CN113926021A - Block chain-based medical infusion pump monitoring method and system - Google Patents
Block chain-based medical infusion pump monitoring method and system Download PDFInfo
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- A61M5/00—Devices for bringing media into the body in a subcutaneous, intra-vascular or intramuscular way; Accessories therefor, e.g. filling or cleaning devices, arm-rests
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- A61M5/00—Devices for bringing media into the body in a subcutaneous, intra-vascular or intramuscular way; Accessories therefor, e.g. filling or cleaning devices, arm-rests
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Abstract
The invention discloses a block chain-based medical infusion pump monitoring method and system, which solve the problem of low precision of the conventional medical infusion pump flow rate monitoring method, and comprise a medical infusion pump, a monitoring system and a 5G network module, wherein: the medical infusion pump is used for controlling the number of infusion drops or the infusion flow rate, and ensuring that liquid and medicine can be accurately and safely infused into a patient body at a set speed; the monitoring system is used for remotely connecting the medical infusion pump, exchanging information and receiving and sending instructions for controlling the infusion pump; and the 5G network module is used for interconnection and remote interaction between the medical infusion pump and the monitoring system, and realizes information collection, information return, control instruction receiving and sending and monitoring.
Description
Technical Field
The invention relates to the technical field of medical infusion pump monitoring, in particular to a block chain-based medical infusion pump monitoring method and system.
Background
In order to improve the working efficiency of hospitals and reduce the workload of medical staff, an infusion pump and an injection pump become two kinds of automatic medical equipment with wide application; the infusion pump is a medical device which can accurately control the number of infusion drops or the flow rate of infusion, ensure that liquid and medicine can enter the body of a patient to play a role in a uniform speed, accurate dosage and safety
Meanwhile, a plurality of technologies in the past are integrated by utilizing a block chain technology, so that the medical information has the characteristics of ' unforgeability ', ' whole-course trace ', traceability ', ' public transparency ', ' collective maintenance ' and the like, and the safety of on-line information transmission and access is ensured.
In order to know the running condition of the infusion pump, the hospital monitors the infusion pump in real time through a monitoring system. Because the number of the infusion pumps to be monitored is too large, when the flow rate of the infusion pump is abnormally monitored in the prior art, the flow rate information is not comprehensive and complete, the multi-dimensional information classification is not accurate enough, a certain error rate exists, and the monitoring precision needs to be improved.
Disclosure of Invention
In order to overcome the defects of the prior art, one of the objectives of the present invention is to provide a block chain-based method and a system for monitoring a medical infusion pump, which solve the problem of low accuracy of the existing method for monitoring the flow rate of the medical infusion pump.
One of the purposes of the invention is realized by adopting the following technical scheme:
a medical infusion pump monitoring method based on a block chain,
the method comprises the following steps:
step 1: after the medical infusion pump is coded, the medical infusion pump is input into a monitoring system, and remote communication is established between the medical infusion pump and the monitoring system;
step 2: when a starting signal of the medical infusion pump is received, the monitoring system collects flow rate information of the medical infusion pump at regular time;
and step 3: analyzing the real-time flow rate information of the medical infusion pump, analyzing the real-time flow rate information through a monitoring system according to the collected flow rate information, and judging whether the abnormal condition exists in the infusion of the patient;
and 4, step 4: the monitoring system displays the flow rate information of the medical infusion pump in real time and prompts abnormal conditions.
Further, the method for analyzing the flow rate information of the medical infusion pump in the step 3 comprises the following steps:
step S100: the method comprises the steps that flow rate information of a medical infusion pump is collected in a timing mode to obtain first timing flow rate information, and information pre-analysis is carried out on the first timing flow rate information to obtain second timing flow rate information;
step S200: inputting the second timing flow velocity information into a Gaussian mixing module to perform set-to-characteristic learning, and obtaining a first set-to-information group;
step S300: dividing the first set into information groups according to the first set to generate a first labeling exercise information group;
step S400: obtaining a first test exercise information set of the medical infusion pump;
step S500: and inputting the first testing exercise information group into the first abnormal inspection module to obtain first output information, wherein the first output information comprises an abnormal inspection classification result.
Further, step S200 further includes:
step S210: generating a first normal information group and a first abnormal condition information group by classifying the normal flow rate information and the abnormal flow rate information of the second timing flow rate information;
step S220: performing set division operation in a Gaussian mixture module according to the first normal information group and the first abnormal condition information group to obtain a first set division result, wherein the first set division result is a set division optimal characteristic;
step S230: and generating the first set and dividing into information groups according to the first set dividing result.
Further, step S100 further includes:
step S110: creating an information quality evaluation module according to the plurality of evaluation data;
step S120: inputting the second timing flow velocity information into the information quality evaluation module, and obtaining an information quality factor according to the information quality evaluation module;
step S130: judging whether secondary information collection is needed or not according to the information quality factor, and if so, obtaining a first information collection standard;
step S140: and obtaining third real-time flow rate information according to the first information collection specification.
Further, step S300 further includes:
step S310: training an SVM module by taking the first labeling training information group as input information to obtain a first SVM module;
step S320: obtaining a first module optimization parameter through a particle swarm algorithm calculation mode, and optimizing the first SVM module according to the first module optimization parameter to obtain a second SVM module;
step S330: and taking the second SVM module as the first abnormal condition detection module.
Further, in the above-mentioned case,
step S320 further includes:
step S321: performing module inspection on the first SVM module to obtain a first inspection result, wherein the first inspection result comprises a first result and a second result, the first result is that the inspection is passed, and the second result is that the inspection is not passed;
step S322: when the first inspection result is that the inspection is passed, inputting the exercise data of the first SVM module into a first index function;
step S323: and judging whether the expected alternation times are reached or not according to the first index function, and if the expected alternation times are reached by the first index function, obtaining the first module optimization data.
Further, step S323 further includes:
step S3231: if the first index function does not reach the expected alternation times;
step S3232: the control system obtains a first blending indication;
step S3233: according to the first allocation instruction, allocating a particle group algorithm calculation mode to generate a first improvement factor;
step S3234: and obtaining the first module optimization parameter according to the first improvement factor.
Further, step S230 further includes:
step S231: dividing the first set into results to be evaluated based on an implicit Dirichlet distribution calculation formula to obtain a first evaluation index;
step S232: judging whether the first evaluation index is in a predicted evaluation requirement or not;
step S233: when the first evaluation index is in the estimated evaluation requirement, obtaining a first output indication;
step S234: and according to the first output indication, obtaining a first optimal set dividing characteristic, and dividing the first optimal set into features as the first set to divide the first optimal set into information groups.
Furthermore, a 5G network module is arranged and used for providing information of the medical infusion pump to be uploaded to a monitoring system and interacting with the monitoring system, the monitoring system uploads the information of the medical infusion pump to a block chain through the 5G network module, and the stored medical information is overlapped for big data analysis.
A medical infusion pump system based on a block chain,
including medical infusion pump, monitored control system, 5G network module, wherein:
the medical infusion pump is used for controlling the number of infusion drops or the infusion flow rate, and ensuring that liquid and medicine can be accurately and safely infused into a patient body at a set speed;
the monitoring system is used for remotely connecting the medical infusion pump, exchanging information and receiving and sending instructions for controlling the infusion pump;
and the 5G network module is used for interconnection and remote interaction between the medical infusion pump and the monitoring system, and realizes information collection, information return, control instruction receiving and sending and monitoring.
Compared with the prior art, the invention has the advantages that:
1. the real-time flow rate information of the medical infusion pump is collected, so that the information pre-analysis is carried out on the real-time collected information.
Then, feature aggregation is carried out on the preprocessed information based on Gaussian mixture module-linear recognition analysis (MOG-hidden Dirichlet distribution), information labeling is carried out according to aggregation results, original monitoring information of the medical infusion pump is obtained, the labeled information is used as exercise information to practice an SVM module, the SVM module is optimized in a particle swarm optimization algorithm calculation mode to obtain a most preferred module, original test information is used as basic information to be input into the optimized most preferred module, and further an abnormal inspection classification result is output, so that the most preferred feature of classification through information extraction is achieved, and the technical effects of improving abnormal inspection accuracy of the flow rate of the medical infusion pump and reducing error rate are further improved.
2. The information is subjected to set division processing and labeled information groups through set division characteristic learning, and the self-adaptive capacity of the information groups is improved by normal form groups and abnormal form groups, so that the method can be suitable for dynamic conversion of network conditions, and the confidentiality of the information characteristics is ensured.
3. Because the trained module is optimized by using a particle swarm optimization support vector machine (particle swarm optimization-SVM), the output result of the module has higher inspection precision and lower error rate.
The foregoing description is only an overview of the technical solutions of the present invention, and in order to make the technical means of the present invention more clearly understood, the present invention may be implemented according to the content of the description, and in order to make the above and other objects, features, and advantages of the present invention more clearly understood, the following preferred embodiments are described in detail with reference to the accompanying drawings.
Drawings
Fig. 1 is a block diagram of a monitoring system for a medical infusion pump according to the present embodiment.
Detailed Description
The present invention will be further described with reference to the accompanying drawings and the detailed description, and it should be noted that any combination of the embodiments or technical features described below can be used to form a new embodiment without conflict.
It will be noted that when an element is referred to as being "secured to" another element, it can be directly on the other element or intervening elements may also be present. When a component is referred to as being "connected" to another component, it can be directly connected to the other component or intervening components may also be present. When a component is referred to as being "disposed on" another component, it can be directly on the other component or intervening components may also be present. The terms "vertical," "horizontal," "left," "right," and the like as used herein are for illustrative purposes only.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
A medical infusion pump monitoring method based on a block chain is based on the medical block chain of medical information interaction, the medical block chain comprises an information group used for storing medical information and an application program used for remotely controlling a medical infusion pump, and all modules carry out remote communication through a 5G network module based on built-in programs. The information group can be provided with a plurality of servers according to the information quantity and the like, information connection is established among the servers, the network module provides a medical information interaction interface, medical information interaction is carried out with the medical block chain at regular time, and the stored medical information is overlaid.
A medical infusion pump monitoring method based on a block chain,
the method comprises the following steps:
step 1: after the medical infusion pump is coded, the medical infusion pump is input into a monitoring system, and remote communication is established between the medical infusion pump and the monitoring system;
step 2: when a starting signal of the medical infusion pump is received, the monitoring system collects flow rate information of the medical infusion pump at regular time;
and step 3: analyzing the real-time flow rate information of the medical infusion pump, analyzing the real-time flow rate information through a monitoring system according to the collected flow rate information, and judging whether the abnormal condition exists in the infusion of the patient;
and 4, step 4: the monitoring system displays the flow rate information of the medical infusion pump in real time and prompts abnormal conditions.
The method for analyzing the flow rate information of the medical infusion pump in the step 3 comprises the following steps:
step S100: the method comprises the steps that flow rate information of a medical infusion pump is collected in a timing mode to obtain first timing flow rate information, and information pre-analysis is carried out on the first timing flow rate information to obtain second timing flow rate information;
specifically, the information of the medical infusion pump is collected at regular time under the condition of a specific occasion, wherein the information collection process can also finish information collection after the information group of the infusion flow rate system of a certain medical infusion pump manufacturer is in communication connection, and the collected information also needs to collect the information of the production flow rate influence factors of the medical infusion pump. And analyzing deviation of the accumulated information such as the flow rate to obtain a flow rate deviation set of a specific time interval, and collecting the rest information through time intervals and the like. The information pre-analysis is information normalization processing, different evaluation data often have different physical quantities and physical quantity units in collected original information, the information analysis result is influenced under the condition, information normalization processing is required to be carried out to eliminate the physical quantity influence among the data, the comparability among the information data is solved, the processed information can be located at the same number level and is suitable for generalized comparison and evaluation, and therefore the information is collected at regular time and is subjected to information pre-analysis before practice, and the information analysis accuracy is improved.
Step S200: inputting the second timing flow velocity information into a Gaussian mixing module to perform set division characteristic learning, and obtaining a first set division information group;
step S300: dividing the first set into information groups according to the first set, and generating a first labeling exercise information group;
specifically, the Gaussian mixture module-linear recognition analysis set is divided into characteristic learning, which is a set division calculation mode based on a label-free information group, the Gaussian mixture module-linear recognition analysis set is divided into characteristic learning sets belonging to a supervision-free intelligent learning calculation mode, which can replace the manual identification mode in the past to classify the sampled information group, when some points are too close to the boundary of the given set division, the points are possibly wrongly recorded, the information can be accurately grouped into sets through the Gaussian mixture module, because the flow of grouping divides the labeled normal or abnormal samples into sets, the information groups are divided into the first set according to the generated set to label the categories, namely the normal and abnormal classification information, and the refreshing mode dynamically generates the nearest normal form group and abnormal form group to improve the self-adaptive capacity of the information groups, the method can be suitable for dynamic conversion of network conditions, information groups are marked, training and analysis of information are facilitated, and classification accuracy is improved.
Step S400: obtaining a first test exercise information set of the medical infusion pump;
step S500: and inputting the first test exercise information group into a first abnormal inspection module to obtain first output information, wherein the first output information comprises an abnormal inspection classification result.
Specifically, a first test practice information group of the medical infusion pump is obtained and input into a first abnormal inspection module, wherein the first test practice information group is used as basic information of the medical infusion pump and input into the module, and the first abnormal inspection module is the most preferable module, so that the abnormal inspection classification result input by the first abnormal inspection module is high in precision and accuracy, the most preferable characteristic of classification through information extraction is achieved, and the technical effects of improving the abnormal inspection precision of the flow rate of the medical infusion pump and reducing the error rate are achieved.
Step S200 further includes:
step S210: generating a first normal information group and a first abnormal condition information group by classifying the normal flow rate information and the abnormal flow rate information of the second timing flow rate information;
step S220: performing set division operation in a Gaussian mixture module according to the first normal information group and the first abnormal condition information group to obtain a first set division result, wherein the first set division result is the most preferable characteristic of set division;
step S230: and generating a first set division information group according to the first set division result.
Step S100 further includes:
step S110: creating an information quality evaluation module according to the plurality of evaluation data;
step S120: inputting the second timing flow velocity information into an information quality evaluation module, and obtaining an information quality factor according to the information quality evaluation module;
step S130: judging whether secondary information collection is needed or not according to the information quality factor, and if so, obtaining a first information collection standard;
step S140: third real-time flow rate information is obtained according to the first information collection specification.
Step S300 further includes:
step S310: training the SVM module by taking the first labeling training information group as input information to obtain a first SVM module;
step S320: obtaining a first module optimization parameter through a particle swarm algorithm calculation mode, and optimizing the first SVM module according to the first module optimization parameter to obtain a second SVM module;
step S330: the second SVM module is used as the first abnormal-state detection module.
Step S320 further includes:
step S321: performing module inspection on the first SVM module to obtain a first inspection result, wherein the first inspection result comprises a first result and a second result, the first result is that the inspection is passed, and the second result is that the inspection is not passed;
step S322: when the first inspection result is that the inspection is passed, inputting the exercise data of the first SVM module into a first index function;
step S323: and judging whether the expected alternation times are reached or not according to the first index function, and if the expected alternation times are reached by the first index function, obtaining the first module optimization data.
Step S323 further includes:
step S3231: if the first index function does not reach the expected alternation times;
step S3232: the control system obtains a first blending indication;
step S3233: according to the first blending instruction, blending a particle group algorithm calculation mode to generate a first improvement factor;
step S3234: and obtaining a first module optimization parameter according to the first improvement factor.
Step S230 further includes:
step S231: dividing the first set into results to be evaluated based on an implicit Dirichlet distribution calculation formula to obtain a first evaluation index;
step S232: judging whether the first evaluation index is in a predicted evaluation requirement or not;
step S233: when the first evaluation index is in the estimated evaluation requirement, obtaining a first output indication;
step S234: and according to the first output indication, obtaining a first optimal set dividing characteristic, and dividing the first optimal set into features as a first set to divide the first optimal set into information groups.
Specifically, a Gaussian mixture model-linear discriminant analysis (MOG-hidden Dirichlet distribution) set is divided into a formula, normal data and inverse data in an information base are subjected to set division operation by using MOG, and then data divided by each MOG set is evaluated by using a hidden Dirichlet distribution algorithm.
The 5G network module is used for providing information of the medical infusion pump to be uploaded to the monitoring system and interacting with the monitoring system, and the monitoring system uploads the information of the medical infusion pump to the block chain through the 5G network module, so that the stored medical information is overlapped and used for big data analysis.
A medical infusion pump system based on a block chain,
including medical infusion pump, monitored control system, 5G network module, wherein:
the medical infusion pump is used for controlling the number of infusion drops or the infusion flow rate, and ensuring that liquid and medicine can be accurately and safely infused into a patient body at a set speed;
a built-in flowmeter is used for collecting the flow of the data liquid medicine, and meanwhile, a built-in wireless communication module such as NB-IOT, Bluetooth and the like is used, which is not described in detail in the embodiment;
the monitoring system is preferably a PC (personal computer), is provided with a monitoring application program and is used for remotely connecting the medical infusion pump, exchanging information and receiving and sending instructions for controlling the infusion pump;
meanwhile, a large monitoring screen can be arranged, data monitoring can be conveniently carried out in a control room, and an audible and visual alarm module is arranged for prompting and broadcasting when the data are abnormal;
and the 5G network module is used for interconnection and remote interaction between the medical infusion pump and the monitoring system, and realizes information collection, information return, control instruction receiving and sending and monitoring.
The above embodiments are only preferred embodiments of the present invention, and the protection scope of the present invention should not be limited thereby, and any insubstantial changes and substitutions made by those skilled in the art based on the present invention are within the protection scope of the present invention.
Claims (10)
1. A medical infusion pump monitoring method based on a block chain is characterized by comprising the following steps:
step 1: after the medical infusion pump is coded, the medical infusion pump is input into a monitoring system, and remote communication is established between the medical infusion pump and the monitoring system;
step 2: when a starting signal of the medical infusion pump is received, the monitoring system collects flow rate information of the medical infusion pump at regular time;
and step 3: analyzing the real-time flow rate information of the medical infusion pump, analyzing the real-time flow rate information through a monitoring system according to the collected flow rate information, and judging whether the abnormal condition exists in the infusion of the patient;
and 4, step 4: the monitoring system displays the flow rate information of the medical infusion pump in real time and prompts abnormal conditions.
2. The method of claim 1 for blockchain-based medical infusion pump monitoring, wherein: the method for analyzing the flow rate information of the medical infusion pump in the step 3 comprises the following steps:
step S100: the method comprises the steps that flow rate information of a medical infusion pump is collected in a timing mode to obtain first timing flow rate information, and information pre-analysis is carried out on the first timing flow rate information to obtain second timing flow rate information;
step S200: inputting the second timing flow velocity information into a Gaussian mixing module to perform set-to-characteristic learning, and obtaining a first set-to-information group;
step S300: dividing the first set into information groups according to the first set to generate a first labeling exercise information group;
step S400: obtaining a first test exercise information set of the medical infusion pump;
step S500: and inputting the first testing exercise information group into the first abnormal inspection module to obtain first output information, wherein the first output information comprises an abnormal inspection classification result.
3. The method of claim 2 for blockchain-based medical infusion pump monitoring, wherein:
step S200 further includes:
step S210: generating a first normal information group and a first abnormal condition information group by classifying the normal flow rate information and the abnormal flow rate information of the second timing flow rate information;
step S220: performing set division operation in a Gaussian mixture module according to the first normal information group and the first abnormal condition information group to obtain a first set division result, wherein the first set division result is a set division optimal characteristic;
step S230: and generating the first set and dividing into information groups according to the first set dividing result.
4. The method of claim 2 for blockchain-based medical infusion pump monitoring, wherein:
step S100 further includes:
step S110: creating an information quality evaluation module according to the plurality of evaluation data;
step S120: inputting the second timing flow velocity information into the information quality evaluation module, and obtaining an information quality factor according to the information quality evaluation module;
step S130: judging whether secondary information collection is needed or not according to the information quality factor, and if so, obtaining a first information collection standard;
step S140: and obtaining third real-time flow rate information according to the first information collection specification.
5. The method of claim 2 for blockchain-based medical infusion pump monitoring, wherein:
step S300 further includes:
step S310: training an SVM module by taking the first labeling training information group as input information to obtain a first SVM module;
step S320: obtaining a first module optimization parameter through a particle swarm algorithm calculation mode, and optimizing the first SVM module according to the first module optimization parameter to obtain a second SVM module;
step S330: and taking the second SVM module as the first abnormal condition detection module.
6. The method of claim 5 for blockchain-based medical infusion pump monitoring, wherein:
step S320 further includes:
step S321: performing module inspection on the first SVM module to obtain a first inspection result, wherein the first inspection result comprises a first result and a second result, the first result is that the inspection is passed, and the second result is that the inspection is not passed;
step S322: when the first inspection result is that the inspection is passed, inputting the exercise data of the first SVM module into a first index function;
step S323: and judging whether the expected alternation times are reached or not according to the first index function, and if the expected alternation times are reached by the first index function, obtaining the first module optimization data.
7. The method of claim 6 for blockchain-based medical infusion pump monitoring, wherein:
step S323 further includes:
step S3231: if the first index function does not reach the expected alternation times;
step S3232: the control system obtains a first blending indication;
step S3233: according to the first allocation instruction, allocating a particle group algorithm calculation mode to generate a first improvement factor;
step S3234: and obtaining the first module optimization parameter according to the first improvement factor.
8. The method of claim 3 for blockchain-based medical infusion pump monitoring, wherein:
step S230 further includes:
step S231: dividing the first set into results to be evaluated based on an implicit Dirichlet distribution calculation formula to obtain a first evaluation index;
step S232: judging whether the first evaluation index is in a predicted evaluation requirement or not;
step S233: when the first evaluation index is in the estimated evaluation requirement, obtaining a first output indication;
step S234: and according to the first output indication, obtaining a first optimal set dividing characteristic, and dividing the first optimal set into features as the first set to divide the first optimal set into information groups.
9. The method of claim 1 for blockchain-based medical infusion pump monitoring, wherein: the 5G network module is used for providing information of the medical infusion pump to be uploaded to the monitoring system and interacting with the monitoring system, and the monitoring system uploads the information of the medical infusion pump to the block chain through the 5G network module, so that the stored medical information is overlapped and used for big data analysis.
10. The utility model provides a medical treatment infusion pump system based on block chain which characterized in that:
including medical infusion pump, monitored control system, 5G network module, wherein:
the medical infusion pump is used for controlling the number of infusion drops or the infusion flow rate, and ensuring that liquid and medicine can be accurately and safely infused into a patient body at a set speed;
the monitoring system is used for remotely connecting the medical infusion pump, exchanging information and receiving and sending instructions for controlling the infusion pump;
and the 5G network module is used for interconnection and remote interaction between the medical infusion pump and the monitoring system, and realizes information collection, information return, control instruction receiving and sending and monitoring.
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