CN116052851A - Recommendation method and device for ventricular assist device - Google Patents
Recommendation method and device for ventricular assist device Download PDFInfo
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Abstract
The embodiment of the invention provides a recommendation method and device for ventricular assist equipment, and relates to the technical field of medical equipment. The method comprises the following steps: obtaining first data characterizing a real-time state of the heart of the target object, and second data characterizing a historical state of the heart; predicting a target ventricular assist device to be used by the target object based on the first data and the second data; recommending the target ventricular assist device to the target subject. When the scheme provided by the embodiment is used for recommending the ventricular assist device, the recommending efficiency can be improved.
Description
Technical Field
The invention relates to the technical field of medical equipment, in particular to a recommendation method and device of ventricular assist equipment.
Background
Ventricular assist devices for promoting cardiac rehabilitation by providing hemodynamic force. Taking an interventional left ventricular assist device as an example, the interventional left ventricular assist device is a temporary ventricular support device, and is suitable for continuous cardiogenic shock generated within 48 hours after acute myocardial infarction, open-heart surgery or myocardial diseases occur, and the interventional left ventricular assist device can reduce ventricular work and improve necessary circulatory support so as to restore the heart and perform early assessment of residual myocardial functions.
Currently, the devices recommended to the patient are determined by experienced physicians from among the existing ventricular assist devices, however, the recommendation efficiency is lower when the devices recommended to the patient are determined from among the larger number of ventricular assist devices.
Disclosure of Invention
The embodiment of the invention aims to provide a recommendation method and device for ventricular assist devices, so as to improve recommendation efficiency. The specific technical scheme is as follows:
in a first aspect, an embodiment of the present invention provides a method for recommending ventricular assist devices, the method including:
obtaining first data characterizing a real-time state of the heart of the target object, and second data characterizing a historical state of the heart;
predicting a target ventricular assist device to be used by the target object based on the first data and the second data;
recommending the target ventricular assist device to the target subject.
In one embodiment of the present invention, the above-mentioned target ventricular assist device for predicting the target object based on the first data and the second data includes:
determining a first device from among the candidate ventricular assist devices, and a first fitness of the first device for the target object, based on the first data;
Determining, based on the second data, a second device from among the candidate ventricular assist devices, and a second fitness of the second device for the target object;
and determining the target ventricular assist device used by the target object from the first device and the second device based on the first fitting degree corresponding to the first device and the second fitting degree corresponding to the second device.
In an embodiment of the present invention, the determining, from the first device and the second device, the target ventricular assist device used by the target object based on the first fitness corresponding to the first device and the second fitness corresponding to the second device includes:
determining a third device in which repetition occurs in the first device and the second device;
a target ventricular assist device for use by a target object is determined from the third device based on the first and second fitness of the third device.
In one embodiment of the present invention, the above-mentioned target ventricular assist device for predicting the target object based on the first data and the second data includes:
generating third data based on the first data and the second data;
based on the third data, a target ventricular assist device used by the target object is predicted.
In one embodiment of the present invention, the above-mentioned target ventricular assist device for predicting the target object based on the third data includes:
determining a target object class to which the target object belongs based on attribute information of the target object;
a reference object of the target object is determined based on fourth data characterizing the cardiac state of each object contained by the target object class, and third data of the target object, and a ventricular assist device used by the reference object is determined as a target ventricular assist device used by the target object.
In one embodiment of the present invention, the determining the reference object of the target object based on the fourth data of each object included in the target object class and the third data of the target object includes:
for each object contained in the target object class, calculating the similarity between fourth data of the object and third data of the target object;
a reference object of the target object is determined based on the calculated similarity.
In one embodiment of the present invention, the above-mentioned target ventricular assist device for predicting the target object based on the third data includes:
Inputting the third data into a prediction model, obtaining a target identification of ventricular assist devices output by the prediction model, and determining ventricular assist devices corresponding to the target identification as target ventricular assist devices used by the target object;
wherein, the prediction model is: and training the initial neural network model by taking the data representing the heart state of the sample object as a training sample and taking the ventricular assist device used by the sample object as a training reference to obtain a model for predicting the adaptation degree of the ventricular assist device to the heart state of the object.
In a second aspect, an embodiment of the present invention provides a recommendation apparatus for a ventricular assist device, the apparatus including:
a data obtaining module for obtaining first data representing a real-time state of the heart of the target object and second data representing a historical state of the heart;
a device prediction module for predicting a target ventricular assist device to be used by the target subject based on the first data and the second data;
and the device recommending module is used for recommending the target ventricular assist device to the target object.
In one embodiment of the present invention, the device prediction module includes:
A first data determination sub-module for determining a first device from among the candidate ventricular assist devices and a first fitness of the first device for the target object based on the first data;
a second data determination sub-module for determining a second device from among the candidate ventricular assist devices, and a second fitness of the second device for the target object, based on the second data;
the device determination submodule is used for determining target ventricular assist devices used by the target object from the first device and the second device based on the first adaptation degree corresponding to the first device and the second adaptation degree corresponding to the second device.
In one embodiment of the present invention, the device determining submodule is specifically configured to determine a third device in which repetition occurs in the first device and the second device; a target ventricular assist device for use by a target object is determined from the third device based on the first and second fitness of the third device.
In one embodiment of the present invention, the device prediction module includes:
a third data determination sub-module for generating third data based on the first data and the second data;
And the device prediction sub-module is used for predicting target ventricular assist devices used by the target object based on the third data.
In one embodiment of the present invention, the device prediction submodule includes:
an object class determining unit, configured to determine, based on attribute information of a target object, a target object class to which the target object belongs;
and a device determining unit configured to determine a reference object of the target object based on fourth data representing a cardiac state of each object included in the target object class and third data of the target object, and determine a ventricular assist device used by the reference object as a target ventricular assist device used by the target object.
In one embodiment of the present invention, the device determining unit is specifically configured to calculate, for each object included in the target object class, a similarity between fourth data of the object and third data of the target object; a reference object of the target object is determined based on the calculated similarity.
In one embodiment of the present invention, the above device prediction submodule is specifically configured to input the third data into a prediction model, obtain a target identifier of a ventricular assist device output by the prediction model, and determine a ventricular assist device corresponding to the target identifier as a target ventricular assist device used by the target object; wherein, the prediction model is: and training the initial neural network model by taking the data representing the heart state of the sample object as a training sample and taking the ventricular assist device used by the sample object as a training reference to obtain a model for predicting the adaptation degree of the ventricular assist device to the heart state of the object.
In a third aspect, an embodiment of the present invention provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, where the processor, the communication interface, and the memory complete communication with each other through the communication bus;
a memory for storing a computer program;
and a processor, configured to implement the method steps described in the first aspect when executing the program stored in the memory.
In a fourth aspect, embodiments of the present invention provide a computer-readable storage medium having stored therein a computer program which, when executed by a processor, implements the method steps of the first aspect described above.
As can be seen from the above, when the scheme provided by the embodiment of the invention is applied to the recommendation of the ventricular assist device, the ventricular assist device used by the target object is predicted by the electronic device based on the first data and the second data corresponding to the target object, and then the target ventricular assist device is recommended to the target object, so that compared with the ventricular assist device manually determined in the prior art, the recommendation efficiency is improved.
In addition, because the target ventricular assist device used by the target object is predicted based on the first data and the second data, and then the target ventricular assist device is recommended to the target object, and because the first data can reflect the current condition of the heart of the target object and the second data can reflect the heart history condition of the target object, when the target ventricular assist device is predicted, the current condition of the heart of the target object is considered, the heart history condition of the target object is considered, and the two aspects are combined, so that the predicted target ventricular assist device can better conform to the heart condition of the target object, thereby improving the recommendation accuracy of the ventricular assist device
Of course, it is not necessary for any one product or method of practicing the invention to achieve all of the advantages set forth above at the same time.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following description will briefly introduce the drawings that are required to be used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other embodiments may also be obtained according to these drawings to those skilled in the art.
Fig. 1 is a flowchart of a recommendation method of a first ventricular assist device according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating a method for recommending a second ventricular assist device according to an embodiment of the present invention;
FIG. 3 is a flowchart illustrating a third method for recommending ventricular assist devices according to an embodiment of the present invention;
FIG. 4 is a flowchart illustrating a method for recommending a fourth ventricular assist device according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a recommendation device of a ventricular assist device according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, those of ordinary skill in the art will be able to devise all other embodiments that are obtained based on this application and are within the scope of the present invention.
First, an execution body of an embodiment of the present invention will be described.
The execution main body of the embodiment of the invention is an electronic device, and the electronic device can be a server, a cloud server and the like.
The following describes a recommended method of the ventricular assist device according to the embodiment of the present invention.
Referring to fig. 1, fig. 1 is a flowchart of a method for recommending a first ventricular assist device according to an embodiment of the present invention, where the method includes the following steps S101 to S103.
Step S101: first data characterizing a real-time state of the heart of a target object, and second data characterizing a historical state of the heart are obtained.
The target object refers to an object of the ventricular assist device to be recommended, and the object may be a person, an animal, or the like.
Since the first data is data for characterizing the real-time state of the heart of the target object, the first data can be understood as real-time data for reflecting the current situation of the heart of the target object; since the second data is data for characterizing the heart history state of the target object, the second data may be understood as history data for reflecting the heart history of the target object.
The first data may be real-time parameter data including a preset cardiac parameter item of the target object within a preset time period of the recommended time. The recommended time is as follows: the preset time period may be 1h, 6h, 12h, 24h, etc. at the moment when the ventricular assist device needs to be recommended to the target object. The preset cardiac parameter items may include: heart rate, mean arterial pressure, left ventricular end-diastole pressure, etc.
The second data may be parameter data of a preset cardiac parameter item of the target object before the preset time interval of the recommended time, and the preset time interval may be 24h, 48h, or the like.
Specifically, the medical diagnosis apparatus may perform real-time diagnosis with respect to the heart of the target object, and the electronic apparatus may obtain the data diagnosed by the medical diagnosis apparatus, for example, the medical diagnosis apparatus may transmit the diagnosed data to the electronic apparatus, and of course, the medical diagnosis apparatus may manually input the data diagnosed by the medical diagnosis apparatus into the electronic apparatus by the medical staff. The obtained data is taken as first data representing the real-time state of the heart of the target object.
After each diagnosis of cardiac state data characterizing the subject, the cardiac state data of the subject may be stored, based on which second data characterizing the cardiac history state of the target subject may be looked up from the stored data. In particular, the second data characterizing the heart history state of the target object may be looked up from the recorded data based on identification information of the target object, e.g. the name of the target object.
Step S102: based on the first data and the second data, a target ventricular assist device used by the target object is predicted.
The first data can reflect the current heart condition of the target object, and the second data can reflect the heart history condition of the target object, and the target ventricular assist device is predicted based on the first data and the second data, so that in the process, the current heart condition of the target object is considered, the heart history condition of the target object is considered, and the target ventricular assist device obtained through the prediction in the two aspects can be more consistent with the heart condition of the target object, thereby improving the accuracy of predicting the target ventricular assist device.
The specific manner of predicting the target ventricular assist device may be found in the embodiments corresponding to fig. 2 and 3, which are not described in detail herein.
Step S103: a target ventricular assist device is recommended to the target subject.
Specifically, the identification information of the target ventricular assist device, such as the name, model, etc., of the ventricular assist device may be displayed through a user interface configured by the electronic device. The identification information of the target ventricular assist device can also be broadcast through a microphone configured by the electronic device.
As can be seen from the above, when the scheme provided by the embodiment is applied to the recommendation of the ventricular assist device, the ventricular assist device used by the target object is predicted by the electronic device based on the first data and the second data corresponding to the target object, so that the target ventricular assist device is recommended to the target object, and compared with the ventricular assist device manually determined in the prior art, the recommendation efficiency is improved.
In addition, the target ventricular assist device used by the target object is predicted based on the first data and the second data, so that the target ventricular assist device is recommended to the target object, and the first data can reflect the current heart condition of the target object, and the second data can reflect the heart history condition of the target object, so that the current heart condition of the target object is considered and the heart history condition of the target object is considered when the target ventricular assist device is predicted, and the two aspects are combined, so that the predicted target ventricular assist device can better conform to the heart condition of the target object, and the recommendation accuracy of the ventricular assist device is improved.
For step S102 of the embodiment shown in fig. 1, two types of prediction methods are provided in the embodiment of the present invention: the first type of prediction mode is to combine the first data and the second data to predict the target ventricular assist device; the second type of prediction mode is to predict the target ventricular assist device based on the first data and the second data, respectively, and these two types of prediction modes can be seen in the embodiments corresponding to fig. 2-4 described below.
Referring to fig. 2, fig. 2 is a flowchart of a recommended method of a second ventricular assist device according to an embodiment of the present invention, where the method includes the following steps S201 to S204.
Step S201: first data characterizing a real-time state of the heart of a target object, and second data characterizing a historical state of the heart are obtained.
The step S201 is the same as the step S101 of the embodiment shown in fig. 1, and will not be described here again.
Step S202: third data is generated based on the first data and the second data.
Since the third data is generated based on the first data and the second data, the third data can sufficiently reflect the heart state of the target object.
The third data may be generated in accordance with the following two embodiments.
In a first embodiment, for each preset cardiac parameter item, data fusion is performed on the first data and the second data of the preset cardiac parameter item, and the fused data is used as third data. The data fusion method may be a kalman filter method, a maximum likelihood estimation method, a least square method, or the like.
In a second embodiment, since the second data may be data obtained at each historical time, one preset cardiac parameter item may correspond to a plurality of second data, based on which, for each preset cardiac parameter item, a first range formed by each second data of the preset cardiac parameter item is determined, and if the first data of the preset cardiac parameter item is located in the first range, the first range is used as the third data of the preset cardiac parameter item; if the first data of the preset cardiac parameter item is not in the first range, determining a second range based on the first data and the first range, and taking the second range as third data of the preset cardiac parameter item.
Taking a preset heart parameter item as an example of heart rate, each second data of the heart rate is: 68bpm, 70bpm, 80bpm, 90bpm, 100bpm, 130bpm, each second data forming a first range of: [68, 130] if the first data of the heart rate is 120bpm, since 120bpm is within the first range, the first range [68, 130] is the third data of the heart rate; if the first data of the heart rate is 140bpm, since 140bpm is not located in the first range, the right boundary of the first range is updated to the first data, and [68, 140] is obtained as the second range, and the second range [68, 140] is the third data of the heart rate.
Step S203: based on the third data, a target ventricular assist device used by the target object is predicted.
Because the third data is determined based on the first data and the second data, the third data can comprehensively reflect the heart state of the target object, and the target ventricular assist device predicted based on the third data can be more in line with the heart state of the target object, so that the accuracy of the target ventricular assist device prediction is improved.
In one embodiment, the target ventricular assist device described above may be predicted in a deep learning manner. Specifically, the third data may be input into the prediction model, a target identification of the ventricular assist device output by the prediction model is obtained, and the ventricular assist device corresponding to the target identification is determined as the target ventricular assist device used by the target object.
The prediction model is used for calculating the adaptation degree of each candidate ventricular assist device to the heart state of the target object based on the third data, and outputting the target identification of the ventricular assist device with the highest adaptation degree.
The prediction model is a model which is obtained by training an initial neural network model by taking data representing the heart state of a sample object as a training sample and taking ventricular assist devices used by the sample object as training references and is used for predicting the adaptation degree of the ventricular assist devices to the heart state of the object. The initial neural network may be CNN (Convolutional Neural Network ), RNN (Recurrent Neural Network, recurrent neural network).
The prediction model is obtained by training by taking data representing the heart state of the sample object as a training sample and taking the ventricular assist device used by the sample object as a training standard, and can accurately learn the characteristic of the adaptation degree of the ventricular assist device to the heart state of the sample object based on the data of the heart state, so that the prediction model can accurately determine the target ventricular assist device used by the target object based on the characteristic.
The prediction of the target ventricular assist device may also be performed according to steps S303-S304 of the corresponding embodiment of fig. 3, which is not described in detail herein.
Step S204: a target ventricular assist device is recommended to the target subject.
The step S204 is the same as the step S103 in the embodiment shown in fig. 1, and will not be described here again.
Because the third data can comprehensively reflect the heart state of the target object, the target ventricular assist device obtained based on the prediction of the third data can be more in line with the heart state of the target object, and therefore the accuracy of ventricular assist device recommendation is improved.
In step S203, when predicting the target ventricular assist device to be used by the target object based on the third data, the prediction may be performed by deep learning, and steps S303 to S304 may be performed according to the following embodiment of fig. 3.
Referring to fig. 3, fig. 3 is a flowchart illustrating a recommendation method of a third ventricular assist device according to an embodiment of the present invention. The method includes the following steps S301-S305.
Step S301: first data characterizing a real-time state of the heart of a target object, and second data characterizing a historical state of the heart are obtained.
Step S302: third data is generated based on the first data and the second data.
The steps S301 to S302 are the same as the steps S201 to S202 in the embodiment shown in fig. 2, and are not described here again.
Step S303: and determining the target object class to which the target object belongs based on the attribute information of the target object.
The target object class refers to a group to which the target object belongs, and the target object class includes a plurality of objects similar to the target object.
The attribute information of the target object may include: age, sex, region, etc.
In one embodiment, the screening condition may be determined based on the attribute information of the target object, and the object satisfying the screening condition may be determined from the existing objects based on the screening condition as the target object class to which the target object belongs.
For example: the attribute information of the target object is: age 60 years, sex men, region a, and based on the above attribute information, the determined screening conditions are: men with ages of 50-70 years old, sex men, and area A are identified from the existing subjects, men with ages of 50-70 years old and area A are identified as the target subject class to which the target subject belongs.
Step S304: a reference object of the target object is determined based on fourth data characterizing the cardiac state of each object contained in the target object class and the third data of the target object, and the ventricular assist device used by the reference object is determined as the target ventricular assist device used by the target object.
In one embodiment, for each object included in the target object class, a similarity between fourth data of the object and third data of the target object may be calculated; a reference object of the target object is determined based on the calculated similarity.
In calculating the similarity, a euclidean distance, a cosine similarity, or a manhattan distance between the fourth data and the third data may be calculated as the similarity between the fourth data and the third data.
After the similarity is calculated, an object corresponding to the highest similarity can be determined as a reference object of the target object; the object corresponding to the preset number of highest similarities may also be determined as a reference object of the target object.
Step S305: a target ventricular assist device is recommended to the target subject.
The step S305 is the same as the step S204 of the embodiment shown in fig. 2, and will not be described here again.
The reference object is determined based on fourth data and third data, the fourth data is data representing the heart state of each object contained in the target object class to which the target object belongs, and the third data is data representing the heart state of the target object, so that the parameter object of the target object can be accurately determined based on the third data and the fourth data, and the determined target ventricular assist device is enabled to be more in line with the heart state of the target object.
In the step S102 of the embodiment shown in fig. 1, when predicting the target ventricular assist device used by the target object based on the first data and the second data, the method of predicting the target ventricular assist device by combining the first data and the second data in the embodiment corresponding to fig. 2 and 3 may be adopted, and steps S402 to S404 in the embodiment corresponding to fig. 4 may be adopted.
Step S401: first data characterizing a real-time state of the heart of a target object, and second data characterizing a historical state of the heart are obtained.
The step S401 is the same as the step S101 of the embodiment shown in fig. 1, and will not be described here again.
Step S402: based on the first data, a first device is determined from the candidate ventricular assist devices, and a first fitness of the first device for the target object.
The first fitness represents the fitness of the first device for the target object. The higher the first fitness is, the higher the first device is for the target object, and the lower the first fitness is, the lower the first device is for the target object.
In one embodiment, the candidate ventricular assist device configures a parameter data range of the applicable preset cardiac parameter items in advance, based on the parameter data range, if data in the parameter data range corresponding to the candidate ventricular assist exists in the first data, the candidate ventricular assist device is determined as the first device, and a proportion of the number of the cardiac parameter items in the data in the parameter data range in the total number of the cardiac parameter items in the first data is calculated, and the proportion is taken as a first adaptation degree; if the first data is not in the parameter data range corresponding to the alternative ventricular assist device, the alternative ventricular assist device is not the first device.
For example: the first data are shown in table 1 below.
TABLE 1
| Heart rate of heart | Mean arterial pressure | Left ventricular pressure | End diastole of left ventricle |
| P1 | P2 | P3 | P4 |
The parameter data ranges of the preset cardiac parameter items for which the alternative ventricular assist device S1 is preconfigured are shown in table 2 below.
TABLE 2
| Heart rate of heart | Mean arterial pressure | Left ventricular pressure | End diastole of left ventricle |
| A1～A2 | A3～A4 | A5～A6 | A7～A8 |
Wherein, the parameter data P1 of the heart rate contained in the first data is located in the heart rate parameter data range corresponding to the device S1; the parameter data P2 of the mean arterial pressure contained in the first data is located in the range of the mean arterial pressure parameter data corresponding to the device S1. Since the first data includes the parameter data range corresponding to the ventricular assist device S1, the ventricular assist device S1 is the first device, and the number of parameter items of the first data within the parameter data range corresponding to the ventricular assist device S1 is 2, the total number of preset cardiac parameter items of the first data is 4, and the calculated duty ratio is 2/4=50%, which is used as the first fitness.
Step S403: based on the second data, a second device is determined from the candidate ventricular assist devices, and a second fitness of the second device for the target object.
The second fitness represents the fitness of the second device for the target object. The higher the second fitness is, the higher the second device is for the target object, and the lower the second fitness is, the lower the second device is for the target object.
In one embodiment, the candidate ventricular assist device configures in advance a parameter data range of the applicable preset cardiac parameter items, based on which, if there is data in the second data that is located in the parameter data range corresponding to the candidate ventricular assist device, the candidate ventricular assist device is determined as the second device, and a proportion of the number of cardiac parameter items of the data located in the parameter data range in the total number of cardiac parameter items of the second data is calculated, and the proportion is taken as the second fitness; if the second data is not in the parameter data range corresponding to the alternative ventricular assist device, the alternative ventricular assist device is not the second device.
Step S404: and determining the target ventricular assist device used by the target object from the first device and the second device based on the first fitting degree corresponding to the first device and the second fitting degree corresponding to the second device.
In one embodiment, a third device in which repetition occurs between the first device and the second device may be determined, and a target ventricular assist device used by the target object is determined from the third device based on the first fitness and the second fitness of the third device.
Specifically, an average value of the first fitting degree and the second fitting degree of the third device may be calculated, and the third device with the highest average value is taken as the target ventricular assist device used by the target object.
For example: the first device comprises ventricular assist devices Sp1, sp2, sp3, sp4, and the second device comprises ventricular assist devices Sp3, sp4, sp5, sp6, wherein Sp3, sp4 are third devices that repeat in the first device and the second device. The average value of the first matching degree and the second matching degree of Sp3 is 75%, the average value of the first matching degree and the second matching degree is calculated to be 62.5%, the first matching degree of Sp4 is 80%, the second matching degree is calculated to be 60%, the average value of the first matching degree and the second matching degree is calculated to be 70%, and 70% >62.5%, so that the ventricular assist device Sp4 is determined as the target ventricular assist device used by the target subject.
The third device is the device which is repeated in the first device and the second device, so that the third device can be in line with the current heart state of the target object and in line with the heart history state of the target object, and the target ventricular assist device determined from the third device can be comprehensively applied to the target object, and the accuracy of determining the target ventricular assist device is improved.
Step S405: a target ventricular assist device is recommended to the target subject.
The step S405 is the same as the step S103 in the embodiment shown in fig. 1, and will not be described here again.
The first equipment and the second equipment which are suitable for the target object are respectively determined through the first data and the second data, and then the ventricular assist equipment used by the target object is determined from the first equipment and the second equipment, so that the ventricular assist equipment which is suitable for the target object can be comprehensively determined, and the target ventricular assist equipment used by the target object can be more accurately determined.
Corresponding to the recommendation method of the ventricular assist device, the embodiment of the invention further provides a recommendation device of the ventricular assist device.
Referring to fig. 5, fig. 5 is a schematic structural diagram of a recommending apparatus for ventricular assist devices according to an embodiment of the present invention, where the foregoing apparatus includes the following modules 501-503.
A data obtaining module 501 for obtaining first data characterizing a real-time state of the heart of a target object, and second data characterizing a historical state of the heart;
a device prediction module 502 for predicting a target ventricular assist device to be used by the target subject based on the first data and the second data;
A device recommendation module 503, configured to recommend the target ventricular assist device to the target object.
In one embodiment of the present invention, the device prediction module 502 includes:
a first data determination sub-module for determining a first device from among the candidate ventricular assist devices and a first fitness of the first device for the target object based on the first data;
a second data determination sub-module for determining a second device from among the candidate ventricular assist devices, and a second fitness of the second device for the target object, based on the second data;
the device determination submodule is used for determining target ventricular assist devices used by the target object from the first device and the second device based on the first adaptation degree corresponding to the first device and the second adaptation degree corresponding to the second device.
In one embodiment of the present invention, the device determining submodule is specifically configured to determine a third device in which repetition occurs in the first device and the second device; a target ventricular assist device for use by a target object is determined from the third device based on the first and second fitness of the third device.
In one embodiment of the present invention, the device prediction module 502 includes:
a third data determination sub-module for generating third data based on the first data and the second data;
and the device prediction sub-module is used for predicting target ventricular assist devices used by the target object based on the third data.
In one embodiment of the present invention, the device prediction submodule includes:
an object class determining unit, configured to determine, based on attribute information of a target object, a target object class to which the target object belongs;
and a device determining unit configured to determine a reference object of the target object based on fourth data representing a cardiac state of each object included in the target object class and third data of the target object, and determine a ventricular assist device used by the reference object as a target ventricular assist device used by the target object.
In one embodiment of the present invention, the device determining unit is specifically configured to calculate, for each object included in the target object class, a similarity between fourth data of the object and third data of the target object; a reference object of the target object is determined based on the calculated similarity.
In one embodiment of the present invention, the above device prediction submodule is specifically configured to input the third data into a prediction model, obtain a target identifier of a ventricular assist device output by the prediction model, and determine a ventricular assist device corresponding to the target identifier as a target ventricular assist device used by the target object; wherein, the prediction model is: and training the initial neural network model by taking the data representing the heart state of the sample object as a training sample and taking the ventricular assist device used by the sample object as a training reference to obtain a model for predicting the adaptation degree of the ventricular assist device to the heart state of the object.
Corresponding to the recommended method of the ventricular assist device, the embodiment of the invention also provides electronic equipment.
Referring to fig. 6, fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, which includes a processor 601, a communication interface 602, a memory 603, and a communication bus 604, wherein the processor 601, the communication interface 602, and the memory 603 communicate with each other through the communication bus 604,
a memory 603 for storing a computer program;
the processor 601 is configured to implement the recommendation method of the ventricular assist device according to the embodiment of the present invention when executing the program stored in the memory 603.
The communication bus mentioned above for the electronic devices may be a peripheral component interconnect standard (Peripheral Component Interconnect, PCI) bus or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, etc. The communication bus may be classified as an address bus, a data bus, a control bus, or the like. For ease of illustration, the figures are shown with only one bold line, but not with only one bus or one type of bus.
The communication interface is used for communication between the electronic device and other devices.
The Memory may include random access Memory (Random Access Memory, RAM) or may include Non-Volatile Memory (NVM), such as at least one disk Memory. Optionally, the memory may also be at least one memory device located remotely from the aforementioned processor.
The processor may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; but also digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.
In still another embodiment of the present invention, a computer readable storage medium is provided, where a computer program is stored, where the computer program is executed by a processor to implement a recommendation method for a ventricular assist device provided by an embodiment of the present invention.
In yet another embodiment of the present invention, a computer program product comprising instructions that, when run on a computer, cause the computer to perform the method of recommending ventricular assist devices provided by embodiments of the present invention is also provided.
As can be seen from the above, when the scheme provided by the embodiment is applied to the recommendation of the ventricular assist device, the ventricular assist device used by the target object is predicted by the electronic device based on the first data and the second data corresponding to the target object, so that the target ventricular assist device is recommended to the target object, and compared with the ventricular assist device manually determined in the prior art, the recommendation efficiency is improved.
In addition, because the target ventricular assist device used by the target object is predicted based on the first data and the second data, and then the target ventricular assist device is recommended to the target object, and because the first data can reflect the current condition of the heart of the target object and the second data can reflect the heart history condition of the target object, when the target ventricular assist device is predicted, the current condition of the heart of the target object is considered, the heart history condition of the target object is considered, and the two aspects are combined, so that the predicted target ventricular assist device can better conform to the heart condition of the target object, thereby improving the recommendation accuracy of the ventricular assist device
In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, produces a flow or function in accordance with embodiments of the present invention, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another, for example, by wired (e.g., coaxial cable, optical fiber, digital Subscriber Line (DSL)), or wireless (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains an integration of one or more available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid State Disk (SSD)), etc.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
In this specification, each embodiment is described in a related manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for apparatus, electronic devices, computer readable storage medium embodiments, since they are substantially similar to method embodiments, the description is relatively simple, and relevant references are made to the partial description of method embodiments.
The foregoing description is only of the preferred embodiments of the present invention and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.
Claims (10)
1. A method of recommending ventricular assist devices, the method comprising:
obtaining first data characterizing a real-time state of the heart of the target object, and second data characterizing a historical state of the heart;
predicting a target ventricular assist device to be used by the target object based on the first data and the second data;
recommending the target ventricular assist device to the target subject.
2. The method of claim 1, wherein predicting a target ventricular assist device for use by the target object based on the first data and the second data comprises:
determining a first device from among the candidate ventricular assist devices, and a first fitness of the first device for the target object, based on the first data;
determining, based on the second data, a second device from among the candidate ventricular assist devices, and a second fitness of the second device for the target object;
And determining the target ventricular assist device used by the target object from the first device and the second device based on the first fitting degree corresponding to the first device and the second fitting degree corresponding to the second device.
3. The method of claim 2, wherein determining the target ventricular assist device to be used by the target object from the first device and the second device based on the first fitness corresponding to the first device and the second fitness corresponding to the second device comprises:
determining a third device in which repetition occurs in the first device and the second device;
a target ventricular assist device for use by a target object is determined from the third device based on the first and second fitness of the third device.
4. The method of claim 1, wherein predicting a target ventricular assist device for use by the target object based on the first data and the second data comprises:
generating third data based on the first data and the second data;
based on the third data, a target ventricular assist device used by the target object is predicted.
5. The method of claim 4, wherein predicting a target ventricular assist device for use by the target object based on the third data comprises:
Determining a target object class to which the target object belongs based on attribute information of the target object;
a reference object of the target object is determined based on fourth data characterizing the cardiac state of each object contained by the target object class, and third data of the target object, and a ventricular assist device used by the reference object is determined as a target ventricular assist device used by the target object.
6. The method of claim 5, wherein the determining the reference object for the target object based on the fourth data for each object contained in the target object class and the third data for the target object comprises:
for each object contained in the target object class, calculating the similarity between fourth data of the object and third data of the target object;
a reference object of the target object is determined based on the calculated similarity.
7. The method of claim 4, wherein predicting a target ventricular assist device for use by the target object based on the third data comprises:
inputting the third data into a prediction model, obtaining a target identification of ventricular assist devices output by the prediction model, and determining ventricular assist devices corresponding to the target identification as target ventricular assist devices used by the target object;
Wherein, the prediction model is: and training the initial neural network model by taking the data representing the heart state of the sample object as a training sample and taking the ventricular assist device used by the sample object as a training reference to obtain a model for predicting the adaptation degree of the ventricular assist device to the heart state of the object.
8. A recommendation device for ventricular assist devices, the device comprising:
a data obtaining module for obtaining first data representing a real-time state of the heart of the target object and second data representing a historical state of the heart;
a device prediction module for predicting a target ventricular assist device to be used by the target subject based on the first data and the second data;
and the device recommending module is used for recommending the target ventricular assist device to the target object.
9. The electronic equipment is characterized by comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory are communicated with each other through the communication bus;
a memory for storing a computer program;
a processor for carrying out the method steps of any one of claims 1-7 when executing a program stored on a memory.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored therein a computer program which, when executed by a processor, implements the method steps of any of claims 1-7.
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| CN116943015A (en) * | 2023-09-21 | 2023-10-27 | 安徽通灵仿生科技有限公司 | Control method and device for ventricular assist device |
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| CN116943015A (en) * | 2023-09-21 | 2023-10-27 | 安徽通灵仿生科技有限公司 | Control method and device for ventricular assist device |
| CN116943015B (en) * | 2023-09-21 | 2023-12-15 | 安徽通灵仿生科技有限公司 | Control method and device for ventricular assist device |
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