CN112489746A - Task pushing method and device for data management, electronic equipment and storage medium - Google Patents
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Abstract
The invention relates to the technical field of digital medical treatment, and provides a task pushing method and device for data management, electronic equipment and a storage medium, wherein the method comprises the following steps: creating baseline data for the patient; creating a decision tree from the baseline data; acquiring a plurality of source data fields from the baseline data, and identifying to obtain a patient number and a task branch number; all branch nodes corresponding to the patient numbers and the task branch numbers are selected from the decision tree and sent to a message queue for asynchronous execution, and the decision tree content corresponding to the task branch numbers is obtained; executing the decision tree and returning an execution result; and determining the task to be pushed according to the execution result. According to the invention, the time difference between the task pushing information in each task branch is calculated in the process of establishing the decision tree according to the baseline data, so that the execution sequence of each task is rapidly determined, the task pushing efficiency is improved, and the follow-up efficiency is improved.
Description
Technical Field
The invention relates to the technical field of digital medical treatment, in particular to a task pushing method and device for data management, electronic equipment and a storage medium.
Background
With the improvement of living standard, the population of diabetics is increased year by year, the existing medical resources are relatively deficient, the systematic management of the blood sugar detection results of the diabetics cannot be realized, particularly, a reasonable reminding mechanism is lacked, each task end (such as a nurse end, a doctor end and an online system) cannot clearly specify tasks and specific time to be executed, and the phenomena of low follow-up efficiency and disordered patient data management caused by the fact that nurses, doctors and online follow-up visits are not in place occur.
Disclosure of Invention
In view of the above, it is necessary to provide a task pushing method, a device, an electronic device, and a storage medium for data management, where a time difference between task pushing information in each task branch is calculated in a process of creating a decision tree according to the baseline data, so as to quickly determine an execution sequence of each task, and the pushing improves task pushing efficiency, thereby improving follow-up efficiency.
A first aspect of the present invention provides a task pushing method for data management, where the method includes:
creating baseline data for the patient;
creating a decision tree from the baseline data;
acquiring a plurality of source data fields from the baseline data, and identifying the plurality of source data fields to obtain a patient number and a task branch number;
all branch nodes corresponding to the patient numbers and the task branch numbers are selected from the decision tree and sent to a message queue for asynchronous execution, and the decision tree content corresponding to the task branch numbers is obtained;
executing the decision tree based on the decision tree content and returning an execution result;
and determining the task to be pushed according to the execution result.
Optionally, the creating a decision tree according to the baseline data includes:
analyzing the baseline data to obtain a plurality of task branches and corresponding task pushing information;
selecting task pushing information corresponding to any task branch as target task pushing information;
randomly selecting a target task push message as a root node of the task branch;
calculating the time difference between any other target task push information and the target task push information at the root node;
judging whether a child node with the same time difference exists or not;
when judging that a child node with the same time difference exists, taking the child node as a father node, and taking the rest target task push information as child nodes of the father node;
and when judging that no child node with the same time difference exists, taking the root node as a father node, and taking the rest target task push information as child nodes of the father node, wherein the time difference between the target task push information at the father node and the target task push information at the corresponding child node is taken as the weight of an edge between the father node and the child node.
Optionally, the creating baseline data of the patient includes:
receiving the treatment information of the client, and sending the treatment information to the first service end;
receiving a treatment strategy reported by a first service end, wherein the treatment strategy is set by the first service end according to the treatment information of the patient and a treatment hospital according to a preset treatment rule;
monitoring detection data of the client and acquiring to obtain target detection data;
and generating baseline data for the acquired target detection data according to the visit strategy.
Optionally, after the identifying the plurality of source data fields obtains the patient number and the task branch number, the method further includes:
acquiring the disease grade of the patient according to the patient number;
determining whether to execute the decision tree asynchronously according to the ill grade;
when the decision tree is determined not to be asynchronously executed according to the ill grade, the decision tree content corresponding to the task branch number is obtained; or
And when the decision tree is determined to be executed asynchronously according to the disease level, executing all branch nodes corresponding to the patient numbers and the task branch numbers selected from the decision tree and sending the branch nodes to a message queue for asynchronous execution.
Optionally, the executing the decision tree based on the decision tree content and returning an execution result includes:
identifying a first node in the decision tree content corresponding to the task branch;
executing all nodes at the next stage of the first node to obtain an execution result;
when the execution results returned by all nodes at the next stage are all pushed, returning the execution results;
and when the execution result returned by any node of the next stage is not pushed, determining a second node which is the same as the first node and has a priority lower than that of the first node, executing all nodes of the next stage corresponding to the second node, and returning the execution result until the pushing results returned by all nodes of the next stage are all pushed.
Optionally, the determining, according to the execution result, the task to be pushed includes:
acquiring a node to be pushed from the execution result;
and determining the task to be pushed according to the task type and the task content in the node to be pushed.
Optionally, the method further includes:
executing the task to be pushed to obtain follow-up data;
when abnormal data appear in the follow-up data, judging whether the abnormal data meet a preset condition for triggering a new strategy tree;
triggering a new strategy tree when the abnormal data is determined to meet the preset condition for triggering the new strategy tree;
updating the policy tree based on the anomalous data when it is determined that the anomalous data does not satisfy a preset condition that triggers a new policy tree.
A second aspect of the present invention provides a task pushing device for data management, the device comprising:
a first creation module to create baseline data for a patient;
a second creation module to create a decision tree based on the baseline data;
the identification module is used for acquiring a plurality of source data fields from the baseline data, and identifying the plurality of source data fields to obtain a patient number and a task branch number;
the first execution module is used for selecting all branch nodes corresponding to the patient numbers and the task branch numbers from the decision tree and sending the branch nodes to a message queue for asynchronous execution to obtain the decision tree contents corresponding to the task branch numbers;
the second execution module is used for executing the decision tree based on the decision tree content and returning an execution result;
and the determining module is used for determining the task to be pushed according to the execution result.
A third aspect of the present invention provides an electronic device, which includes a processor and a memory, wherein the processor is configured to implement the task pushing method for data management when executing a computer program stored in the memory.
A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the task pushing method for data management.
In summary, according to the data management task pushing method, the data management task pushing device, the electronic device and the storage medium, on one hand, in the process of creating the decision tree according to the baseline data, the execution sequence of each task in each task branch in the decision tree is rapidly determined by calculating the time difference between task pushing information in each task branch, so that the accuracy and efficiency of task pushing and follow-up can be improved, and the efficiency of data management is further improved; on the other hand, all branch nodes corresponding to the patient numbers and the task branch numbers are selected from the decision tree and sent to a message queue for asynchronous execution, so that the phenomenon of disordered data management caused by task missing pushing when too many patients are caused is avoided, and the accuracy of task pushing management is improved; and finally, if abnormal data appear in the follow-up data obtained by executing the task to be pushed, determining that the ill condition of the patient changes, and triggering a new strategy tree or updating the strategy tree in time according to the abnormal data, so that the timeliness of patient data management is improved.
Drawings
Fig. 1 is a flowchart of a task pushing method for data management according to an embodiment of the present invention.
Fig. 2 is a structural diagram of a task pushing device for data management according to a second embodiment of the present invention.
Fig. 3 is a schematic structural diagram of an electronic device according to a third embodiment of the present invention.
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, a detailed description of the present invention will be given below with reference to the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present invention and features of the embodiments may be combined with each other without conflict.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
Example one
Fig. 1 is a flowchart of a task pushing method for data management according to an embodiment of the present invention.
In this embodiment, the task pushing method for data management may be applied to an electronic device, and for an electronic device that needs to perform task pushing for data management, a function of task pushing for data management provided by the method of the present invention may be directly integrated on the electronic device, or may be run in the electronic device in a form of Software Development Kit (SKD).
As shown in fig. 1, the task pushing method for data management specifically includes the following steps, and the order of the steps in the flowchart may be changed and some may be omitted according to different requirements.
S11, baseline data for the patient is created.
In this embodiment, the baseline data is created according to the visit information of the patient, the hospital information of the hospital for the patient to visit and the diagnosis and treatment data of the patient, and the server obtains the diagnosis and treatment data of the patient by receiving the medical equipment and transmitting the data in real time, wherein the diagnosis and treatment data of the patient includes the historical blood glucose measurement data and the current blood glucose measurement data of the patient.
Optionally, the creating baseline data for the patient comprises:
receiving the treatment information of the client, and sending the treatment information to the first service end;
receiving a treatment strategy reported by a first service end, wherein the treatment strategy is set by the first service end according to the treatment information of the patient and a treatment hospital according to a preset treatment rule;
monitoring detection data of the client and acquiring to obtain target detection data;
and generating baseline data for the acquired target detection data according to the visit strategy.
In this embodiment, the first server is configured to receive the visit information uploaded by the client, and specifically, the first server may be a nurse end, a doctor end, or another end that can be used to enter the visit information.
In this embodiment, the first service end receives the visit information sent by the client, specifically, the visit information may be a text input, or a received video or voice message, and if the visit information received by the first service end is a text, the visit information is directly entered into the first service end; if the visit information received by the first service terminal is a video, extracting the voice information of the patient from the video, converting the voice information into text information and inputting the text information into the first service terminal; if the diagnosis information received by the first service terminal is voice information, the voice information is converted into text information by adopting a voice recognition technology and is recorded into the first service terminal. By receiving different forms of encounter information, the efficiency with which the patient provides encounter information is improved.
In this embodiment, the visit policy is set by the first server according to the visit information of the patient and the visit hospital according to a preset visit rule, and different visit policies are specified for different patients, for example, for patient a, visit policy 1: if the number of times of insulin injection per week exceeds 2 times or continuous 3 times of blood sugar measurement in 5 times per week do not reach the standard, the corresponding doctor is the principal and subordinate doctor B; visit strategy 2: if the number of times of insulin injection per week exceeds 4 times or 5 times of blood glucose measurement per week do not reach the standard, the doctor corresponding to the doctor is expert C; visit strategy 3: if the number of times of insulin injection per week is less than 2 times or 1 time of blood glucose measurement in 5 times per week does not reach the standard, the corresponding doctor is a general doctor D.
In this embodiment, the acquired detection data of the client is generated into the baseline data according to the visit policy, so that different baseline data can be generated according to the acquired detection data of each patient, and the diversity of the baseline data is improved.
S12, creating a decision tree according to the baseline data.
In this embodiment, the decision tree is created according to the baseline data, and specifically, the creating a decision tree according to the baseline data includes:
analyzing the baseline data to obtain a plurality of task branches and corresponding task pushing information;
selecting task pushing information corresponding to any task branch as target task pushing information;
randomly selecting a target task push message as a root node of the task branch;
calculating the time difference between any other target task push information and the target task push information at the root node;
judging whether a child node with the same time difference exists or not;
when judging that a child node with the same time difference exists, taking the child node as a father node, and taking the rest target task push information as child nodes of the father node;
and when judging that no child node with the same time difference exists, taking the root node as a father node, and taking the rest target task push information as child nodes of the father node, wherein the time difference between the target task push information at the father node and the target task push information at the corresponding child node is taken as the weight of an edge between the father node and the child node.
In this embodiment, the task branch includes: nurse follow-up visit task, doctor follow-up visit task, on-line follow-up visit task, patient measurement task and patient injection task etc. each task branch corresponds a plurality of task propelling movement information, for example: the plurality of task pushing information corresponding to the nurse follow-up task branch comprises the following steps:
in this embodiment, the decision tree includes a plurality of task branches, each task branch corresponds to at least one task node, each task node includes specific task push information, the task push information corresponding to one task branch is randomly selected as target task push information, then one target task push information is randomly selected from the target task push information as a root node of the task branch, a time difference between any other target task push information and the target task push information at the root node is calculated, a task execution sequence can be determined according to the calculated time difference, when it is determined that a child node identical to the time difference exists, the child node is used as a parent node, and the other target task push information is used as a child node of the parent node; and when judging that no child node with the same time difference exists, taking the root node as a father node, and taking the rest target task push information as child nodes of the father node, wherein all task branches in the decision tree have the same creation method.
In this embodiment, a time difference between the target task push information at the parent node and the target task push information at the corresponding child node is used as a weight of an edge between the parent node and the child node, and the child nodes at the same level may be prioritized according to the weight, and specifically, the child nodes at the same level may be sequentially ranked from left to right according to a descending order of the weight.
In the embodiment, in the process of creating the decision tree according to the baseline data, the execution sequence of each task in each task branch in the decision tree is quickly determined by calculating the time difference between the task pushing information in each task branch, so that the accuracy and efficiency of task pushing can be improved, and the follow-up efficiency and the data management efficiency are improved.
And S13, acquiring a plurality of source data fields from the baseline data, and identifying the plurality of source data fields to obtain the patient number and the task branch number.
In this embodiment, the source data field may be preset, and different preset source data fields represent different parameters in the baseline data, and specifically, the plurality of source data fields include the patient number and the task branch number, and the patient number and the task branch number may be identified according to the source data field.
In some other embodiments, after the identifying the plurality of source data fields results in the patient number and the task branch number, the method further comprises:
acquiring the disease grade of the patient according to the patient number;
and determining whether to execute the decision tree asynchronously according to the ill grade.
When it is determined that asynchronous execution is not performed on the decision tree according to the ill degree, obtaining content of the decision tree corresponding to the task branch number, and executing S15; or
When it is determined to execute the decision tree asynchronously according to the prevalence level, S14 is performed.
In this embodiment, the disease grade of the patient may be preset, and different grades may be set according to different parameters corresponding to different diseases, for example: for diabetes, if the blood sugar value exceeds M, setting the blood sugar value to be level III, if the blood sugar value is between N, setting the blood sugar value to be level II, and if the blood sugar value is lower than P, setting the blood sugar value to be level I; the strategy tree can be executed in different modes according to different disease levels, so that the diversity of executing the strategy tree is improved.
Illustratively, if the patient's illness level is general, the patient can be directly executed in the decision tree, all branch nodes corresponding to the patient number and the task branch number do not need to be selected from the decision tree and sent to a message queue for asynchronous execution, if the patient's illness level is serious, the patient needs to be followed up in real time, the patient needs to be asynchronously executed, and follow-up efficiency and follow-up quality are improved.
S14, all branch nodes corresponding to the patient numbers and the task branch numbers are selected from the decision tree and sent to a message queue for asynchronous execution, and the decision tree content corresponding to the task branch numbers is obtained.
In this embodiment, the content of the decision tree includes, but is not limited to, the task branch corresponding to the task branch number, all branch nodes corresponding to the task branch, and the task type and task content corresponding to each branch node.
In this embodiment, all branch nodes corresponding to the patient numbers and the task branch numbers are selected from the decision tree and sent to the message queue for asynchronous execution, so that a phenomenon of data management confusion caused by task missing pushing when too many patients are present is avoided, and accuracy of task pushing management is improved.
S15, executing the decision tree based on the decision tree content and returning the execution result.
In this embodiment, since the content of the decision tree includes the direction of the next node, the decision tree is executed based on the content of the decision tree and an execution result is returned.
Optionally, the executing the decision tree based on the decision tree content and returning an execution result includes:
identifying a first node in the decision tree content corresponding to the task branch;
executing all nodes at the next stage of the first node to obtain an execution result;
when the execution results returned by all nodes at the next stage are all pushed, returning the execution results;
and when the execution result returned by any node of the next stage is not pushed, determining a second node which is the same as the first node and has a priority lower than that of the first node, executing all nodes of the next stage corresponding to the second node, and returning the execution result until the pushing results returned by all nodes of the next stage are all pushed.
In this embodiment, the next stage of each task branch may correspond to one node or multiple nodes, and the multiple nodes are sequentially ordered according to priority, and when all the nodes of the next stage of the first node are executed and the execution results are pushed, the returned execution results are pushed of the first node; and when the execution result returned by any node of the next level is not pushed, determining not to push the first node, and possibly pushing all nodes of the next level corresponding to the second priority node of the same level as the first node, until the execution result returned by all nodes of the next level is the node corresponding to pushing.
In this embodiment, since the nodes corresponding to each task branch are different, when only all nodes of the next stage corresponding to all nodes return to push, it is determined that the task corresponding to the node needs to be pushed, and the accuracy of task pushing is improved.
And S16, determining the task to be pushed according to the execution result.
In this embodiment, the execution result includes a task type, a task content, and the like, and after the execution result is obtained, the task to be pushed is determined according to the execution result.
Optionally, the determining, according to the execution result, the task to be pushed includes:
acquiring a node to be pushed from the execution result;
and determining the task to be pushed according to the task type and the task content in the node to be pushed.
In the embodiment, the task to be pushed is determined according to the task type and the task content in the pushing node in the execution result, so that the accuracy of obtaining the task to be pushed is improved, and the management efficiency of task pushing is further improved.
Further, the method further comprises;
executing the task to be pushed to obtain follow-up data;
when abnormal data appear in the follow-up data, judging whether the abnormal data meet a preset condition for triggering a new strategy tree;
triggering a new strategy tree when the abnormal data is determined to meet the preset condition for triggering the new strategy tree;
updating the policy tree based on the anomalous data when it is determined that the anomalous data does not satisfy a preset condition that triggers a new policy tree.
In this embodiment, updating the policy tree includes adding, deleting, or changing a task branch or a next node in the policy tree.
In this embodiment, if it is determined that the diseased condition of the patient has changed when abnormal data occurs in the follow-up data obtained by executing the task to be pushed, a new policy tree needs to be triggered or updated in time according to the abnormal data, so that timeliness of patient data management is improved.
Further, the method further comprises:
and when abnormal data does not appear in the follow-up data, continuously executing the strategy tree.
In summary, the task pushing method for data management according to this embodiment creates baseline data of a patient; creating a decision tree from the baseline data; acquiring a plurality of source data fields from the baseline data, and identifying the plurality of source data fields to obtain a patient number and a task branch number; executing all branch nodes corresponding to the patient numbers and the task branch numbers selected from the decision tree and sending the branch nodes to a message queue for asynchronous execution to obtain the decision tree content corresponding to the task branch numbers; executing the decision tree based on the decision tree content and returning an execution result; and determining the task to be pushed according to the execution result.
In this embodiment, on one hand, in the process of creating the decision tree according to the baseline data, by calculating a time difference between task push information in each task branch, an execution sequence of each task in each task branch in the decision tree is quickly determined, so that accuracy and efficiency of task push can be improved, and further, efficiency of data management is improved; on the other hand, all branch nodes corresponding to the patient numbers and the task branch numbers are selected from the decision tree and sent to a message queue for asynchronous execution, so that the phenomenon of disordered data management caused by task missing push when too many patients are caused is avoided, and the accuracy of task push management is improved; and finally, if abnormal data appear in the follow-up data obtained by executing the task to be pushed, determining that the ill condition of the patient changes, and triggering a new strategy tree or updating the strategy tree in time according to the abnormal data, so that the timeliness of patient data management is improved.
Example two
Fig. 2 is a structural diagram of a task pushing device for data management according to a second embodiment of the present invention.
In some embodiments, the data-managed task pushing device 20 may include a plurality of functional modules composed of program code segments. The program codes of the program segments in the data management task pushing device 20 may be stored in a memory of the electronic device and executed by the at least one processor to perform (see fig. 1 for details) task pushing of data management.
In this embodiment, the task pushing device 20 for data management may be divided into a plurality of functional modules according to the functions executed by the task pushing device. The functional module may include: a first creating module 201, a second creating module 202, an identifying module 203, a first executing module 204, a second executing module 205, a determining module 206 and a judging module 207. The module referred to herein is a series of computer program segments capable of being executed by at least one processor and capable of performing a fixed function and is stored in memory. In the present embodiment, the functions of the modules will be described in detail in the following embodiments.
A first creation module 201 for creating baseline data for a patient.
In this embodiment, the baseline data is created according to the visit information of the patient, the hospital information of the hospital for the patient to visit and the diagnosis and treatment data of the patient, and the server obtains the diagnosis and treatment data of the patient by receiving the medical equipment and transmitting the data in real time, wherein the diagnosis and treatment data of the patient includes the historical blood glucose measurement data and the current blood glucose measurement data of the patient.
Optionally, the first creating module 201 creating the baseline data of the patient comprises:
receiving the treatment information of the client, and sending the treatment information to the first service end;
receiving a treatment strategy reported by a first service end, wherein the treatment strategy is set by the first service end according to the treatment information of the patient and a treatment hospital according to a preset treatment rule;
monitoring detection data of the client and acquiring to obtain target detection data;
and generating baseline data for the acquired target detection data according to the visit strategy.
In this embodiment, the first server is configured to receive the visit information uploaded by the client, and specifically, the first server may be a nurse end, a doctor end, or another end that can be used to enter the visit information.
In this embodiment, the first service end receives the visit information sent by the client, specifically, the visit information may be a text input, or a received video or voice message, and if the visit information received by the first service end is a text, the visit information is directly entered into the first service end; if the visit information received by the first service terminal is a video, extracting the voice information of the patient from the video, converting the voice information into text information and inputting the text information into the first service terminal; if the diagnosis information received by the first service terminal is voice information, the voice information is converted into text information by adopting a voice recognition technology and is recorded into the first service terminal. By receiving different forms of encounter information, the efficiency with which the patient provides encounter information is improved.
In this embodiment, the visit policy is set by the first server according to the visit information of the patient and the visit hospital according to a preset visit rule, and different visit policies are specified for different patients, for example, for patient a, visit policy 1: if the number of times of insulin injection per week exceeds 2 times or continuous 3 times of blood sugar measurement in 5 times per week do not reach the standard, the corresponding doctor is the principal and subordinate doctor B; visit strategy 2: if the number of times of insulin injection per week exceeds 4 times or 5 times of blood glucose measurement per week do not reach the standard, the doctor corresponding to the doctor is expert C; visit strategy 3: if the number of times of insulin injection per week is less than 2 times or 1 time of blood glucose measurement in 5 times per week does not reach the standard, the corresponding doctor is a general doctor D.
In this embodiment, the acquired detection data of the client is generated into the baseline data according to the visit policy, so that different baseline data can be generated according to the acquired detection data of each patient, and the diversity of the baseline data is improved.
A second creating module 202 for creating a decision tree based on the baseline data.
In this embodiment, the decision tree is created according to the baseline data, and specifically, the creating a decision tree by the second creating module 202 according to the baseline data includes:
analyzing the baseline data to obtain a plurality of task branches and corresponding task pushing information;
selecting task pushing information corresponding to any task branch as target task pushing information;
randomly selecting a target task push message as a root node of the task branch;
calculating the time difference between any other target task push information and the target task push information at the root node;
judging whether a child node with the same time difference exists or not;
when judging that a child node with the same time difference exists, taking the child node as a father node, and taking the rest target task push information as child nodes of the father node;
and when judging that no child node with the same time difference exists, taking the root node as a father node, and taking the rest target task push information as child nodes of the father node, wherein the time difference between the target task push information at the father node and the target task push information at the corresponding child node is taken as the weight of an edge between the father node and the child node.
In this embodiment, the task branch includes: nurse follow-up visit task, doctor follow-up visit task, on-line follow-up visit task, patient measurement task and patient injection task etc. each task branch corresponds a plurality of task propelling movement information, for example: the plurality of task pushing information corresponding to the nurse follow-up task branch comprises the following steps:
in this embodiment, the decision tree includes a plurality of task branches, each task branch corresponds to at least one task node, each task node includes specific task push information, the task push information corresponding to one task branch is randomly selected as target task push information, then one target task push information is randomly selected from the target task push information as a root node of the task branch, a time difference between any other target task push information and the target task push information at the root node is calculated, a task execution sequence can be determined according to the calculated time difference, when it is determined that a child node identical to the time difference exists, the child node is used as a parent node, and the other target task push information is used as a child node of the parent node; and when judging that no child node with the same time difference exists, taking the root node as a father node, and taking the rest target task push information as child nodes of the father node, wherein all task branches in the decision tree have the same creation method.
In this embodiment, a time difference between the target task push information at the parent node and the target task push information at the corresponding child node is used as a weight of an edge between the parent node and the child node, and the child nodes at the same level may be prioritized according to the weight, and specifically, the child nodes at the same level may be sequentially ranked from left to right according to a descending order of the weight.
In the embodiment, in the process of creating the decision tree according to the baseline data, the execution sequence of each task in each task branch in the decision tree is quickly determined by calculating the time difference between the task pushing information in each task branch, so that the accuracy and efficiency of task pushing can be improved, and the follow-up efficiency and the data management efficiency are improved.
The identifying module 203 is configured to obtain a plurality of source data fields from the baseline data, and identify the plurality of source data fields to obtain a patient number and a task branch number.
In this embodiment, the source data field may be preset, and different preset source data fields represent different parameters in the baseline data, and specifically, the plurality of source data fields include the patient number and the task branch number, and the patient number and the task branch number may be identified according to the source data field.
In other embodiments, after the identifying module 203 identifies the source data fields to obtain the patient number and the task branch number, the patient grade of the patient is obtained according to the patient number; and determining whether to execute the decision tree asynchronously according to the ill grade.
When the decision tree is determined not to be asynchronously executed according to the ill grade, the decision tree content corresponding to the task branch number is obtained; or
And when the decision tree is determined to be executed asynchronously according to the disease level, executing all branch nodes corresponding to the patient numbers and the task branch numbers selected from the decision tree and sending the branch nodes to a message queue for asynchronous execution.
In this embodiment, the disease grade of the patient may be preset, and different grades may be set according to different parameters corresponding to different diseases, for example: for diabetes, if the blood sugar value exceeds M, setting the blood sugar value to be level III, if the blood sugar value is between N, setting the blood sugar value to be level II, and if the blood sugar value is lower than P, setting the blood sugar value to be level I; the strategy tree can be executed in different modes according to different disease levels, so that the diversity of executing the strategy tree is improved.
Illustratively, if the patient's illness level is general, the patient can be directly executed in the decision tree, all branch nodes corresponding to the patient number and the task branch number do not need to be selected from the decision tree and sent to a message queue for asynchronous execution, if the patient's illness level is serious, the patient needs to be followed up in real time, the patient needs to be asynchronously executed, and follow-up efficiency and follow-up quality are improved.
A first executing module 204, configured to select all branch nodes corresponding to the patient number and the task branch number from the decision tree, and send the branch nodes to a message queue for asynchronous execution, so as to obtain the decision tree content corresponding to the task branch number.
In this embodiment, the content of the decision tree includes, but is not limited to, the task branch corresponding to the task branch number, all branch nodes corresponding to the task branch, and the task type and task content corresponding to each branch node.
In this embodiment, all branch nodes corresponding to the patient numbers and the task branch numbers are selected from the decision tree and sent to the message queue for asynchronous execution, so that a phenomenon of data management confusion caused by task missing pushing when too many patients are present is avoided, and accuracy of task pushing management is improved.
A second executing module 205, configured to execute the decision tree based on the decision tree content and return an execution result.
In this embodiment, since the content of the decision tree includes the direction of the next node, the decision tree is executed based on the content of the decision tree and an execution result is returned.
Optionally, the executing the decision tree by the second executing module 205 based on the decision tree content and returning an execution result includes:
identifying a first node in the decision tree content corresponding to the task branch;
executing all nodes at the next stage of the first node to obtain an execution result;
when the execution results returned by all nodes at the next stage are all pushed, returning the execution results;
and when the execution result returned by any node of the next stage is not pushed, determining a second node which is the same as the first node and has a priority lower than that of the first node, executing all nodes of the next stage corresponding to the second node, and returning the execution result until the pushing results returned by all nodes of the next stage are all pushed.
In this embodiment, the next stage of each task branch may correspond to one node or multiple nodes, and the multiple nodes are sequentially ordered according to priority, and when all the nodes of the next stage of the first node are executed and the execution results are pushed, the returned execution results are pushed of the first node; and when the execution result returned by any node of the next level is not pushed, determining not to push the first node, and possibly pushing all nodes of the next level corresponding to the second priority node of the same level as the first node, until the execution result returned by all nodes of the next level is the node corresponding to pushing.
In this embodiment, since the nodes corresponding to each task branch are different, when only all nodes of the next stage corresponding to all nodes return to push, it is determined that the task corresponding to the node needs to be pushed, and the accuracy of task pushing is improved.
A determining module 206, configured to determine, according to the execution result, a task to be pushed.
In this embodiment, the execution result includes a task type, a task content, and the like, and after the execution result is obtained, the task to be pushed is determined according to the execution result.
Optionally, the determining module 206 determines, according to the execution result, that the task to be pushed includes:
acquiring a node to be pushed from the execution result;
and determining the task to be pushed according to the task type and the task content in the node to be pushed.
In the embodiment, the task to be pushed is determined according to the task type and the task content in the pushing node in the execution result, so that the accuracy of obtaining the task to be pushed is improved, and the management efficiency of task pushing is further improved.
Further, in some other embodiments, the second executing module 205 is further configured to execute the task to be pushed to obtain follow-up data.
A judging module 207, configured to, when abnormal data occurs in the follow-up data, judge whether the abnormal data meets a preset condition for triggering a new policy tree; triggering a new strategy tree when the abnormal data is determined to meet the preset condition for triggering the new strategy tree; updating the policy tree based on the anomalous data when it is determined that the anomalous data does not satisfy a preset condition that triggers a new policy tree.
In this embodiment, updating the policy tree includes adding, deleting, or changing a task branch or a next node in the policy tree.
In this embodiment, if it is determined that the diseased condition of the patient has changed when abnormal data occurs in the follow-up data obtained by executing the task to be pushed, a new policy tree needs to be triggered or updated in time according to the abnormal data, so that timeliness of patient data management is improved.
Further, when abnormal data does not appear in the follow-up data, the strategy tree is continuously executed.
In summary, the task pushing device for data management according to this embodiment creates baseline data of a patient; creating a decision tree from the baseline data; acquiring a plurality of source data fields from the baseline data, and identifying the plurality of source data fields to obtain a patient number and a task branch number; executing all branch nodes corresponding to the patient numbers and the task branch numbers selected from the decision tree and sending the branch nodes to a message queue for asynchronous execution to obtain the decision tree content corresponding to the task branch numbers; executing the decision tree based on the decision tree content and returning an execution result; and determining the task to be pushed according to the execution result.
In this embodiment, on one hand, in the process of creating the decision tree according to the baseline data, by calculating a time difference between task push information in each task branch, an execution sequence of each task in each task branch in the decision tree is quickly determined, so that accuracy and efficiency of task push can be improved, and further, efficiency of data management is improved; on the other hand, all branch nodes corresponding to the patient numbers and the task branch numbers are selected from the decision tree and sent to a message queue for asynchronous execution, so that the phenomenon of disordered data management caused by task missing push when too many patients are caused is avoided, and the accuracy of task push management is improved; and finally, if abnormal data appear in the follow-up data obtained by executing the task to be pushed, determining that the ill condition of the patient changes, and triggering a new strategy tree or updating the strategy tree in time according to the abnormal data, so that the timeliness of patient data management is improved.
EXAMPLE III
Fig. 3 is a schematic structural diagram of an electronic device according to a third embodiment of the present invention. In the preferred embodiment of the present invention, the electronic device 3 comprises a memory 31, at least one processor 32, at least one communication bus 33 and a transceiver 34.
It will be appreciated by those skilled in the art that the configuration of the electronic device shown in fig. 3 does not constitute a limitation of the embodiment of the present invention, and may be a bus-type configuration or a star-type configuration, and the electronic device 3 may include more or less other hardware or software than those shown, or a different arrangement of components.
In some embodiments, the electronic device 3 is an electronic device capable of automatically performing numerical calculation and/or information processing according to instructions set or stored in advance, and the hardware thereof includes but is not limited to a microprocessor, an application specific integrated circuit, a programmable gate array, a digital processor, an embedded device, and the like. The electronic device 3 may also include a client device, which includes, but is not limited to, any electronic product that can interact with a client through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device, for example, a personal computer, a tablet computer, a smart phone, a digital camera, and the like.
It should be noted that the electronic device 3 is only an example, and other existing or future electronic products, such as those that can be adapted to the present invention, should also be included in the scope of the present invention, and are included herein by reference.
In some embodiments, the memory 31 is used for storing program codes and various data, such as the task pushing device 20 installed in the electronic device 3 for data management, and realizes high-speed and automatic access to programs or data during the operation of the electronic device 3. The Memory 31 includes a Read-Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), a One-time Programmable Read-Only Memory (OTPROM), an electronically Erasable rewritable Read-Only Memory (Electrically-Erasable Programmable Read-Only Memory (EEPROM)), an optical Read-Only disk (CD-ROM) or other optical disk Memory, a magnetic disk Memory, a tape Memory, or any other medium readable by a computer capable of carrying or storing data.
In some embodiments, the at least one processor 32 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same or different functions, including one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips. The at least one processor 32 is a Control Unit (Control Unit) of the electronic device 3, connects various components of the electronic device 3 by using various interfaces and lines, and executes various functions and processes data of the electronic device 3 by running or executing programs or modules stored in the memory 31 and calling data stored in the memory 31.
In some embodiments, the at least one communication bus 33 is arranged to enable connection communication between the memory 31 and the at least one processor 32 or the like.
Although not shown, the electronic device 3 may further include a power supply (such as a battery) for supplying power to each component, and optionally, the power supply may be logically connected to the at least one processor 32 through a power management device, so as to implement functions of managing charging, discharging, and power consumption through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device 3 may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.
The integrated unit implemented in the form of a software functional module may be stored in a computer-readable storage medium. The software functional module is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, an electronic device, or a network device) or a processor (processor) to execute parts of the methods according to the embodiments of the present invention.
In a further embodiment, in conjunction with fig. 2, the at least one processor 32 may execute an operating device of the electronic device 3 and various installed application programs (such as the task pushing device 20 for data management), program codes, and the like, for example, the above modules.
The memory 31 has program code stored therein, and the at least one processor 32 can call the program code stored in the memory 31 to perform related functions. For example, the modules described in fig. 2 are program codes stored in the memory 31 and executed by the at least one processor 32, so as to implement the functions of the modules for the purpose of task pushing of data management.
In one embodiment of the present invention, the memory 31 stores a plurality of instructions executed by the at least one processor 32 to implement the function of task pushing for data management.
Specifically, the at least one processor 32 may refer to the description of the relevant steps in the embodiment corresponding to fig. 1, and details are not repeated here.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention 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, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned. Furthermore, it is obvious that the word "comprising" does not exclude other elements or that the singular does not exclude the plural. A plurality of units or means recited in the present invention may also be implemented by one unit or means through software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. A task pushing method for data management, characterized in that the method comprises:
creating baseline data for the patient;
creating a decision tree from the baseline data;
acquiring a plurality of source data fields from the baseline data, and identifying the plurality of source data fields to obtain a patient number and a task branch number;
all branch nodes corresponding to the patient numbers and the task branch numbers are selected from the decision tree and sent to a message queue for asynchronous execution, and the decision tree content corresponding to the task branch numbers is obtained;
executing the decision tree based on the decision tree content and returning an execution result;
and determining the task to be pushed according to the execution result.
2. The data management task pushing method of claim 1, wherein the creating a decision tree from the baseline data comprises:
analyzing the baseline data to obtain a plurality of task branches and corresponding task pushing information;
selecting task pushing information corresponding to any task branch as target task pushing information;
randomly selecting a target task push message as a root node of the task branch;
calculating the time difference between any other target task push information and the target task push information at the root node;
judging whether a child node with the same time difference exists or not;
when judging that a child node with the same time difference exists, taking the child node as a father node, and taking the rest target task push information as child nodes of the father node;
and when judging that no child node with the same time difference exists, taking the root node as a father node, and taking the rest target task push information as child nodes of the father node, wherein the time difference between the target task push information at the father node and the target task push information at the corresponding child node is taken as the weight of an edge between the father node and the child node.
3. The data management task pushing method of claim 1, wherein the creating baseline data for a patient comprises:
receiving the treatment information of the client, and sending the treatment information to the first service end;
receiving a treatment strategy reported by a first service end, wherein the treatment strategy is set by the first service end according to the treatment information of the patient and a treatment hospital according to a preset treatment rule;
monitoring detection data of the client and acquiring to obtain target detection data;
and generating baseline data for the acquired target detection data according to the visit strategy.
4. The method for task pushing for data management of claim 1, wherein after identifying the plurality of source data fields for a patient number and a task branch number, the method further comprises:
acquiring the disease grade of the patient according to the patient number;
determining whether to execute the decision tree asynchronously according to the ill grade;
when the decision tree is determined not to be asynchronously executed according to the ill grade, the decision tree content corresponding to the task branch number is obtained; or
And when the decision tree is determined to be executed asynchronously according to the disease level, executing all branch nodes corresponding to the patient numbers and the task branch numbers selected from the decision tree and sending the branch nodes to a message queue for asynchronous execution.
5. The data management task pushing method of claim 1, wherein the executing the decision tree based on the decision tree content and returning an execution result comprises:
identifying a first node in the decision tree content corresponding to the task branch;
executing all nodes at the next stage of the first node to obtain an execution result;
when the execution results returned by all nodes at the next stage are all pushed, returning the execution results;
and when the execution result returned by any node of the next stage is not pushed, determining a second node which is the same as the first node and has a priority lower than that of the first node, executing all nodes of the next stage corresponding to the second node, and returning the execution result until the pushing results returned by all nodes of the next stage are all pushed.
6. The task pushing method for data management according to claim 1, wherein the determining the task to be pushed according to the execution result comprises:
acquiring a node to be pushed from the execution result;
and determining the task to be pushed according to the task type and the task content in the node to be pushed.
7. A data management task pushing method according to any one of claims 1 to 6, characterized in that the method further comprises:
executing the task to be pushed to obtain follow-up data;
when abnormal data appear in the follow-up data, judging whether the abnormal data meet a preset condition for triggering a new strategy tree;
triggering a new strategy tree when the abnormal data is determined to meet the preset condition for triggering the new strategy tree;
updating the policy tree based on the anomalous data when it is determined that the anomalous data does not satisfy a preset condition that triggers a new policy tree.
8. A task pushing apparatus for data management, the apparatus comprising:
a first creation module to create baseline data for a patient;
a second creation module to create a decision tree based on the baseline data;
the identification module is used for acquiring a plurality of source data fields from the baseline data, and identifying the plurality of source data fields to obtain a patient number and a task branch number;
the first execution module is used for selecting all branch nodes corresponding to the patient numbers and the task branch numbers from the decision tree and sending the branch nodes to a message queue for asynchronous execution to obtain the decision tree contents corresponding to the task branch numbers;
the second execution module is used for executing the decision tree based on the decision tree content and returning an execution result;
and the determining module is used for determining the task to be pushed according to the execution result.
9. An electronic device, characterized in that the electronic device comprises a processor and a memory, the processor is configured to implement the task pushing method for data management according to any one of claims 1 to 7 when executing the computer program stored in the memory.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements a task pushing method for data management according to any one of claims 1 to 7.
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| CN110135590A (en) * | 2019-04-15 | 2019-08-16 | 平安科技（深圳）有限公司 | Information processing method, device, medium and electronic equipment |
| CN111640512A (en) * | 2020-06-02 | 2020-09-08 | 戴松世 | Kidney replacement therapy starting strategy evaluation method and device and electronic equipment |
| CN112039709A (en) * | 2020-09-02 | 2020-12-04 | 北京首都在线科技股份有限公司 | Resource scheduling method, device, equipment and computer readable storage medium |
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| CN114936040A (en) * | 2022-05-31 | 2022-08-23 | 北京达佳互联信息技术有限公司 | Program data processing method, program data processing device, electronic device, and storage medium |
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