CN113517077A - Control method, system and storage medium for predicting efficacy of hip external inversion - Google Patents
Control method, system and storage medium for predicting efficacy of hip external inversion Download PDFInfo
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- CN113517077A CN113517077A CN202110676525.7A CN202110676525A CN113517077A CN 113517077 A CN113517077 A CN 113517077A CN 202110676525 A CN202110676525 A CN 202110676525A CN 113517077 A CN113517077 A CN 113517077A
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- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
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Abstract
The invention discloses a control method, a control system and a storage medium for predicting hip external inversion therapeutic effect, which can be applied to the field of neural networks. The method comprises the following steps: acquiring a plurality of first physical sign information of a target patient, and preprocessing the first physical sign information to obtain second physical sign information; according to the second sign information, adopting an artificial neural network model to predict the success rate of the external inversion of multiple types; the success rates of the multiple types of external inversion are sent to a display device. According to the invention, the second sign information is obtained after the obtained multiple pieces of first sign information are preprocessed, then the success rate prediction of the multiple types of external inversion is carried out by adopting the artificial neural network model according to the second sign information, the success rates of the multiple types of external inversion are obtained, and the success rates of the multiple types of external inversion are sent to the display device, so that a professional can quickly select an accurate treatment scheme when the clinical experience is lacked.
Description
Technical Field
The invention relates to the field of neural networks, in particular to a control method, a control system and a storage medium for predicting hip external inversion therapeutic effect.
Background
The hip position is a clinically common fetal position abnormality, and the incidence rate in term pregnancy is about 3% -4%. Premature rupture of fetal membranes, prolapse of umbilical cord and difficulty in back head during natural delivery at the hip position, and infant and mother adverse fatalities such as asphyxia of newborn, intracranial hemorrhage, brachial plexus injury, puerperal soft birth canal injury and the like often occur. Hip external inversion is to transform the hip fetus into the head position by manipulation, providing another option for hip delivery besides cesarean section. At present, when a hip position phenomenon is found clinically, a specific hip position external inversion operation is selected for a patient mainly according to clinical experience, so that a treatment process is caused, and a doctor with less clinical experience is difficult to effectively make a proper selection.
Disclosure of Invention
The present invention is directed to solving at least one of the problems of the prior art. Therefore, the invention provides a control method, a system and a storage medium for predicting the curative effect of hip external inversion, which can enable professionals to quickly make accurate treatment plans.
In a first aspect, an embodiment of the present invention provides a control method for predicting a hip external inversion therapeutic effect, including the following steps:
acquiring a plurality of first physical sign information of a target patient, and preprocessing the first physical sign information to obtain second physical sign information;
according to the second sign information, adopting an artificial neural network model to predict the success rate of the external inversion of multiple types;
sending the success rates of the multiple types of external inversion to a display device, wherein the display device is used for displaying the types of the external inversion and the corresponding success rates.
The control method for predicting the hip external inversion therapeutic effect provided by the embodiment of the invention has the following beneficial effects:
in the embodiment, the second body characteristic information is obtained by preprocessing the acquired multiple pieces of first body characteristic information, then the success rate prediction of the multiple types of external inversion is performed by adopting the artificial neural network model according to the second body characteristic information, the success rates of the multiple types of external inversion are obtained, and the success rates of the multiple types of external inversion are sent to the display device, so that a professional can quickly select an accurate treatment scheme when the clinical experience is short.
Optionally, the preprocessing the first body information to obtain second body information includes:
judging the data type of the first sign information;
when the data type is a non-numerical type, converting first sign information corresponding to the non-numerical type into second sign information represented by numerical values;
and when the data type is a missing type, supplementing the first sign information corresponding to the missing type by adopting an interpolation method to obtain corresponding second sign information.
Optionally, the supplementing, by using an interpolation method, the first sign information corresponding to the missing type includes:
obtaining an average value of first sign information corresponding to the missing type;
and replacing the first sign information corresponding to the missing type by using the average value.
Optionally, the supplementing, by using an interpolation method, the first sign information corresponding to the missing type includes:
and replacing the first sign information corresponding to the missing type by a preset numerical value.
Optionally, the artificial neural network model comprises input layer neurons, intermediate layer neurons, and a linear activation module; the input layer neuron and the middle layer neuron are connected in a full connection mode;
the input layer neuron is used for carrying out preliminary data fitting on the input second somatic information;
the intermediate layer neurons are used for fitting the second somatic information after the input layer neurons are fitted again;
the linear activation module is used for calculating the success rate of the external inversion operation of multiple types according to the sign information after the middle-layer neuron is fitted again.
Optionally, the dimension size of the input layer neurons is equal to the number of second morphological information.
Optionally, before the step of predicting the success rate of the external inversion operation of multiple types by using the artificial neural network model, the method further includes a training step of the artificial neural network model:
acquiring a plurality of pieces of third sign information acquired on various devices;
training the artificial neural network model by adopting the third sign information;
acquiring the theoretical success rate of external inversion predetermined according to the third body information;
comparing the theoretical success rate with a training result;
and adjusting the model parameters of the artificial neural network model according to the comparison result.
In a second aspect, an embodiment of the present invention provides a control system for predicting the efficacy of external hip inversion, including:
the data preprocessing device is used for acquiring a plurality of first physical sign information of a target patient and preprocessing the first physical sign information to obtain second physical sign information;
the data analysis device adopts an artificial neural network model to predict the success rate of the external inversion of multiple types according to the second sign information and sends the success rate of the external inversion of multiple types to the display device;
and the display device is used for displaying the type of the external inversion and the corresponding success rate.
In a third aspect, an embodiment of the present invention provides a control system for predicting hip external inversion therapeutic effect, including:
at least one memory for storing a program;
at least one processor is used for loading the program to execute the control method for predicting the curative effect of the hip external inversion operation provided by the embodiment of the first aspect.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, in which a program executable by a processor is stored, and the program executable by the processor is used for executing the control method for predicting the hip external inversion therapeutic effect provided in the first aspect.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The invention is further described with reference to the following figures and examples, in which:
FIG. 1 is a flowchart of a control method for predicting hip external inversion efficacy in accordance with an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of an artificial neural network model according to an embodiment of the present invention;
FIG. 3 is a block diagram of a control system for predicting hip external inversion efficacy in accordance with an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
In the description of the present invention, the meaning of a plurality is one or more, the meaning of a plurality is two or more, and the above, below, exceeding, etc. are understood as excluding the present numbers, and the above, below, within, etc. are understood as including the present numbers. If the first and second are described for the purpose of distinguishing technical features, they are not to be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated or implicitly indicating the precedence of the technical features indicated.
In the description of the present invention, unless otherwise explicitly defined, terms such as set, etc. should be broadly construed, and those skilled in the art can reasonably determine the specific meanings of the above terms in the present invention in combination with the detailed contents of the technical solutions.
In the description of the present invention, reference to the description of the terms "one embodiment," "some embodiments," "an illustrative embodiment," "an example," "a specific example," or "some examples," etc., means that a particular feature or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Referring to fig. 1, an embodiment of the present invention provides a control method for predicting hip external inversion therapeutic effect, and the embodiment may be applied to a server or a background processor corresponding to a medical prediction platform.
In the application process, the embodiment includes the following steps:
s11, acquiring a plurality of first sign information of the target patient, and preprocessing the first sign information to obtain second sign information.
In the present embodiment, the target patient is a pregnant woman who may currently need to perform hip inversion. The first sign information is all information of the pregnant woman currently undergoing pregnancy test in the whole pregnancy period, such as age, pregnancy times, production times, pregnancy week, pregnancy precursor body mass index BMI, BMI during operation, gestational diabetes GDM, abnormal nail function, orientation of fetus and the like. The second sign information is obtained by performing deficiency completion and numerical type conversion on the first sign information.
In the embodiment of the application, after the data type of the first sign information is judged, when the data type is a non-numerical type, the first sign information corresponding to the non-numerical type is converted into second sign information represented by numerical values; and when the data type is a missing type, supplementing the first sign information corresponding to the missing type by adopting an interpolation method to obtain corresponding second sign information. Specifically, after the average value of the first sign information corresponding to the missing type is obtained, the average value corresponding to the information is used to replace the first sign information corresponding to the missing type. In other embodiments, a preset numerical value may be further used to replace the first sign information corresponding to the missing type. For example, as shown in table 1, for specific first sign information, a corresponding processing method is adopted:
TABLE 1
And S12, according to the second sign information, adopting an artificial neural network model to predict the success rate of the external inversion operation of multiple types.
In the embodiment of the present application, as shown in fig. 2, the artificial neural network model includes input layer neurons, intermediate layer neurons, and a linear activation module; the input layer neuron and the middle layer neuron are connected in a full connection mode; the input layer neuron is used for carrying out preliminary data fitting on the input second somatotropism information; the middle layer neuron is used for fitting the second somatotropism information after the input layer neuron is fitted again; the linear activation module is used for calculating the success rate of the external inversion operation of multiple types according to the sign information after the middle-layer neuron is fitted again. And the dimension n of the input layer neurons is equal to the number of the input second sign information. For example, when the input second sign information is 20, the input layer neurons also include 20, and the input sign information has a one-to-one correspondence relationship with each of the input layer neurons. The dimension m of the middle layer neurons can be adjusted according to actual needs, for example, set to 30 neurons.
In some embodiments, before the artificial neural network model is applied to the actual operation, the artificial neural network model needs to be trained, wherein the training step comprises:
and acquiring a plurality of pieces of third sign information acquired on various devices. The type and processing mode of the third sign information are the same as those of the first sign information.
And training the artificial neural network model by adopting third body information. Specifically, the processed third body information is adopted for training.
And obtaining the theoretical success rate of the external inversion predetermined according to the third body information. The theoretical success rate is the success rate deduced by experts according to clinical experience.
And comparing the theoretical success rate with the training result, and adjusting the model parameters of the artificial neural network model according to the comparison result.
In the embodiment of the application, the prediction accuracy of the model is improved by continuously adjusting the parameters of the model in the training process.
S13, transmitting success rates of the plurality of types of external inversion to a display device. The display device is used for displaying the type and the corresponding success rate of the external inversion operation, and the displayed content provides preoperative reference information for medical staff.
In conclusion, the above embodiments can enable the professional to quickly select the accurate treatment scheme even in the absence of clinical experience.
Referring to fig. 3, an embodiment of the present invention provides a control system for predicting hip external inversion therapeutic effect, including:
the data preprocessing device is used for acquiring a plurality of first physical sign information of a target patient and preprocessing the first physical sign information to obtain second physical sign information;
the data analysis device adopts an artificial neural network model to predict the success rate of the external inversion of multiple types according to the second sign information and sends the success rate of the external inversion of multiple types to the display device;
and the display device is used for displaying the type of the external inversion and the corresponding success rate.
The content of the embodiment of the method of the invention is all applicable to the embodiment of the system, the function of the embodiment of the system is the same as the embodiment of the method, and the beneficial effect achieved by the embodiment of the system is the same as the beneficial effect achieved by the method.
The embodiment of the invention provides a control system for predicting hip external inversion therapeutic effect, which comprises:
at least one memory for storing a program;
at least one processor for loading the program to execute the control method for predicting the efficacy of external hip inversion as shown in fig. 1.
The content of the embodiment of the method of the invention is all applicable to the embodiment of the system, the function of the embodiment of the system is the same as the embodiment of the method, and the beneficial effect achieved by the embodiment of the system is the same as the beneficial effect achieved by the method.
An embodiment of the present invention provides a computer-readable storage medium in which a processor-executable program is stored, which, when being executed by a processor, is used for executing the control method for predicting the efficacy of hip external inversion surgery shown in fig. 1.
The embodiment of the invention also discloses a computer program product or a computer program, which comprises computer instructions, and the computer instructions are stored in a computer readable storage medium. The computer instructions may be read by a processor of a computer device from a computer-readable storage medium, and executed by the processor to cause the computer device to perform the method illustrated in fig. 1.
The embodiments of the present invention have been described in detail with reference to the accompanying drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge of those skilled in the art without departing from the gist of the present invention. Furthermore, the embodiments of the present invention and the features of the embodiments may be combined with each other without conflict.
Claims (10)
1. A control method for predicting the efficacy of hip external inversion surgery, comprising the steps of:
acquiring a plurality of first physical sign information of a target patient, and preprocessing the first physical sign information to obtain second physical sign information;
according to the second sign information, adopting an artificial neural network model to predict the success rate of the external inversion of multiple types;
sending the success rates of the multiple types of external inversion to a display device, wherein the display device is used for displaying the types of the external inversion and the corresponding success rates.
2. The control method of claim 1, wherein the preprocessing the first body information to obtain second body information comprises:
judging the data type of the first sign information;
when the data type is a non-numerical type, converting first sign information corresponding to the non-numerical type into second sign information represented by numerical values;
and when the data type is a missing type, supplementing the first sign information corresponding to the missing type by adopting an interpolation method to obtain corresponding second sign information.
3. The control method for predicting the efficacy of external hip inversion surgery as claimed in claim 2, wherein said supplementing the first sign information corresponding to the missing type by interpolation comprises:
obtaining an average value of first sign information corresponding to the missing type;
and replacing the first sign information corresponding to the missing type by using the average value.
4. The control method for predicting the efficacy of external hip inversion surgery as claimed in claim 2, wherein said supplementing the first sign information corresponding to the missing type by interpolation comprises:
and replacing the first sign information corresponding to the missing type by a preset numerical value.
5. The control method for predicting the efficacy of external hip inversion surgery according to claim 1, wherein the artificial neural network model comprises input layer neurons, intermediate layer neurons, and a linear activation module; the input layer neuron and the middle layer neuron are connected in a full connection mode;
the input layer neuron is used for carrying out preliminary data fitting on the input second somatic information;
the intermediate layer neurons are used for fitting the second somatic information after the input layer neurons are fitted again;
the linear activation module is used for calculating the success rate of the external inversion operation of multiple types according to the sign information after the middle-layer neuron is fitted again.
6. The control method of claim 5, wherein the input layer neurons have dimensions equal to the second somatotrophic information.
7. The control method for predicting the efficacy of external hip inversion surgery according to claim 1, further comprising the step of training the artificial neural network model before the step of predicting the success rate of the external inversion surgery of multiple types using the artificial neural network model:
acquiring a plurality of pieces of third sign information acquired on various devices;
training the artificial neural network model by adopting the third sign information;
acquiring the theoretical success rate of external inversion predetermined according to the third body information;
comparing the theoretical success rate with a training result;
and adjusting the model parameters of the artificial neural network model according to the comparison result.
8. A control system for predicting efficacy of hip external inversion surgery, comprising:
the data preprocessing device is used for acquiring a plurality of first physical sign information of a target patient and preprocessing the first physical sign information to obtain second physical sign information;
the data analysis device adopts an artificial neural network model to predict the success rate of the external inversion of multiple types according to the second sign information and sends the success rate of the external inversion of multiple types to the display device;
and the display device is used for displaying the type of the external inversion and the corresponding success rate.
9. A control system for predicting efficacy of hip external inversion surgery, comprising:
at least one memory for storing a program;
at least one processor configured to load the program to perform the control method for predicting hip external inversion efficacy of any of claims 1-7.
10. A computer-readable storage medium, in which a program executable by a processor is stored, wherein the program executable by the processor is adapted to perform a control method for predicting efficacy of external hip inversion surgery as set forth in any one of claims 1 to 7 when executed by the processor.
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