CN113870987A - Use method of medical intelligent bed cabinet - Google Patents
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Abstract
The invention discloses a use method of a medical intelligent bed cabinet, which comprises the following steps: acquiring related information data of people using the bed cabinet in the fixed area, and classifying and storing the related data in a database; collecting characteristic data required by a sorting classification model, and sorting and classifying the characteristic data by using the sorting classification model; and carrying out characteristic matching on the sorted and classified data and the data which are classified and stored in the database, if the matching degree is higher than a preset value, successfully matching, and distributing accompanying personnel, and when an emergency event occurs in the fixed area, quickly screening out the empty bed cabinet and the corresponding accompanying personnel by using a screening algorithm, thereby realizing the intelligent application of the medical bed cabinet. The intelligent bedside cabinet combines the intelligent bedside cabinet with the accompanying service, realizes reasonable distribution and full utilization of hospital bed resources, improves accompanying service quality, and reduces patient expenditure.
Description
Technical Field
The invention relates to the technical field of intelligent bed cabinets, in particular to a using method of a medical intelligent bed cabinet.
Background
In recent years, along with the progress of urbanization, the population density is gradually increased, and thus the problem of allocating medical resources is becoming prominent, and such a problem is particularly reflected in the field of allocation management of hospital beds. For the distribution of hospital beds, the existing treatment mode is to allocate patients manually according to real-time patient information and bed information by allocating full-time bed management nurses, and the whole process of the mode is dominated by people by the distribution process of medical resources, so that the problems of unreasonable distribution and the like are easily caused, even worse doctor-patient relationship is caused, and severe social events are caused.
Therefore, how to realize reasonable distribution and full utilization of hospital bed resources is a technical problem which needs to be solved urgently at present.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and the title of the invention of this application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.
The present invention has been made in view of the above-mentioned conventional problems.
Therefore, the technical problem solved by the invention is as follows: the prior art can not realize the reasonable distribution and the full utilization of hospital bed resources.
In order to solve the technical problems, the invention provides the following technical scheme: obtaining relevant information data of people using the bed cabinet in a fixed area, and classifying and storing the relevant data in a database; collecting characteristic data required by a sorting classification model, and sorting and classifying the characteristic data by using the sorting classification model; and performing characteristic matching on the sorted and classified data and the data stored in the database in a classified manner, and if the matching degree is higher than a preset value, successfully matching and allocating accompanying personnel.
As a preferred scheme of the using method of the medical intelligent bed cabinet, the medical intelligent bed cabinet comprises the following steps: the related information data of the person using the bed cabinet in the fixed area comprises the position IP of the bed cabinet, codes, application time, money amount and end time.
As a preferred scheme of the using method of the medical intelligent bed cabinet, the medical intelligent bed cabinet comprises the following steps: the feature data comprise dimension features of accompanying persons, user dimension features, cross features and distance features.
As a preferred scheme of the using method of the medical intelligent bed cabinet, the medical intelligent bed cabinet comprises the following steps: also comprises the following steps of (1) preparing,
dimension characteristics of the accompanying person: range of care, gender, age, cost, discount, performance, rating, click rate;
characteristics of user dimensions: user grade, user attribute and client type of the user;
cross characteristics: clicking, collecting and purchasing accompanying personnel by a user;
distance characteristics: the distance of the user's real-time geographic location, frequent geographic location, place of work, place of residence, and poi;
presenting the feature in scalar data form.
As a preferred scheme of the using method of the medical intelligent bed cabinet, the medical intelligent bed cabinet comprises the following steps: performing a normalization process on the feature values, the normalized conversion function including,
wherein, XmaxIs the maximum value of the sample data, XminIs the minimum value of the sample data;
and converting the characteristic value into a continuous value between 0 and 1 by using the conversion function.
As a preferred scheme of the using method of the medical intelligent bed cabinet, the medical intelligent bed cabinet comprises the following steps: establishing the ranked classification model may include,
constructing a sequencing classification model by using a weighting algorithm:
wherein, wiRepresenting the weight of each sample, R representing a random number between 0 and 1 generated when traversing each sample, SiRepresenting a sampling fraction of each sample;
and carrying out classification sorting according to the output sampling scores, sorting according to the sampling scores from high to low, and constructing a data set.
As a preferred scheme of the using method of the medical intelligent bed cabinet, the medical intelligent bed cabinet comprises the following steps: performing data pre-processing on the feature matching data set, the pre-processing comprising,
cleaning vacancy values, format contents, logic errors and non-demand information;
performing feature construction, data grading and data quantization on the data set;
carrying out data statistics on the data after data transformation, and merging the data into a unified data storage;
and detecting and removing samples which are possibly abnormal in the data samples by using an outlier sample detection strategy based on clustering.
As a preferred scheme of the using method of the medical intelligent bed cabinet, the medical intelligent bed cabinet comprises the following steps: the feature matching includes the steps of,
and performing feature matching on the data by utilizing a similarity calculation strategy:
in the correlation between the event a and the event B, k01 represents the number of times that the event a and the event B co-occur, k02 represents the occurrence of the event B, the number of times that the event a does not occur, k11 represents the occurrence of the event a, the number of times that the event B does not occur, and k12 represents the number of times that neither the event a nor the event B occurs, then:
logLikelihoodRatio＝2*(matrixEntropy-rowEntropy-columnEntropy)
wherein, rowEntrophy (k11, k12) + entcopy (k21, k22) column Entrophy (k11, k21) + entcopy (k12, k22) matrix Entrophy (k11, k12, k21, k22), and entcopy represents the Shannon entropy of a system composed of several elements.
As a preferred scheme of the using method of the medical intelligent bed cabinet, the medical intelligent bed cabinet comprises the following steps: the establishment of the screening algorithm includes that,
wherein x is1,x2,…xmRepresenting an input variable, xiDenotes the ith data, F denotes the output variable, ci、bi、pi、x0The parameters of the model are represented by,i represents any one of 1 to m.
The invention has the beneficial effects that: the intelligent bedside cabinet combines the intelligent bedside cabinet with the accompanying service, realizes reasonable distribution and full utilization of hospital bed resources, improves accompanying service quality, and reduces patient expenditure.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise. Wherein:
fig. 1 is a basic flow chart of a method for using a medical intelligent bed cabinet according to an embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, specific embodiments accompanied with figures are described in detail below, and it is apparent that the described embodiments are a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making creative efforts based on the embodiments of the present invention, shall fall within the protection scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
Furthermore, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
The present invention will be described in detail with reference to the drawings, wherein the cross-sectional views illustrating the structure of the device are not enlarged partially in general scale for convenience of illustration, and the drawings are only exemplary and should not be construed as limiting the scope of the present invention. In addition, the three-dimensional dimensions of length, width and depth should be included in the actual fabrication.
Meanwhile, in the description of the present invention, it should be noted that the terms "upper, lower, inner and outer" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of describing the present invention and simplifying the description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation and operate, and thus, cannot be construed as limiting the present invention. Furthermore, the terms first, second, or third are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected and connected" in the present invention are to be understood broadly, unless otherwise explicitly specified or limited, for example: can be fixedly connected, detachably connected or integrally connected; they may be mechanically, electrically, or directly connected, or indirectly connected through intervening media, or may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Example 1
Referring to fig. 1, in an embodiment of the present invention, a method for using a medical intelligent bed cabinet is provided, including:
s1: acquiring related information data of people using the bed cabinet in the fixed area, and classifying and storing the related data in a database;
it should be noted that the relevant information data of the person using the bed cabinet in the fixed area includes the bed cabinet position IP, the code, the application time, the amount of money, and the end time.
The range of the fixed area is preset according to the demand and supply of different grade cities, the acquisition mode of the related position IP includes but is not limited to Bluetooth, dynamic two-dimensional codes and RFID electronic tags, and other data are directly generated by an electronic system.
Further, the principle of classified storage is as follows: in the same area range, the application time is sorted according to the geographic position, then the difference between the application time and the ending time is used as the application time, and finally the sum is used.
S2: collecting characteristic data required by a sorting classification model, and sorting and classifying the characteristic data by using the sorting classification model;
it should be noted that the feature data includes a caregiver dimension feature, a user dimension feature, a cross feature, and a distance feature.
Wherein, the dimension characteristic of accompanying person: range of care, gender, age, cost, discount, performance, rating, click rate;
characteristics of user dimensions: user grade, user attribute and client type of the user;
cross characteristics: clicking, collecting and purchasing accompanying personnel by a user;
distance characteristics: the distance of the user's real-time geographic location, frequent geographic location, place of work, place of residence, and poi;
the features are presented in scalar data form.
And normalizing the characteristic value, wherein the normalized conversion function comprises the following steps:
wherein, XmaxIs the maximum value of the sample data, XminIs the minimum value of the sample data;
and converting the characteristic value into a continuous value between 0 and 1 by using a conversion function.
Further, the establishing of the ranking classification model comprises:
constructing a sequencing classification model by using a weighting algorithm:
wherein, wiRepresenting the weight of each sample, R representing a random number between 0 and 1 generated when traversing each sample, SiRepresenting a sampling fraction of each sample;
and carrying out classification sorting according to the output sampling scores, sorting from high to low according to the sampling scores, and constructing a data set.
Performing data preprocessing on the feature matching data set, wherein the preprocessing comprises the following steps:
cleaning vacancy values, format contents, logic errors and non-demand information;
carrying out feature construction, data grading and data quantization on the data set;
carrying out data statistics on the data after data transformation, and merging the data into a unified data storage;
and detecting and removing samples which are possibly abnormal in the data samples by using an outlier sample detection strategy based on clustering.
S3: performing feature matching on the sorted and classified data and the data stored in the database in a classified manner, and if the matching degree is higher than a preset value, successfully matching and allocating accompanying personnel;
it should be noted that the feature matching includes:
and performing feature matching on the data by utilizing a similarity calculation strategy:
in the correlation between the event a and the event B, k01 represents the number of times that the event a and the event B co-occur, k02 represents the occurrence of the event B, the number of times that the event a does not occur, k11 represents the occurrence of the event a, the number of times that the event B does not occur, and k12 represents the number of times that neither the event a nor the event B occurs, then:
logLikelihoodRatio＝2*(matrixEntropy-rowEntropy-columnEntropy)
wherein, rowEntrophy (k11, k12) + entcopy (k21, k22) column Entrophy (k11, k21) + entcopy (k12, k22) matrix Entrophy (k11, k12, k21, k22), and entcopy represents the Shannon entropy of a system composed of several elements.
S4: when an emergency event occurs in the fixed area, the empty bed cabinet and the corresponding accompanying personnel are quickly screened out by utilizing a screening algorithm, so that the intelligent application of the medical bed cabinet is realized.
It should be noted that the establishment of the screening algorithm includes:
wherein x is1,x2,…xmRepresenting an input variable, xiDenotes the ith data, F denotes the output variable, ci、bi、pi、x0Represents a model parameter, and i represents any one of 1 to m.
Specifically, the bed cabinets in the empty state are screened out when the emergency event occurs in the region to which the emergency event belongs, the screened bed cabinets are set to be in the usable state, and after the emergency event is finished, the bed cabinets are set to be in the unusable state.
When the bed cabinet is in an emergency available state, screening out the accompanying personnel in the emergency vacant state, informing the screened out accompanying personnel to the designated area, and positioning the accompanying personnel.
Example 2
The embodiment is another embodiment of the invention, which is different from the first embodiment, and provides a verification test of a using method of the medical intelligent bed cabinet.
The traditional technical scheme is as follows: the degree of intellectualization is low, so that the accompanying service with better performance price can not be quickly and accurately matched for each patient, and the method has higher personnel matching speed and accuracy compared with the traditional method for verifying. In this embodiment, the traditional method for selecting the maintenance worker and the method for selecting the maintenance worker are adopted to respectively perform real-time measurement and comparison on the maintenance worker matching precision of the simulation bed cabinet.
And (3) testing environment: the method comprises the steps of simulating the requirements of the workers in different regions and simulating the use of a bed cabinet on a simulation platform, and respectively performing distribution test of the workers by manual operation of a traditional method and obtaining test result data. By adopting the method, the automatic test equipment is started, MATLB software is used for programming to realize the simulation test of the method, and simulation data is obtained according to the experimental result, and the result is shown in the following table.
Table 1: the experimental results are shown in a comparison table.
| Test specimen | Conventional methods | The method of the invention |
| Speed of matching | ≥5min | 0.5s |
| Efficiency of | 60％ | 99％ |
| Accuracy of | 75％ | 96％ |
Compared with the traditional method, the method has higher matching precision, thereby reducing the cost of customers and improving the satisfaction.
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may 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, which should be covered by the claims of the present invention.
Claims (9)
1. A use method of a medical intelligent bed cabinet is characterized by comprising the following steps:
obtaining relevant information data of people using the bed cabinet in a fixed area, and classifying and storing the relevant data in a database;
collecting characteristic data required by a sorting classification model, and sorting and classifying the characteristic data by using the sorting classification model;
performing feature matching on the sorted and classified data and the data stored in the database in a classified manner, and if the matching degree is higher than a preset value, successfully matching and allocating accompanying personnel;
when an emergency event occurs in the fixed area, the empty bed cabinet and the corresponding accompanying personnel are quickly screened out by using a screening algorithm, so that the intelligent application of the medical bed cabinet is realized.
2. The use method of the medical intelligent bed cabinet as claimed in claim 1, characterized in that: the related information data of the person using the bed cabinet in the fixed area comprises the position IP of the bed cabinet, codes, application time, money amount and end time.
3. The use method of the medical intelligent bed cabinet as claimed in claim 1, characterized in that: the feature data comprise dimension features of accompanying persons, user dimension features, cross features and distance features.
4. The use method of the medical intelligent bed cabinet as claimed in claim 1, characterized in that: also comprises the following steps of (1) preparing,
dimension characteristics of the accompanying person: range of care, gender, age, cost, discount, performance, rating, click rate;
characteristics of user dimensions: user grade, user attribute and client type of the user;
cross characteristics: clicking, collecting and purchasing accompanying personnel by a user;
distance characteristics: the distance of the user's real-time geographic location, frequent geographic location, place of work, place of residence, and poi;
presenting the feature in scalar data form.
5. The use method of the medical intelligent bed cabinet as claimed in claim 1, characterized in that: performing a normalization process on the feature values, the normalized conversion function including,
wherein, XmaxIs the maximum value of the sample data, XminIs the minimum value of the sample data;
and converting the characteristic value into a continuous value between 0 and 1 by using the conversion function.
6. The use method of the medical intelligent bed cabinet as claimed in claim 1, characterized in that: establishing the ranked classification model may include,
constructing a sequencing classification model by using a weighting algorithm:
wherein, wiRepresenting the weight of each sample, R representing a random number between 0 and 1 generated when traversing each sample, SiRepresenting a sampling fraction of each sample;
and carrying out classification sorting according to the output sampling scores, sorting according to the sampling scores from high to low, and constructing a data set.
7. The use method of the medical intelligent bed cabinet as claimed in claim 1, characterized in that: performing data pre-processing on the feature matching data set, the pre-processing comprising,
cleaning vacancy values, format contents, logic errors and non-demand information;
performing feature construction, data grading and data quantization on the data set;
carrying out data statistics on the data after data transformation, and merging the data into a unified data storage;
and detecting and removing samples which are possibly abnormal in the data samples by using an outlier sample detection strategy based on clustering.
8. The use method of the medical intelligent bed cabinet as claimed in claim 1, characterized in that: the feature matching includes the steps of,
and performing feature matching on the data by utilizing a similarity calculation strategy:
in the correlation between the event a and the event B, k01 represents the number of times that the event a and the event B co-occur, k02 represents the occurrence of the event B, the number of times that the event a does not occur, k11 represents the occurrence of the event a, the number of times that the event B does not occur, and k12 represents the number of times that neither the event a nor the event B occurs, then:
logLikelihoodRatio＝2*(matrixEntropy-rowEntropy-columnEntropy)
wherein, rowEntrophy (k11, k12) + entcopy (k21, k22) column Entrophy (k11, k21) + entcopy (k12, k22) matrix Entrophy (k11, k12, k21, k22), and entcopy represents the Shannon entropy of a system composed of several elements.
9. The use method of the medical intelligent bed cabinet as claimed in claim 8, characterized in that: the establishment of the screening algorithm includes that,
wherein x is1,x2,…xmRepresenting an input variable, xiDenotes the ith data, F denotes the output variable, ci、bi、pi、x0Represents a model parameter, and i represents any one of 1 to m.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114628014A (en) * | 2022-05-07 | 2022-06-14 | 湖南工商大学 | Intelligent vaccine distribution method |
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