CN110890146A - Bedside intelligent interaction system for intelligent ward - Google Patents

Bedside intelligent interaction system for intelligent ward Download PDF

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CN110890146A
CN110890146A CN201911064287.3A CN201911064287A CN110890146A CN 110890146 A CN110890146 A CN 110890146A CN 201911064287 A CN201911064287 A CN 201911064287A CN 110890146 A CN110890146 A CN 110890146A
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CN110890146B (en
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王洪平
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Guangdong Deao Smart Medical Technology Co Ltd
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • 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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    • G16H40/67—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H80/00—ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring

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Abstract

The utility model provides a bedside intelligence interactive system for wisdom ward, includes bedside digital terminal, nurse station host computer and backstage management system, bedside digital terminal provides human-computer interaction interface, including patient function interface, doctor function interface and nurse function interface, nurse station host computer sets up in the nurse station department of each ward of hospital, including information display interface, information management interface and remote call interface, backstage management system includes backstage login interface, operation monitoring module, information statistics analysis platform, data storage module and doctor workstation. The invention has the beneficial effects that: the bedside intelligent interaction system for the intelligent ward combines the internet technology and medical treatment, realizes information interaction among medical treatment, nursing treatment and patients through the bedside digital terminal, the nurse station host and the background management system, and realizes intellectualization, visualization, precision and high efficiency of medical treatment.

Description

Bedside intelligent interaction system for intelligent ward
Technical Field
The invention relates to the field of intelligent wards, in particular to a bedside intelligent interaction system for an intelligent ward.
Background
With the rapid development of information technology, more and more hospitals in China are also accelerating to implement the overall construction based on an information-based service platform and system so as to improve the service level and the core competitiveness of the hospitals. At present, China has integrally gone into an intelligent era, and in terms of nursing disciplines, important contents such as continuously improving nursing service quality, gradually implementing hospital nursing post management, strengthening nursing informatization construction and the like are also put forward in a nursing career development planning outline, so that the fact that the nursing career goes into informatization and intellectualization becomes a necessary trend of the era development.
Disclosure of Invention
In view of the above problems, the present invention provides a bedside intelligent interactive system for an intelligent ward.
The purpose of the invention is realized by the following technical scheme:
a bedside intelligent interaction system for an intelligent ward comprises a bedside digital terminal, a nurse station host and a background management system, wherein the bedside digital terminal provides a human-computer interaction interface which comprises a patient function interface, a doctor function interface and a nurse function interface, the patient function interface is used for providing system functions for a patient, the doctor function interface is used for providing system functions for a doctor, the nurse function interface is used for providing system functions for a nurse, the nurse station host is arranged at a nurse station of each ward of a hospital and used for providing the human-computer interaction interface for the nurse, the nurse station host comprises an information display interface, an information management interface and a remote call interface, the information display interface is used for displaying information, the information management interface is used for the nurse to enter and modify the information in the bedside digital terminal, and the nurse can establish video connection with the bedside intelligent terminal through the remote call interface, thereby carry out long-range call to the patient, backstage management system includes backstage login interface, operation monitoring module, information statistics analysis platform, data storage module and doctor's workstation, backstage login interface is used for user input account and password to get into backstage management system, operation monitoring module is used for monitoring bedside digital terminal's running state, information statistics analysis platform is used for gathering bedside digital terminal's data, and goes forward the analysis to the data of gathering, data storage module is used for saving the data that information statistics analysis platform gathered, and the doctor can pass through doctor's workstation inquires the information in the bedside digital terminal, also can establish video connection through doctor's workstation and bedside digital terminal, realizes the remote consultation to the patient.
The beneficial effects created by the invention are as follows: the bedside intelligent interaction system for the intelligent ward combines the internet technology and medical treatment, realizes information interaction among medical treatment, nursing treatment and patients through the bedside digital terminal, the nurse station host and the background management system, and realizes intellectualization, visualization, precision and high efficiency of medical treatment.
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The invention is further described with the aid of the accompanying drawings, in which, however, the embodiments do not constitute any limitation to the invention, and for a person skilled in the art, without inventive effort, further drawings may be derived from the following figures.
FIG. 1 is a schematic diagram of the present invention.
Reference numerals:
a bedside digital terminal; a nurse station host; and (4) a background management system.
Detailed Description
The invention is further described with reference to the following examples.
Referring to fig. 1, the bedside intelligent interaction system for the intelligent ward of the embodiment includes a bedside digital terminal, a nurse station host and a background management system, the bedside digital terminal provides a human-computer interaction interface, which includes a patient function interface, a doctor function interface and a nurse function interface, the patient function interface is used for providing system functions for patients, the doctor function interface is used for providing system functions for doctors, the nurse function interface is used for providing system functions for nurses, the nurse station host is disposed at a nurse station of each ward of a hospital and is used for providing a human-computer interaction interface for nurses, which includes an information display interface, an information management interface and a remote call interface, the information display interface is used for displaying information, and the information management interface is used for nurses to enter and modify information in the bedside digital terminal, the nurse can establish video connection with the bedside intelligent terminal through a remote call interface so as to remotely call a patient, the background management system comprises a background login interface, an operation monitoring module, an information statistical analysis platform, a data storage module and a doctor workstation, the background login interface is used for inputting an account number and a password by a user so as to enter the background management system, the operation monitoring module is used for monitoring the operation state of the bedside digital terminal, the information statistical analysis platform is used for acquiring the data of the bedside digital terminal and analyzing the acquired data, the data storage module is used for storing the data acquired by the information statistical analysis platform, the doctor can inquire the information in the bedside digital terminal through the doctor workstation and establish video connection with the bedside digital terminal through the doctor workstation, and remote consultation of the patient is realized.
Preferably, the patient function interface is used for providing system functions for the patient, and comprises an intelligent bedside card, self-service business handling and information display, wherein the intelligent bedside card is used for displaying patient information, hospital departments, main doctors, responsibility nurses, nursing levels, attention points and the like, the self-service business handling comprises a self-service inquiry unit, a payment recharging unit, a nutrition ordering unit, an insurance service unit and a leisure service unit, the self-service handling of services such as self-service inquiry, payment recharging, remote calling and leisure service is performed on the patient, and the information display comprises a hospital introduction unit, a health promotion and education unit, an intelligent reminding unit, a satisfaction evaluation unit and a questionnaire investigation unit.
Preferably, doctor function interface is used for providing system function to the doctor, including doctor's advice inquiry unit, record unit, call nurse unit, vital sign inquiry unit and remote consultation unit of making an appointment, doctor's advice inquiry unit is used for the doctor to look over the doctor's advice and the execution conditions, record unit is used for the doctor to record this patient's condition of making an appointment, and when the nurse was not in the ward, the doctor can reach the ward through calling nurse unit real-time call nurse, vital sign inquiry unit is used for the doctor to look over patient's vital sign condition at any time, remote consultation unit realizes the remote consultation of bedside through the doctor workstation of connecting backstage management system.
This preferred embodiment provides a bedside intelligence interactive system for wisdom ward, combines internet technology and medical treatment, through bedside digital terminal, nurse station host computer and backstage management system, has realized the information interaction between doctor, nurse, the sick, has realized intelligent, visual, accurate and the high-efficient of medical treatment.
Preferably, nurse functional interface is used for providing system function to the nurse, including doctor's advice inquiry unit, call doctor's unit, vital sign enter unit and risk assessment unit, doctor's advice inquiry unit is used for nurse's inquiry doctor's advice information, and the nurse can pass through call doctor's unit and doctor workstation establish video connection to visually talkback with the doctor, vital sign enter unit is used for the nurse to look over or record patient vital sign data, risk assessment unit is used for according to the patient vital sign data that vital sign entered the unit and assesses patient's current health status.
Preferably, the risk assessment unit adopts a neural network model to assess the health state of the patient, the neural network model adopted by the risk assessment unit is marked as a second neural network model, the patient vital sign data input by the vital sign input unit is taken as an input value of the second neural network model, and the output result of the output layer of the second neural network model is the health state of the patient obtained through assessment; the method comprises the steps of adopting a collected patient vital sign sample set to train a second neural network model, wherein the data preprocessing part adopts the neural network model to cluster the collected patient vital sign sample set, the neural network model adopted by the data preprocessing part is marked as a first neural network model, and the clustering result of the first neural network model is used as the training sample set of the second neural network model.
In the preferred embodiment, the collected patient vital sign data set is adopted to train the second neural network model adopted by the risk assessment unit, and the vital sign sample set contains a small amount of labeled vital sign data and a large amount of unlabeled vital sign data, so that before the second neural network model is trained, the first neural network model is introduced to carry out semi-supervised clustering preprocessing on the collected patient vital sign data set, and the obtained clustering result and the labels corresponding to the clustering result are adopted to carry out supervised training on the second neural network model, thereby improving the accuracy of the training result of the second neural network model.
Preferably, the first neural network model is used for clustering a collected patient vital sign sample set, and the collected patient vital sign sample set is set as D, where the sample set D includes a labeled sample subset D1And unlabeled exemplar subset D2Given an initial cluster center number c0In the sample subset D1In turn select c0An initial cluster center defining a subset of samples D1The priority of data points competing for the initial clustering center is rho, when the data points are selected as the clustering centers, the data points do not participate in the competition of the next initial clustering center any more, and a sample subset D is defined1Middle data point xiCompeting initial cluster centers v1Has a priority of ρ (x)i，v1) Then ρ (x)i，v1) The calculation formula of (2) is as follows:
Figure RE-GDA0002325274940000041
Figure RE-GDA0002325274940000042
in the formula, xj、xmAnd xnAre the data points, x, in the sample set D, respectivelyiAs a subset of samples D1N is the number of data points in the sample set D, N1As a subset of samples D1Number of data points in, v1Selecting a first initial clustering center;
define the remaining data points xlCompeting initial cluster centers vk(k＝2，...，c0) Has a priority of ρ (x)l，vk) Then ρ (x)l，vk) The calculation formula of (2) is as follows:
Figure RE-GDA0002325274940000043
Figure RE-GDA0002325274940000044
in the formula, xj、xmAnd xnAre the data points, x, in the sample set D, respectivelylAnd xzAs a subset of samples D1N is the number of data points in the sample set D, N1As a subset of samples D1Number of data points in, vkFor the selected k-th initial cluster center, vk-1The selected (k-1) th initial clustering center is obtained;
c to be selected0Inputting data points corresponding to the initial clustering centers into the first neural network model, and inputting c0And taking the data points as initial weights of the connection of hidden layer neurons and input layer neurons of the first neural network model, and taking the labels of the input data points as the labels of the corresponding neurons.
The preferred embodiment defines the priority of selecting the initial clustering center by the data points, selects the data point with the highest priority as the initial clustering center, and the defined priority calculation method comprehensively considers the distance information between the current data point and all the data points in the sample set and the local information of the current data point, so that the global distribution information of the sample space and the local information of the data points are comprehensively utilized, the selected clustering center is more consistent with the distribution condition of the sample data set, and the accuracy of the clustering result is improved.
Preferably, c0After the data points corresponding to the initial clustering centers are input into the first neural network model, sequentially inputting the data points in the sample set D into the first neural network model, and setting xiFor an input data point, at data point xiWhen an already existing neuron is fallen into, calculate data point xiWith the nerve falling intoDistance of element, when data point xiIf the data is label-free data, then selecting a distance data point xiRecent neuron addition; when data point xiWhen the label data exists, a distance data point x is selectediNearest neuron uaIs a data point xiSelecting a distance data point xiSecond proximal neuron ubIs a data point xiWhen the neuron u is a second candidate neuronaAnd ubIs the same, and data point xiOf (3) and neurons uaIs the same, data point xiAdding neuron uaAnd applying the following formula to neurons uaUpdating the weight value:
Figure RE-GDA0002325274940000051
in the formula (I), the compound is shown in the specification,
Figure RE-GDA0002325274940000052
represents the neuron u at the (t-1) th iterationaThe weight of (a) is calculated,
Figure RE-GDA0002325274940000053
to adjust the posterior neuron uaβ represents the learning rate;
when neuron uaAnd ubIf the labels of (1) are different, the following formula is adopted to treat the neuron uaAnd ubUpdating the weight value:
Figure RE-GDA0002325274940000054
Figure RE-GDA0002325274940000055
Figure RE-GDA0002325274940000056
Figure RE-GDA0002325274940000057
in the formula, q (x)i，ua) Is the first candidate neuron uaQ (x) ofi，ub) Is the first candidate neuron ubF (x) ofi，ua) To determine the function, at data point xiAnd neuron uaIf the label of (b) is the same, f (x)i，ua) 1, at data point xiAnd neuron uaIf the labels of (1) are different, f (x)i，ua)＝0；f(xi，ub) To determine the function, at data point xiAnd neuron ubIf the label of (b) is the same, f (x)i，ub) 1, at data point xiAnd neuron ubIf the labels of (1) are different, f (x)i，ub)＝0；
When data point xiIf not in any existing neuron, data point x is assignediAs a new hidden layer neuron, let data point xiAs the initial weight of the neuron, at data point xiIf there is label data, let data point xiAs a label for the neuron; when data point xiWhen there is no label data, the label of the neuron is determined by the following formula:
B(xi)＝B(ul)
Figure RE-GDA0002325274940000061
in the formula, B (x)i) And B (u)l) Respectively represent data points xiAnd neuron ulThe label of (a) is used,
Figure RE-GDA0002325274940000062
and
Figure RE-GDA0002325274940000063
are respectively neuron ulAnd ujN is a distance data point xiThe nearest n neurons, m (u)l) Is neuron ulThe data point in (a) belongs to the sample subset D1Number of data points of, M (u)l) Is neuron ulThe total number of data points.
In the preferred embodiment, after the initial clustering center is determined, the data points in the sample set D are sequentially input into the first neural network model, the weights of the neurons in the hidden layer in the first neural network model are adjusted according to the characteristics of the input data points, the first candidate neuron and the second candidate neuron are selected according to the condition that the data points fall into the existing neurons, a new weight adjustment mode is provided for the condition that the first candidate neuron and the second candidate neuron have label conflict, and a new weight adjustment mode is provided by the data point xiThe distance between the label conflict area and the first candidate neuron is measured according to the distance of the first candidate neuron, so that the adjustment parameter of the weight of the first candidate neuron is determined, and the adjustment parameter of the second candidate neuron is determined in the same way, so that the data point labels in the adjusted neurons have higher uniformity, the sample space can be better adapted, and the accuracy of a clustering result is improved; when the label of the non-label neuron is predicted, the label of the neuron with a short distance is determined, reliable data distribution characteristics are recorded in the neuron, and compared with the traditional mode that the label of the neuron is determined through a single data point, the method has high reliability; in addition, when determining the label of the unlabeled neuron by using the neuron, compared with the conventional method of only considering the distance factor, the preferred embodiment comprehensively introduces the number of labeled data points in the neuron with a short distance, and the neuron has more labeled data, that is, the reliability of the label of the neuron is higher, thereby improving the reliability of label prediction.
Preferably, the clustering result of the first neural network model is used as a training sample set of a second neural network model, the second neural network model adopts a radial basis function neural network and comprises an input layer, a hidden layer and an output layer, the number of neurons of the given hidden layer is S, and the training of the second neural network model is setThe training sample set is H { (c { (C)i，yi) 1, 2.., M }, wherein ciAs the output class of the first neural network model, yiIs of class ciAnd (3) corresponding expected values, wherein M is the number of samples in the sample set H, the samples in the sample set H are input into an input layer of the second neural network model, so that the neurons of the hidden layer of the second neural network model are iteratively trained, the priority g of the samples participating in the neuron training of the hidden layer is defined, and then the samples ciThe priority participating in the training of the s-th neuron of the hidden layer at the t-th iteration is
Figure RE-GDA0002325274940000064
And is
Figure RE-GDA0002325274940000065
qt(s) is the connection weight of the s-th neuron of the hidden layer and the output layer in the t-th iteration, and the weight q is obtained by adopting the following formulat(s) and priority
Figure RE-GDA0002325274940000066
Carrying out iterative updating;
Figure RE-GDA0002325274940000071
Figure RE-GDA0002325274940000072
in the formula (I), the compound is shown in the specification,
Figure RE-GDA0002325274940000073
is a normalization factor, and
Figure RE-GDA0002325274940000074
Figure RE-GDA0002325274940000075
for the s-th neuron of the hidden layer, the sample c is aligned at the t-th iterationiPredicted value of (a), yiIs a sample ciCorrespond toThe expected value of (c) is,
Figure RE-GDA0002325274940000076
represents the number of samples, ρ, involved in training of hidden layer neurons s at the t-th iterationt-1(ci) Represents the sample c up to the (t-1) th iterationiThe total number of times that the neuron is misclassified in all neurons of the hidden layer;
when the sample ciPriority of participation in training of s-th neuron of hidden layer at t-th iteration
Figure RE-GDA0002325274940000077
Then sample ciParticipating in the training of the (t +1) th iteration of the s-th neuron of the hidden layer when
Figure RE-GDA0002325274940000078
Then sample ciDoes not participate in training of the (t +1) th iteration of the s-th neuron of the hidden layer, where Rt(s) sample control threshold corresponding to the set s-th neuron of the hidden layer at the t-th iteration, and
Figure RE-GDA0002325274940000079
and stopping the training of the second neural network model when the prediction error values of the hidden layer neurons are all less than 0.3.
In the preferred embodiment, the output set of the first neural network model is used for training the second neural network model, the priority of each sample in the sample set H participating in training hidden layer neurons is defined, the priority of the sample participating in the hidden layer neuron training is iteratively updated, the priority of the sample participating in the neuron training is reduced when the sample is correctly classified by the neurons, the priority of the sample participating in the neuron training is increased when the sample is incorrectly classified by the neurons, a sample control threshold is set for screening the samples participating in the hidden layer neuron training, the samples with higher priorities are selected to participate in the neuron training, and the samples which are difficult to classify for the neurons are used for carrying out re-classification on the neuronsThe point training is carried out, thereby improving the classification performance of the neuron, and in addition, when the priority of the sample is calculated, the preferred embodiment introduces an inhibition item in consideration of the condition of the sample itself
Figure RE-GDA00023252749400000710
Adjusting the updating of the priority of the sample, counting the times of the sample being classified incorrectly in the neurons of the whole hidden layer, judging that the sample has a problem when the times of the sample being classified incorrectly in the neurons of the whole hidden layer are more, and inhibiting the updating of the priority of the sample, so that the phenomenon that the prediction error rate of the neurons rises when the problem sample is used for repeatedly training the neurons is avoided, the connection weight of the neurons is influenced, and the performance and the weight of each neuron of the hidden layer are more balanced by introducing an inhibition item, so that the accuracy of the evaluation result of the second neural network model is improved; the set sample control threshold value is adaptively changed along with the self condition of the neuron, when the prediction error of the neuron is higher, the value of the neuron sample control threshold value is reduced, so that more samples participate in the training of the neuron, the accuracy of the neuron training result is improved, when the prediction error of the neuron is smaller, the value of the sample control threshold value is increased, the number of the samples participating in the training of the neuron is reduced, and the convergence speed of the second neural network model is improved.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the protection scope of the present invention, although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims (7)

1. A bedside intelligent interaction system for a smart ward is characterized by comprising a bedside digital terminal, a nurse station host and a background management system, wherein the bedside digital terminal provides a human-computer interaction interface which comprises a patient function interface, a doctor function interface and a nurse function interface, the patient function interface is used for providing system functions for patients, the doctor function interface is used for providing system functions for doctors, the nurse function interface is used for providing system functions for nurses, the nurse station host is arranged at a nurse station of each ward area of a hospital and comprises an information display interface, an information management interface and a remote call interface, the information display interface is used for displaying information, the information management interface is used for allowing nurses to enter and modify information in the bedside digital terminal, and the nurses can establish video connection through the remote call interface and the bedside intelligent terminal, thereby carry out long-range call to the patient, backstage management system includes backstage login interface, operation monitoring module, information statistics analysis platform, data storage module and doctor's workstation, backstage login interface is used for user input account and password to get into backstage management system, operation monitoring module is used for monitoring bedside digital terminal's running state, information statistics analysis platform is used for gathering bedside digital terminal's data, and goes forward the analysis to the data of gathering, data storage module is used for saving the data that information statistics analysis platform gathered, and the doctor can pass through doctor's workstation inquires the information in the bedside digital terminal, also can establish video connection through doctor's workstation and bedside digital terminal, realizes the remote consultation to the patient.
2. The bedside intelligent interactive system for intelligent wards as claimed in claim 1, wherein the bedside digital terminal, the nurse station host and the background management system are connected through an IP network or a WiFi network.
3. The bedside intelligent interactive system for the intelligent ward of claim 2, wherein the nurse functional interface comprises an order inquiry unit, a doctor calling unit, a vital sign entry unit and a risk assessment unit, the order inquiry unit is used for nurses to inquire order information, nurses can establish video connection with a doctor workstation by calling the doctor calling unit to realize visual talkback with doctors, the vital sign entry unit is used for nurses to check or record vital sign data of patients, and the risk assessment unit is used for assessing the current physical health status of patients according to the vital sign data of the patients entered by the vital sign entry unit.
4. The bedside intelligent interactive system for an intelligent ward according to claim 3, wherein the risk assessment unit assesses the health state of a patient using a neural network model, the neural network model used by the risk assessment unit is recorded as a second neural network model, patient vital sign data recorded by a vital sign recording unit is used as an input value of the second neural network model, and an output result of an output layer of the second neural network model is the assessed health state of the patient; the method comprises the steps of adopting a collected patient vital sign sample set to train a second neural network model, wherein the data preprocessing part adopts the neural network model to cluster the collected patient vital sign sample set, the neural network model adopted by the data preprocessing part is marked as a first neural network model, and the clustering result of the first neural network model is used as the training sample set of the second neural network model.
5. The bedside intelligent interactive system for intelligent wards, as claimed in claim 4, wherein the first neural network model is used for clustering the collected patient vital sign sample sets, and the collected patient vital sign sample sets are set as
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The sample set
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Including a subset of labeled exemplars
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And unlabeled exemplar subsets
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Given initial cluster center number
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In the sample subset
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In turn select
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An initial cluster center defining a subset of samples
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The priority of data points competing for the initial cluster center in (1) is
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When the data point is selected as the clustering center, the data point does not participate in the competition of the next initial clustering center any more, and the sample subset is defined
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Data point in
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Competing initial cluster centers
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Has a priority of
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Then, then
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The calculation formula of (2) is as follows:
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in the formula (I), the compound is shown in the specification,
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、
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and
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are respectively a sample set
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The data point in (a) is,
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as a subset of samples
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The data point in (a) is,
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as a sample set
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The number of data points in (1),
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as a subset of samples
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The number of data points in (1),
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to be selectedA first initial cluster center;
defining remaining data points
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Competing initial cluster centers
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Has a priority of
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Then, then
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The calculation formula of (2) is as follows:
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in the formula (I), the compound is shown in the specification,
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、
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and
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are respectively a sample set
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The data point in (a) is,
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and
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is a sampleSubsets
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The data point in (a) is,
Figure 732637DEST_PATH_IMAGE016
as a sample set
Figure 918768DEST_PATH_IMAGE001
The number of data points in (1),
Figure 203119DEST_PATH_IMAGE017
as a subset of samples
Figure 553329DEST_PATH_IMAGE002
The number of data points in (1),
Figure 266814DEST_PATH_IMAGE026
to a selected one
Figure DEST_PATH_IMAGE027
The center of each initial cluster is determined,
Figure 386079DEST_PATH_IMAGE028
to a selected one
Figure DEST_PATH_IMAGE029
An initial clustering center;
to be selected
Figure 700386DEST_PATH_IMAGE004
Inputting data points corresponding to the initial clustering centers into the first neural network model, and inputting
Figure 288624DEST_PATH_IMAGE004
The data points are used as initial weight values of the connection of hidden layer neurons and input layer neurons of the first neural network model, and the labels of the input data points are used as corresponding nervesA meta tag.
6. The bedside intelligent interactive system for intelligent wards of claim 5, wherein,
Figure 792418DEST_PATH_IMAGE004
inputting data points corresponding to the initial clustering centers into the first neural network model, and then collecting the sample set
Figure 94086DEST_PATH_IMAGE001
The data points in (1) are sequentially input into the first neural network model
Figure 641611DEST_PATH_IMAGE006
For an input data point, when the data point
Figure 966413DEST_PATH_IMAGE006
When an existing neuron is fallen into, a data point is calculated
Figure 21701DEST_PATH_IMAGE006
Distance from the neuron that falls in, as data points
Figure 912296DEST_PATH_IMAGE006
When the data is label-free data, selecting a distance data point
Figure 647034DEST_PATH_IMAGE006
Recent neuron addition; when the data point is
Figure 442821DEST_PATH_IMAGE006
Selecting distance data points when the data is labeled
Figure 553996DEST_PATH_IMAGE006
Nearest neuron
Figure 564677DEST_PATH_IMAGE030
Are data points
Figure 221049DEST_PATH_IMAGE006
Selecting distance data points
Figure 254864DEST_PATH_IMAGE006
Second proximal neuron
Figure DEST_PATH_IMAGE031
Are data points
Figure 153419DEST_PATH_IMAGE006
A second candidate neuron of (1), when the neuron is
Figure 956290DEST_PATH_IMAGE030
And
Figure 780633DEST_PATH_IMAGE031
are identical, and data points
Figure 301744DEST_PATH_IMAGE006
Of (3) and neurons
Figure 738410DEST_PATH_IMAGE030
Is the same, data point
Figure 723684DEST_PATH_IMAGE006
Adding neurons
Figure 971126DEST_PATH_IMAGE030
And applying the following formula to neurons
Figure 730265DEST_PATH_IMAGE030
Updating the weight value:
Figure DEST_PATH_IMAGE033
in the formula (I), the compound is shown in the specification,
Figure 908306DEST_PATH_IMAGE034
is shown as
Figure DEST_PATH_IMAGE035
Sub-iterative time neuron
Figure 685769DEST_PATH_IMAGE030
The weight of (a) is calculated,
Figure 851915DEST_PATH_IMAGE036
for regulating posterior neuron
Figure 82039DEST_PATH_IMAGE030
The weight of (a) is calculated,
Figure DEST_PATH_IMAGE037
represents a learning rate;
when neuron
Figure 126087DEST_PATH_IMAGE030
And
Figure 758057DEST_PATH_IMAGE031
if the labels are different, the neuron is treated by the following formula
Figure 98034DEST_PATH_IMAGE030
And
Figure 81033DEST_PATH_IMAGE031
updating the weight value:
Figure DEST_PATH_IMAGE039
Figure DEST_PATH_IMAGE041
Figure DEST_PATH_IMAGE043
Figure DEST_PATH_IMAGE045
in the formula (I), the compound is shown in the specification,
Figure 224045DEST_PATH_IMAGE046
to determine the function, when the data point
Figure 772838DEST_PATH_IMAGE006
And neurons
Figure 47830DEST_PATH_IMAGE030
When the labels are the same, then
Figure DEST_PATH_IMAGE047
When a data point
Figure 940962DEST_PATH_IMAGE006
And neurons
Figure 139863DEST_PATH_IMAGE030
When the labels are different, then
Figure 480845DEST_PATH_IMAGE048
；
Figure DEST_PATH_IMAGE049
To determine the function, when the data point
Figure 864422DEST_PATH_IMAGE006
And neurons
Figure 140175DEST_PATH_IMAGE031
When the labels are the same, then
Figure 877187DEST_PATH_IMAGE050
When a data point
Figure 338255DEST_PATH_IMAGE006
And neurons
Figure 955050DEST_PATH_IMAGE031
When the labels are different, then
Figure DEST_PATH_IMAGE051
；
When the data point is
Figure 868779DEST_PATH_IMAGE006
If not in any existing neuron, the data point is assigned
Figure 97898DEST_PATH_IMAGE006
As a new hidden layer neuron, let the data point
Figure 679052DEST_PATH_IMAGE006
As an initial weight for the neuron, at the data point
Figure 201169DEST_PATH_IMAGE006
When the data is tagged, the data point is ordered
Figure 867773DEST_PATH_IMAGE006
As a label for the neuron; when the data point is
Figure 946588DEST_PATH_IMAGE006
When there is no label data, the label of the neuron is determined by the following formula:
Figure DEST_PATH_IMAGE053
Figure DEST_PATH_IMAGE055
in the formula (I), the compound is shown in the specification,
Figure 254685DEST_PATH_IMAGE056
and
Figure DEST_PATH_IMAGE057
respectively represent data points
Figure 698436DEST_PATH_IMAGE006
And neurons
Figure 603069DEST_PATH_IMAGE058
The label of (a) is used,
Figure DEST_PATH_IMAGE059
and
Figure 610208DEST_PATH_IMAGE060
are neurons respectively
Figure 962692DEST_PATH_IMAGE058
And
Figure DEST_PATH_IMAGE061
the weight of (a) is calculated,
Figure 528409DEST_PATH_IMAGE062
as distance data points
Figure 904027DEST_PATH_IMAGE006
More recent
Figure 511595DEST_PATH_IMAGE062
The number of the nerve cells is one,
Figure DEST_PATH_IMAGE063
is a neuron
Figure 921847DEST_PATH_IMAGE058
The data point in (1) belongs to the sample subset
Figure 458133DEST_PATH_IMAGE002
The number of data points of (a),
Figure 321047DEST_PATH_IMAGE064
is a neuron
Figure 732306DEST_PATH_IMAGE058
The total number of data points.
7. The bedside intelligent interactive system for intelligent ward of claim 6, wherein the clustering result of the first neural network model is used as a training sample set of the second neural network model, the second neural network model is a radial basis neural network, and comprises an input layer, an implicit layer and an output layer, the number of neurons in the given implicit layer is
Figure DEST_PATH_IMAGE065
The set of training samples for the second neural network model is
Figure 997065DEST_PATH_IMAGE066
Wherein, in the step (A),
Figure DEST_PATH_IMAGE067
for the output class of the first neural network model,
Figure 966901DEST_PATH_IMAGE068
is a category
Figure 317111DEST_PATH_IMAGE067
The corresponding desired value is set to the desired value,
Figure DEST_PATH_IMAGE069
as a set of samples
Figure 204165DEST_PATH_IMAGE070
Number of samples in (1), set the samples into a set
Figure 385747DEST_PATH_IMAGE070
The samples in (1) are input into an input layer of a second neural network model, so that the neurons of the hidden layer of the second neural network model are subjected to iterative training, and the priority of the samples participating in the training of the neurons of the hidden layer is defined
Figure DEST_PATH_IMAGE071
Then sample
Figure 467098DEST_PATH_IMAGE067
In the first place
Figure 553872DEST_PATH_IMAGE072
Participation in hidden layer number one at sub-iteration
Figure DEST_PATH_IMAGE073
The training of each neuron has priority of
Figure 57665DEST_PATH_IMAGE074
And is and
Figure DEST_PATH_IMAGE075
，
Figure 716923DEST_PATH_IMAGE076
is at the first
Figure 530028DEST_PATH_IMAGE072
Implicit layer number at sub-iteration
Figure 917147DEST_PATH_IMAGE073
The connection weight of each neuron and the output layer adopts the following formula to the weight
Figure 959052DEST_PATH_IMAGE076
And priority
Figure 803642DEST_PATH_IMAGE074
Carrying out iterative updating;
Figure 600697DEST_PATH_IMAGE078
Figure 147216DEST_PATH_IMAGE080
in the formula (I), the compound is shown in the specification,
Figure DEST_PATH_IMAGE081
is a normalization factor, and
Figure 445342DEST_PATH_IMAGE082
，
Figure DEST_PATH_IMAGE083
as a hidden layer
Figure 141509DEST_PATH_IMAGE073
One neuron is in the first place
Figure 47148DEST_PATH_IMAGE072
Pair samples at sub-iteration
Figure 330231DEST_PATH_IMAGE067
The predicted value of (a) is determined,
Figure 776256DEST_PATH_IMAGE068
is a sample
Figure 844706DEST_PATH_IMAGE067
The corresponding desired value is set to the desired value,
Figure 671979DEST_PATH_IMAGE084
is shown as
Figure 193090DEST_PATH_IMAGE072
Involvement of hidden layer neurons at sub-iteration
Figure 442806DEST_PATH_IMAGE073
The number of samples to be trained is,
Figure DEST_PATH_IMAGE085
is shown to
Figure 552713DEST_PATH_IMAGE086
Sample to sub-iteration
Figure 547958DEST_PATH_IMAGE067
The total number of times that the neuron is misclassified in all neurons of the hidden layer;
when the sample is
Figure 618682DEST_PATH_IMAGE067
In the first place
Figure 609771DEST_PATH_IMAGE072
Participation in hidden layer number one at sub-iteration
Figure 636502DEST_PATH_IMAGE073
Priority of individual neuron training
Figure DEST_PATH_IMAGE087
Then sample
Figure 54845DEST_PATH_IMAGE067
Participating in the hidden layer one
Figure 35702DEST_PATH_IMAGE073
The first of each neuron
Figure 830482DEST_PATH_IMAGE088
Training in a sub-iteration when
Figure DEST_PATH_IMAGE089
Then sample
Figure 711720DEST_PATH_IMAGE067
Does not participate in the hidden layer
Figure 300964DEST_PATH_IMAGE073
The first of each neuron
Figure 31766DEST_PATH_IMAGE088
And (c) training of a sub-iteration, wherein,
Figure 364659DEST_PATH_IMAGE090
for setting the hidden layer
Figure 913452DEST_PATH_IMAGE073
One neuron is in the first place
Figure 188444DEST_PATH_IMAGE072
The corresponding sample at the time of the sub-iteration controls the threshold, an
Figure DEST_PATH_IMAGE091
；
And stopping the training of the second neural network model when the prediction error values of the hidden layer neurons are all less than 0.3.
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