CN117409908A - Tumor whole course management method, device, equipment and medium based on RPA - Google Patents
Tumor whole course management method, device, equipment and medium based on RPA Download PDFInfo
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Abstract
The embodiment of the invention discloses a tumor whole course management method, device, equipment and medium based on RPA. The method comprises the following steps: acquiring index data of each tumor patient, and grouping each tumor patient according to each index data; acquiring disease course information of a target user in the target group, and determining whether the target user is abnormal or not according to the disease course information; under the condition that the abnormal grade of the target user meets the preset condition, feeding back the abnormal grade to a target doctor matched with the target user, receiving a processing strategy fed back by the target doctor, and feeding back the processing strategy to the target user; wherein the processing policy includes at least one of: review in time, take medications on time, strengthen exercise, and pay attention to diet. The scheme of the embodiment of the invention can manage the course of the tumor patients, reduce the personnel cost and accurately know the conditions of each tumor patient in real time.
Description
Technical Field
The embodiment of the invention relates to the technical field of computers, in particular to a tumor whole course management method, device, equipment and medium based on RPA.
Background
Along with the continuous development of computer and internet technologies, the development of medical informatization is also followed, but currently, the whole medical industry has no good technical means for the whole tumor course management based on RPA, for example, the postoperative recovery condition of tumor patients is difficult to monitor in real time; most hospitals only need to visit the hospital for re-diagnosis according to the orders, and some doctors can inquire the recent situation of the tumor patients through a telephone follow-up mode. However, such a method is too costly for personnel and it is difficult to know the condition of each tumor patient in real time and accurately.
How to manage the course of each tumor patient, reduce personnel costs, and to know the condition of each tumor patient in real time and accurately is a major problem in the industry.
Disclosure of Invention
The embodiment of the invention provides an RPA-based tumor whole course management method, an RPA-based tumor whole course management device, RPA-based tumor whole course management equipment and an RPA-based tumor whole course management medium, so that the disease course of tumor patients is managed, the personnel cost is reduced, and the conditions of all tumor patients can be accurately known in real time.
According to an aspect of the embodiment of the invention, there is provided an RPA-based tumor whole course management method, applied to a course management system, including:
Acquiring index data of each tumor patient, and grouping each tumor patient according to each index data;
acquiring disease course information of a target user in a target group, and determining whether the target user is abnormal or not according to the disease course information;
under the condition that the abnormal grade of the target user meets the preset condition, feeding back the abnormal grade to a target doctor matched with the target user, receiving a processing strategy fed back by the target doctor, and feeding back the processing strategy to the target user;
wherein the processing policy includes at least one of: review in time, take medications on time, strengthen exercise, and pay attention to diet.
According to another aspect of the embodiments of the present invention, there is provided an RPA-based tumor whole course management device, applied to a course management system, including:
the index data acquisition module is used for acquiring index data of each tumor patient and grouping each tumor patient according to each index data;
the disease course information acquisition module is used for acquiring disease course information of target users in the target group and determining whether the target users have abnormality according to the disease course information;
The processing strategy feedback module is used for feeding back the abnormal grade to a target doctor matched with the target user under the condition that the abnormal grade of the target user meets a preset condition, receiving a processing strategy fed back by the target doctor and feeding back the processing strategy to the target user;
wherein the processing policy includes at least one of: review in time, take medications on time, strengthen exercise, and pay attention to diet.
According to another aspect of an embodiment of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the RPA-based tumor whole course management method according to any one of the embodiments of the present invention.
According to another aspect of the embodiments of the present invention, there is provided a computer readable storage medium storing computer instructions for implementing the RPA-based tumor total course management method according to any one of the embodiments of the present invention when executed by a processor.
According to the technical scheme, index data of each tumor patient are obtained, and each tumor patient is grouped according to each index data; acquiring disease course information of a target user in a target group, and determining whether the target user is abnormal or not according to the disease course information; under the condition that the abnormal grade of the target user meets the preset condition, feeding back the abnormal grade to a target doctor matched with the target user, receiving a processing strategy fed back by the target doctor, and feeding back the processing strategy to the target user; wherein the processing policy includes at least one of: the method has the advantages of timely review, taking medicines on time, strengthening exercise and paying attention to diet, can manage the course of the tumor patients, reduces personnel cost, and can accurately know the conditions of each tumor patient in real time.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention, nor is it intended to be used to limit the scope of the embodiments of the invention. Other features of embodiments of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of an RPA-based tumor whole course management method according to a first embodiment of the present invention;
FIG. 2 is a flowchart of a method for managing tumor progression based on RPA according to a second embodiment of the invention;
FIG. 3 is a schematic diagram of a disease process management system according to a second embodiment of the present invention;
fig. 4 is a schematic structural diagram of an RPA-based tumor whole course management device according to a third embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device for implementing the RPA-based tumor whole course management method according to an embodiment of the present invention.
Detailed Description
In order to make the embodiments of the present invention better understood by those skilled in the art, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are intended to be within the scope of the embodiments of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the embodiments of the present invention and the above-described drawings are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
Fig. 1 is a flowchart of an RPA-based tumor whole course management method according to an embodiment of the present invention, where the method may be performed by an RPA-based tumor whole course management device, and the RPA-based tumor whole course management device may be implemented in hardware and/or software, and the RPA-based tumor whole course management device may be configured in an electronic device such as a computer, a server, or a tablet computer.
Specifically, referring to fig. 1, the method specifically includes the following steps:
step 110, obtaining index data of each tumor patient, and grouping each tumor patient according to each index data.
The tumor patients may be lung cancer patients, liver cancer patients, colon cancer patients, or the like discharged after surgery, or patients with tumor that have been diagnosed but not subjected to surgical resection, and the present embodiment is not limited thereto.
In this embodiment, the index data of each tumor patient may include disease information, operation condition, post-operation condition, medication information, and the like of each tumor patient, and is not limited in this embodiment. By way of example, the index data for tumor patient a may include: age, sex, tumor distribution, operation time, operation procedure, postoperative recovery, medication category, medication dosage, etc. of tumor patients.
In an alternative implementation of this embodiment, after the index data of each tumor patient is acquired, each tumor patient may be further grouped according to each index data. For example, tumor patients may be grouped according to condition information in the index data, e.g., tumor patients having the same tumor and having the same tumor grade may be grouped into one group; the individual tumor patients may also be grouped according to their age, as well as tumor size, which is not limited in this embodiment.
Step 120, acquiring the disease course information of the target user in the target group, and determining whether the target user has an abnormality according to the disease course information.
The target group can be any group obtained by grouping each tumor patient according to each index data; the target users in the target group may be any tumor patient in the group, which is not limited in this embodiment.
In this embodiment, the disease course information of the target user in the target packet may include at least one of the following: user behavior data, inspection data, and questionnaire data. Illustratively, the course information for tumor patient a may include: the work and rest time, diet content, the items and exercise time for taking part in physical exercise, each report of post-operation examination, and feedback data of a questionnaire for the tumor patient, etc. each day after discharge of the tumor patient are not limited in this embodiment.
In an optional implementation manner of this embodiment, after the disease course information of the target user is obtained, whether the target user has an abnormality may be further determined according to the disease course information of the target user, where in this embodiment, whether the target user has an abnormality may be determined by one or more disease course information of the target user; for example, if the target user is not eating after discharge and the daily meal size is gradually decreasing, it may be preliminarily determined that the target user is abnormal; if the target user has regular daily work and rest, has normal diet, and can participate in a certain amount of physical exercises, it can be preliminarily determined that the target user has no abnormality.
In another optional implementation manner of this embodiment, in a case where it is determined that the target user has an abnormality, the abnormality level of the target user may be further determined; wherein, the abnormal level can be low level, medium level or high level; the anomaly level may also be represented by a number, for example, 0-9 for different users, which is not limited in this embodiment.
In this embodiment, the abnormal level of the target user may be determined according to the amount of the target user's food intake; for example, if the target user does not eat any food for a plurality of consecutive days, it may be determined that the abnormality level of the target user is high; if the target user cannot normally defecation for three consecutive days, determining that the abnormal grade of the target user is grade 7; if the target user cannot normally defecate for five continuous days and symptoms such as lack of strength of the whole body appear, the abnormal grade of the target user can be determined to be 9.
And 130, under the condition that the abnormal grade of the target user meets the preset condition, feeding back the abnormal grade to a target doctor matched with the target user, receiving a processing strategy fed back by the target doctor, and feeding back the processing strategy to the target user.
Wherein the processing policy includes at least one of: review in time, take medications on time, strengthen exercise, and pay attention to diet.
In an optional implementation manner of this embodiment, after determining that the target user has an abnormality and determining an abnormality level of the target user, it may further be determined whether the abnormality level of the target user meets a preset condition; the preset condition may be an abnormal grade condition, for example, a medium grade, or a grade 5, etc., which is not limited in this embodiment.
Optionally, in this embodiment, after determining that the target user has an abnormality and determining the abnormality level of the target user, if it is determined that the abnormality level of the target user is greater than a preset abnormality level, it may be determined that the abnormality level of the target user meets a preset condition; further, the anomaly level of the target user may be transmitted to a target doctor who matches the target user; the target doctor can determine the processing strategy of the target user according to the abnormal grade, index data, disease course information and the like of the target user; furthermore, the processing strategy of the target user can be fed back to the target user, so that the target user can solve the abnormal situation in time.
According to the technical scheme of the embodiment, index data of each tumor patient are obtained, and each tumor patient is grouped according to each index data; acquiring disease course information of a target user in a target group, and determining whether the target user is abnormal or not according to the disease course information; under the condition that the abnormal grade of the target user meets the preset condition, feeding back the abnormal grade to a target doctor matched with the target user, receiving a processing strategy fed back by the target doctor, and feeding back the processing strategy to the target user; wherein the processing policy includes at least one of: the method has the advantages of timely review, taking medicines on time, strengthening exercise and paying attention to diet, can manage the course of the tumor patients, reduces personnel cost, and can accurately know the conditions of each tumor patient in real time.
Example two
Fig. 2 is a flowchart of a method for managing a tumor whole course based on RPA according to a second embodiment of the present invention, where the technical solutions in this embodiment are further refined, and the technical solutions in this embodiment may be combined with each of the alternatives in one or more embodiments.
Before describing the method for managing the tumor total course based on RPA in the embodiment specifically, it should be further described that the method for managing the tumor total course based on RPA in the embodiment may be applied to a course management system; fig. 3 is a schematic structural diagram of a disease process management system according to a second embodiment of the present invention, which mainly includes: a control center 310, the control center 310 including a data acquisition robot; the control center also comprises a management robot; the data acquisition robot is in communication connection with the management robot;
as shown in fig. 2, the RPA-based tumor whole course management method may include the following steps:
step 210, obtaining index data of each tumor patient, and grouping each tumor patient according to each index data.
In an optional implementation manner of this embodiment, acquiring the index data of each tumor patient may include: under the condition that the preset acquisition time is reached, acquiring each index data uploaded by each tumor patient through a patient client by the data acquisition robot of the control center.
The preset collection time can be a fixed time of each day, each week or each month; for example, ten am each day; eight pm every week or twelve pm every month, etc.; the preset collection time may be a period of time, for example, the index data of each tumor patient is obtained once every two days, which is not limited in this embodiment.
Optionally, in this embodiment, each index data uploaded by each tumor patient at the client may be collected once by the data collection robot in the control center at each set time interval; for example, the disease information, the operation condition, the postoperative condition, the medication information, and the like of each tumor patient can be acquired.
In an optional implementation manner of this embodiment, the grouping each tumor patient according to each index data may include: determining at least one conditional expression by a management robot of the control center; inputting each index data into each conditional expression respectively, and determining a target expression matched with each index data; and determining target groups corresponding to the tumor patients according to the target expression.
Wherein each conditional expression is associated with each index data; for example, a conditional expression may be determined based on the condition information; for example, the age is greater than a set age threshold and the tumor grade is greater than a set tumor grade; an expression can also be determined from the condition information and the surgical condition; for example, the tumor grade is larger than the set tumor grade and the operation is smooth, etc., and the content and form of each conditional expression are not particularly limited in this embodiment.
Note that each conditional expression in the present embodiment corresponds to one packet, respectively; for example, the conditional expression "age greater than the set age threshold and tumor grade greater than the set tumor grade" corresponds to group one; the conditional expression "the tumor grade is greater than the set tumor grade and the surgery is smooth" corresponds to two groups, which is not limited in this embodiment.
Optionally, in an optional implementation manner of this embodiment, after the data acquisition robot of the control center acquires each index data, each index data may be sent to the management robot, after the management robot receives each index data, each index data of each tumor patient may be respectively input into each predetermined conditional expression, and a target expression matched with each index data is determined, and further, a target group corresponding to each tumor patient may be determined according to the target expression.
The target packet may be any packet corresponding to each conditional expression, and is not limited in this embodiment.
Step 220, acquiring the disease course information of the target users in the target group, and determining whether the target users have abnormality according to the disease course information.
As shown in fig. 3, the disease process management system according to this embodiment further includes an actuator 320, where the actuator 320 is communicatively connected to the control center 310.
In another alternative implementation of this embodiment, the disease course management system may further include a designer 330, the designer 330 being communicatively coupled to the control center 310; the designer may convert the designed flow into a robot script, which is then issued to the control center. A designer typically contains a set of design tools that can create, edit, and test a robotic process automation (Robotic process automation, RPA) process.
In an optional implementation manner of this embodiment, the obtaining the disease course information of the target user in the target group and determining whether the target user has an abnormality according to the disease course information may include: acquiring disease course information of the target user through the data acquisition robot of the control center, and sending the disease course information to the executor; the executor analyzes the course information to determine whether the target user is abnormal; wherein the course information includes at least one of: user behavior data, inspection data, and questionnaire data.
Step 230, inputting the course information to a predetermined abnormality level determination module through the executor, and determining an abnormality level of the target user according to the abnormality level determination module; and sending the abnormal grade of the target user to a management robot of the control center.
In an optional implementation manner of this embodiment, after determining, by the actuator, that the target user has an abnormality, the acquired course information of the target user may be further input by the actuator to a predetermined abnormality level determining module, and the abnormality level of the target user may be determined according to the abnormality level determining module; the abnormal grade of the target user is sent to a management robot of the control center; the anomaly level includes one of: stable, substantially stable, dangerous or very dangerous. The anomaly level may be a first level, a second level, a third level, a fourth level, or the like, and is not limited in this embodiment.
Alternatively, in this embodiment, the anomaly level determining module may determine the anomaly level by: acquiring a plurality of abnormal course information, and marking the abnormal course information in a grade way; constructing a training sample according to the abnormal course information and the grade marking result corresponding to the abnormal course information; and sequentially inputting the training samples into a target convolutional neural network for training to obtain the abnormal grade determination model.
In this embodiment, after the information of each abnormal course is obtained, the information of each abnormal course may be automatically marked by a computer program, or may be manually marked, which is not limited in this embodiment.
And 240, under the condition that the abnormal grade of the target user meets the preset condition, feeding back the abnormal grade to a target doctor matched with the target user, receiving a processing strategy fed back by the target doctor, and feeding back the processing strategy to the target user.
In an optional implementation manner of this embodiment, feeding back the abnormality level to a target doctor matched with the target user, receiving a processing policy fed back by the target doctor, and feeding back the processing policy to the target user may include: determining a target doctor matched with the target user through the management robot, and pushing live broadcast data and disease course information of the target user to the target doctor; receiving a processing strategy which is fed back by the target doctor and matched with the target user; and feeding back the processing strategy to the target user.
In order to better understand the RPA-based tumor whole course management method according to the present embodiment, a specific example will be explained below:
after a disease course management system user logs in the disease course management system by using an account number password, a process layout tool can be used for layout of a process (the existing process supports multiplexing) according to a disease course management period, and the process supports five nodes, namely a task node, a clock node, a conditional expression node, an approval node and a notification node.
The task node has the functions of issuing tasks such as a declaration task, a follow-up task and an inspection task; the function of the clock node is timing, e.g. weekly/daily/hourly timing; the function of the conditional expression node is to set rules, and the NAND logic processing of single or multiple index thresholds; the approval node has the function of manual approval, for example, after a doctor clicks a pass button, the next flow can be executed; the function of the notification node is to initiate message notification and support notification types such as short messages, micro messages and the like.
The user adopts the flow engine WorkFolw of the designer to design a data acquisition task node, sets the task starting time, acquires the attributes such as an address and a port, distributes the node to an executor RPA-Robot, and performs unified management and monitoring by a control center Commander to perform operations such as distribution, suspension and the like on the task.
The data acquisition robot periodically acquires index data from hospital systems such as HIS, LIS and the like through task nodes, and performs grouping management on patients meeting the conditions after judging through conditional expression nodes.
The system administrator of the course management system can see all the grouped patients in the management background, and the health files of the patients and the execution states of the patients.
The doctor can adopt intelligent form SmartForms of the designer in the management background, and make a follow-up access volume form by using the control in a code-free mode of dragging. Meanwhile, a doctor can check grouped patients on the small program at the mobile phone end, can label the patients, remind the messages, send follow-up visit volumes, send out the contents of the announcements, prescribe the prescriptions, answer the patient questions and other patient management works, and set management paths for the patients. For example, the doctor selects the follow-up access volume form at the applet side, then clicks the send button after the patient list page has selected or multiple patients, and the system control layer receives the request and sends a message to the patient side applet via socket communications.
The patient can check the doctor of his own supervisor on the small program of the mobile phone end, and can see the backlog according to the schedule, finish backlog according to administrative route and correspondent time node that doctor set up, for example fill out the questionnaire, take medicine on time, check, move and punch card, diet punch card, etc..
And the background of the disease course management system statistically analyzes behavior data of the patient according to the buried points and the log data, such as whether to see the declaration article, whether to punch cards in sports and diet, and whether to finish filling in questionnaires.
The data collector continuously collects index data of the patient at regular intervals, integrates management compliance of the patient, e.g. counts compliance of the patient in a time dimension, supplements the corresponding collected index data to a health file of the patient, and pushes the index data to a doctor through a message. Furthermore, the doctor optimizes and improves the management method setting of the patient according to the patient data, and continuously tracks the illness state of the patient. In this embodiment, the whole disease course management system uses the patient curative effect and the doctor work efficiency improvement as the principle, and when the patient's illness state is significantly relieved or the current disease course management period is ended, the process is ended.
The scheme of the embodiment of the invention provides a personalized tumor whole course management scheme for patients. Improves the doctor experience of the patient and improves the working efficiency of doctors.
In the technical scheme of the embodiment of the invention, the acquisition, storage, application and the like of the related user personal information (such as face information, voice information and the like) accord with the regulations of related laws and regulations, and the public order welcome is not violated.
Example III
Fig. 4 is a schematic structural diagram of an RPA-based tumor whole course management device according to a third embodiment of the present invention. As shown in fig. 4, the apparatus includes: index data acquisition module 410, course information acquisition module 420, and processing strategy feedback module 430.
An index data obtaining module 410, configured to obtain index data of each tumor patient, and group each tumor patient according to each index data;
the disease course information acquisition module 420 is configured to acquire disease course information of a target user in a target group, and determine whether the target user has an abnormality according to the disease course information;
a processing policy feedback module 430, configured to, when determining that an abnormal level of the target user meets a preset condition, feedback the abnormal level to a target doctor matched with the target user, receive a processing policy fed back by the target doctor, and feed back the processing policy to the target user;
wherein the processing policy includes at least one of: review in time, take medications on time, strengthen exercise, and pay attention to diet.
According to the scheme of the embodiment, index data of each tumor patient is acquired through an index data acquisition module, and each tumor patient is grouped according to each index data; acquiring the disease course information of a target user in a target group through a disease course information acquisition module, and determining whether the target user is abnormal or not according to the disease course information; under the condition that the abnormal grade of the target user meets the preset condition is determined by the processing strategy feedback module, the abnormal grade is fed back to a target doctor matched with the target user, the processing strategy fed back by the target doctor is received, and the processing strategy is fed back to the target user, so that the course of a tumor patient can be managed, the personnel cost is reduced, and the situation of each tumor patient can be known in real time and accurately.
In an alternative implementation of this embodiment, the disease course management system includes a control center including a data acquisition robot;
the index data obtaining module 410 is specifically configured to obtain, by using the data collection robot of the control center, each index data uploaded by each tumor patient through the patient client when a preset collection time is reached;
the index data includes at least one of: illness state information, operation condition, postoperative condition and medication information.
In an optional implementation manner of this embodiment, the control center further includes a management robot; the data acquisition robot is in communication connection with the management robot;
the index data obtaining module 410 is further specifically configured to determine at least one conditional expression through a management robot of the control center;
inputting each index data into each conditional expression respectively, and determining a target expression matched with each index data;
and determining target groups corresponding to the tumor patients according to the target expression.
In an alternative implementation of this embodiment, the disease course management system further includes an actuator, the actuator being communicatively coupled to the control center;
The disease course information acquisition module 420 is specifically configured to acquire disease course information of the target user through the data acquisition robot of the control center, and send the disease course information to the executor;
the executor analyzes the course information to determine whether the target user is abnormal;
wherein the course information includes at least one of: user behavior data, inspection data, and questionnaire data.
In an optional implementation manner of this embodiment, after determining that the target user has an abnormality according to the course information, an abnormality level determining module is configured to input, by the executor, the course information to a predetermined abnormality level determining module, and determine an abnormality level of the target user according to the abnormality level determining module;
the abnormal grade of the target user is sent to a management robot of the control center;
the anomaly level includes one of: stable, substantially stable, dangerous or very dangerous.
In an alternative implementation manner of this embodiment, the anomaly level determining module determines by:
acquiring a plurality of abnormal course information, and marking the abnormal course information in a grade way;
Constructing a training sample according to the abnormal course information and the grade marking result corresponding to the abnormal course information;
and sequentially inputting the training samples into a target convolutional neural network for training to obtain the abnormal grade determination model.
In an optional implementation manner of this embodiment, the processing policy feedback module 430 is specifically configured to determine, by using the management robot, a target doctor matching the target user, and push live broadcast data and disease course information of the target user to the target doctor;
receiving a processing strategy which is fed back by the target doctor and matched with the target user;
and feeding back the processing strategy to the target user.
The tumor full course management device based on the RPA provided by the embodiment of the invention can execute the tumor full course management method based on the RPA provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Example IV
Fig. 5 shows a schematic diagram of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the embodiments of the invention described and/or claimed herein.
As shown in fig. 5, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as the RPA-based tumor total course management method.
In some embodiments, the RPA-based tumor total course management method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the RPA-based tumor total course management method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the RPA-based tumor whole course management method in any other suitable manner (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for implementing the methods of embodiments of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of embodiments of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the embodiments of the present invention may be performed in parallel, sequentially or in a different order, so long as the desired result of the technical solution of the embodiments of the present invention can be achieved, which is not limited herein.
The above detailed description should not be construed as limiting the scope of the embodiments of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the embodiments of the present invention should be included in the scope of the embodiments of the present invention.
Claims (10)
1. An RPA-based tumor whole course management method applied to a course management system is characterized by comprising the following steps:
acquiring index data of each tumor patient, and grouping each tumor patient according to each index data;
acquiring disease course information of a target user in a target group, and determining whether the target user is abnormal or not according to the disease course information;
Under the condition that the abnormal grade of the target user meets the preset condition, feeding back the abnormal grade to a target doctor matched with the target user, receiving a processing strategy fed back by the target doctor, and feeding back the processing strategy to the target user;
wherein the processing policy includes at least one of: review in time, take medications on time, strengthen exercise, and pay attention to diet.
2. The method of claim 1, wherein the course management system comprises a control center comprising a data acquisition robot;
the obtaining the index data of each tumor patient comprises the following steps:
under the condition that the preset acquisition time is reached, acquiring each index data uploaded by each tumor patient through a patient client by the data acquisition robot of the control center;
the index data includes at least one of: illness state information, operation condition, postoperative condition and medication information.
3. The method of claim 2, wherein the control center further comprises a management robot; the data acquisition robot is in communication connection with the management robot;
The grouping each of the tumor patients according to each of the index data includes:
determining at least one conditional expression by a management robot of the control center;
inputting each index data into each conditional expression respectively, and determining a target expression matched with each index data;
and determining target groups corresponding to the tumor patients according to the target expression.
4. The method of claim 3, wherein the course management system further comprises an actuator, the actuator being communicatively coupled to the control center;
the obtaining the disease course information of the target user in the target group, and determining whether the target user has an abnormality according to the disease course information, includes:
acquiring disease course information of the target user through the data acquisition robot of the control center, and sending the disease course information to the executor;
the executor analyzes the course information to determine whether the target user is abnormal;
wherein the course information includes at least one of: user behavior data, inspection data, and questionnaire data.
5. The method of claim 4, further comprising, after determining that the target user has an abnormality based on the course information:
inputting the course information to a predetermined abnormality level determination module through the actuator, and determining an abnormality level of the target user according to the abnormality level determination module;
the abnormal grade of the target user is sent to a management robot of the control center;
the anomaly level includes one of: stable, substantially stable, dangerous or very dangerous.
6. The method of claim 5, wherein the anomaly level determination module determines by:
acquiring a plurality of abnormal course information, and marking the abnormal course information in a grade way;
constructing a training sample according to the abnormal course information and the grade marking result corresponding to the abnormal course information;
and sequentially inputting the training samples into a target convolutional neural network for training to obtain the abnormal grade determination model.
7. The method of claim 5, wherein the feeding back the anomaly level to a target doctor matching the target user, receiving a processing strategy fed back by the target doctor, and feeding back the processing strategy to the target user, comprises:
Determining a target doctor matched with the target user through the management robot, and pushing live broadcast data and disease course information of the target user to the target doctor;
receiving a processing strategy which is fed back by the target doctor and matched with the target user;
and feeding back the processing strategy to the target user.
8. An RPA-based tumor whole course management device applied to a course management system, comprising:
the index data acquisition module is used for acquiring index data of each tumor patient and grouping each tumor patient according to each index data;
the disease course information acquisition module is used for acquiring disease course information of target users in the target group and determining whether the target users have abnormality according to the disease course information;
the processing strategy feedback module is used for feeding back the abnormal grade to a target doctor matched with the target user under the condition that the abnormal grade of the target user meets a preset condition, receiving a processing strategy fed back by the target doctor and feeding back the processing strategy to the target user;
wherein the processing policy includes at least one of: review in time, take medications on time, strengthen exercise, and pay attention to diet.
9. An electronic device, the electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the RPA-based tumor total course management method of any one of claims 1-7.
10. A computer readable storage medium storing computer instructions for causing a processor to perform the RPA-based tumor whole course management method of any one of claims 1-7.
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