Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a grouping distribution management system and method based on different chronic diseases, which can carry out grouping management according to the illness state information of patients and configure corresponding diagnosis and treatment paths, realize the personalized diagnosis and treatment process of chronic patients and improve the prevention and treatment level and the service quality of the chronic diseases.
In order to achieve the above purpose, the present invention provides the following technical solutions:
In one aspect, the present invention provides a method for grouping and assigning management based on different chronic diseases, specifically comprising the steps of:
S1: establishing a chronic disease base database, the chronic disease base database comprising standard chronic disease groupings;
S2: configuring a standardized diagnosis and treatment path for the standard chronic disease group;
S3: acquiring disease data of chronic diseases of patients, and making a disease grouping strategy based on the disease data to group the chronic diseases;
s4: based on chronic disease grouping, making diagnosis and treatment path matching strategies to allocate diagnosis and treatment paths, and performing personalized adjustment;
s5: and (5) repeating the steps S3-S4 when a preset time period is reached, and updating the chronic disease grouping and diagnosis and treatment path. As a further improvement of the present invention, the step S1 specifically further includes:
obtaining grouping standards of the chronic diseases, wherein the grouping standards comprise definitions, clinical characteristics, disease characteristics, laboratory examination characteristics, image examination characteristics and pathology examination characteristics of the chronic diseases;
based on the chronic disease grouping standard, the chronic diseases are hierarchically grouped, and the method specifically comprises the following steps:
obtaining a first order grouping of chronic diseases based on definitions of chronic diseases and disease characteristics;
obtaining a secondary group of chronic diseases based on clinical characteristics of the chronic diseases under the primary group;
Under the secondary grouping, a tertiary grouping is obtained based on laboratory examination features, image examination features, pathology examination features.
As a further improvement of the present invention, the acquired condition data includes disease data, clinical data, laboratory test data, image test data, pathology test data;
The illness state grouping strategy comprises the following steps:
s31: extracting keywords of the disease data in the disease data;
s32: extracting disease feature keywords of each first-level grouping based on n first-level groupings;
s33: calculating the similarity between the keywords of the disease data and the keywords of the first-level group disease characteristics;
S34: chronic diseases are assigned to the primary group with the greatest similarity.
As a further improvement of the present invention, the condition grouping strategy further comprises the steps of:
S35: extracting keywords of the clinical data in the illness state data;
S36: extracting clinical feature keywords of each secondary group under the corresponding primary group based on m secondary groups;
s37: calculating the similarity between the keywords of the clinical data and the keywords of the secondary grouping clinical characteristics;
s38: chronic diseases are assigned to secondary groupings with the greatest similarity.
As a further improvement of the present invention, the condition grouping strategy further comprises the steps of:
s39: extracting keywords of laboratory examination data, image examination data and pathology examination data in the illness state data;
S310: extracting keywords of laboratory inspection features, image inspection features and pathology inspection features of each tertiary grouping under the corresponding secondary grouping based on p tertiary groupings;
S311: calculating the similarity between laboratory examination data and laboratory examination characteristics, between image examination data and image examination characteristics and between pathology examination data and pathology examination characteristics, and carrying out weighted summation to obtain three-level grouping similarity, wherein the calculation formula is as follows:
wherein,Similarity between keywords representing laboratory test data and keywords of laboratory test features,A similarity between the keywords representing the image inspection data and the keywords of the image inspection feature,Similarity between the keywords representing the pathology examination data and the keywords of the pathology examination feature,Representing the weight coefficient;
s312: configuring a three-level grouping similarity threshold, and distributing the chronic diseases to the three-level grouping with the maximum similarity when the three-level grouping similarity is greater than or equal to the three-level grouping similarity threshold;
when the three-level grouping similarity is smaller than the three-level grouping similarity threshold, calculating the comprehensive risk of the chronic disease to obtain a chronic disease risk score;
s313: configuring a risk threshold;
when the risk score of the chronic diseases is smaller than the risk threshold, the chronic diseases are distributed into three-level groups with the maximum similarity;
When the risk score of the chronic disease is greater than or equal to the risk threshold, a tertiary group is added in the corresponding secondary group, and the chronic disease and the corresponding illness state data are stored.
As a further improvement of the present invention, extracting keywords of the condition data, and calculating the similarity between the keywords specifically includes:
preprocessing disease data, specifically including stop word removal, punctuation mark removal, stem extraction and spelling correction;
calculating TF-IDF values of the words of each illness state data, and extracting the first q words with high TF-IDF values as keywords of illness state data; calculating TF-IDF values of words of each grouping standard, and extracting the first q words with high TF-IDF values as keywords of the grouping standard;
Combining the condition data and TF-IDF values of keywords extracted by grouping standards into vectors, wherein the expression form is as follows:
wherein,A keyword vector representing the extraction of the condition data,A TF-IDF value corresponding to the ith keyword of the disease data,Represents the keyword vector extracted by the grouping criteria,The TF-IDF value corresponding to the ith keyword of the grouping standard is represented, i is 1,2, … …, q and q are the number of the keywords;
and calculating the similarity between the keywords extracted by the illness state data and the keywords extracted by the grouping standards, wherein the calculation formula is as follows:
wherein,Representing the similarity between the condition data keywords and the grouping standard keywords,A weight coefficient representing the corresponding keyword; Representing the limiting factor.
As a further improvement of the present invention, the step of calculating the risk score for chronic disease is as follows:
Obtaining chronic disease physical examination data including age, sex, weight, blood pressure and examination data;
based on the preprocessed physical examination data, extracting potential risk factors of the chronic diseases to form a data set X, wherein the expression form is as follows:
wherein,K=1, 2, … …, M is the total number of risk potential factors;
constructing a Cox single factor regression model to select and determine risk factors, wherein the formula is as follows:
wherein,Representing the risk function at time t under given conditions,Represents an exponential function based on a natural constant e,As a function of the risk of the reference,Representing regression coefficients corresponding to the kth potential risk factors;
Calculating to obtain a regression coefficient of each potential risk factor and a corresponding significance level p value, and when the p value is smaller than a preset significance level threshold value, indicating that significant correlation exists between the potential risk factor and the survival time of the chronic disease, wherein the regression coefficient and the corresponding significance level p value are determined risk factors;
based on the determined risk factors, constructing a multi-factor Cox proportional risk model, and calculating a risk score of the chronic disease, wherein the calculation formula is as follows:
wherein,A risk score indicative of chronic disease; Representing the baseline survival, j=1, 2, … …, m, m is the number of risk factors determined by the Cox single factor regression method,A risk factor is determined for the j-th,Is the regression coefficient corresponding to the j-th determined risk factor,Is the mean of the j-th determined risk factors,Is a cumulative risk correction factor.
As a further improvement of the present invention, the diagnosis and treatment path matching policy in S4 includes:
When the chronic diseases are distributed to the three-level grouping with the maximum similarity, automatically matching the chronic diseases to a standardized diagnosis and treatment path configured by the three-level grouping;
when the chronic diseases are distributed into the newly added three-level groups, the diagnosis and treatment path is manually configured based on the chronic disease condition data, and the diagnosis and treatment path is used as a standardized diagnosis and treatment path of the groups.
As a further improvement of the present invention, the personalized adjustment in S4 specifically includes:
based on a chronic disease knowledge system and clinical practice data, establishing a logic rule base;
based on physical examination data of the chronic diseases, corresponding personalized management information is generated from a logic rule base;
converting the result output by the logic rule base into natural language and transmitting the natural language to the server;
And collecting feedback information of the server side, and optimizing and adjusting the logic rule base.
In a second aspect, the invention provides a grouping distribution management system based on different chronic diseases, which comprises a chronic disease database module, a standardized diagnosis and treatment path module, a data acquisition module, a grouping distribution module, a diagnosis and treatment path matching module, a personalized adjustment module and a dynamic update module, wherein:
The chronic disease database module is used for storing standard chronic disease multistage grouping;
the standardized diagnosis and treatment path module is used for configuring a standardized diagnosis and treatment path for the standard chronic disease grouping;
The data acquisition module is used for acquiring disease data and physical examination data of the chronic disease, wherein the disease data comprises disease data, clinical data, laboratory examination data, image examination data and pathology examination data;
The grouping distribution module is used for grouping chronic diseases according to the illness state grouping strategy and comprises a similarity analysis unit for calculating the similarity between illness state data and keywords in the standard chronic disease grouping; a risk scoring unit for risk assessment of chronic diseases;
the diagnosis and treatment path matching module is used for distributing diagnosis and treatment paths according to diagnosis and treatment path matching strategies;
The personalized adjustment module is used for generating personalized management information according to physical examination data of the chronic diseases;
The dynamic updating module is used for adjusting grouping distribution and diagnosis and treatment paths of the chronic diseases.
In a third aspect, the present invention provides an electronic device comprising: a memory for storing instructions; and a processor for executing the instructions to cause the apparatus to perform operations implementing a grouping assignment management method based on different chronic diseases.
In a fourth aspect, the present invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method of grouping assignment management based on different chronic diseases.
The invention has the beneficial effects that:
By constructing a set of standardized grouping system which covers all kinds of chronic diseases, and aiming at each type of standard chronic disease grouping, a standardized diagnosis and treatment path with wide applicability is prepared; collecting and integrating disease information of chronic patients, matching the similarity of disease data keywords and grouping keywords of the chronic diseases, and carrying out refinement grouping on the chronic diseases of which the matching similarity does not reach a similarity threshold value by calculating a chronic disease risk score, so that the chronic disease patients in a critical disease development stage or facing a large health risk can be focused;
According to the grouping situation, the diagnosis and treatment paths of the slow patients under the corresponding grouping are matched, dynamic adjustment can be performed according to living habits, eating habits and the like of the slow patients, reminding is pushed to the slow patients, compliance of the patients and management efficiency of the slow patients can be enhanced, and the grouping and diagnosis and treatment paths of the slow patients are dynamically updated, so that the diagnosis and treatment process can meet actual demands of the patients, and development of the slow diseases is prolonged.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. It will be apparent that the described embodiments are some, but not all, embodiments of the invention. All other embodiments, which can be made by a person skilled in the art without creative efforts, based on the described embodiments of the present invention fall within the protection scope of the present invention.
Unless defined otherwise, technical or scientific terms used herein should be given the ordinary meaning as understood by one of ordinary skill in the art to which this invention belongs. The terms "first," "second," and the like, as used herein, do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that elements or items preceding the word are included in the element or item listed after the word and equivalents thereof, but does not exclude other elements or items. The terms "connected" or "connected," and the like, are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", etc. are used merely to indicate relative positional relationships, which may also be changed when the absolute position of the object to be described is changed.
In order to keep the following description of the embodiments of the present invention clear and concise, the detailed description of known functions and known components thereof have been omitted.
Example 1
Referring to fig. 1 to 3, the embodiment of the grouping and distribution management method based on different chronic diseases according to the present invention specifically includes the following steps:
S1: establishing a chronic disease base database, the chronic disease base database comprising standard chronic disease groupings;
collecting standards and guidelines for various chronic diseases issued by the country, and extracting definition, clinical manifestation, diagnosis standards, disease stage and other contents of the chronic diseases from the guidelines;
based on the collected data, a basic database containing a plurality of chronic diseases is established, and diagnosis standards such as chronic disease definition, clinical manifestation, imaging data, required laboratory examination and the like are included under each chronic disease;
According to the national diagnosis grouping standard, the chronic diseases of different types are independently grouped according to the characteristics of the diseases and the management requirements, the chronic diseases of the same type are further subdivided and classified to obtain standardized grouping of the chronic diseases, and a detailed disease grouping database is established for each grouping;
S1 specifically comprises the following steps:
Obtaining chronic disease grouping criteria, wherein the grouping criteria comprise definition, clinical characteristics, disease characteristics, laboratory examination characteristics, image examination characteristics and pathology examination characteristics of chronic diseases;
based on the chronic disease grouping standard, the chronic diseases are hierarchically grouped, and the method specifically comprises the following steps:
obtaining a first order grouping of chronic diseases based on definitions of chronic diseases and disease characteristics;
obtaining a secondary group of chronic diseases based on clinical characteristics of the chronic diseases under the primary group;
Under the secondary grouping, a tertiary grouping is obtained based on laboratory examination features, image examination features, pathology examination features.
S2: configuring corresponding standardized diagnosis and treatment paths for standard chronic disease groups;
For each standard group, a multidisciplinary expert team formulates a corresponding standard diagnosis and treatment path according to the latest medical evidence, clinical diagnosis and treatment guidelines and practical experience; the standard diagnosis and treatment path comprises early screening and prevention of chronic diseases, preliminary diagnosis and identification procedures, treatment schemes, disease monitoring, complications, rehabilitation plans, diet suggestions, life style guidance and periodic re-diagnosis.
S3: acquiring disease data of chronic diseases of patients, and making a disease grouping strategy based on the disease data to group the chronic diseases;
The acquired disease data comprise disease data, clinical data, laboratory examination data, image examination data and pathology examination data;
Extracting keywords of chronic disease condition data, calculating the similarity of the keywords with standard chronic disease grouping standards, performing matching grouping, and grouping chronic patients with matching similarity not reaching a similarity threshold value by calculating comprehensive risk scores;
the extracting of the keywords of the chronic disease condition data and calculating the similarity between the keywords specifically comprises the following steps:
Calculating TF-IDF values of the words of each illness state data according to word frequency-inverse document frequency, and extracting the first q words with high TF-IDF values as keywords of illness state data; calculating TF-IDF values of words of each grouping standard, and extracting the first q words with high TF-IDF values as keywords of the grouping standard;
Combining the condition data and TF-IDF values of keywords extracted by grouping standards into vectors, wherein the expression form is as follows:
wherein,A keyword vector representing the extraction of the condition data,A TF-IDF value corresponding to the ith keyword of the disease data,Represents the keyword vector extracted by the grouping criteria,The TF-IDF value corresponding to the ith keyword of the grouping standard is represented, i is 1,2, … …, q and q are the number of the keywords;
Similarity between keywords extracted from illness data and keywords extracted from grouping standards of chronic diseases is calculated, and a calculation formula is as follows:
wherein,Representing the similarity between the condition data keywords and the grouping standard keywords,A weight coefficient representing the corresponding keyword; Representing a limiting factor for ensuring that the similarity is between 0 and 1; the closer the calculated value is to 1, the higher the similarity between the patient condition information and the disease packet.
The illness state grouping strategy comprises the following steps:
s31: extracting keywords of the disease data in the disease data;
s32: extracting disease feature keywords of each first-level grouping based on n first-level groupings;
s33: calculating the similarity between the keywords of the disease data and the keywords of the first-level group disease characteristics;
s34: assigning the chronic diseases to the primary group with the greatest similarity;
S35: extracting keywords of the clinical data in the illness state data;
S36: extracting clinical feature keywords of each secondary group under the corresponding primary group based on m secondary groups;
s37: calculating the similarity between the keywords of the clinical data and the keywords of the secondary grouping clinical characteristics;
s38: assigning chronic diseases to secondary groupings with the greatest similarity;
s39: extracting keywords of laboratory examination data, image examination data and pathology examination data in the illness state data;
S310: extracting keywords of laboratory inspection features, image inspection features and pathology inspection features of each tertiary grouping under the corresponding secondary grouping based on p tertiary groupings;
S311: calculating the similarity between laboratory examination data and laboratory examination characteristics, between image examination data and image examination characteristics and between pathology examination data and pathology examination characteristics, and carrying out weighted summation to obtain three-level grouping similarity, wherein the calculation formula is as follows:
wherein,Similarity between keywords representing laboratory test data and keywords of laboratory test features,A similarity between the keywords representing the image inspection data and the keywords of the image inspection feature,Similarity between the keywords representing the pathology examination data and the keywords of the pathology examination feature,Representing weight coefficients, and setting according to experience;
s312: configuring a three-level grouping similarity threshold, and distributing the chronic diseases to the three-level grouping with the maximum similarity when the three-level grouping similarity is greater than or equal to the three-level grouping similarity threshold;
when the three-level grouping similarity is smaller than the three-level grouping similarity threshold, calculating the comprehensive risk of the chronic disease to obtain a chronic disease risk score;
s313: configuring a risk threshold;
when the risk score of the chronic diseases is smaller than the risk threshold, the chronic diseases are distributed into three-level groups with the maximum similarity;
When the risk score of the chronic disease is greater than or equal to the risk threshold, a tertiary group is added in the corresponding secondary group, and the chronic disease and the corresponding illness state data are stored.
Wherein, the slow disease risk score calculation steps are as follows:
Obtaining chronic disease physical examination data including age, sex, weight, blood pressure and examination data;
Preprocessing the physical examination data, specifically comprising cleaning and standardizing the data, and eliminating missing values and abnormal values; based on the preprocessed physical examination data, extracting potential risk factors of the chronic diseases to form a data set X, wherein the expression form is as follows:
wherein,K=1, 2, … …, M is the total number of risk potential factors;
And constructing a Cox single factor regression model, namely selecting the determined risk factors, wherein the constructed Cox single factor regression model is as follows:
wherein,Representing the risk function at time t under given conditions,Represents an exponential function based on a natural constant e,As a function of the risk of the reference,Representing regression coefficients corresponding to the kth potential risk factors;
estimating the model by using statistical software SaS, calculating to obtain regression coefficient and corresponding significance level p value of each potential risk factor, when the p value is smaller than a preset significance level threshold value, indicating that the potential risk factor has significant association with survival time of slow patients and is a definite risk factor, when the p value is smaller than the preset significance level threshold value, determining that the potential risk factor has significant association with survival time of slow patientsDescription of the related artIncreased risk, whenDescription of the related artAn increase in (2), a decrease in risk;
based on the determined risk factors, constructing a multi-factor Cox proportional risk model, and calculating a risk score of the chronic disease, wherein the calculation formula is as follows:
wherein,A risk score indicative of chronic disease; Representing the baseline survival, j=1, 2, … …, m, m is the number of risk factors determined by the Cox single factor regression method,A risk factor is determined for the j-th,Is the regression coefficient corresponding to the j-th determined risk factor,Is the mean of the j-th determined risk factors,Is a cumulative risk correction factor.
By calculating the similarity between the keywords of the illness data and the keywords of the standard chronic disease grouping standard, common chronic diseases can be scientifically and reasonably grouped according to the characteristics and types of the diseases, and the chronic disease classification can be refined by combining the comprehensive risk scoring mode of the chronic diseases, so that patients in the critical disease development stage or facing greater health risks can be accurately identified and included in the specific grouping which needs to be subjected to intensified intervention and detection, and the pertinence and the efficiency of medical services are improved.
S4: based on chronic disease grouping, making diagnosis and treatment path matching strategies to allocate diagnosis and treatment paths, and performing personalized adjustment;
personalized management advice and reminders are pushed to chronic disease patients, including medication reminders, review appointments and sports diet advice;
the way of matching the diagnosis and treatment path for the slow patient is as follows:
Under the condition that the slow patients are matched with the standardized slow disease group with the maximum similarity, the slow patients are automatically matched with the corresponding diagnosis and treatment path; when the chronic patients are separately grouped, doctors configure corresponding diagnosis and treatment paths according to the illness state information of the chronic patients.
The personalized adjustment specifically further comprises:
according to the knowledge system and clinical practice of medical specialists, a logic rule base is established, each rule is associated with a specific patient and is combined with personal conditions and preferences of the patient, and personalized management suggestions corresponding to the specific patient are generated, wherein the personalized management suggestions comprise medication adjustment, living habit improvement and sports suggestions;
Converting the results output by the logic rules and algorithms into natural language suggestions by using Natural Language Generation (NLG) artificial intelligence technology;
pushing personalized management advice to a patient in a plurality of modes to remind the patient of a healthy life style;
And collecting feedback information of the server side, and optimizing and adjusting the logic rule base.
S5: and (5) repeating the steps S3-S4 when a preset time period is reached, and updating the chronic disease grouping and diagnosis and treatment path.
The preset time period is set by a user according to experience, grouping and diagnosis and treatment path matching are carried out again according to dynamic data of a slow patient, and accordingly accurate medical care can be obtained for the patient all the time along with the time and the change of illness state.
Example 2
Referring to fig. 4, this embodiment is a second embodiment of the present invention; based on the same inventive concept as embodiment 1, this embodiment describes a specific implementation manner of a grouping distribution management system based on different chronic diseases, where the system includes a chronic disease database module, a standardized diagnosis and treatment path module, a data acquisition module, a grouping distribution module, a diagnosis and treatment path matching module, a personalized adjustment module, and a dynamic update module, where:
The chronic disease database module is used for storing standard chronic disease multistage grouping;
the standardized diagnosis and treatment path module is used for configuring a standardized diagnosis and treatment path for the standard chronic disease grouping;
The data acquisition module is used for acquiring disease data and physical examination data of the chronic disease, wherein the disease data comprises disease data, clinical data, laboratory examination data, image examination data and pathology examination data;
The grouping distribution module is used for grouping chronic diseases according to the illness state grouping strategy and comprises a similarity analysis unit for calculating the similarity between illness state data and keywords in the standard chronic disease grouping; a risk scoring unit for risk assessment of chronic diseases;
the diagnosis and treatment path matching module is used for distributing diagnosis and treatment paths according to diagnosis and treatment path matching strategies;
The personalized adjustment module is used for generating personalized management information according to physical examination data of the chronic diseases;
The dynamic updating module is used for adjusting grouping distribution and diagnosis and treatment paths of the chronic diseases.
Example 3
Based on the same inventive concept as the other embodiments, this embodiment introduces an electronic device, including a memory and a processor, where the memory is configured to store instructions, and the processor is configured to execute the instructions, so that the computer device performs a method for implementing the group allocation management based on different chronic diseases provided in the foregoing embodiments.
Since the electronic device described in this embodiment is an electronic device used to implement the method for managing grouping assignment based on different chronic diseases in the embodiment of the present application, based on the method for managing grouping assignment based on different chronic diseases described in the embodiment of the present application, those skilled in the art can understand the specific implementation of the electronic device and various modifications thereof, so how to implement the method in the embodiment of the present application for this electronic device will not be described in detail herein. Electronic devices used by those skilled in the art to implement the grouping distribution management method based on different chronic diseases in the embodiments of the present application are all within the scope of the present application.
Example 4
The present embodiment introduces a computer-readable storage medium on which a computer program is stored, which when executed by a processor, implements the grouping assignment management method based on different chronic diseases provided by the above embodiments, based on the same inventive concept as other embodiments.
Working principle and effect:
the invention establishes a set of standardized multi-level grouping which covers all kinds of chronic diseases, and establishes a standardized diagnosis and treatment path with wide applicability aiming at each type of standard chronic disease grouping; collecting disease data integrating chronic diseases, carrying out multistage grouping through extracting similarity between disease data keywords and standard chronic disease grouping standard keywords, and further grouping chronic patients with low similarity to the standard chronic disease grouping through comprehensive risk evaluation indexes of the chronic patients, so that the grouping of the chronic patients can be thinned, and patients in a critical disease development stage or facing a large health risk can be focused on;
The diagnosis and treatment paths under the corresponding grouping are matched for the slow patients according to the grouping condition, the diagnosis and treatment paths can be dynamically adjusted according to the living habits, the eating habits and the like of the slow patients, and reminding is pushed to the patients, so that the compliance of the patients and the management efficiency of the slow patients can be enhanced; according to the dynamic variability of the slow disease development, when reaching a preset time node, the slow disease grouping and diagnosis and treatment path of the slow disease is timely adjusted according to the latest disease data, so that the whole diagnosis and treatment process meets the actual requirements of patients, and the slow disease development is delayed.
Furthermore, although exemplary embodiments have been described in the present disclosure, the scope thereof includes any and all embodiments having equivalent elements, modifications, omissions, combinations (e.g., of the various embodiments across), adaptations or alterations as would be appreciated by those in the art. The elements in the claims are to be construed broadly based on the language employed in the claims and are not limited to examples described in the present specification or during the practice of the application, which examples are to be construed as non-exclusive. It is intended, therefore, that the specification and examples be considered as exemplary only, with a true scope and spirit being indicated by the following claims and their full scope of equivalents.
The above description is intended to be illustrative and not restrictive. For example, the above-described examples (or one or more aspects thereof) may be used in combination with each other. For example, other embodiments may be used by those of ordinary skill in the art upon reading the above description. In addition, in the above detailed description, various features may be grouped together to streamline the invention. This is not to be interpreted as an intention that the disclosed features not being claimed are essential to any claim. Rather, inventive subject matter may lie in less than all features of a particular disclosed embodiment. Thus, the claims are hereby incorporated into the detailed description as examples or embodiments, with each claim standing on its own as a separate embodiment, and it is contemplated that these embodiments may be combined with one another in various combinations or permutations. The scope of the invention should be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.
The above embodiments are only exemplary embodiments of the present invention and are not intended to limit the present invention, the scope of which is defined by the claims. Various modifications and equivalent arrangements of this invention will occur to those skilled in the art, and are intended to be within the spirit and scope of the invention.