CN112489746B - 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 according to the baseline data; acquiring a plurality of source data fields from the baseline data, and identifying and obtaining a patient number and a task branch number; 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 decision tree contents corresponding to the task branch numbers; executing the decision tree and returning an execution result; and determining a 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 creating 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 visit efficiency is improved.
Description
Technical Field
The present invention relates to the field of digital medical technology, and in particular, to a task pushing method and apparatus for data management, an electronic device, and a storage medium.
Background
With the improvement of living standard, the population of diabetics rises year by year, the existing medical resources are relatively deficient, the management of a system cannot be realized aiming at the blood sugar detection result of the patients, particularly a reasonable reminding mechanism is lacking, each task end (such as a nurse end, a doctor end and an online system) cannot clearly execute tasks and specific time, nurses, doctors and online follow-up cannot be achieved, follow-up efficiency is low, and the phenomenon of disorder of patient data management is caused.
Disclosure of Invention
In view of the foregoing, it is necessary to provide a task pushing method, device, electronic device and storage medium for data management, where the task pushing efficiency is improved by calculating the time difference between task pushing information in each task branch in the process of creating a decision tree according to the baseline data, so as to quickly determine the execution sequence of each task, and the task pushing efficiency is improved, thereby improving the follow-up efficiency.
A first aspect of the present invention provides a task pushing method for data management, the method including:
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;
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 decision tree contents corresponding to the task branch numbers;
Executing the decision tree based on the decision tree content and returning an execution result;
and determining a task to be pushed according to the execution result.
Optionally, the creating a decision tree from 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 one task branch as target task pushing information;
Randomly selecting target task pushing information as a root node of the task branch;
Calculating the time difference between any one of the other target task pushing information and the target task pushing information at the root node;
Judging whether a child node which is the same as the time difference exists or not;
When judging that the 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 the child node of the father node;
And when judging that the child node which is the same as the time difference does not exist, taking the root node as a father node, and taking the rest target task pushing information as the child node of the father node, wherein the time difference between the target task pushing information at the father node and the target task pushing information at the corresponding child node is taken as the weight of the 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 side and sending the treatment information to a first service side;
Receiving a diagnosis strategy reported by a first service end, wherein the diagnosis strategy is set by the first service end according to the diagnosis information of the patient and a preset diagnosis rule of a diagnosis hospital;
monitoring the detection data of the client and acquiring target detection data;
and generating baseline data for the acquired target detection data according to the diagnosis 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 level of the patient according to the patient number;
Determining whether to asynchronously execute the decision tree according to the disease level;
when the decision tree is determined not to be asynchronously executed according to the disease level, acquiring decision tree content corresponding to the task branch number; or alternatively
When the decision tree is determined to be executed asynchronously according to the disease level, executing the step of selecting all branch nodes corresponding to the patient number and the task branch number 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 corresponding to the task branch in the decision tree content;
executing all nodes of the next stage of the first node to obtain an execution result;
returning an execution result when the execution results returned by all the nodes at the next stage are push;
And when the execution result returned by any node of the next stage is not push, determining a second node which is the same level as the first node and has a priority smaller than that of the first node, executing all nodes of the next stage corresponding to the second node, and returning an execution result until the push results returned by all nodes of the next stage are push.
Optionally, the determining the task to be pushed according to the execution result 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 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 the preset condition for triggering a new decision tree;
Triggering a new decision tree when the abnormal data is determined to meet the preset condition for triggering the new decision tree;
and updating the decision tree based on the abnormal data when the abnormal data is determined not to meet the preset condition for triggering a new decision tree.
A second aspect of the present invention provides a task pushing device for data management, the device comprising:
a first creation module for creating baseline data for the patient;
The second creating module is used for creating a decision tree according to the baseline data;
The identification module is used for acquiring a plurality of source data fields from the baseline data, and identifying the 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, sending the branch nodes to a message queue for asynchronous execution, and obtaining 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 comprising a processor and a memory, the processor being configured to implement the task push method of 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 push method of data management.
In summary, according to the task pushing method, device, electronic equipment and storage medium for data management, 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 data management confusion caused by task missing pushing when patients are too many is avoided, and the accuracy rate of task pushing management is improved; finally, if abnormal data appear in follow-up data obtained by executing the task to be pushed, the occurrence of the change of the diseased condition of the patient is determined, and a new decision tree is required to be triggered or updated in time according to the abnormal data, so that 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 block 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-recited objects, features and advantages of the present invention will be more clearly understood, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings. It should be noted that, without conflict, the embodiments of the present invention and features in the embodiments may be combined with each other.
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 herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
Example 1
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 task pushing for data management, the 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 the form of a software development kit (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 according to different requirements, and some may be omitted.
S11, creating baseline data of the patient.
In this embodiment, the baseline data is created according to the patient's diagnosis information, the patient's hospital information of the patient's diagnosis hospital, and the patient's diagnosis and treatment data, and the server obtains the patient's diagnosis and treatment data by receiving real-time transmission from the medical device, where the patient's diagnosis and treatment data includes the patient's historical blood glucose measurement data and current blood glucose measurement data.
Optionally, the creating baseline data for the patient includes:
receiving the treatment information of the client side and sending the treatment information to a first service side;
Receiving a diagnosis strategy reported by a first service end, wherein the diagnosis strategy is set by the first service end according to the diagnosis information of the patient and a preset diagnosis rule of a diagnosis hospital;
monitoring the detection data of the client and acquiring target detection data;
and generating baseline data for the acquired target detection data according to the diagnosis strategy.
In this embodiment, the first service side is configured to receive the doctor information uploaded by the client, and specifically, the first service side may be a nurse side, a doctor side, or other sides that may be used to input the doctor information.
In this embodiment, the first service side receives the diagnosis information sent by the client, specifically, the diagnosis information may be text input, or may be received video or voice information, and if the diagnosis information received by the first service side is text, the diagnosis information is directly input to the first service side; if the treatment information received by the first service end is video, extracting the voice information of the patient from the video, converting the voice information into text information and recording the text information into the first service end; and if the diagnosis information received by the first service end is voice information, converting the voice information into text information by adopting a voice recognition technology and inputting the text information into the first service end. By receiving the different forms of the treatment information, the efficiency of the patient to provide the treatment information is improved.
In this embodiment, the treatment policy is set by the first server according to the treatment information of the patient and the treatment hospital according to a preset treatment rule, and different treatment policies are specified for different patients, for example, for patient a, treatment policy 1: if the number of times of insulin injection exceeds 2 times per week or 3 times of continuous blood glucose measurement are not up to standard in 5 times per week, the corresponding doctor is the doctor B; visit strategy 2: if the number of times of insulin injection exceeds 4 times per week or the continuous 5 times of blood glucose measurement are not up to standard, the corresponding doctor is expert C; visit strategy 3: if the number of insulin injections per week is less than 2 or 1 out of 5 blood glucose measurements per week does not reach the standard, the corresponding doctor is doctor D.
In this embodiment, by generating baseline data according to the diagnosis policy for the collected detection data of the client, different baseline data may be generated according to the collected detection data of each patient, thereby improving diversity of baseline data.
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 one task branch as target task pushing information;
Randomly selecting target task pushing information as a root node of the task branch;
Calculating the time difference between any one of the other target task pushing information and the target task pushing information at the root node;
Judging whether a child node which is the same as the time difference exists or not;
When judging that the 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 the child node of the father node;
And when judging that the child node which is the same as the time difference does not exist, taking the root node as a father node, and taking the rest target task pushing information as the child node of the father node, wherein the time difference between the target task pushing information at the father node and the target task pushing information at the corresponding child node is taken as the weight of the edge between the father node and the child node.
In this embodiment, the task branches include: a nurse follow-up task, a doctor follow-up task, an online follow-up task, a patient measurement task, a patient injection task and the like, and each task branch corresponds to a plurality of task pushing information.
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 pushing information, task pushing information corresponding to one task branch is randomly selected as target task pushing information, then one target task pushing information is randomly selected from the target task pushing information as a root node of the task branch, a time difference between any other target task pushing information and the target task pushing information at the root node is calculated, a task execution sequence can be determined according to the calculated time difference, when it is judged that a child node identical to the time difference exists, the child node is used as a parent node, and the rest target task pushing information is used as a child node of the parent node; and when judging that the child node which is the same as the time difference does not exist, taking the root node as a parent node, and taking the other target task pushing information as the child node of the parent node, wherein all task branch creation methods in the decision tree are the same.
In this embodiment, a time difference between the target task pushing information at the parent node and the target task pushing information at the corresponding child node is used as a weight of an edge between the parent node and the child node, the child nodes at the same level may be ranked in priority according to the size of the weight, and specifically, the child nodes at the same level may be ranked from left to right in order of the weight from big to small.
In this embodiment, in the process of creating the decision tree according to the baseline data, by calculating the time difference between the task pushing information in each task branch, the execution sequence of each task in each task branch in the decision tree is quickly determined, so that the accuracy and efficiency of task pushing can be improved, and the efficiency of follow-up visit and the efficiency of data management are further improved.
S13, acquiring a plurality of source data fields from the baseline data, and identifying the source data fields to obtain a patient number and a task branch number.
In this embodiment, the source data fields may be preset, and different preset source data fields represent different parameters in the baseline data, specifically, the plurality of source data fields include the patient number, the task branch number, and the like, and the patient number and the task branch number may be identified according to the source data fields.
In some other embodiments, after the identifying the plurality of source data fields results in a patient number and a task branch number, the method further comprises:
Acquiring the disease level of the patient according to the patient number;
and determining whether to asynchronously execute the decision tree according to the disease level.
When the decision tree is determined not to be asynchronously executed according to the disease level, acquiring decision tree content corresponding to the task branch number, and executing S15; or alternatively
When it is determined that the decision tree is asynchronously executed according to the disease level, S14 is executed.
In this embodiment, the patient's disease level may be preset, and different levels may be set according to different parameters corresponding to different diseases, for example: for diabetes, if the blood glucose level exceeds M, the blood glucose level is set to be level III, if the blood glucose level is between N, the blood glucose level is set to be level II, and if the blood glucose level is lower than P, the blood glucose level is set to be level I; the decision tree can be executed in different ways according to different disease grades, so that the diversity of executing the decision tree is improved.
The method is characterized in that the method can be directly executed in the decision tree if the patient's disease level is normal, 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, and if the patient's disease level is serious, the patient needs to be visited in real time for asynchronous execution, so that the follow-up efficiency and the follow-up quality are improved.
S14, 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 decision tree contents corresponding to the task branch numbers.
In this embodiment, the decision tree content includes, but is not limited to, a task branch corresponding to the task branch number, all branch nodes corresponding to the task branch, and task types and task contents corresponding to each branch node.
In this embodiment, all branch nodes corresponding to the patient number and the task branch number are selected from the decision tree and sent to a message queue for asynchronous execution, so that the phenomenon of data management confusion caused by task missing pushing when patients are too many is avoided, and the accuracy of task pushing management is improved.
S15, executing the decision tree based on the decision tree content and returning an execution result.
In this embodiment, since the decision tree content includes the direction of the next node, the decision tree is executed based on the decision tree content and the 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 corresponding to the task branch in the decision tree content;
executing all nodes of the next stage of the first node to obtain an execution result;
returning an execution result when the execution results returned by all the nodes at the next stage are push;
And when the execution result returned by any node of the next stage is not push, determining a second node which is the same level as the first node and has a priority smaller than that of the first node, executing all nodes of the next stage corresponding to the second node, and returning an execution result until the push results returned by all nodes of the next stage are push.
In this embodiment, the next stage of each task branch may correspond to one node or a plurality of nodes, and the plurality of nodes are sequentially ordered according to priority, and when all the nodes in the next stage after the execution of the first node obtain that the execution result is push, the returned execution result is push of the first node; when the execution result returned by any node of the next stage is not pushing, determining that the first node is not pushing, and possibly pushing all nodes of the next stage corresponding to the second priority node of the same level as the first node until the pushing result returned by all nodes of the next stage is pushing, and returning the execution result to the node corresponding to pushing.
In the embodiment, because the nodes corresponding to each task branch are different, when all the nodes of the next stage corresponding to all the nodes return to push, the task corresponding to the node is determined to need to be pushed, and the accuracy of task pushing is improved.
S16, determining a task to be pushed according to the execution result.
In this embodiment, the execution result includes a task type, 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 the task to be pushed according to the execution result 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 further, the management efficiency of task pushing is 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 the preset condition for triggering a new decision tree;
Triggering a new decision tree when the abnormal data is determined to meet the preset condition for triggering the new decision tree;
and updating the decision tree based on the abnormal data when the abnormal data is determined not to meet the preset condition for triggering a new decision tree.
In this embodiment, updating the decision tree includes adding, deleting, or modifying a task branch or a next node in the decision tree.
In this embodiment, if abnormal data appears in the follow-up data obtained by executing the task to be pushed, it is determined that the patient's disease condition changes, and a new decision 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 no abnormal data appear in the follow-up data, continuing to execute the decision tree.
In summary, in the task pushing method for data management described in this embodiment, baseline data of a patient is created; 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 the step of 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 decision tree contents corresponding to the task branch numbers; executing the decision tree based on the decision tree content and returning an execution result; and determining a task to be pushed according to the execution result.
In the embodiment, 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 the task pushing information in each task branch, so that the accuracy and the efficiency of task pushing can be improved, and the efficiency of data management is further improved; on the other hand, executing the step of 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, so that the phenomenon of data management confusion caused by task missing pushing when patients are too many is avoided, and the accuracy rate of task pushing management is improved; finally, if abnormal data appear in follow-up data obtained by executing the task to be pushed, the occurrence of the change of the diseased condition of the patient is determined, and a new decision tree is required to be triggered or updated in time according to the abnormal data, so that timeliness of patient data management is improved.
Example two
Fig. 2 is a block diagram of a task pushing device for data management according to a second embodiment of the present invention.
In some embodiments, the task pushing device 20 for data management may include a plurality of functional modules composed of program code segments. Program code of each program segment in the data-managed 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 performed by the task pushing device. The functional module may include: the device comprises a first creation module 201, a second creation module 202, an identification module 203, a first execution module 204, a second execution module 205, a determination module 206 and a judgment module 207. The module referred to in the present invention refers to a series of computer program segments capable of being executed by at least one processor and of performing a fixed function, stored in a memory. In the present embodiment, the functions of the respective modules will be described in detail in the following embodiments.
A first creation module 201 for creating baseline data of the patient.
In this embodiment, the baseline data is created according to the patient's diagnosis information, the patient's hospital information of the patient's diagnosis hospital, and the patient's diagnosis and treatment data, and the server obtains the patient's diagnosis and treatment data by receiving real-time transmission from the medical device, where the patient's diagnosis and treatment data includes the patient's historical blood glucose measurement data and current blood glucose measurement data.
Optionally, the first creating module 201 creates baseline data of the patient includes:
receiving the treatment information of the client side and sending the treatment information to a first service side;
Receiving a diagnosis strategy reported by a first service end, wherein the diagnosis strategy is set by the first service end according to the diagnosis information of the patient and a preset diagnosis rule of a diagnosis hospital;
monitoring the detection data of the client and acquiring target detection data;
and generating baseline data for the acquired target detection data according to the diagnosis strategy.
In this embodiment, the first service side is configured to receive the doctor information uploaded by the client, and specifically, the first service side may be a nurse side, a doctor side, or other sides that may be used to input the doctor information.
In this embodiment, the first service side receives the diagnosis information sent by the client, specifically, the diagnosis information may be text input, or may be received video or voice information, and if the diagnosis information received by the first service side is text, the diagnosis information is directly input to the first service side; if the treatment information received by the first service end is video, extracting the voice information of the patient from the video, converting the voice information into text information and recording the text information into the first service end; and if the diagnosis information received by the first service end is voice information, converting the voice information into text information by adopting a voice recognition technology and inputting the text information into the first service end. By receiving the different forms of the treatment information, the efficiency of the patient to provide the treatment information is improved.
In this embodiment, the treatment policy is set by the first server according to the treatment information of the patient and the treatment hospital according to a preset treatment rule, and different treatment policies are specified for different patients, for example, for patient a, treatment policy 1: if the number of times of insulin injection exceeds 2 times per week or 3 times of continuous blood glucose measurement are not up to standard in 5 times per week, the corresponding doctor is the doctor B; visit strategy 2: if the number of times of insulin injection exceeds 4 times per week or the continuous 5 times of blood glucose measurement are not up to standard, the corresponding doctor is expert C; visit strategy 3: if the number of insulin injections per week is less than 2 or 1 out of 5 blood glucose measurements per week does not reach the standard, the corresponding doctor is doctor D.
In this embodiment, by generating baseline data according to the diagnosis policy for the collected detection data of the client, different baseline data may be generated according to the collected detection data of each patient, thereby improving diversity of baseline data.
A second creation module 202 is configured to create 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, by the second creating module 202, the 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 one task branch as target task pushing information;
Randomly selecting target task pushing information as a root node of the task branch;
Calculating the time difference between any one of the other target task pushing information and the target task pushing information at the root node;
Judging whether a child node which is the same as the time difference exists or not;
When judging that the 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 the child node of the father node;
And when judging that the child node which is the same as the time difference does not exist, taking the root node as a father node, and taking the rest target task pushing information as the child node of the father node, wherein the time difference between the target task pushing information at the father node and the target task pushing information at the corresponding child node is taken as the weight of the edge between the father node and the child node.
In this embodiment, the task branches include: a nurse follow-up task, a doctor follow-up task, an online follow-up task, a patient measurement task, a patient injection task and the like, and each task branch corresponds to a plurality of task pushing information.
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 pushing information, task pushing information corresponding to one task branch is randomly selected as target task pushing information, then one target task pushing information is randomly selected from the target task pushing information as a root node of the task branch, a time difference between any other target task pushing information and the target task pushing information at the root node is calculated, a task execution sequence can be determined according to the calculated time difference, when it is judged that a child node identical to the time difference exists, the child node is used as a parent node, and the rest target task pushing information is used as a child node of the parent node; and when judging that the child node which is the same as the time difference does not exist, taking the root node as a parent node, and taking the other target task pushing information as the child node of the parent node, wherein all task branch creation methods in the decision tree are the same.
In this embodiment, a time difference between the target task pushing information at the parent node and the target task pushing information at the corresponding child node is used as a weight of an edge between the parent node and the child node, the child nodes at the same level may be ranked in priority according to the size of the weight, and specifically, the child nodes at the same level may be ranked from left to right in order of the weight from big to small.
In this embodiment, in the process of creating the decision tree according to the baseline data, by calculating the time difference between the task pushing information in each task branch, the execution sequence of each task in each task branch in the decision tree is quickly determined, so that the accuracy and efficiency of task pushing can be improved, and the efficiency of follow-up visit and the efficiency of data management are further improved.
And 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 fields may be preset, and different preset source data fields represent different parameters in the baseline data, specifically, the plurality of source data fields include the patient number, the task branch number, and the like, and the patient number and the task branch number may be identified according to the source data fields.
In other embodiments, the identifying module 203 obtains the patient number and the task branch number from the plurality of source data fields, and then obtains the patient's disease level according to the patient number; and determining whether to asynchronously execute the decision tree according to the disease level.
When the decision tree is determined not to be asynchronously executed according to the disease level, acquiring decision tree content corresponding to the task branch number; or alternatively
When the decision tree is determined to be executed asynchronously according to the disease level, executing the step of selecting all branch nodes corresponding to the patient number and the task branch number from the decision tree and sending the branch nodes to a message queue for asynchronous execution.
In this embodiment, the patient's disease level may be preset, and different levels may be set according to different parameters corresponding to different diseases, for example: for diabetes, if the blood glucose level exceeds M, the blood glucose level is set to be level III, if the blood glucose level is between N, the blood glucose level is set to be level II, and if the blood glucose level is lower than P, the blood glucose level is set to be level I; the decision tree can be executed in different ways according to different disease grades, so that the diversity of executing the decision tree is improved.
The method is characterized in that the method can be directly executed in the decision tree if the patient's disease level is normal, 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, and if the patient's disease level is serious, the patient needs to be visited in real time for asynchronous execution, so that the follow-up efficiency and the follow-up quality are improved.
The first execution module 204 is 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 decision tree content corresponding to the task branch number.
In this embodiment, the decision tree content includes, but is not limited to, a task branch corresponding to the task branch number, all branch nodes corresponding to the task branch, and task types and task contents corresponding to each branch node.
In this embodiment, all branch nodes corresponding to the patient number and the task branch number are selected from the decision tree and sent to a message queue for asynchronous execution, so that the phenomenon of data management confusion caused by task missing pushing when patients are too many is avoided, and the accuracy of task pushing management is improved.
A second execution module 205, configured to execute the decision tree based on the decision tree content and return an execution result.
In this embodiment, since the decision tree content includes the direction of the next node, the decision tree is executed based on the decision tree content and the execution result is returned.
Optionally, the second executing module 205 executes the decision tree based on the decision tree content and returns an execution result includes:
identifying a first node corresponding to the task branch in the decision tree content;
executing all nodes of the next stage of the first node to obtain an execution result;
returning an execution result when the execution results returned by all the nodes at the next stage are push;
And when the execution result returned by any node of the next stage is not push, determining a second node which is the same level as the first node and has a priority smaller than that of the first node, executing all nodes of the next stage corresponding to the second node, and returning an execution result until the push results returned by all nodes of the next stage are push.
In this embodiment, the next stage of each task branch may correspond to one node or a plurality of nodes, and the plurality of nodes are sequentially ordered according to priority, and when all the nodes in the next stage after the execution of the first node obtain that the execution result is push, the returned execution result is push of the first node; when the execution result returned by any node of the next stage is not pushing, determining that the first node is not pushing, and possibly pushing all nodes of the next stage corresponding to the second priority node of the same level as the first node until the pushing result returned by all nodes of the next stage is pushing, and returning the execution result to the node corresponding to pushing.
In the embodiment, because the nodes corresponding to each task branch are different, when all the nodes of the next stage corresponding to all the nodes return to push, the task corresponding to the node is determined to need to be pushed, and the accuracy of task pushing is improved.
And the determining module 206 is configured to determine a task to be pushed according to the execution result.
In this embodiment, the execution result includes a task type, 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 further, the management efficiency of task pushing is improved.
Further, in some other embodiments, the second execution module 205 is further configured to execute the task to be pushed to obtain the follow-up data.
A judging module 207, configured to judge, when abnormal data occurs in the follow-up data, whether the abnormal data meets a preset condition for triggering a new decision tree; triggering a new decision tree when the abnormal data is determined to meet the preset condition for triggering the new decision tree; and updating the decision tree based on the abnormal data when the abnormal data is determined not to meet the preset condition for triggering a new decision tree.
In this embodiment, updating the decision tree includes adding, deleting, or modifying a task branch or a next node in the decision tree.
In this embodiment, if abnormal data appears in the follow-up data obtained by executing the task to be pushed, it is determined that the patient's disease condition changes, and a new decision 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 no abnormal data appears in the follow-up data, continuing to execute the decision tree.
In summary, the task pushing device for data management according to the 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 the step of 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 decision tree contents corresponding to the task branch numbers; executing the decision tree based on the decision tree content and returning an execution result; and determining a task to be pushed according to the execution result.
In the embodiment, 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 the task pushing information in each task branch, so that the accuracy and the efficiency of task pushing can be improved, and the efficiency of data management is further improved; on the other hand, executing the step of 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, so that the phenomenon of data management confusion caused by task missing pushing when patients are too many is avoided, and the accuracy rate of task pushing management is improved; finally, if abnormal data appear in follow-up data obtained by executing the task to be pushed, the occurrence of the change of the diseased condition of the patient is determined, and a new decision tree is required to be triggered or updated in time according to the abnormal data, so that 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 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 is not limiting of the embodiments of the present invention, and that either a bus-type configuration or a star-type configuration is possible, and that the electronic device 3 may also include more or less other hardware or software than that 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 a preset or stored instruction, and its hardware 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 further include a client device, where the client device includes, but is not limited to, any electronic product that can interact with a client by way of a keyboard, a mouse, a remote control, a touch pad, or a voice control device, such as a personal computer, a tablet computer, a smart phone, a digital camera, etc.
It should be noted that the electronic device 3 is only used as an example, and other electronic products that may be present in the present invention or may be present in the future are also included in the scope of the present invention by way of reference.
In some embodiments, the memory 31 is used to store program codes and various data, such as the task pushing device 20 for data management installed in the electronic device 3, and to implement high-speed, automatic access to programs or data during operation of the electronic device 3. The Memory 31 includes Read-Only Memory (ROM), programmable Read-Only Memory (Programmable Read-Only Memory, PROM), erasable programmable Read-Only Memory (Erasable Programmable Read-Only Memory, EPROM), one-time programmable Read-Only Memory (OTPROM), electrically erasable rewritable Read-Only Memory (EEPROM), compact disc Read-Only Memory (Compact Disc Read-Only Memory, CD-ROM) or other optical disc Memory, magnetic tape Memory, or any other medium that can be used for carrying or storing data.
In some embodiments, the at least one processor 32 may be comprised of an integrated circuit, such as a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functionality, including one or more central processing units (Central Processing unit, CPU), microprocessors, digital processing chips, graphics processors, combinations of various control chips, and the like. The at least one processor 32 is a Control Unit (Control Unit) of the electronic device 3, connects the respective components of the entire electronic device 3 using various interfaces and lines, and executes various functions of the electronic device 3 and processes data 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 connected 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 source (such as a battery) for powering the various components, and optionally, the power source may be logically connected to the at least one processor 32 via a power management device, thereby implementing functions such as managing charging, discharging, and power consumption by the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The electronic device 3 may further include various sensors, bluetooth modules, wi-Fi modules, etc., which will not be described herein.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
The integrated units implemented in the form of software functional modules described above may be stored in a computer readable storage medium. The software functional modules described above are stored in a storage medium and include instructions for causing a computer device (which may be a personal computer, an electronic device, or a network device, etc.) or a processor (processor) to perform portions of the methods described in the various embodiments of the invention.
In a further embodiment, in connection with fig. 2, the at least one processor 32 may execute the operating device of the electronic device 3 and various installed applications (e.g. the task pushing device 20 for data management), program code, etc., such as the above-mentioned modules.
The memory 31 has program code stored therein, and the at least one processor 32 can invoke the program code stored in the memory 31 to perform related functions. For example, each of the modules depicted in fig. 2 is a program code stored in the memory 31 and executed by the at least one processor 32 to perform the functions of the respective modules for task pushing purposes of data management.
In one embodiment of the present invention, the memory 31 stores a plurality of instructions that are executed by the at least one processor 32 to implement the function of task pushing for data management.
Specifically, the specific implementation method of the above instruction by the at least one processor 32 may refer to the description of the relevant steps in the corresponding embodiment of fig. 1, which is not repeated herein.
In the several embodiments provided by the present invention, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
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 characteristics 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 will be obvious that the term "comprising" does not exclude other elements or that the singular does not exclude a plurality. The units or means stated in the invention may also be implemented by one unit or means, either by software or hardware. The terms first, second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (7)
1. A task pushing method for data management, the method comprising:
Creating baseline data for the patient, comprising: receiving the treatment information of the client side and sending the treatment information to a first service side; receiving a diagnosis strategy reported by a first service end, wherein the diagnosis strategy is set by the first service end according to the diagnosis information of the patient and a preset diagnosis rule of a diagnosis hospital; monitoring the detection data of the client and acquiring target detection data; generating baseline data for the collected target detection data according to the diagnosis strategy, wherein the baseline data is created according to the diagnosis information of the patient, the hospital information of the diagnosis hospital where the patient is in a diagnosis and the diagnosis and treatment data of the patient, and the diagnosis and treatment data of the patient comprises historical blood sugar measurement data and current blood sugar measurement data of 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;
Acquiring the disease level of the patient according to the patient number;
Determining whether to asynchronously execute the decision tree according to the disease level;
When the decision tree is determined not to be asynchronously executed according to the disease level, acquiring decision tree content corresponding to the task branch number; or when the decision tree is determined to be asynchronously executed according to the disease level, selecting all branch nodes corresponding to the patient number and the task branch number from the decision tree, and sending the branch nodes to a message queue for asynchronous execution to obtain decision tree content corresponding to the task branch number;
executing the decision tree based on the decision tree content and returning an execution result, including: identifying a first node corresponding to the task branch number in the decision tree content; executing all nodes of the next stage of the first node to obtain an execution result; returning an execution result when the execution results returned by all the nodes at the next stage are push; when the execution result returned by any node of the next stage is not push, determining a second node which is the same level as the first node and has a priority smaller than that of the first node, executing all nodes of the next stage corresponding to the second node until the push results returned by all nodes of the next stage are push, and returning the execution result;
and determining a task to be pushed according to the execution result.
2. The task pushing method of data management according to claim 1, wherein said creating a decision tree from said 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 one task branch as target task pushing information;
Randomly selecting target task pushing information as a root node of the task branch;
Calculating the time difference between any one of the other target task pushing information and the target task pushing information at the root node;
Judging whether a child node which is the same as the time difference exists or not;
When judging that the 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 the child node of the father node;
And when judging that the child node which is the same as the time difference does not exist, taking the root node as a father node, and taking the rest target task pushing information as the child node of the father node, wherein the time difference between the target task pushing information at the father node and the target task pushing information at the corresponding child node is taken as the weight of the edge between the father node and the child node.
3. The method for pushing tasks for data management as claimed in claim 1, wherein said determining tasks to be pushed according to said 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.
4. A task push method of data management according to any one of claims 1 to 3, wherein 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 the preset condition for triggering a new decision tree;
Triggering a new decision tree when the abnormal data is determined to meet the preset condition for triggering the new decision tree;
and updating the decision tree based on the abnormal data when the abnormal data is determined not to meet the preset condition for triggering a new decision tree.
5. A task pushing device for data management, the device comprising:
A first creation module for creating baseline data for a patient, comprising: receiving the treatment information of the client side and sending the treatment information to a first service side; receiving a diagnosis strategy reported by a first service end, wherein the diagnosis strategy is set by the first service end according to the diagnosis information of the patient and a preset diagnosis rule of a diagnosis hospital; monitoring the detection data of the client and acquiring target detection data; generating baseline data for the collected target detection data according to the diagnosis strategy, wherein the baseline data is created according to the diagnosis information of the patient, the hospital information of the diagnosis hospital where the patient is in a diagnosis and the diagnosis and treatment data of the patient, and the diagnosis and treatment data of the patient comprises historical blood sugar measurement data and current blood sugar measurement data of the patient;
The second creating module is used for creating a decision tree according to the baseline data;
The identification module is used for acquiring a plurality of source data fields from the baseline data, and identifying the source data fields to obtain a patient number and a task branch number; acquiring the disease level of the patient according to the patient number; determining whether to asynchronously execute the decision tree according to the disease level;
The first execution module is used for acquiring the decision tree content corresponding to the task branch number when the decision tree is determined not to be asynchronously executed according to the disease level;
The first execution module is further used for selecting all branch nodes corresponding to the patient number and the task branch number from the decision tree and sending the branch nodes to a message queue for asynchronous execution when the decision tree is determined to be executed asynchronously according to the disease level, so as to obtain decision tree content corresponding to the task branch number;
the second execution module is used for executing the decision tree based on the decision tree content and returning an execution result, and comprises the following steps: identifying a first node corresponding to the task branch number in the decision tree content; executing all nodes of the next stage of the first node to obtain an execution result; returning an execution result when the execution results returned by all the nodes at the next stage are push; when the execution result returned by any node of the next stage is not push, determining a second node which is the same level as the first node and has a priority smaller than that of the first node, executing all nodes of the next stage corresponding to the second node until the push results returned by all nodes of the next stage are push, and returning the execution result;
And the determining module is used for determining the task to be pushed according to the execution result.
6. An electronic device comprising a processor and a memory, wherein the processor is configured to implement the task pushing method for data management according to any one of claims 1 to 4 when executing a computer program stored in the memory.
7. A computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements a task push method of data management according to any of claims 1 to 4.
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