CN114887169A - Intelligent control decision method and system for breathing machine - Google Patents
Intelligent control decision method and system for breathing machine Download PDFInfo
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Abstract
The invention belongs to the field of medical equipment intellectualization, and provides a breathing machine intelligent control decision method and a system, which comprises the steps of obtaining various physiological state parameters of a patient and operation state parameters of a breathing machine; calculating the change values and the transformation rates of different parameters within a certain time based on various physiological state parameters of the patient and the operating state parameters of the breathing machine; the simulation training of the ventilation decision of the breathing machine is carried out on the basis of the trained fuzzy neural network control model by combining the change values and the change rates of different parameters within a certain time and the current physiological state parameters of the patient; obtaining a simulated ventilation parameter setting result, and judging whether the simulated ventilation parameter setting result is abnormal; if the abnormal condition exists, alarming; if not, outputting the setting result of the simulated ventilation parameters to the respirator; according to the invention, the corresponding ventilation scheme is adaptively formulated according to the specific conditions of different patients, and compared with the original heavy and complex adjustment process, the workload of medical staff is reduced.
Description
Technical Field
The invention belongs to the technical field of medical equipment intellectualization, and particularly relates to a breathing machine intelligent control decision method and system.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The global influence of new coronary epidemic situation increases the number of new coronary pneumonia patients rapidly, so that the demand of medical equipment such as a breathing machine and the like and corresponding medical care personnel is increased rapidly. By 2022, the situation of new coronary epidemic is still serious and is increasingly developed, and every country in the world needs to prepare for a long-term battle, which means more ventilators and medical staff skilled in operating the ventilators are needed, however, not only the research and development and production of the ventilators need a certain period, but also the skilled operation of the ventilators needs long-term clinical practice experience. With the rapid increase of the number of new patients with coronary pneumonia and the occurrence of a series of problems of global atmospheric environment deterioration, overload work of people and the like caused by the industrial development, patients with respiratory systems and related diseases are increased, however, the problems of serious loss and shortage of medical care personnel in China exist all the time, and under the current situation, few medical care personnel who can make decision and judge and are skilled in adjusting the breathing machine according to the real-time change of the physiological state of the patients are few.
In actual operation, medical care personnel must pay close attention to the change of the physiological state of the patient, then doctors give orders for adjusting the breathing machine according to various physiological state indexes of the patient, and nurses repeatedly adjust the breathing machine according to the orders until the patient wearing the breathing machine is in the most comfortable mechanical ventilation state. The above-mentioned complicated and complicated of process is concerned with life safety again, has occupied medical personnel a large amount of time energy, and patient's physiological state changes often, leads to medical personnel to bear the double pressure of mind and body for a long time, and its overload operating condition is urgently needed to be alleviated.
Disclosure of Invention
In order to solve the problems, the invention provides a breathing machine intelligent control decision method and a breathing machine intelligent control decision system, which organically combine a mechanical ventilation training model, a mechanical ventilation rule and a breathing machine intelligent control algorithm, and compared with the original heavy and complex observation, repeated diagnosis and other processes, the method and the system reduce the workload of medical staff, ensure the safety and the effectiveness of ventilation decision and simultaneously improve the ventilation comfort of patients.
According to some embodiments, a first aspect of the present invention provides a method for intelligent control decision-making of a ventilator, which adopts the following technical solutions:
a ventilator intelligent control decision method comprises the following steps:
acquiring various physiological state parameters of a patient and operating state parameters of a breathing machine;
calculating the change values and the transformation rates of different parameters within a certain time based on various physiological state parameters of the patient and the operating state parameters of the breathing machine;
the simulation training of the ventilation decision of the breathing machine is carried out on the basis of the trained fuzzy neural network control model by combining the change values and the change rates of different parameters within a certain time and the current physiological state parameters of the patient;
obtaining a simulated ventilation parameter setting result, and judging whether the simulated ventilation parameter setting result is abnormal or not;
if the abnormal condition exists, alarming; and if not, outputting the result of the setting of the simulated ventilation parameters to the respirator.
Further, the forming of the ventilator ventilation decision specifically includes:
determining the mechanical ventilation treatment target of the patient according to the height, weight, age, sex and disease information of the patient;
selecting an optimal ventilation mode according to the mechanical ventilation treatment target, determining the ventilation state parameters of the patient and the breathing machine to be monitored in the mode, and using the parameters as parameter bases for ventilation state perception;
determining the corresponding relation between ventilation state parameters and breathing machine parameter adjustment settings under the guidance of an expert knowledge base to form mechanical ventilation rules;
based on the mechanical ventilation rule, training is carried out by utilizing a fuzzy neural network to obtain a ventilation decision of the breathing machine.
Further, the mechanical ventilation therapy target is to set a value to be reached and a time to be stably maintained by an oxygenation parameter represented by a blood oxygen saturation and a ventilation parameter represented by a carbon dioxide partial pressure of the patient under the mechanical ventilation therapy.
Further, the training by using the fuzzy neural network based on the mechanical ventilation rule to obtain the ventilator ventilation decision includes:
based on the mechanical ventilation rule, generating a mechanical ventilation fuzzy control rule through the fuzzy logic algorithm structure determination and ventilation rule fuzzification processing processes of a fuzzy system;
training a fuzzy neural network based on the mechanical ventilation fuzzy control rule to obtain a trained fuzzy neural network control model;
and controlling the mechanical ventilation simulation gas circuit model by using the trained fuzzy neural network control model to obtain the ventilation decision of the breathing machine.
Further, training the fuzzy neural network based on the mechanical ventilation fuzzy control rule to obtain a trained fuzzy neural network control model, including:
simulating membership functions representing fuzzy sets of different input states in a fuzzy mechanical control rule by using neurons to obtain fuzzy neurons;
receiving the information fuzzified by the fuzzy neuron through the WTA neuron, and comparing fuzzy quantities of various input states to determine an output state;
determining the number of layers of a neural network and the number of neurons on each layer according to mechanical ventilation rules of different patients to obtain a fuzzy neural network control model;
and respectively training the constructed fuzzy neural network control model according to the physiological state parameters of the typical patient and the operating parameters of the breathing machine to obtain the trained fuzzy neural network control model.
Further, the physiological state parameters of the patient comprise blood gas analysis results of the patient and respiratory physiological data of the patient;
the blood gas analysis result of the patient refers to the clinical application of a blood gas analyzer to carry out blood gas analysis and detection on the patient so as to obtain various respiratory function indexes and acid-base balance state indexes of the patient.
Further, the ventilator adjusts the operating parameters according to the simulated ventilation parameter setting result.
According to some embodiments, the second aspect of the present invention provides a ventilator intelligent control decision system, which adopts the following technical solutions:
a ventilator intelligence control decision making system, comprising:
the data acquisition module is configured to acquire various physiological state parameters of a patient and the operating state parameters of a breathing machine;
the data processing module is configured to calculate change values and transformation rates of different parameters within a certain time based on various physiological state parameters of the patient and the operating state parameters of the breathing machine;
the breathing simulation training module is configured to combine the change values and the change rates of different parameters within a certain time and various current physiological state parameters of the patient, and perform simulation training of breathing machine ventilation decision based on a trained fuzzy neural network control model;
the breath control execution module is configured to obtain a simulated ventilation parameter setting result and judge whether the simulated ventilation parameter setting result is abnormal;
if not, outputting the setting result of the simulated ventilation parameters to the respirator; otherwise, alarming.
According to some embodiments, a third aspect of the invention provides a computer-readable storage medium.
A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out the steps of a method for intelligent control decision-making for a ventilator as described in the first aspect above.
According to some embodiments, a fourth aspect of the invention provides a computer apparatus.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of a method for intelligent ventilator control decision making as described in the first aspect above when executing the program.
Compared with the prior art, the invention has the beneficial effects that:
the invention organically combines a mechanical ventilation training model, a mechanical ventilation rule and a breathing machine intelligent control algorithm to form a fuzzy neural network control model, utilizes the trained fuzzy neural network control model to carry out simulation training ventilation on different physiological parameters of different patients to obtain different ventilation parameter setting results for different patients, and compared with the original heavy and complex processes of observation, repeated diagnosis and the like, the method and the system reduce the workload of medical personnel, ensure the safety and the effectiveness of ventilation decision and simultaneously improve the ventilation comfort of the patients.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
Fig. 1 is a flowchart of a method for making a decision on intelligent control of a ventilator according to an embodiment of the present invention;
fig. 2 is a flowchart of an intelligent control implementation according to an embodiment of the present invention.
Detailed Description
The invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. 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.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The embodiments and features of the embodiments of the invention may be combined with each other without conflict.
Example one
As shown in fig. 1-fig. 2, the present embodiment provides a method for intelligent control decision of a ventilator, and the present embodiment is illustrated by applying the method to a server, it is to be understood that the method may also be applied to a terminal, and may also be applied to a system including a terminal and a server, and implemented by interaction between the terminal and the server. The server may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a network server, cloud communication, middleware service, a domain name service, a security service CDN, a big data and artificial intelligence platform, and the like. The terminal may be, but is not limited to, a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and the like. The terminal and the server may be directly or indirectly connected through wired or wireless communication, and the application is not limited herein. In this embodiment, the method includes the steps of:
acquiring various physiological state parameters of a patient and operating state parameters of a breathing machine;
calculating the change values and the transformation rates of different parameters within a certain time based on various physiological state parameters of the patient and the operating state parameters of the breathing machine;
combining the change values and the change rates of different parameters in a certain time and various current physiological state parameters of the patient, and carrying out simulation training of the ventilation decision of the breathing machine based on the trained fuzzy neural network control model;
obtaining a simulated ventilation parameter setting result, and judging whether the simulated ventilation parameter setting result is abnormal;
if the abnormal condition exists, alarming; and if not, outputting the simulation ventilation parameter setting result to the respirator.
The physiological state parameters of the patient comprise a blood gas analysis result of the patient and respiratory physiological data of the patient;
the blood gas analysis result of the patient refers to the clinical application of a blood gas analyzer to carry out blood gas analysis and detection on the patient so as to obtain various respiratory function indexes and acid-base balance state indexes of the patient.
The respiratory physiological data of the patient is acquired by using various sensors, wherein the various sensors include but are not limited to a series of sensors including a gas pressure sensor, a gas flow sensor, a carbon dioxide sensor, a blood oxygen saturation sensor, a carbon dioxide partial pressure sensor, an oxygen concentration sensor and the like, and are used for monitoring various physiological states of the patient in real time and transmitting the monitored physiological state parameters and the blood gas analysis result to an intelligent control implementation part in real time in a data form.
The gas pressure sensor is used for monitoring various pressure changes in the respiratory tract of the human body including but not limited to inspiratory positive airway pressure, end-expiratory positive airway pressure and the like of the human body; the gas flow sensor is used for monitoring relevant parameters including but not limited to flow velocity and flow in human respiratory tract; the carbon dioxide sensor is used for monitoring relevant carbon dioxide parameters including but not limited to the carbon dioxide concentration of the expired gas of the human body; the blood oxygen saturation sensor is used for monitoring the blood oxygen saturation of the human body; the carbon dioxide partial pressure sensor is used for monitoring the carbon dioxide partial pressure of arterial blood of a human body; the oxygen concentration sensor is used for monitoring the oxygen concentration of the gas inhaled and exhaled by the human body. The operating state parameters of the breathing machine include but are not limited to the working parameters of the breathing machine including the actually output ventilation frequency, the positive expiratory phase airway pressure, the positive inspiratory phase airway channel, the inspiratory oxygen concentration, the positive end expiratory pressure and the like.
Based on the blood gas analysis result of the patient, the respiration physiological data of the patient and the operation state parameters of the breathing machine, the mechanical ventilation clinical experience in the existing expert knowledge base is combined, and a ventilation decision result is finally made in the ventilation decision result to control and adjust the current operating breathing machine or displayed to the current medical staff responsible for breathing machine adjustment through a human-computer interaction interface by the processes of mechanical ventilation rule making, mechanical ventilation intelligent control algorithm generation, mechanical ventilation intelligent control program realization, state acquisition and perception, training model pre-verification and the like, so that the intelligent automatic adjustment of the breathing machine or the auxiliary adjustment of the medical staff is realized.
In a specific embodiment, the forming of the ventilator ventilation decision specifically includes:
determining the mechanical ventilation treatment target of the patient according to the height, weight, age, sex and disease information of the patient;
selecting an optimal ventilation mode according to the mechanical ventilation treatment target, determining the ventilation state parameters of the patient and the breathing machine to be monitored in the mode, and using the parameters as parameter bases for ventilation state perception;
determining the corresponding relation between ventilation state parameters and breathing machine parameter adjustment settings under the guidance of an expert knowledge base to form mechanical ventilation rules;
based on the mechanical ventilation rule, training is carried out by utilizing a fuzzy neural network to obtain a ventilation decision of the breathing machine.
The diseases include, but are not limited to, three diseases of obstructive ventilation dysfunction diseases, limited ventilation dysfunction diseases and ventilation dysfunction diseases, wherein the obstructive ventilation dysfunction diseases comprise specific diseases such as chronic obstructive pulmonary acute exacerbation and asthma acute attack; the diseases of restricted ventilation dysfunction include neuromuscular diseases, interstitial lung diseases, thoracic trauma, thoracic deformity and the like; the diseases of ventilation dysfunction include acute respiratory distress syndrome and severe pneumonia.
The mechanical ventilation target is to set the value and stable maintaining time of the oxygenation parameter represented by the blood oxygen saturation and the ventilation parameter represented by the carbon dioxide partial pressure of the patient under the mechanical ventilation treatment.
Based on the mechanical ventilation rule, training by using a fuzzy neural network to obtain a ventilation decision of the breathing machine, comprising the following steps of:
based on the mechanical ventilation rule, generating a mechanical ventilation fuzzy control rule through the fuzzy logic algorithm structure determination and ventilation rule fuzzification processing processes of a fuzzy system;
training a fuzzy neural network based on the mechanical ventilation fuzzy control rule to obtain a trained fuzzy neural network control model;
and controlling the mechanical ventilation simulation gas circuit model by using the trained fuzzy neural network control model to obtain the ventilation decision of the breathing machine.
Specifically, based on the mechanical ventilation fuzzy control rule, training a fuzzy neural network to obtain a trained fuzzy neural network control model, including:
simulating membership functions representing fuzzy sets of different input states in a fuzzy mechanical control rule by using neurons to obtain fuzzy neurons;
receiving the information fuzzified by the fuzzy neuron through the WTA neuron, and comparing fuzzy quantities of various input states to determine an output state;
determining the number of layers of a neural network and the number of neurons on each layer according to mechanical ventilation rules of different patients to obtain a fuzzy neural network control model;
and respectively training the constructed fuzzy neural network control model according to the physiological state parameters of the typical patient and the operating parameters of the breathing machine to obtain the trained fuzzy neural network control model.
Wherein, the mechanical ventilation rule is firstly subjected to the fuzzy logic algorithm structure determination of the fuzzy system, the ventilation rule fuzzification processing and other processes to generate the mechanical ventilation fuzzy control rule,
then inputting the fuzzy control rule into a fuzzy neural network for training;
the fuzzy neural network receives the fuzzy control rule and generates a fuzzy neural network control algorithm through the processes of designing a fuzzy neuron, defuzzifying, designing a neural network structure, setting an initial connection weight, training empirical data, designing the overall fuzzy neural network controller and the like.
Specifically, the designing of the fuzzy neuron means that the neuron is used for simulating membership functions representing fuzzy sets of different input states in a fuzzy control rule, the traditional neuron does not have a fuzzification function, the fuzzy neuron is designed to enable curves of input data to be continuous, and various neural network algorithms can be conveniently used in the next step.
The defuzzification processing means that the information defuzzified by the fuzzy neurons is received through the WTA neurons, and then the WTA neurons compare fuzzy quantities of various input states to determine states which can be output, so that the purposes of state perception and state identification are achieved.
The design of the neural network structure refers to determining the number of layers of the neural network and the number of neurons on each layer according to the ventilation rules of different patients, and further building a fuzzy neural network controller for patient state identification.
The overall design of the fuzzy neural network controller comprises the fuzzy neural network controller for patient state recognition and the fuzzy neural network controller for decision making, the design idea of the decision making controller is basically similar to that of the recognition controller, clinical experience data can be input after the overall design is completed to train the fuzzy neural network controller to help the fuzzy neural network controller to perform verification and optimization, and a fuzzy neural network control algorithm is generated after the verification and optimization.
Then the algorithm is output to a mechanical ventilation simulation gas circuit and a Codesys control software platform in a training model, the simulation application and the algorithm function block packaging are respectively carried out,
the process refines the ventilation control rule, and the parameter adjustment of the breathing machine is changed from step adjustment with large range interval into dynamic stepless adjustment, so that the patient can have corresponding breathing machine ventilation mode parameter decision even under the condition of slight physiological state change.
The state sensing part monitors the parameters of the patient and the breathing machine required to be monitored in the ventilation state after determining the parameters, and calculates the change values and the change rates of different parameters within a certain time.
The ventilation state monitoring parameter represents the current physiological ecology of the patient, and if the blood oxygen saturation value of the patient is 85%, the patient can be considered to be in an oxygen deficiency state, and measures such as increasing the concentration of inhaled oxygen or increasing the pressure of inhaled gas can be taken for improvement.
The change value and the change rate of a certain parameter in a certain time can represent the state change of the patient and are used for predicting the state change trend of the patient, for example, the blood oxygen saturation value of the patient continuously decreases by 9% at a rate of 3% per minute in the last 3 minutes, the patient can be considered to be in the oxygen deficiency gradual aggravation state, and more powerful improvement measures based on the above should be taken.
The parameters representing the current state and the variation trend are respectively input into a training model and a control program to assist the training model in ventilation decision simulation and actual operation.
The training model comprises a human body physiological model and a mechanical ventilation simulation gas circuit and is used for receiving data of the state perception part and carrying out ventilation simulation so as to ensure that a ventilation decision obtained by a control algorithm is safe and effective.
The human body physiological model and the mechanical ventilation simulation gas circuit are equivalent to the simulation of a patient and a breathing machine.
The human body physiological model comprises a respiratory mechanics model and a gas exchange model, is built based on a Simulink software platform, and is used for simply simulating processes of lung ventilation, gas transportation, tissue ventilation and the like in the human body respiratory process so as to estimate and analyze the mechanical ventilation condition of a patient according to the situation of parameters.
The respiratory mechanics model is a simplified lung electrical characteristic model, wherein the method used in the method comprises but is not limited to fitting a pressure-volume curve by using a Newton iteration method to represent the dynamic compliance of the lung, and the static airway resistance is calculated by using a formula to replace the dynamic airway resistance.
The gas exchange model is based on a double-cavity lung model, the processes of diffusion, dispersion, oxygenation, dissolution and the like of oxygen and carbon dioxide are described by a mathematical formula according to the states of the oxygen and the carbon dioxide in different gas exchange processes, the gas exchange physiological state parameters such as arterial blood oxygen saturation, venous blood oxygen saturation, intrapulmonary bypass values, ventilation perfusion ratios, oxygen and carbon dioxide metabolism quantities and the like are mathematically estimated, the set value of the model parameters is estimated according to a patient information database, the model can well simulate the gas exchange condition in a patient body, and the initial physiological state of different patients to be ventilated and the physiological state which is constantly changed in the ventilation treatment process can be simulated according to certain rules.
The patient information database includes doctor diagnosis and treatment information, basic information, physiological state information and disease information for different patients. The basic information of the patient includes but is not limited to the name, sex, age, height and weight of the patient; the patient physiological state comprises but is not limited to physiological state information such as blood oxygen saturation, respiratory rate, end-tidal carbon dioxide concentration, tidal volume, respiratory function index and acid-base equilibrium state index of the patient during ventilation; the patient condition information includes but is not limited to information such as a disease from which the patient has; the doctor diagnosis and treatment information includes but is not limited to ventilation decision and the like of the corresponding patient.
The mechanical ventilation simulation gas circuit mainly comprises a ventilation loop and a controller, is used for simply simulating ventilation of a human body physiological model, and is built based on a Simulink software platform.
The controller is used for analog control of the fuzzy neural network control algorithm, and the ventilation loop is used for being connected with a human body physiological model to carry out analog ventilation. The human body physiological model can simulate and set physiological states of different patients during ventilation treatment according to data transmitted by the state sensing part, after the mechanical ventilation simulation gas circuit receives state parameters simulated by the human body physiological model, the designed fuzzy neural network control algorithm in the controller is used for simulating ventilation of the human body physiological model, and the physiological state of the human body physiological model can be correspondingly adjusted and changed after the human body physiological model receives simulated ventilation, so that a dynamic mechanical ventilation simulation process is realized.
The training model is used as real-time simulation contrast of ventilation, a simulation ventilation result can be continuously output in the process of ventilation simulation, and the simulation ventilation result can guide the specific application of a real ventilation decision so as to ensure that a patient can carry out safe and effective mechanical ventilation treatment.
The Codesys control software platform is used for packaging a fuzzy neural network control algorithm into a functional block to form an intelligent control program, then acquiring patient state perception data through an EtherCAT communication environment, inputting the state perception data into the control program as an input value, and finally obtaining a ventilation parameter setting result as a ventilation decision, wherein the ventilation decision result can judge whether to output according to a simulated ventilation result of a training model, if an abnormal result occurs, an alarm is selected, if the abnormal result does not occur in the simulated ventilation, the decision result is selected to be output to control and adjust a currently running respirator or displayed to a current medical care worker in charge of respirator adjustment through a human-computer interaction interface, and intelligent automatic adjustment of the respirator or auxiliary adjustment of the medical care worker is realized.
Example two
The embodiment provides a ventilator intelligent control decision system, which comprises:
the data acquisition module is configured to acquire various physiological state parameters of a patient and the operating state parameters of a breathing machine;
the data processing module is configured to calculate change values and transformation rates of different parameters within a certain time based on various physiological state parameters of the patient and the operating state parameters of the breathing machine;
the breathing simulation training module is configured to combine the change values and the change rates of different parameters within a certain time and various current physiological state parameters of the patient, and perform simulation training of breathing machine ventilation decision based on a trained fuzzy neural network control model;
the breath control execution module is configured to obtain a simulated ventilation parameter setting result and judge whether the simulated ventilation parameter setting result is abnormal;
if not, outputting the setting result of the simulated ventilation parameters to the respirator; otherwise, alarming.
The modules are the same as the corresponding steps in the implementation example and application scenarios, but are not limited to the disclosure of the first embodiment. It should be noted that the modules described above as part of a system may be implemented in a computer system such as a set of computer-executable instructions.
In the foregoing embodiments, the descriptions of the embodiments have different emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
The proposed system can be implemented in other ways. For example, the above-described system embodiments are merely illustrative, and for example, the division of the above-described modules is merely a logical functional division, and in actual implementation, there may be another division, for example, a plurality of modules may be combined or may be integrated into another system, or some features may be omitted, or not executed.
EXAMPLE III
The present embodiment provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps in a method for intelligent control decision-making for a ventilator as described in the first embodiment above.
Example four
The present embodiment provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the program, the steps in an intelligent control decision method for a ventilator as described in the above embodiment are implemented.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive changes in the technical solutions of the present invention.
Claims (10)
1. An intelligent control decision method for a breathing machine is characterized by comprising the following steps:
acquiring various physiological state parameters of a patient and operating state parameters of a breathing machine;
calculating the change values and the transformation rates of different parameters within a certain time based on various physiological state parameters of the patient and the operating state parameters of the breathing machine;
the simulation training of the ventilation decision of the breathing machine is carried out on the basis of the trained fuzzy neural network control model by combining the change values and the change rates of different parameters within a certain time and the current physiological state parameters of the patient;
obtaining a simulated ventilation parameter setting result, and judging whether the simulated ventilation parameter setting result is abnormal;
if the abnormal condition exists, alarming; and if not, outputting the simulation ventilation parameter setting result to the respirator.
2. The method for intelligently controlling and deciding the ventilation of a ventilator according to claim 1, wherein the ventilation decision of the ventilator is formed by:
determining the mechanical ventilation treatment target of the patient according to the height, weight, age, sex and disease information of the patient;
selecting an optimal ventilation mode according to the mechanical ventilation treatment target, determining the ventilation state parameters of the patient and the breathing machine to be monitored in the mode, and using the parameters as parameter bases for ventilation state perception;
determining the corresponding relation between ventilation state parameters and breathing machine parameter adjustment settings under the guidance of an expert knowledge base to form mechanical ventilation rules;
based on the mechanical ventilation rule, training is carried out by utilizing a fuzzy neural network to obtain a ventilation decision of the breathing machine.
3. The method of claim 2, wherein the goal of mechanical ventilation therapy is to set a value and a time for which an oxygenation parameter represented by a blood oxygen saturation level and a ventilation parameter represented by a carbon dioxide partial pressure of the patient need to be reached under mechanical ventilation therapy.
4. The method for intelligent ventilator control decision-making according to claim 2, wherein the training using the fuzzy neural network based on the mechanical ventilation rule to obtain the ventilator ventilation decision-making comprises:
based on a mechanical ventilation rule, generating a mechanical ventilation fuzzy control rule through the fuzzy logic algorithm structure determination of a fuzzy system and the ventilation rule fuzzification processing process;
training a fuzzy neural network based on the mechanical ventilation fuzzy control rule to obtain a trained fuzzy neural network control model;
and controlling the mechanical ventilation simulation gas circuit model by using the trained fuzzy neural network control model to obtain the ventilation decision of the breathing machine.
5. The method as claimed in claim 4, wherein the training of the fuzzy neural network based on the mechanical ventilation fuzzy control rule to obtain the trained fuzzy neural network control model comprises:
simulating membership function representing fuzzy sets of different input states in a fuzzy mechanical control rule by using the neurons to obtain fuzzy neurons;
receiving the information fuzzified by the fuzzy neuron through the WTA neuron, and comparing fuzzy quantities of various input states to determine an output state;
determining the number of layers of a neural network and the number of neurons on each layer according to mechanical ventilation rules of different patients to obtain a fuzzy neural network control model;
and respectively training the constructed fuzzy neural network control model according to the physiological state parameters of the typical patient and the operating parameters of the breathing machine to obtain the trained fuzzy neural network control model.
6. The intelligent control decision-making method for the breathing machine according to claim 1, wherein the physiological state parameters of the patient comprise blood gas analysis results of the patient and breathing physiological data of the patient;
the blood gas analysis result of the patient refers to the clinical application of a blood gas analyzer to carry out blood gas analysis and detection on the patient so as to obtain various respiratory function indexes and acid-base balance state indexes of the patient.
7. The method of claim 1, wherein the ventilator adjusts the operating parameters based on simulated ventilation parameter settings.
8. A ventilator intelligence control decision making system, comprising:
the data acquisition module is configured to acquire various physiological state parameters of a patient and the operating state parameters of a breathing machine;
the data processing module is configured to calculate change values and transformation rates of different parameters within a certain time based on various physiological state parameters of the patient and the operating state parameters of the breathing machine;
the breathing simulation training module is configured to combine the change values and the change rates of different parameters within a certain time and various current physiological state parameters of the patient, and perform simulation training of breathing machine ventilation decision based on a trained fuzzy neural network control model;
the breath control execution module is configured to obtain a simulated ventilation parameter setting result and judge whether the simulated ventilation parameter setting result is abnormal;
if not, outputting the setting result of the simulated ventilation parameters to the respirator; otherwise, alarming.
9. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of a method for intelligent control decision-making of a breathing apparatus according to any one of claims 1-7.
10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps in a method of intelligent control decision making for a ventilator as claimed in any one of claims 1-7.
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