Intelligent monitoring system of surgical intensive care unit based on Internet of Things

Abstract

The invention belongs to the technical field of intensive care unit intelligent monitoring, and provides a surgical intensive care unit intelligent monitoring system based on the Internet of Things, which comprises a physiological data acquisition module, a data processing classification control system, a remote information display processing device capable of being carried by medical staff, and a display, the system further comprises a cross-platform data synchronization and sharing module. Through comprehensive acquisition of the physiological data acquisition module, classification processing and threshold alarm of the data processing classification control system, real-time information display of the remote information display processing device and seamless connection of the cross-platform data synchronization and sharing module, comprehensive monitoring of the physical condition of a patient is realized; the timeliness, the accuracy, the convenience and the efficiency of medical services are improved, and meanwhile, the accuracy and the effectiveness of monitoring are further improved through continuous learning and optimization of the adaptive learning and optimization module.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent monitoring in intensive care units, and in particular to an intelligent monitoring system for surgical intensive care units based on the Internet of Things. Background Art

[0002] In the existing surgical intensive care unit environment, medical staff need to monitor the patient's physiological data in real time to ensure that abnormal conditions of the patient can be discovered and handled in a timely manner; however, traditional monitoring systems often have problems such as inaccurate data collection, untimely data processing, and medical staff's inability to obtain patient information anytime and anywhere, which seriously affects the efficiency of medical services and the patient's treatment effect.

[0003] Specifically, traditional monitoring systems can usually only collect limited physiological data, such as heart rate, blood pressure, etc., and cannot fully reflect the patient's physical condition; at the same time, delays in data processing and transmission also cause medical staff to be unable to obtain the patient's latest information in a timely manner, thus missing the best time for treatment.

[0004] To this end, those skilled in the art have proposed an intelligent monitoring system for surgical intensive care units based on the Internet of Things to solve the problems raised by the background technology. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides an intelligent monitoring system for a surgical intensive care unit based on the Internet of Things, so as to solve the problem that the monitoring system in the prior art can usually only collect limited physiological data, such as heart rate, blood pressure, etc., but cannot fully reflect the patient's physical condition; at the same time, the delay in data processing and transmission also causes medical staff to be unable to obtain the latest information of the patient in time, thereby missing the best treatment opportunity and other problems.

[0006] An intelligent monitoring system for surgical intensive care units based on the Internet of Things, including a physiological data acquisition module, a data processing and classification control system, a remote information display processing device and a display that can be carried by medical staff;

[0007] The physiological data collection module is used to collect various physiological data information of the guardian, including electroencephalogram, electrocardiogram, respiratory data, arterial pacing data, body fluid data, blood and urine dialysis data, etc.

[0008] The data processing classification control system is used to classify the collected physiological data information, set the thresholds of the physiological data information of each type of guardian in important data information, ordinary data information and minor data information, and when the physiological data detector detects that the physiological data information of the guardian exceeds the preset threshold, the control directly alarms the remote information display processing device;

[0009] The remote information display processing device is a mobile phone or a wearable device, which is connected to the data processing classification control system through the Internet of Things to receive and display the physiological data information and abnormal alarm information of the guardian;

[0010] The remote information display processing device is a mobile phone or a wearable device, which is connected to the data processing classification control system through the Internet of Things to receive and display the physiological data information and abnormal alarm information of the guardian;

[0011] It also includes: a cross-platform data synchronization and sharing module, which enables the intelligent monitoring system in the intensive care unit to be seamlessly connected with other information systems of the hospital (including electronic medical record systems, medical image storage and transmission systems); medical staff can view the patient's monitoring data and imaging materials in real time on any terminal device (including computers, mobile phones, tablets), thereby improving the efficiency and convenience of medical services.

[0012] Preferably, the important data information classified in the data processing classification control system includes the electroencephalogram and electrocardiogram information of the guardian; the ordinary data information includes respiratory data and arterial pacing data; and the minor data information includes body fluid data and blood and urine dialysis data.

[0013] Preferably, the data processing classification control system also includes a breathing asynchronous adjustment system and an infusion pump adjustment system, which are used to perform automatic adjustments based on the physiological data information of the guardian.

[0014] Preferably, the data processing classification control system also includes a precise drug management module, which automatically calculates and recommends appropriate drug dosage and administration time based on the patient's real-time vital signs data and disease prediction results; at the same time, the smart medicine cabinet and infusion pump are connected through the Internet of Things technology to realize automatic drug dispensing, administration and monitoring, thereby ensuring the accuracy and safety of drug use.

[0015] Preferably, the intelligent monitoring system also includes an adaptive learning and optimization module; this module can continuously learn and adapt to individual differences and changes in the patient's condition, automatically adjust monitoring parameters, warning settings and data analysis algorithms to improve the accuracy and effectiveness of monitoring; at the same time, through cooperation and communication with other medical institutions and scientific research institutions, knowledge sharing and continuous optimization of algorithms can be achieved.

[0016] Preferably, a GPS positioning system is provided on the remote information display processing device. When the command system detects that the positioning data of the GPS positioning system is in front of the bed of the monitored person, slight data information abnormalities of the monitored person are displayed on the remote guidance display through the Internet of Things.

[0017] Preferably, it also includes: a real-time image monitoring module, which uses a high-definition camera and image processing technology to capture the image information of patients in the intensive care unit in real time; combined with a machine learning algorithm, it performs intelligent analysis of the images, such as detecting the patient's activity status, facial expression changes, etc., to assist medical staff in judging the patient's consciousness and comfort.

[0018] A processor is configured to execute the above-mentioned smart monitoring system for surgical intensive care units based on the Internet of Things.

[0019] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned intelligent monitoring system for surgical intensive care units based on the Internet of Things.

[0020] Compared with the prior art, the present invention has the following beneficial effects:

[0021] 1. The present invention can comprehensively collect various physiological data information of patients through the physiological data acquisition module, including electroencephalogram, electrocardiogram, respiratory data, arterial pacing data, body fluid data, blood and urine dialysis data, etc., so as to realize comprehensive monitoring of the patient's physical condition;

[0022] 2. The present invention classifies the collected physiological data information through a data processing classification control system, and sets different data information thresholds. When it is detected that the patient's physiological data information exceeds the preset threshold, abnormal alarm information can be sent to medical staff in a timely manner, thereby improving the timeliness and accuracy of medical services;

[0023] 3. The present invention uses a remote information display processing device to enable medical staff to receive and display the patient's physiological data information and abnormal alarm information anytime and anywhere, thereby improving the convenience and flexibility of medical services;

[0024] 4. The present invention also includes a cross-platform data synchronization and sharing module, which can realize the seamless connection between the intelligent monitoring system in the intensive care unit and other information systems of the hospital, so that medical staff can view the patient's monitoring data and imaging materials in real time on any terminal device, further improving the efficiency and convenience of medical services;

[0025] 5. The present invention can continuously learn and adapt to the individual differences and changes in the condition of patients through the adaptive learning and optimization module, automatically adjust the monitoring parameters, early warning settings and data analysis algorithms, thereby improving the accuracy and effectiveness of monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 This is a framework diagram of the surgical intensive care unit intelligent monitoring system based on the Internet of Things of the present invention. DETAILED DESCRIPTION

[0027] The following embodiments of the present invention are described in further detail in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0028] Embodiment: The present invention provides an intelligent monitoring system for surgical intensive care units based on the Internet of Things, such as Figure 1 As shown, it includes a physiological data acquisition module, a data processing classification control system, a remote information display processing device and a display that can be carried by medical staff;

[0029] The physiological data collection module is used to collect various physiological data information of the guardian, including electroencephalogram, electrocardiogram, respiratory data, arterial pacing data, body fluid data, blood and urine dialysis data, etc.

[0030] The data processing classification control system is used to classify the collected physiological data information, set the thresholds of the physiological data information of each type of guardian in important data information, ordinary data information and minor data information, and when the physiological data detector detects that the physiological data information of the guardian exceeds the preset threshold, the control directly alarms the remote information display processing device;

[0031] The remote information display processing device is a mobile phone or a wearable device, which is connected to the data processing classification control system through the Internet of Things to receive and display the physiological data information and abnormal alarm information of the guardian;

[0032] The remote information display processing device is a mobile phone or a wearable device, which is connected to the data processing classification control system through the Internet of Things to receive and display the physiological data information and abnormal alarm information of the guardian;

[0033] It also includes: cross-platform data synchronization and sharing modules, which enable the intelligent monitoring system in the intensive care unit to be seamlessly connected with other information systems of the hospital (including electronic medical record system, medical image storage and transmission system); in this way, medical staff can view the patient's monitoring data and image materials in real time on any terminal device (including computers, mobile phones, tablets), improving the efficiency and convenience of medical services.

[0034] From the above, it can be seen that the present invention realizes comprehensive monitoring of the patient's physical condition through comprehensive collection of physiological data acquisition modules, classification processing and threshold alarm of data processing classification control system, real-time information display of remote information display processing device, and seamless connection of cross-platform data synchronization and sharing modules, thereby improving the timeliness, accuracy, convenience and efficiency of medical services. At the same time, through continuous learning and optimization of adaptive learning and optimization modules, the accuracy and effectiveness of monitoring are further improved.

[0035] Furthermore, in the physiological data acquisition module, the wavelet transform algorithm is used to pre-process the collected physiological data, remove noise and extract key features. The wavelet transform formula is as follows:

[0036]

[0037] Among them, f(t) is the original signal, ψ(t) is the wavelet function, a is the scale parameter, and b is the translation parameter.

[0038] From the above, we can see that by using the wavelet transform algorithm to preprocess the collected physiological data, the algorithm can effectively remove noise and extract key features, thereby improving the accuracy and reliability of the physiological data and providing a strong guarantee for subsequent data processing and analysis.

[0039] Furthermore, in the remote information display processing device, a threshold detection method is used to construct an abnormality detection model to monitor the patient's physiological data in real time, detect abnormalities in time and issue an early warning. The steps of the threshold detection method are: if the measured value x of a certain physiological data exceeds the preset threshold T, an early warning is triggered, that is, an alarm is issued when x>T.

[0040] Furthermore, the system also includes a health status assessment module, which includes building a health status assessment model based on a neural network algorithm to conduct real-time assessment of the patient's health status. The output formula of the neural network algorithm neuron is as follows:

[0041]

[0042] Among them, x i is the input feature, w i is the weight, b is the bias term, and f is the activation function.

[0043] From the above, we can see that by introducing the health status assessment module and building a health status assessment model based on the neural network algorithm, the patient's health status can be assessed in real time and accurately; at the same time, through this model, the system can comprehensively consider the patient's various physiological data, and use the powerful learning ability of the neural network to discover the potential correlation and rules between the data, so as to more accurately judge the patient's health status and provide strong decision-making support for medical staff.

[0044] Furthermore, the important data information classified in the data processing classification control system includes the electroencephalogram and electrocardiogram information of the guardian; the ordinary data information includes respiratory data and arterial pacing data; and the minor data information includes body fluid data and blood and urine dialysis data.

[0045] Furthermore, the data processing classification control system also includes a breathing asynchronous adjustment system and an infusion pump adjustment system, which are used to perform automatic adjustments based on the physiological data information of the guardian.

[0046] Furthermore, the data processing classification control system also includes a precise drug management module, which automatically calculates and recommends appropriate drug dosage and administration time based on the patient's real-time vital signs data and disease prediction results; that is, the pharmacokinetic model is used to calculate and recommend appropriate drug dosage and administration time. The pharmacokinetic model: the formula for the change in the drug's in vivo concentration is as follows:

[0047]

[0048] Where D is the dosage, k a is the absorption rate constant, k e is the elimination rate constant, V is the volume of distribution;

[0049] At the same time, the smart medicine cabinet and infusion pump are connected through the Internet of Things technology to realize automatic dispensing, administration and monitoring of drugs, ensuring the accuracy and safety of drug use.

[0050] From the above, we can see that combining the patient's real-time vital signs data and disease prediction results, using a pharmacokinetic model to calculate and recommend appropriate drug dosages and administration times can ensure personalized and precise drug use; through the pharmacokinetic model, the system can comprehensively consider the drug's absorption, distribution, metabolism and excretion processes, as well as the patient's individual differences and changes in the disease, so as to calculate the most suitable drug dosage and administration time for the patient, which helps to improve the effectiveness and safety of drug treatment.

[0051] Furthermore, the intelligent monitoring system also includes an adaptive learning and optimization module, which can continuously learn and adapt to individual differences and changes in the condition of patients, automatically adjust monitoring parameters, warning settings and data analysis algorithms to improve the accuracy and effectiveness of monitoring; at the same time, through cooperation and communication with other medical institutions and scientific research institutions, knowledge sharing and continuous optimization of algorithms can be achieved;

[0052] In the adaptive learning and optimization module, the reinforcement learning algorithm is used to enable the system to continuously learn and adapt to the individual differences and changes in the condition of patients, and automatically adjust the monitoring parameters, warning settings and data analysis algorithms. The objective function of reinforcement learning is expressed as maximizing the cumulative reward, that is:

[0053]

[0054] Where π is the strategy, γ is the discount factor, and r t is the reward obtained at time t.

[0055] From the above, we can see that in the intelligent monitoring system, the adaptive learning and optimization module can continuously learn and adapt to the individual differences and changes in the condition of patients by adopting the reinforcement learning algorithm, and automatically adjust the monitoring parameters, warning settings and data analysis algorithms; the beneficial effect of this module is that it can significantly improve the accuracy and effectiveness of monitoring, making the system more intelligent and personalized; at the same time, through cooperation and communication with other medical institutions and scientific research institutions, knowledge sharing and continuous optimization of algorithms are achieved, which further improves the adaptability and reliability of the system and provides patients with more accurate and efficient monitoring services.

[0056] Furthermore, a GPS positioning system is provided on the remote information display processing device. When the command system detects that the positioning data of the GPS positioning system is in front of the bed of the monitored person, the slightly abnormal data of the monitored person is displayed on the remote guidance display through the Internet of Things.

[0057] From the above, it can be seen that by setting up a GPS positioning system on the remote information display processing device of the monitored person, and when the command system detects that the positioning data is in front of the bed, the IoT remote guidance display will display the minor data information abnormalities of the monitored person. This design can promptly and accurately remind medical staff or family members of the patient's current abnormal condition; this targeted information display method helps medical staff to respond quickly and reduce potential risks, while also enhancing the sense of security of the patient's family members and improving the efficiency and effectiveness of overall monitoring.

[0058] Furthermore, it also includes: a real-time image monitoring module, which uses high-definition cameras and image processing technology to capture the image information of patients in the intensive care unit in real time; combined with machine learning algorithms, it performs intelligent analysis of the images, such as detecting the patient's activity status, expression changes, etc., to assist medical staff in judging the patient's consciousness and comfort level;

[0059] Specifically, the convolutional neural network (CNN) algorithm is introduced for image feature extraction, and its formula is as follows:

[0060] y=f(W·x+b);

[0061] Where x is the input image, W is the convolution kernel weight, b is the bias term, f is the activation function, and y is the extracted feature;

[0062] At the same time, the support vector machine (SVM) algorithm is combined for image classification and recognition, and its decision function can be expressed as:

[0063] sign(w T φ(x)+b)

[0064] Among them, φ(x) is the function that maps the input x to a high-dimensional space, and w and b are the SVM model parameters.

[0065] From the above, we can see that through high-definition cameras and advanced image processing technology, the image information of patients in the intensive care unit can be captured and analyzed in real time, providing medical staff with more intuitive and comprehensive patient monitoring data; combined with machine learning algorithms, the system can intelligently analyze the patient's activity status, facial expression changes, etc., to assist medical staff in accurately judging the patient's consciousness and comfort, so as to take necessary medical measures in time and improve the accuracy and effectiveness of intensive care.

[0066] Working principle: This system comprehensively collects various physiological data information of patients, such as electroencephalogram, electrocardiogram, respiratory data, etc. through the physiological data acquisition module; then, these data are sent to the data processing classification control system for classification and processing, and different data information thresholds are set; when it is detected that the patient's physiological data information exceeds the preset threshold, the system can promptly send abnormal alarm information to the remote information display processing device carried by medical staff; at the same time, the cross-platform data synchronization and sharing module ensures the seamless connection between the intelligent monitoring system in the intensive care unit and other information systems of the hospital, so that medical staff can view the patient's monitoring data and image materials in real time on any terminal device; in addition, the adaptive learning and optimization module can continuously learn and adapt to the individual differences and changes in the patient's condition, and automatically adjust the monitoring parameters, early warning settings and data analysis algorithms; and the real-time image monitoring module uses high-definition cameras and image processing technology to capture and analyze the patient's image information in real time, providing medical staff with more intuitive and comprehensive patient monitoring data; these modules work together to achieve comprehensive, real-time and accurate monitoring of the patient's physical condition.

[0067] The effects of the surgical intensive care unit intelligent monitoring system based on the Internet of Things in the embodiment and the traditional monitoring system (comparative example) are compared to obtain the following table:

[0068]

[0069]

[0070] It can be seen from the above table that the intelligent monitoring system of the surgical intensive care unit based on the Internet of Things has significant advantages over the traditional monitoring system in many aspects. It can monitor the patient's physical condition comprehensively, in real time and accurately, and improve the timeliness, accuracy, convenience and efficiency of medical services.

[0071] The embodiment of the present application provides an electronic device, which is applicable to the above-mentioned intelligent monitoring system for surgical intensive care units based on the Internet of Things, including:

[0072] Memory, used to protect computer programs and data;

[0073] Processor, used to run system programs.

[0074] The embodiment of the present application provides a computer storage medium, which is applicable to the above-mentioned surgical intensive care unit intelligent monitoring system based on the Internet of Things, and performs hierarchical confidentiality management on the above-mentioned system and data in accordance with confidentiality management requirements.

[0075] Those skilled in the art will appreciate that the embodiments of the present application may be provided as a system or a computer program product. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0076] The present application is described with reference to the flowcharts and / or block diagrams of the devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0077] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0078] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0079] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0080] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0081] Computer readable media include permanent and non-permanent, removable and non-removable media, and can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0082] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, commodity or device including the elements.

[0083] The embodiments of the present invention are provided for the purpose of illustration and description. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations of the present invention. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. An intelligent monitoring system for surgical intensive care units based on the Internet of Things, including a physiological data acquisition module, a data processing and classification control system, a remote information display processing device and a display that medical staff can carry with them, characterized in that: The physiological data acquisition module is used to collect various physiological data information of the guardian, including electroencephalogram, electrocardiogram, respiratory data, arterial pacing data, body fluid data, and blood and urine dialysis data; The data processing classification control system is used to classify the collected physiological data information, set thresholds of physiological data information of various types of guardians in important data information, ordinary data information and minor data information, and when the physiological data detector detects that the physiological data information of the guardian exceeds the preset threshold, control to directly alarm the remote information display processing device; The remote information display processing device is a mobile phone or a wearable device, which is connected to the data processing classification control system through the Internet of Things to receive and display the physiological data information and abnormal alarm information of the guardian; It also includes: a cross-platform data synchronization and sharing module, which enables the intelligent monitoring system in the intensive care unit to be seamlessly connected with other information systems in the hospital.

2. The intelligent monitoring system for surgical intensive care units based on the Internet of Things as claimed in claim 1, characterized in that: The important data information classified in the data processing classification control system includes the electroencephalogram and electrocardiogram information of the guardian; the ordinary data information includes respiratory data and arterial pacing data; and the minor data information includes body fluid data and blood and urine dialysis data.

3. The intelligent monitoring system for surgical intensive care units based on the Internet of Things as claimed in claim 1, characterized in that: The data processing classification control system also includes a breathing asynchronous adjustment system and an infusion pump adjustment system, which are used to perform automatic adjustments based on the physiological data information of the guardian.

4. The intelligent monitoring system for surgical intensive care units based on the Internet of Things as claimed in claim 1, characterized in that: The data processing classification control system also includes a precise drug management module.

5. The intelligent monitoring system for surgical intensive care units based on the Internet of Things as claimed in claim 1, characterized in that: The intelligent monitoring system also includes an adaptive learning and optimization module, which can continuously learn and adapt to individual differences and changes in the condition of patients, and automatically adjust monitoring parameters, early warning settings and data analysis algorithms.

6. The intelligent monitoring system for surgical intensive care units based on the Internet of Things as claimed in claim 1, characterized in that: The remote information display processing device is provided with a GPS positioning system. When the command system detects that the positioning data of the GPS positioning system is in front of the bed of the monitored person, the display is remotely guided to display the slightly abnormal data of the monitored person through the Internet of Things.

7. The intelligent monitoring system for surgical intensive care units based on the Internet of Things as claimed in claim 1, characterized in that: Also includes: The real-time image monitoring module uses high-definition cameras and image processing technology to capture real-time image information of patients in the intensive care unit.

8. A processor, characterized in that: The invention is configured to execute the surgical intensive care unit intelligent monitoring system based on the Internet of Things according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, the intelligent monitoring system for surgical intensive care units based on the Internet of Things as described in any one of claims 1 to 7 is implemented.

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