CN117313954A - Method and system for predicting hospitalization cost of tumor patient based on bp neural network - Google Patents

Method and system for predicting hospitalization cost of tumor patient based on bp neural network Download PDF

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CN117313954A
CN117313954A CN202311465912.1A CN202311465912A CN117313954A CN 117313954 A CN117313954 A CN 117313954A CN 202311465912 A CN202311465912 A CN 202311465912A CN 117313954 A CN117313954 A CN 117313954A
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张伯慧
张冉
倪建伟
车涛锋
李云峰
陈一超
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Digital Health China Technologies Co Ltd
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Abstract

The invention provides a method and a system for predicting hospitalization cost of a tumor patient based on a bp neural network, wherein the method comprises the following steps: s1: acquiring the hospitalization data of the existing tumor patient, extracting data features in the hospitalization data, and taking the extracted features as influence factors of the hospitalization cost of the tumor patient; s2: constructing a bp neural network, taking hospitalization cost influence factors and hospitalization total cost data as a training set and a testing set according to the proportion of 8:2, and training and testing the bp neural network; s3: current patient medical data is input into the bp neural network and hospitalization cost is output. The method and the system for predicting the hospitalization cost of the tumor patient based on the bp neural network can train the bp neural network model by utilizing the hospitalization data of the existing tumor patient, thereby predicting the hospitalization cost of the actual tumor patient.

Description

Method and system for predicting hospitalization cost of tumor patient based on bp neural network
Technical Field
The invention belongs to the technical field of medical data processing, and particularly relates to a method and a system for predicting hospitalization cost of a tumor patient based on a bp neural network.
Background
At present, for the study of the hospitalization cost of malignant tumor patients, the multi-factor analysis of the hospitalization cost of single malignant tumor patients is basically remained, such as multi-factor multiple linear regression analysis of the hospitalization cost of certain malignant tumor patients. However, this method cannot perform data integration analysis on different types of nausea tumors, and the accuracy of the analysis is still to be improved.
Disclosure of Invention
The method and the system for predicting the hospitalization cost of the tumor patient based on the bp neural network can train the bp neural network model by utilizing the hospitalization data of the existing tumor patient, so that the hospitalization cost of the actual tumor patient is predicted, and the problems in the prior art can be overcome.
The technical scheme for solving the technical problems is as follows:
in a first aspect, the invention provides a method for predicting hospitalization cost of a tumor patient based on a bp neural network, comprising the following steps:
s1: acquiring the hospitalization data of the existing tumor patient, extracting data features in the hospitalization data, and taking the extracted data features as influence factors of the hospitalization cost of the tumor patient;
s2: constructing a bp neural network, taking hospitalization cost influence factors and hospitalization cost data as input and output of training data respectively, taking the training data as a training set and a testing set according to the proportion of 8:2, and training and testing the bp neural network;
s3: current patient medical data is input into the bp neural network and hospitalization cost is output.
In some embodiments, the hospitalization cost influencing factors include diagnosis and treatment costs, traditional Chinese medicine costs, western medicine costs, examination costs, surgical costs, assay costs, and tumor categories.
In some embodiments, the S1 further comprises:
s11: according to the corresponding relation between hospitalization cost and hospitalization cost influence factors, converting hospitalization data into a vector form;
s12: normalizing the hospitalized data in the vector form;
s13: initial weights are given to the hospitalization cost impact factors.
In some embodiments, the S2 comprises:
s21: constructing a bp neural network structure with 7 nodes as an input layer, 5 nodes as an hidden layer and 1 node as an output layer;
s22: taking hospitalization cost influence factors in vector form hospitalization data as input, hospitalization cost as expected output, and training the bp neural network according to initial weights;
s23: updating the initial weight until the loss function converges.
In a second aspect, the present invention provides a bp neural network-based tumour patient hospitalization cost prediction system, comprising:
the data acquisition module is used for acquiring the hospitalization data of the existing tumor patient, extracting the data characteristics in the hospitalization data, and taking the extracted data characteristics as the influence factors of the hospitalization cost of the tumor patient;
the model training module is used for constructing a bp neural network, taking hospitalization cost influence factors and hospitalization cost data as a training set and a testing set according to the proportion of 8:2, and training and testing the bp neural network;
and the expense prediction module is used for inputting the current patient medical data into the bp neural network and outputting hospitalization expense.
In some embodiments, the hospitalization cost influencing factors include diagnosis and treatment costs, traditional Chinese medicine costs, western medicine costs, examination costs, surgical costs, assay costs, and tumor categories.
In some embodiments, the data acquisition module comprises:
the vector conversion sub-module is used for converting hospitalization data into a vector form according to the corresponding relation between hospitalization cost and hospitalization cost influence factors;
the normalization sub-module is used for carrying out normalization processing on the hospitalization data in the vector form;
and the initialization weight sub-module is used for giving initial weight to the hospitalization expense influence factors.
In some embodiments, the model training module comprises:
the model construction submodule is used for constructing a bp neural network structure with 7 nodes in an input layer, 5 nodes in an hidden layer and 1 node in an output layer;
the training sub-module is used for taking the hospitalization cost influence factors in the vector form hospitalization data as input, taking the hospitalization cost as expected output, and training the bp neural network according to the initial weight;
and the weight updating sub-module updates the initial weight until the loss function converges.
In a third aspect, the present invention provides a computer comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing a prediction method as claimed in any one of the preceding claims when executing the computer program.
In a fourth aspect, the present invention provides a readable storage medium having stored thereon a computer program which when executed by a processor implements a prediction method as claimed in any one of the preceding claims.
The beneficial effects of this application are:
the method and the system for predicting the hospitalization cost of the tumor patient based on the bp neural network can train the bp neural network model by utilizing the hospitalization data of the existing tumor patient, so that the hospitalization cost of the actual tumor patient is predicted, and the problems in the prior art can be overcome.
Drawings
FIG. 1 is a flow chart of a method for predicting hospitalization cost of a tumor patient based on a bp neural network;
FIG. 2 is a sub-flowchart of step S1 of the present application;
fig. 3 is a sub-flowchart of step S2 of the present application.
Detailed Description
The principles and features of the present invention are described below with reference to the drawings, the examples are illustrated for the purpose of illustrating the invention and are not to be construed as limiting the scope of the invention.
In order that the above-recited objects, features and advantages of the present application will be more clearly understood, a more particular description of the application will be rendered by reference to the appended drawings and examples. It is to be understood that the described embodiments are some, but not all, of the embodiments of the present application. The specific embodiments described herein are to be considered in an illustrative rather than a restrictive sense. All other embodiments obtained by a person of ordinary skill in the art based on the described embodiments of the present application are within the scope of the protection of the present application.
It should be noted that in this document, 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.
FIG. 1 is a flow chart of a method for predicting hospitalization cost of a tumor patient based on a bp neural network.
The method for predicting the hospitalization cost of the tumor patient based on the bp neural network comprises the following steps in combination with fig. 1:
s1: acquiring the hospitalization data of the existing tumor patient, extracting data features in the hospitalization data, and taking the extracted data features as influence factors of the hospitalization cost of the tumor patient;
in some embodiments, the hospitalization cost influencing factors include diagnosis and treatment costs, traditional Chinese medicine costs, western medicine costs, examination costs, surgical costs, assay costs, and tumor categories.
In some embodiments, in conjunction with fig. 2, i.e. the sub-flowchart of step S1 of the present application, the step S1 further includes:
s11: according to the corresponding relation between hospitalization cost and hospitalization cost influence factors, converting hospitalization data into a vector form;
s12: normalizing the hospitalized data in the vector form;
s13: initial weights are given to the hospitalization cost impact factors.
Specifically, the scheme is based on the existing hospitalization data of the tumor patients, extracts data features from the existing hospitalization data of the tumor patients, and uses the extracted data features as influence factors of hospitalization cost of the tumor patients for subsequent model training. For the hospitalization data of the tumor patients, the scheme is summarized into 7 categories, including diagnosis and treatment cost, traditional Chinese medicine cost, western medicine cost, examination cost, operation cost, assay cost and tumor category, wherein the 7 categories are main influencing factors of the hospitalization cost of the tumor patients. Furthermore, in order to facilitate the input of the bp neural network model, the hospitalization data is converted into a vector representation form of the correspondence between hospitalization cost and the hospitalization cost influencing factors, and the vector representation form can be specifically represented by the following formula:
，
wherein,hospitalization cost representing group i hospitalization data, +.>-/>The diagnosis and treatment fee, the Chinese medicine fee, the western medicine fee, the examination fee, the operation fee, the assay fee and the tumor category corresponding to the hospitalization fee of the ith group of hospitalization data are respectively shown.
For the input data of the bp neural network, besides vector representation, normalization processing is needed to be carried out on the input data so as to speed up the training speed of the model. Further, after preprocessing of the hospitalization data is completed, each hospitalization cost influencing factor needs to be given its initial weight for training of the model. Specifically, a random generator can be utilized to generate a group of random numbers of-0.5 to 0.5, which are used as initial weights of the bp neural network.
S2: constructing a bp neural network, taking hospitalization cost influence factors and hospitalization cost data as input and output of training data respectively, taking the training data as a training set and a testing set according to the proportion of 8:2, and training and testing the bp neural network;
specifically, according to the vectorized hospitalization data and the initial weight, the hospitalization cost influencing factors and the hospitalization cost data are used as a training set and a testing set according to the proportion of 8:2, and the bp neural network is trained and tested.
In some embodiments, in conjunction with fig. 3, which is a sub-flowchart of step S2 of the present application, the S2 includes:
s21: constructing a bp neural network structure with 7 nodes as an input layer, 5 nodes as an hidden layer and 1 node as an output layer;
s22: taking hospitalization cost influence factors in vector form hospitalization data as input, hospitalization cost as expected output, and training the bp neural network according to initial weights;
s23: updating the initial weight until the loss function converges.
Furthermore, the hospitalization cost influencing factors in vector form hospitalization data are used as input of the bp neural network model, corresponding hospitalization cost is used as expected output of the bp neural network model, according to the category number of the hospitalization cost influencing factors, namely 7 categories of diagnosis and treatment cost, traditional Chinese medicine cost, western medicine cost, examination cost, operation cost, assay cost and tumor category, the input layer of the bp neural network structure is set to 7 nodes, the corresponding output layer is set to 1 node, the hidden layer is set to 75% of the number of the nodes of the input layer, namely 5 hidden layer nodes, and note that the node number of the hidden layer is not necessarily fixed to 5, and the models of the hidden layers comprising 4, 5 and 6 nodes can be compared, so that the most reasonable bp neural network structure is determined. For the specific calculation of the bp neural network, the scheme adopts a sigmiod function as an activation function, and the output of an implicit layer is as follows:
；
wherein,representing the output of the hidden layer, S being the activation function, n representing the number of nodes of the input layer, i representing the ith input, j representing the jth hidden layer,/->For inputting layer to hidden layer weights, +.>Representing the i-th input hospitalization cost influencing factor, < ->Representing implicit layer bias.
The output of the output layer is:
；
wherein,representing the output of the output layer, l being the number of hidden layer nodes, in this case 4 or 5 or 6,/for this case>Representing weights of hidden layer to output layer, +.>Representing the output layer bias.
The error is:
；
where E represents an error, m represents the number of output layer nodes (1 in this scheme), and Y represents a desired output.
Thus, the update of the weights is:
；
wherein,is a preset learning rate.
Therefore, according to the bp neural network model, hospitalization cost influence factors in vector form hospitalization data are used as input of the bp neural network model, corresponding hospitalization cost is used as expected output of the bp neural network model, and training is carried out until errors are converged, so that the bp neural network model with updated weight is obtained. Furthermore, the hospitalization cost of the tumor patient can be predicted by inputting the hospitalization data of the actual tumor patient into the model.
The second aspect of the present invention also provides a tumor patient hospitalization cost prediction system based on a bp neural network, comprising:
the data acquisition module is used for acquiring the hospitalization data of the existing tumor patient, extracting the data characteristics in the hospitalization data, and taking the extracted data characteristics as the influence factors of the hospitalization cost of the tumor patient;
the model training module is used for constructing a bp neural network, taking hospitalization cost influence factors and hospitalization cost data as a training set and a testing set according to the proportion of 8:2, and training and testing the bp neural network;
and the expense prediction module is used for inputting the current patient medical data into the bp neural network and outputting hospitalization expense.
In some embodiments, the hospitalization cost influencing factors include diagnosis and treatment costs, traditional Chinese medicine costs, western medicine costs, examination costs, surgical costs, assay costs, and tumor categories.
In some embodiments, the data acquisition module comprises:
the vector conversion sub-module is used for converting hospitalization data into a vector form according to the corresponding relation between hospitalization cost and hospitalization cost influence factors;
the normalization sub-module is used for carrying out normalization processing on the hospitalization data in the vector form;
and the initialization weight sub-module is used for giving initial weight to the hospitalization expense influence factors.
The third aspect of the present invention also provides a computer comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the prediction method as claimed in any one of the preceding claims when executing the computer program.
The fourth aspect of the present invention also provides a readable storage medium having stored thereon a computer program, characterized in that the program, when executed by a processor, implements a prediction method as described in any of the above.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.
In the embodiments provided in the present disclosure, it should be understood that the disclosed apparatus/computer device and method may be implemented in other manners. For example, the apparatus/computer device embodiments described above are merely illustrative, e.g., the division of modules or elements is merely a logical functional division, and there may be additional divisions of actual implementations, multiple elements or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present disclosure may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present disclosure may implement all or part of the flow of the method of the above-described embodiments, or may be implemented by a computer program to instruct related hardware, and the computer program may be stored in a computer readable storage medium, where the computer program, when executed by a processor, may implement the steps of the method embodiments described above. The computer program may comprise computer program code, which may be in source code form, object code form, executable file or in some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the content of the computer readable medium can be appropriately increased or decreased according to the requirements of the jurisdiction's jurisdiction and the patent practice, for example, in some jurisdictions, the computer readable medium does not include electrical carrier signals and telecommunication signals according to the jurisdiction and the patent practice.
Those skilled in the art will appreciate that while some embodiments described herein include some features but not others included in other embodiments, combinations of features of different embodiments are meant to be within the scope of the present application and form different embodiments.
Those skilled in the art will appreciate that the descriptions of the various embodiments are each focused on, and that portions of one embodiment that are not described in detail may be referred to as related descriptions of other embodiments.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and alterations without departing from the spirit and scope of the present invention, and such modifications and alterations fall within the scope of the appended claims, which are to be construed as merely illustrative of this invention, but the scope of the invention is not limited thereto, and various equivalent modifications and substitutions will be readily apparent to those skilled in the art within the scope of the present invention, and are intended to be included within the scope of the invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
The present invention is not limited to the above embodiments, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the present invention, and these modifications and substitutions are intended to be included in the scope of the present invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.

Claims (10)

1. The method for predicting the hospitalization cost of the tumor patient based on the bp neural network is characterized by comprising the following steps of:
s1: acquiring the hospitalization data of the existing tumor patient, extracting data features in the hospitalization data, and taking the extracted data features as influence factors of the hospitalization cost of the tumor patient;
s2: constructing a bp neural network, taking hospitalization cost influence factors and hospitalization cost data as input and output of training data respectively, taking the training data as a training set and a testing set according to the proportion of 8:2, and training and testing the bp neural network;
s3: current patient medical data is input into the bp neural network and hospitalization cost is output.
2. The bp neural network-based method of claim 1, wherein the hospitalization cost influencing factors include diagnosis and treatment costs, chinese medicine costs, western medicine costs, examination costs, operation costs, assay costs, and tumor categories.
3. The bp neural network-based method of predicting hospitalization cost for a tumor patient according to claim 2, wherein S1 further comprises:
s11: according to the corresponding relation between hospitalization cost and hospitalization cost influence factors, converting hospitalization data into a vector form;
s12: normalizing the hospitalized data in the vector form;
s13: initial weights are given to the hospitalization cost impact factors.
4. The bp neural network-based method of predicting hospitalization cost of a tumor patient according to claim 3, wherein S2 comprises:
s21: constructing a bp neural network structure with 7 nodes as an input layer, 5 nodes as an hidden layer and 1 node as an output layer;
s22: taking hospitalization cost influence factors in vector form hospitalization data as input, hospitalization cost as expected output, and training the bp neural network according to initial weights;
s23: updating the initial weight until the loss function converges.
5. The tumor patient hospitalization cost prediction system based on the bp neural network is characterized by comprising the following steps:
the data acquisition module is used for acquiring the hospitalization data of the existing tumor patient, extracting the data characteristics in the hospitalization data, and taking the extracted data characteristics as the influence factors of the hospitalization cost of the tumor patient;
the model training module is used for constructing a bp neural network, taking hospitalization cost influence factors and hospitalization cost data as input and output of training data respectively, taking the training data as a training set and a testing set according to the proportion of 8:2, and training and testing the bp neural network;
and the expense prediction module is used for inputting the current patient medical data into the bp neural network and outputting hospitalization expense.
6. The bp neural network-based tumor patient hospitalization cost prediction system of claim 5, wherein the hospitalization cost influencing factors comprise diagnosis and treatment costs, traditional Chinese medicine costs, western medicine costs, examination costs, surgery costs, assay costs, and tumor categories.
7. The bp neural network-based tumor patient hospitalization cost prediction system of claim 6, wherein the data acquisition module comprises:
the vector conversion sub-module is used for converting hospitalization data into a vector form according to the corresponding relation between hospitalization cost and hospitalization cost influence factors;
the normalization sub-module is used for carrying out normalization processing on the hospitalization data in the vector form;
and the initialization weight sub-module is used for giving initial weight to the hospitalization expense influence factors.
8. The bp neural network-based tumor patient hospitalization cost prediction system of claim 7, wherein the model training module comprises:
the model construction submodule is used for constructing a bp neural network structure with 7 nodes in an input layer, 5 nodes in an hidden layer and 1 node in an output layer;
the training sub-module is used for taking the hospitalization cost influence factors in the vector form hospitalization data as input, taking the hospitalization cost as expected output, and training the bp neural network according to the initial weight;
and the weight updating sub-module updates the initial weight until the loss function converges.
9. A computer comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the prediction method of any one of claims 1 to 4 when the computer program is executed.
10. A readable storage medium having stored thereon a computer program, which when executed by a processor implements the prediction method according to any of claims 1 to 4.
CN202311465912.1A 2023-11-07 2023-11-07 Method and system for predicting hospitalization cost of tumor patient based on bp neural network Pending CN117313954A (en)

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