugar exposure: averaging blood glucose and estimating a glycation profile; sixth step, close-up of standard reaching rate: blood sugar distribution range map.
In steps S402 to S404, the timestamp in the tag combination includes at least one of: time periods and time points. Some of the data are temporal, such as 21/5/2021, and the glycated hemoglobin content of the target is 8.3%, which is out of limits. The data mark that the sugar control effect 90-120 days before the time point is "not up to standard, over standard". However, in 21.8.8.2021, the glycated hemoglobin is reduced to 6.1%, indicating that 90 days of sugar control reached the standard before 21.8.1 in 2021. As another example, if the target subject has hypoglycemia at 20% of the night between 2-6 points, there is a "nighttime hypoglycemia" label, where 2-6 points are also timestamps. As another example, there are timestamps that are relative time information, such as 4 hours of hypoglycemia after a meal; the time of the glycemic minimum of insulin excess is 3 hours, etc., and here 4 hours after meal and 3 hours of the time of the glycemic minimum are also time stamps.
The embodiment of the application surrounds the correlation of a medical knowledge graph or a medical affair graph, and aims at the personal health service of a target object, intelligently generates a report which corresponds to the target object and has executable significance, generates the report through a small data graph which corresponds to the target object, gives the making right of the small data graph to professionals related to medical health instead of programming engineers, reduces the code requirement, and thus, the system and the small data graph can be continuously improved. Moreover, the resulting report is not a single simple data report, but a comprehensive analytical report of an ever-expanding derivative. And through different templates, different forms of reports are generated, and various analysis report interpretations of medical data are generated by using a language which can be understood by non-professional common people, so that the effective popular science is achieved, repeated interpretation work of doctors and nurses is reduced, and the understanding and compliance of target objects can be improved. The steps in the embodiment of the application can also be applied to other medical health processes, and are suitable for other diseases or other scenes, such as old age-care scenes.
Fig. 6 is a block diagram of a data processing apparatus according to an embodiment of the present application, as shown in fig. 6, the apparatus including:
a first obtaining module 60, configured to obtain physiological data of a target subject, where the physiological data includes health data of a current target subject collected by a medical detection device or a portable device;
a determination module 62 for determining a target node corresponding to the physiological data and a derivative node associated with the target node in the target knowledge-graph;
a second obtaining module 64, configured to obtain, when the derived node is of a specified type, supplemental data input by the target object, where the supplemental data is used to verify validity of the derived node of the specified type, and the specified type is a node that needs to interact with the target object;
a generating module 66, configured to generate a tag set corresponding to the target object according to at least the supplementary data and the data corresponding to the target node, where a tag in the tag set is used to represent state data of the target object; and generating report information corresponding to the target object according to the labels in the label set, and outputting the report information.
In the above data processing apparatus, the nodes in the target knowledge-graph include: entity information in the medical health data, and edges in the target knowledge graph represent the incidence relation between two nodes connected by the edges.
In the second obtaining module 64, the supplementary data input by the target object is obtained, which is specifically represented as: generating prompt information under the condition that the derived node is of a specified type, wherein the prompt information is used for prompting a target object to input the associated information of the derived node; and receiving the associated information input by the target object according to the prompt information. When there are a plurality of prompt messages, data corresponding to the plurality of prompt messages input by the target object is received.
After receiving the associated information input by the target object according to the prompt information, the following steps are specifically required to be implemented: and judging whether the received associated information meets the condition corresponding to the derivative node of the specified type, and if so, taking the associated information as supplementary data.
In the generating module 66, a tag set corresponding to the target object is generated at least according to the supplementary data and the data corresponding to the target node, which is specifically represented as: determining a timestamp of the supplemental data and a timestamp of data corresponding to the target node, wherein the timestamp comprises at least one of: collecting time, relative time information between the current collecting time and historical collecting time; generating a tag according to the supplementary data and the timestamp of the supplementary data, and the data corresponding to the target node and the timestamp of the data corresponding to the target node, wherein the timestamp in the tag set comprises at least one of the following: time periods and time points.
In the generating module 66, after generating the tag set corresponding to the target object at least according to the supplementary data and the data corresponding to the target node, the following process needs to be specifically implemented: encoding the tags in the tag set to obtain data indexes corresponding to the tags; and searching a database corresponding to the tag according to the data index, wherein the database stores corresponding information for processing the tag.
It should be noted that the data processing apparatus shown in fig. 6 is used for executing the data processing methods shown in fig. 2 to 5, and therefore the explanations related to the data processing methods are also applicable to the data processing apparatus, and are not repeated here.
Fig. 7 is a flowchart of a data processing method based on medical health data according to an embodiment of the present application, and as shown in fig. 7, a target knowledge graph or a target event graph as shown in fig. 8 is first established, where the aspects of "hypoglycemia rate" symptom, existing risk, intervention scheme, drug control, diet, and the like are recorded, and the various aspects are finely divided, and physiological data of a target object detected by medical equipment is obtained, and the target knowledge graph or the target event graph is intelligently analyzed according to the physiological data of the target object, and a target node corresponding to the physiological data can be directly searched in the target knowledge graph, and then the target nodes are directly added into an analysis data set; when other nodes related to the physiological data are indirectly found in the target knowledge graph through the edges of the graph, namely the association relationship between two nodes, the nodes are called as derived nodes, and meanwhile, the derived nodes are also added into the analysis data set, and the current node and the derived nodes can be in a one-hop relationship or a multi-hop relationship.
In the process of expanding the derivative nodes, under the condition that the derivative nodes are of the specified type, the specified type is the nodes needing to interact with the target object, if more supplementary information is needed, the target object can be required to input the relevant information by generating prompt information in an interaction mode with the target object, when the relevant information input by the target object meets the sufficient condition corresponding to the derivative nodes of the specified type, the relevant data is used as supplementary data and added into the analysis data set, and a label set corresponding to the target object is generated according to the nodes in the analysis data set. And determining a time stamp of data corresponding to the node in the analysis data set, and forming a small data map corresponding to the target object, wherein the time stamp comprises at least one of the following: relative time information between the acquisition time, the current acquisition time and the historical acquisition time.
And coding the tags in the tag set to obtain a data index corresponding to the tags, and searching a database corresponding to the tags according to the data index, wherein the database stores corresponding information for processing the tags. Algorithms and data query results such as corresponding intervention schemes, report tactical templates, corresponding charts, control targets, follow-up plans and the like can also be generated from small data maps corresponding to the target object so as to achieve the purposes of science popularization medical knowledge, interpretation reports and the like, generate long-term 'continuous cases' of medical health of the target object, manage control and follow-up plans and form a health management continuous evidence-based medical historical data set.
It should be noted that the flowchart of the data processing method based on medical health data shown in fig. 7 is based on the data processing methods shown in fig. 2 to 5, and therefore the explanation of the data processing method is also applicable to the flowchart of the data processing method based on medical health data, and is not repeated here.
The embodiment of the application also provides a nonvolatile storage medium, which comprises a stored program, wherein when the program runs, the device where the nonvolatile storage medium is located is controlled to execute the following data processing method:
acquiring physiological data of a target object, wherein the physiological data comprises health data of the current target object acquired by medical detection equipment or portable equipment;
determining a target node corresponding to the physiological data and a derivative node associated with the target node in a target knowledge graph;
under the condition that the derived node is of the designated type, acquiring supplementary data input by the target object, wherein the supplementary data are used for verifying the effectiveness of the derived node of the designated type, and the designated type is a node needing to interact with the target object;
generating a tag set corresponding to the target object at least according to the supplementary data and the data corresponding to the target node, wherein tags in the tag set are used for representing state data of the target object;
and generating report information corresponding to the target object according to the labels in the label set, and outputting the report information.
In the data processing method, the nodes in the target knowledge graph include: entity information in the medical health data, and edges in the target knowledge graph represent the incidence relation between two nodes connected by the edges.
The method for acquiring the supplementary data input by the target object specifically comprises the following steps: generating prompt information under the condition that the derived node is of a specified type, wherein the prompt information is used for prompting a target object to input the associated information of the derived node; and receiving the associated information input by the target object according to the prompt information. When there are a plurality of prompt messages, data corresponding to the plurality of prompt messages input by the target object is received.
After receiving the associated information input by the target object according to the prompt information, the following steps are also required to be implemented: and judging whether the received associated information meets the condition corresponding to the derivative node of the specified type, and if so, taking the associated information as supplementary data.
Generating a label set corresponding to the target object at least according to the supplementary data and the data corresponding to the target node, which is specifically represented as follows: determining a timestamp of the supplemental data and a timestamp of data corresponding to the target node, wherein the timestamp comprises at least one of: collecting time, relative time information between the current collecting time and historical collecting time; generating a tag according to the supplementary data and the timestamp of the supplementary data, and the data corresponding to the target node and the timestamp of the data corresponding to the target node, wherein the timestamp in the tag set comprises at least one of the following: time periods and time points.
After generating a tag set corresponding to the target object at least according to the supplementary data and the data corresponding to the target node, the method further comprises: encoding the tags in the tag set to obtain data indexes corresponding to the tags; and searching a database corresponding to the tag according to the data index, wherein the database stores corresponding information for processing the tag.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present application, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present application and it should be noted that those skilled in the art can make several improvements and modifications without departing from the principle of the present application, and these improvements and modifications should also be considered as the protection scope of the present application.
Claims (10)
1. A data processing method, comprising:
acquiring physiological data of a target object;
determining a target node corresponding to the physiological data and a derivative node associated with the target node in a target knowledge graph;
under the condition that the derived node is of a specified type, acquiring supplementary data input by the target object, wherein the supplementary data is used for verifying the validity of the derived node of the specified type;
generating a tag set corresponding to the target object at least according to the supplementary data and the data corresponding to the target node, wherein tags in the tag set are used for representing state data of the target object;
and generating report information corresponding to the target object according to the labels in the label set, and outputting the report information.
2. The method of claim 1, wherein the nodes in the target knowledge-graph comprise: entity information in the medical health data, wherein an edge in the target knowledge graph represents an incidence relation between two nodes connected by the edge.
3. The method of claim 1, wherein the obtaining supplemental data input by the target object comprises:
generating prompt information under the condition that the derivative node is of the specified type, wherein the prompt information is used for prompting the target object to input the association information of the derivative node;
and receiving the associated information input by the target object according to the prompt information.
4. The method according to claim 3, wherein when there are a plurality of the prompt messages, data corresponding to at least one of the plurality of prompt messages input by the target object is received.
5. The method according to claim 3, wherein after receiving the association information input by the target object according to the prompt message, the method comprises:
and judging whether the received associated information meets the condition corresponding to the derivative node of the specified type, and if so, taking the associated information as the supplementary data.
6. The method of claim 1, wherein generating the set of tags corresponding to the target object based at least on the supplemental data and the data corresponding to the target node comprises:
determining a timestamp of the supplemental data and a timestamp of data corresponding to the target node, wherein the timestamp comprises at least one of: collecting time, relative time information between the current collecting time and the historical collecting time;
and generating the label according to the supplementary data, the timestamp of the supplementary data, the data corresponding to the target node and the timestamp of the data corresponding to the target node.
7. The method of claim 1, wherein after generating the set of tags corresponding to the target object at least according to the supplemental data and the data corresponding to the target node, the method further comprises: and encoding the tags in the tag set to obtain a data index corresponding to the tags, and searching a database corresponding to the tags according to the data index, wherein the database stores corresponding information for processing the tags.
8. A data processing apparatus, comprising:
the first acquisition module is used for acquiring physiological data of a target object;
a determination module to determine a target node corresponding to the physiological data and a derivative node associated with the target node in a target knowledge-graph;
the second acquisition module is used for acquiring supplementary data input by the target object under the condition that the derived node is of a specified type, wherein the supplementary data is used for verifying the validity of the derived node of the specified type;
a generating module, configured to generate a tag set corresponding to the target object at least according to the supplementary data and data corresponding to the target node, where a tag in the tag set is used to represent state data of the target object; and generating report information corresponding to the target object according to the labels in the label set, and outputting the report information.
9. An electronic device, comprising:
a memory for storing program instructions;
a processor coupled to the memory and configured to perform the following functions when executing the program instructions: acquiring physiological data of a target object; determining a target node corresponding to the physiological data and a derivative node associated with the target node in a target knowledge graph; under the condition that the derived node is of a specified type, acquiring supplementary data input by the target object, wherein the supplementary data is used for verifying the validity of the derived node of the specified type; generating a tag set corresponding to the target object at least according to the supplementary data and the data corresponding to the target node, wherein tags in the tag set are used for representing state data of the target object; and generating report information corresponding to the target object according to the labels in the label set, and outputting the report information.
10. A non-volatile storage medium, comprising a stored program, wherein when the program is executed, a device in which the non-volatile storage medium is located is controlled to execute the data processing method according to any one of claims 1 to 7.
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