CN116468300A - Army general hospital discipline assessment method and system based on neural network - Google Patents
Army general hospital discipline assessment method and system based on neural network Download PDFInfo
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Abstract
The invention discloses a method and a system for evaluating general hospital disciplines of army based on a neural network, and relates to the technical field of hospital discipline evaluation. The method comprises the following specific steps: determining a subject to be evaluated and establishing a hospital subject evaluation system according to the subject to be evaluated, wherein the hospital subject evaluation system comprises primary indexes and secondary indexes corresponding to the primary indexes; establishing an initial BP neural network model, and training the initial BP neural network model by using secondary index data to obtain an optimized BP neural network model; and inputting the acquired secondary index data into an optimized BP neural network model to obtain an evaluation result of the subject to be evaluated. According to the invention, the army high-level hospital disciplines are subjected to omnibearing longitudinal comparison evaluation from five dimensions, and the discipline evaluation is performed by adopting the neural network algorithm, so that the problem that the evaluation accuracy is low due to the fact that the traditional evaluation algorithm needs to manually judge weights and relies on subjective evaluation of people excessively is solved.
Description
Technical Field
The invention relates to the technical field of hospital discipline assessment, in particular to a method and a system for evaluating general hospital disciplines of army based on a neural network.
Background
Currently, the following problems are common in the high-level hospital discipline construction of the army: (1) tracking dynamics is difficult. The subject construction index data are scattered in various departments such as clinic, auxiliary diagnosis and the like, and the army high-level hospitals lack special subject systems, so that the current state of subject development cannot be monitored in real time. (2) The data quality is not high. Because there is not unified matching rule, cause subject data to be not standard, and scientific research output is the key index of judgement subject construction level, still adopts the mode that clinical department filled when statistics scientific research output at present, and not only waste time and energy, the problem of reporting by mistake easily appears moreover, and the data quality is low. (3) The data statistics caliber is wide. At present, most of the existing data of hospitals are wide-caliber statistical data, public statistical data often cannot focus on disciplines, and accurate and scientific evaluation of discipline level is difficult. (4) Discipline evaluation is difficult. Because discipline evaluation business logic is complex and a special discipline evaluation system and tools are lacked, the self-discipline evaluation of a hospital is difficult, the discipline evaluation based on the analytic hierarchy process in the prior art needs to be manually judged to be weighted, and the evaluation accuracy is low. Therefore, how to use machine learning theory for military hospital discipline assessment is a highly desirable problem for those skilled in the art.
Disclosure of Invention
In view of the above, the invention provides a method and a system for evaluating the subject of a general hospital of the army based on a neural network, so as to solve the problems in the background technology.
In order to achieve the above purpose, the present invention adopts the following technical scheme: a method for evaluating the subject of a general hospital of the army based on a neural network comprises the following specific steps:
determining a subject to be evaluated and establishing a hospital subject evaluation system according to the subject to be evaluated, wherein the hospital subject evaluation system comprises primary indexes and secondary indexes corresponding to the primary indexes;
establishing an initial BP neural network model, and training the initial BP neural network model by using secondary index data to obtain an optimized BP neural network model;
and inputting the acquired secondary index data into an optimized BP neural network model to obtain an evaluation result of the subject to be evaluated.
Optionally, the first-level index comprises clinical work, teaching and learning team and resource, talent culture, scientific research and army service; the second-level indexes corresponding to the clinical work comprise daily average clinic volume and inpatients; the secondary indexes corresponding to the resources of the teacher and resource team comprise the number of the teacher and resource, the quality of the teacher and resource and the number of the discipline support platforms; the talent culture corresponding secondary indexes comprise graduate just-in-place data and hospital-in-place data; the second-level indexes corresponding to the scientific research comprise academic paper publishing conditions and academic result conversion conditions; the secondary indexes corresponding to the military service comprise social task participation data and exercise task participation data.
Optionally, the initial BP neural network model is a three-layer structure, including an input layer, an implicit layer and an output layer, the second-level index is used as the input of the initial BP neural network model, and the evaluation level is used as the output of the initial BP neural network.
Optionally, in the initial BP neural network model, a function from the input layer to the hidden layer is:
wherein n is the number of neurons in the input layer, v ij For the input of weights of layer i neurons to hidden layer j neurons, i=1, 2,..n, j=1, 2,..n.
Optionally, according to the hospital discipline assessment system, subjective and objective assessment is performed on the primary index and the secondary index, a comprehensive weight value of the discipline to be assessed is calculated, and the comprehensive weight value is used as an initial weight value of the initial BP neural network model for learning training.
Optionally, subjective weights of the primary index and the secondary index are determined based on triangle fuzzy numbers, and objective weights of the primary index and the secondary index are determined by using an entropy weight method.
Optionally, the method further comprises the steps of performing data cleaning processing on the collected primary index data and secondary index data of the subject to be evaluated, and performing normalization processing on the data after the data cleaning processing.
On the other hand, the invention provides a army general hospital discipline assessment method based on a neural network, which comprises an assessment system determination module, a neural network model establishment module and an assessment module which are connected in sequence; wherein,,
the assessment system determination module is used for determining subjects to be assessed and establishing a hospital subject assessment system according to the subjects to be assessed, wherein the hospital subject assessment system comprises primary indexes and secondary indexes corresponding to the primary indexes;
the neural network model building module is used for building an initial BP neural network model, and training the initial BP neural network model by utilizing the secondary index data to obtain an optimized BP neural network model;
the evaluation module is used for inputting the acquired secondary index data into the optimized BP neural network model to obtain an evaluation result of the subject to be evaluated.
Compared with the prior art, the invention discloses a method and a system for evaluating the subject of the army general hospital based on the neural network, which have the following beneficial technical effects: through establishing a discipline assessment system, the discipline simulation self-assessment is realized by carrying out omnibearing longitudinal comparison assessment on the high-level hospital disciplines of the army in five dimensions from clinical work, teaching and material teams and resources, talent culture, scientific research and military service, and the problem that the traditional assessment algorithm needs to manually judge weights and relies on subjective assessment of people excessively to cause low assessment accuracy is solved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present invention, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of the method of the present invention;
fig. 2 is a system configuration diagram 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. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The embodiment of the invention discloses a method for evaluating the subject of a general hospital of the army based on a neural network, which is shown in figure 1 and comprises the following specific steps:
s1, determining a subject to be evaluated, and establishing a hospital subject evaluation system according to the subject to be evaluated, wherein the hospital subject evaluation system comprises primary indexes and secondary indexes corresponding to the primary indexes;
s2, an initial BP neural network model is established, and the initial BP neural network model is trained by utilizing the secondary index data, so that an optimized BP neural network model is obtained;
s3, inputting the acquired second-level index data into the optimized BP neural network model to obtain an evaluation result of the subject to be evaluated.
Further, the first-level indexes comprise clinical work, teaching and learning team and resource, talent culture, scientific research and army service; the second-level indexes corresponding to clinical work comprise daily average clinic volume and hospitalization number; the secondary indexes corresponding to the resources of the teacher and resource team comprise the number of the teacher and resource, the quality of the teacher and resource and the number of the discipline support platforms; the second-level index corresponding to talent cultivation comprises graduate employment data and hospital-presence data; the second-level indexes corresponding to scientific research comprise academic paper publishing conditions and academic result conversion conditions; the secondary indexes corresponding to the military service comprise social task participation data and exercise task participation data.
According to a hospital discipline assessment system, subjective and objective assessment is carried out on the primary index and the secondary index, a comprehensive weight value of the discipline to be assessed is calculated, and learning and training are carried out by taking the comprehensive weight value as an initial weight value of an initial BP neural network model.
Further, subjective weights of the primary index and the secondary index are determined based on the triangular fuzzy number, and the specific mode is as follows:
s11, scoring the importance degree of the secondary index according to experience and different services by experts, and obtaining a judgment matrix by each person;
s12, integrating the decision matrix of the experts into a decision matrix, and determining the triangular fuzzy number;
s13, determining the importance degree among all the attributes according to the triangular fuzzy number;
s14, carrying out alignment transformation on the fuzzy matrix to calculate subjective weight values of the secondary indexes.
Determining objective weights of the primary index and the secondary index by using an entropy weight method, wherein the formula is as follows:
wherein,,for objective weight, E j Entropy value of information of j-th index, E k Entropy of information of kth index, E l The information entropy value of the first index.
The method also comprises the steps of carrying out data cleaning processing on the collected primary index data and secondary index data of the subject to be evaluated, and carrying out normalization processing on the data after the data cleaning processing.
In addition, when training sample data is less or training is excessive, the situation that an algorithm model is over-fitted usually occurs, that is, as the complexity of the model increases, although the output error of a training set gradually decreases, the error on a verification data set gradually increases, which can cause poor generalization capability of the model and seriously affect the performance of the neural network model, so that the situation that the model is over-fitted is prevented by using a regularization method in the training process of the neural network model.
Further, the initial BP neural network model is of a three-layer structure and comprises an input layer, an implicit layer and an output layer, the second-level index is used as the input of the initial BP neural network model, and the evaluation grade is used as the output of the initial BP neural network.
In the initial BP neural network model, the function of the input layer to the hidden layer is:
wherein n is the number of neurons in the input layer, v ij For the input of weights of layer i neurons to hidden layer j neurons, i=1, 2,..n, j=1, 2,..n.
The evaluation grade was divided into 5 grades:
the comprehensive score is more than or equal to 8 and less than or equal to 10, and the evaluation grade is good;
the comprehensive score is less than or equal to 6 and less than 8, and the evaluation grade is better;
the comprehensive score is less than or equal to 4 and less than or equal to 6, and the evaluation grade is general;
a comprehensive score of less than or equal to 0.2 and less than or equal to 4, and the evaluation grade is poorer;
and 0 is less than or equal to the comprehensive score <2, and the evaluation grade is poor.
The embodiment 2 of the invention provides a army general hospital discipline assessment method based on a neural network, which is shown in fig. 2 and comprises an assessment system determination module, a neural network model establishment module and an assessment module which are connected in sequence; wherein,,
the assessment system determination module is used for determining subjects to be assessed and establishing a hospital subject assessment system according to the subjects to be assessed, wherein the hospital subject assessment system comprises primary indexes and secondary indexes corresponding to the primary indexes;
the neural network model building module is used for building an initial BP neural network model, training the initial BP neural network model by utilizing the secondary index data, and obtaining an optimized BP neural network model;
the evaluation module is used for inputting the acquired second-level index data into the optimized BP neural network model to obtain an evaluation result of the subject to be evaluated.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. The army general hospital discipline assessment method based on the neural network is characterized by comprising the following specific steps of:
determining a subject to be evaluated and establishing a hospital subject evaluation system according to the subject to be evaluated, wherein the hospital subject evaluation system comprises primary indexes and secondary indexes corresponding to the primary indexes;
establishing an initial BP neural network model, and training the initial BP neural network model by using secondary index data to obtain an optimized BP neural network model;
and inputting the acquired secondary index data into an optimized BP neural network model to obtain an evaluation result of the subject to be evaluated.
2. The neural network-based general hospital discipline assessment method for troops of claim 1, wherein the primary index comprises clinical work, teaching and learning team and resource, talent training, scientific research, army service; the second-level indexes corresponding to the clinical work comprise daily average clinic volume and inpatients; the secondary indexes corresponding to the resources of the teacher and resource team comprise the number of the teacher and resource, the quality of the teacher and resource and the number of the discipline support platforms; the talent culture corresponding secondary indexes comprise graduate just-in-place data and hospital-in-place data; the second-level indexes corresponding to the scientific research comprise academic paper publishing conditions and academic result conversion conditions; the secondary indexes corresponding to the military service comprise social task participation data and exercise task participation data.
3. The method for evaluating the subject of the army total hospital based on the neural network according to claim 1, wherein the initial BP neural network model is of a three-layer structure and comprises an input layer, an implicit layer and an output layer, the secondary index is used as the input of the initial BP neural network model, and the evaluation grade is used as the output of the initial BP neural network.
4. A method of general hospital discipline assessment for the army based on a neural network according to claim 3, characterized in that in the initial BP neural network model the function of the input layer to the hidden layer is:
wherein n is input layer godNumber of menstruation elements, v ij For the input of weights of layer i neurons to hidden layer j neurons, i=1, 2,..n, j=1, 2,..n.
5. The method for evaluating the general hospital discipline of the army based on the neural network according to claim 1, wherein the primary index and the secondary index are subjected to subjective and objective evaluation according to the hospital discipline evaluation system, the comprehensive weight value of the discipline to be evaluated is calculated, and the comprehensive weight value is used as the initial weight value of the initial BP neural network model for learning training.
6. The method for evaluating the subject of a general hospital for the army based on the neural network according to claim 5, wherein subjective weights of the primary index and the secondary index are determined based on triangle ambiguity numbers, and objective weights of the primary index and the secondary index are determined by using an entropy weight method.
7. The method for evaluating the general hospital discipline of the army based on the neural network according to claim 1, further comprising the steps of performing data cleaning processing on the collected primary index data and secondary index data of the discipline to be evaluated, and performing normalization processing on the data after the data cleaning processing.
8. The army general hospital discipline assessment method based on the neural network is characterized by comprising an assessment system determination module, a neural network model establishment module and an assessment module which are connected in sequence; wherein,,
the assessment system determination module is used for determining subjects to be assessed and establishing a hospital subject assessment system according to the subjects to be assessed, wherein the hospital subject assessment system comprises primary indexes and secondary indexes corresponding to the primary indexes;
the neural network model building module is used for building an initial BP neural network model, and training the initial BP neural network model by utilizing the secondary index data to obtain an optimized BP neural network model;
the evaluation module is used for inputting the acquired secondary index data into the optimized BP neural network model to obtain an evaluation result of the subject to be evaluated.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116882845A (en) * | 2023-09-05 | 2023-10-13 | 北京中电普华信息技术有限公司 | Scientific and technological achievement assessment information system |
| CN117352152A (en) * | 2023-12-06 | 2024-01-05 | 济宁医学院附属医院 | Assessment method of comprehensive evaluation index system for hospital discipline construction |
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- 2022-10-09 CN CN202211226442.9A patent/CN116468300A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116882845A (en) * | 2023-09-05 | 2023-10-13 | 北京中电普华信息技术有限公司 | Scientific and technological achievement assessment information system |
| CN117352152A (en) * | 2023-12-06 | 2024-01-05 | 济宁医学院附属医院 | Assessment method of comprehensive evaluation index system for hospital discipline construction |
| CN117352152B (en) * | 2023-12-06 | 2024-02-09 | 济宁医学院附属医院 | Assessment method of comprehensive evaluation index system for hospital discipline construction |
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