Alarm prompting system for infusion nursing

Abstract

The invention relates to the technical field of medical care, and discloses an alarm prompt system for infusion nursing, which comprises a liquid medicine monitoring module used for identifying the real-time infusion state of an infusion patient and setting a multi-dimensional physiological parameter detection network of the infusion patient; the abnormal correlation analysis module is used for identifying the risk level of the infusion patient; the alarm threshold setting module is used for determining the optimal infusion speed and the residual liquid medicine amount of the infusion patient and creating a grading early warning system of the infusion patient; the grading early warning module is used for generating personalized intervention suggestions of infusion patients; the data display module is used for displaying the real-time liquid medicine flow rate, the residual liquid medicine amount and personalized intervention suggestions of the infusion patient; and the alarm prompt module is used for outputting an alarm prompt result of the infusion patient in combination with the abnormal correlation analysis unit, the grading early warning system and the user interaction interface. The infusion nursing efficiency and safety can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of medical care, and particularly to an alarm prompt system for infusion care. Background Art

[0002] Infusion care refers to a series of professional nursing operations and monitoring behaviors during the intravenous infusion process of patients, specifically including the management of the entire infusion process, such as ensuring the correct connection and operation of the infusion device, observing the patient's physical reactions, monitoring the infusion speed and the remaining amount of the liquid medicine, etc., to ensure the safe and effective progress of the infusion treatment. In the field of modern medicine, infusion is one of the extremely important means of treating diseases. The intravenous infusion therapy allows the infusion needle to be directly inserted into the patient's vein, and it is used to input drugs into the blood vessels or for blood transfusion. The possibility of inpatients receiving infusion treatment through the vein is 60% to 80%. However, during the intravenous infusion process, when the liquid in the infusion bottle is about to run out, the liquid needs to be replaced in a timely manner. Especially when it comes to patients receiving chemotherapy by intravenous infusion, it is necessary to continuously monitor the patient's condition and replace the infusion bottle in a timely manner.

[0003] However, traditional infusion care mainly relies on manual observation and manual operation, which requires nursing staff to frequently check the patient's infusion situation and manually adjust the infusion speed. This method not only increases the time cost of nursing staff, but may also have an adverse impact on the patient's health due to untimely handling of infusion abnormalities and inaccurate control of the infusion speed, thereby resulting in low efficiency and safety of infusion care. Summary of the Invention

[0004] The present invention provides an alarm prompt system for infusion care, and its main purpose is to improve the efficiency and safety of infusion care.

[0005] To achieve the above object, an alarm prompt system for infusion care provided by the present invention includes: a liquid medicine monitoring module, an abnormal correlation analysis module, an alarm threshold setting module, a hierarchical early warning module, a data display module, and an alarm prompt module;

[0006] The liquid medicine monitoring module is used to obtain the infusion container and the infusion tube of the infusion patient, configure the liquid medicine monitor of the infusion patient based on the infusion container and the infusion tube, collect the historical infusion data of the infusion patient, and perform error compensation processing on the liquid medicine monitor according to the historical infusion data to obtain an optimized liquid medicine monitor;

[0007] The abnormal correlation analysis module is used to identify the real-time infusion state of the infusion patient according to the optimized liquid medicine monitor, set the multi-dimensional physiological parameter detection network of the infusion patient based on the real-time infusion state and the liquid medicine monitor, and construct the abnormal correlation analysis unit of the infusion patient according to the optimized liquid medicine monitor and the multi-dimensional physiological parameter detection network;

[0008] The alarm threshold setting module is used to identify the real-time liquid medicine flow rate and the remaining liquid medicine volume of the infusion tube based on the optimized liquid medicine monitor, determine the optimal infusion speed of the infusion patient according to the real-time liquid medicine flow rate and the real-time infusion state, and set the infusion alarm threshold of the infusion patient based on the optimal infusion speed, the remaining liquid medicine volume and the real-time infusion state;

[0009] The hierarchical early warning module is used to identify the risk level of the infusion patient according to the abnormal correlation analysis unit, create a hierarchical early warning system for the infusion patient based on the risk level and the infusion alarm threshold, and generate personalized intervention suggestions for the infusion patient according to the real-time infusion state and the hierarchical early warning system;

[0010] The data display module is used to construct a user interaction interface for the infusion patient based on the real-time liquid medicine flow rate, the remaining liquid medicine volume, the personalized intervention suggestions and the hierarchical early warning system;

[0011] The alarm prompt module is used to output the alarm prompt result of the infusion patient by combining the abnormal correlation analysis unit, the hierarchical early warning system and the user interaction interface.

[0012] Optionally, configuring the liquid medicine monitor for the infusion patient based on the infusion container and the infusion tube includes:

[0013] Identifying the infusion liquid medicine in the infusion container, and setting a liquid level sensing unit, a flow rate sensing unit and a pressure sensing unit for the infusion liquid medicine based on the infusion container and the infusion tube;

[0014] Constructing a real-time monitoring network for the infusion liquid medicine based on the liquid level sensing unit, the flow rate sensing unit and the pressure sensing unit;

[0015] Collecting multi-modal signal parameters of the infusion liquid medicine according to the real-time monitoring network;

[0016] Creating a multi-modal parameter controller for the infusion liquid medicine based on the multi-modal signal parameters;

[0017] Setting an abnormal alarm mechanism and a blockchain data storage unit for the infusion liquid medicine according to the multi-modal parameter controller;

[0018] Constructing a data sharing platform for the infusion patient based on the blockchain data storage unit;

[0019] Configure the liquid medicine monitor for the infusion patient in combination with the real-time monitoring network, the multi-modal parameter controller, the abnormal alarm mechanism, the blockchain data storage unit, and the data sharing platform.

[0020] Optionally, performing error compensation processing on the liquid medicine monitor according to the historical infusion data to obtain an optimized liquid medicine monitor, including:

[0021] Identify the error influence factors of the liquid medicine monitor according to the historical infusion data;

[0022] Determine the measurement error of the liquid medicine monitor based on the error influence factors;

[0023] Calculate the influence degree of the error influence factors on the measurement error;

[0024] Construct a real-time output confidence scoring mechanism for the liquid medicine monitor according to the influence degree and the historical infusion data;

[0025] Extract the sensor measurement data from the historical infusion data, and perform time series analysis on the sensor measurement data to obtain a time series analysis result;

[0026] Identify the long-term drift trend of the sensor of the liquid medicine monitor based on the time series analysis result;

[0027] Perform error compensation processing on the liquid medicine monitor by combining the influence degree, the real-time output confidence scoring mechanism, and the long-term drift trend of the sensor to obtain an optimized liquid medicine monitor.

[0028] Optionally, setting up a multi-dimensional physiological parameter detection network for the infusion patient based on the real-time infusion state and the liquid medicine monitor, including:

[0029] Identify the physiological response of the infusion patient based on the real-time infusion state;

[0030] Determine the type of monitored physiological signal of the infusion patient according to the physiological response, and collect the liquid medicine monitoring parameters of the liquid medicine monitor;

[0031] Construct a physiological signal correlation matrix for the infusion patient based on the type of monitored physiological signal and the liquid medicine monitoring parameters;

[0032] Divide the infusion stage of the infusion patient according to the liquid medicine monitoring parameters and the physiological response;

[0033] Create a stage perception network for the infusion patient based on the infusion stage;

[0034] Extract the error perception signals of the stage perception network according to the monitored physiological signal types;

[0035] Construct a fault tolerance processing unit of the stage perception network based on the error perception signals;

[0036] Set up a multi-dimensional physiological parameter detection network for the infusion patient by combining the physiological signal correlation matrix, the stage perception network, and the fault tolerance processing unit.

[0037] Optionally, constructing the physiological signal correlation matrix of the infusion patient based on the monitored physiological signal types and the liquid medicine monitoring parameters includes:

[0038] Calculate the correlation coefficients between the monitored physiological signal types and the liquid medicine monitoring parameters;

[0039] Construct a signal topology graph of the monitored physiological signal types and the liquid medicine monitoring parameters based on the correlation coefficients;

[0040] Extract the non-linear signal features of the monitored physiological signal types and the liquid medicine monitoring parameters according to the signal topology graph;

[0041] Extract the correlation index parameters of the monitored physiological signal types and the liquid medicine monitoring parameters based on the correlation coefficients;

[0042] Identify the interaction strength between the monitored physiological signal types and the liquid medicine monitoring parameters according to the correlation coefficients and the correlation index parameters;

[0043] Construct the physiological signal correlation matrix of the infusion patient based on the non-linear features and the interaction strength.

[0044] Optionally, constructing the abnormal correlation analysis unit of the infusion patient according to the optimized liquid medicine monitor and the multi-dimensional physiological parameter detection network includes:

[0045] Collect the infusion parameters and physiological index parameters corresponding to the optimized liquid medicine monitor and the multi-dimensional physiological parameter detection network, and identify the normal parameter ranges of the infusion parameters and the physiological index parameters;

[0046] Analyze the causal relationship between the infusion parameters and the physiological index parameters;

[0047] Create a causal relationship graph of the optimized liquid medicine monitor and the multi-dimensional physiological parameter detection network based on the causal relationship;

[0048] Identify the abnormal data points in the optimized liquid medicine monitor and the multi-dimensional physiological parameter detection network according to the normal parameter ranges;

[0049] Perform the relationship association processing between the abnormal data points and the causal relationship diagram to obtain an associated causal relationship diagram;

[0050] Based on the associated causal relationship diagram, identify the abnormal association patterns between the infusion parameters and the physiological index parameters;

[0051] According to the abnormal association patterns and the associated causal relationship diagram, construct an abnormal association analysis unit for the infusion patient.

[0052] Optionally, the determining the optimal infusion rate of the infusion patient according to the real-time liquid medicine flow rate and the real-time infusion state includes:

[0053] Based on the real-time infusion state, collect the basic medical parameters of the infusion patient, and extract the drip drugs of the infusion patient and their corresponding drug components;

[0054] According to the drug components, identify the pharmacokinetic characteristics of the drip drugs;

[0055] Based on the real-time liquid medicine flow rate and the pharmacokinetic characteristics, calculate the blood drug concentration in the body of the infusion patient;

[0056] According to the basic medical parameters, identify the disease characteristics of the infusion patient;

[0057] Based on the disease characteristics, determine the infusion goal of the infusion patient;

[0058] According to the disease characteristics and the infusion goal, set the target concentration range of the blood drug concentration;

[0059] Based on the basic medical parameters and the blood drug concentration, analyze the liquid medicine clearance ability of the infusion patient;

[0060] According to the pharmacokinetic characteristics, analyze the concentration change trend of the blood drug concentration;

[0061] According to the disease characteristics and the liquid medicine clearance ability, identify the physical condition of the infusion patient;

[0062] Combining the target concentration range, the concentration change trend and the physical condition, determine the optimal infusion rate of the infusion patient

[0063] Optionally, the identifying the risk level of the infusion patient according to the abnormal association analysis unit includes:

[0064] According to the abnormal association analysis unit, extract the abnormal association parameters of the infusion patient;

[0065] Set the risk assessment index of the infusion patient according to the abnormal correlation parameter;

[0066] Identify the risk status of the infusion patient based on the risk assessment index;

[0067] Analyze the evolution trend of the risk status according to the abnormal correlation analysis unit;

[0068] Monitor the real-time condition change of the infusion patient based on the evolution trend;

[0069] Set the adaptive evaluation threshold of the risk assessment index according to the real-time condition change;

[0070] Identify the risk level of the infusion patient based on the adaptive evaluation threshold and the risk assessment index.

[0071] Optionally, create a hierarchical early warning system for the infusion patient based on the risk level and the infusion alarm threshold, including:

[0072] Set the hierarchical early warning trigger condition for the infusion patient based on the risk level and the infusion alarm threshold;

[0073] Analyze the reason for the infusion abnormality of the infusion patient according to the infusion alarm threshold;

[0074] Identify the severity of the abnormality of the infusion patient based on the reason for the infusion abnormality;

[0075] Construct a threshold dynamic adjustment mechanism for the infusion alarm threshold according to the severity of the abnormality;

[0076] Set the hierarchical alarm mode for the infusion patient according to the risk level and the severity of the abnormality;

[0077] Create the hierarchical early warning system for the infusion patient by combining the hierarchical early warning trigger condition, the threshold dynamic adjustment mechanism and the hierarchical alarm mode.

[0078] Optionally, construct a user interaction interface for the infusion patient based on the real-time liquid medicine flow rate, the remaining liquid medicine volume, the personalized intervention suggestion and the hierarchical early warning system, including:

[0079] Extract the hierarchical early warning information of the infusion patient based on the hierarchical early warning system;

[0080] Generate a data display unit for the infusion patient by combining the hierarchical early warning information, the real-time liquid medicine flow rate, the remaining liquid medicine volume and the personalized intervention suggestion;

[0081] Set an information query entry for the infusion patient according to the data display unit;

[0082] Extract the user access instruction of the information query entry, and set the user access permission of the data display unit based on the user access instruction;

[0083] Create an interactive information feedback mechanism for the infusion patient according to the user access permission;

[0084] Construct a user interaction interface for the infusion patient by combining the data display unit, the information query entry and the interactive information feedback mechanism.

[0085] In the embodiments of the present invention, by configuring the liquid medicine monitor for the infusion patient, information such as the infusion speed, infusion volume, and remaining liquid medicine volume of the liquid medicine can be monitored in real time, realizing precise liquid medicine infusion management. And according to the historical infusion data, error compensation processing of the liquid medicine monitor is performed to obtain an optimized liquid medicine monitor, which can reduce errors caused by equipment precision limitations, environmental interference, or patient individual differences, and improve the stability of monitoring. Further, in the embodiments of the present invention, by setting up a multi-dimensional physiological parameter detection network for the infusion patient based on the real-time infusion state and the liquid medicine monitor, it can help medical staff understand the overall physical condition of the patient during the infusion process more comprehensively and accurately, and formulate personalized alarm thresholds and nursing plans. In the embodiments of the present invention, by constructing an abnormal correlation analysis unit for the infusion patient according to the optimized liquid medicine monitor and the multi-dimensional physiological parameter detection network, potential risks and abnormal situations can be identified more accurately, thereby providing more targeted alarm prompts. Further, in the embodiments of the present invention, by setting the infusion alarm threshold for the infusion patient based on the optimal infusion speed, the remaining liquid medicine volume, and the real-time infusion state, abnormal situations during the infusion process can be detected in a timely manner, improving the safety of nursing work, and helping nursing staff meet the infusion needs of each patient more precisely, and enhancing the overall nursing quality. In the embodiments of the present invention, by creating a hierarchical early warning system for the infusion patient based on the risk level and the infusion alarm threshold, patients can be clearly divided into different risk levels, enabling nursing staff to quickly identify patients who need key attention, avoiding waste of nursing resources, and improving the efficiency of nursing work. Further, in the embodiments of the present invention, by generating personalized intervention suggestions for the infusion patient according to the real-time infusion state and the hierarchical early warning system, the individual needs of the patient can be better met, avoiding nursing deficiencies or over-care caused by the limitations of alarm prompts, and reducing the probability of adverse events. In the embodiments of the present invention, by constructing a user interaction interface for the infusion patient based on the real-time liquid medicine flow rate, the remaining liquid medicine volume, the personalized intervention suggestions, and the hierarchical early warning system, it can help medical staff quickly formulate and implement precise nursing decisions, avoid the inapplicability brought by empirical or general decisions, and enhance the nursing effect. Finally, in the embodiments of the present invention, by combining the abnormal correlation analysis unit, the hierarchical early warning system, and the user interaction interface, the alarm prompt result for the infusion patient is output, which can detect potential risks in advance, quickly make response measures, improve work efficiency, and help medical staff accurately grasp the risk level, handle it in a timely manner, avoid over-warning or delayed warning, and ensure patient safety. Therefore, an alarm prompt system for infusion nursing provided by the embodiments of the present invention can improve the efficiency and safety of infusion nursing. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] Figure 1The functional module diagram of an alarm and reminder system for infusion care provided by an embodiment of the present invention;

[0087] Figure 2 The flowchart of a method for alarm and reminder in infusion care provided by an embodiment of the present invention;

[0088] The implementation, functional features, and advantages of the present invention will be further described with reference to the embodiments and the accompanying drawings. Specific embodiments

[0089] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0090] In addition, the sequence of steps in the following method embodiments is only an example and is not strictly limited.

[0091] In fact, the server device deployed by an alarm and reminder system for infusion care may be composed of one or more devices. The above-mentioned alarm and reminder system for infusion care can be implemented as: a service instance, a virtual machine, or a hardware device. For example, the above-mentioned alarm and reminder system for infusion care can be implemented as a service instance deployed on one or more devices in a cloud node. Briefly, the above-mentioned alarm and reminder system for infusion care can be understood as a software deployed on a cloud node, which is used to provide an alarm and reminder service for infusion care for each client. Or, the above-mentioned alarm and reminder system for infusion care can also be implemented as a virtual machine deployed on one or more devices in a cloud node. An application software for managing each client is installed in the virtual machine. Or, the above-mentioned alarm and reminder system for infusion care can also be implemented as a server composed of many identical or different types of hardware devices, and one or more hardware devices are set to provide an alarm and reminder service for infusion care for each client.

[0092] In terms of implementation form, an alarm and reminder system for infusion care and the client adapt to each other. That is, if the alarm and reminder system for infusion care is an application installed on a cloud service platform, the client is a client that establishes a communication connection with the application; or if the alarm and reminder system for infusion care is implemented as a website, the client is implemented as a web page; or if the alarm and reminder system for infusion care is implemented as a cloud service platform, the client is implemented as a small program in an instant messaging application.

[0093] Refer to Figure 1As shown in the figure, it is a functional module diagram of an alarm and reminder system for infusion care provided by an embodiment of the present invention.

[0094] The alarm and reminder system 100 for infusion care according to the present invention can be set in a cloud server. In terms of implementation form, it can be used as one or more service devices, or can be installed as an application on the cloud (such as a server for alarm and reminder of infusion care, a server cluster, etc.), or can also be developed into a website. According to the functions achieved, the alarm and reminder system 100 for infusion care includes a liquid medicine monitoring module 101, an abnormal correlation analysis module 102, an alarm threshold setting module 103, a hierarchical early warning module 104, a data display module 105, and an alarm and reminder module 106.

[0095] In the embodiment of the present invention, in the tracking of the alarm and reminder for infusion care, each of the above modules can be independently implemented and called with other modules. Here, the call can be understood as that a certain module can be connected to multiple modules of another type and provide corresponding services for the multiple modules it is connected to. In the alarm and reminder system for infusion care provided by the embodiment of the present invention, without modifying the program code, the applicable range of the alarm and reminder architecture for infusion care can be adjusted by adding modules and directly calling, so as to achieve cluster-level horizontal expansion, so as to achieve the purpose of quickly and flexibly expanding the alarm and reminder system for infusion care. In practical applications, the above modules can be set in the same device or different devices, or can also be set in virtual devices, such as service instances in a cloud server.

[0096] Next, in combination with specific embodiments, the respective components and specific working processes of the alarm and reminder system for infusion care will be described separately.

[0097] The liquid medicine monitoring module 101 is used to obtain the infusion container and infusion tube of the infusion patient, configure the liquid medicine monitor of the infusion patient based on the infusion container and the infusion tube, collect the historical infusion data of the infusion patient, and perform error compensation processing on the liquid medicine monitor according to the historical infusion data to obtain an optimized liquid medicine monitor.

[0098] In the embodiment of the present invention, by obtaining the infusion container and infusion tube of the infusion patient, a basis can be provided for the configuration of the liquid medicine monitor. The infusion patient refers to a patient who is receiving infusion treatment. The infusion container refers to a container for storing the infused liquid medicine, such as an infusion bottle. The infusion tube refers to a tube connecting the infusion container and the patient's vein.

[0099] Furthermore, in the embodiment of the present invention, by configuring a liquid medicine monitor for the infusion patient based on the infusion container and the infusion tube, information such as the infusion speed, infusion volume, and remaining liquid medicine volume of the liquid medicine can be monitored in real time, realizing precise liquid medicine infusion management. The liquid medicine monitor refers to a medical device for real-time monitoring of the state of liquid medicine during the infusion process.

[0100] As an embodiment of the present invention, the configuration of the liquid medicine monitor for the infusion patient based on the infusion container and the infusion tube includes:

[0101] Identifying the infused liquid medicine in the infusion container, and based on the infusion container and the infusion tube, setting a liquid level sensing unit, a flow rate sensing unit, and a pressure sensing unit for the infused liquid medicine;

[0102] Based on the liquid level sensing unit, the flow rate sensing unit, and the pressure sensing unit, constructing a real-time monitoring network for the infused liquid medicine;

[0103] According to the real-time monitoring network, collecting multi-modal signal parameters of the infused liquid medicine;

[0104] Based on the multi-modal signal parameters, creating a multi-modal parameter controller for the infused liquid medicine;

[0105] According to the multi-modal parameter controller, setting an abnormal alarm mechanism and a blockchain data storage unit for the infused liquid medicine;

[0106] Based on the blockchain data storage unit, constructing a data sharing platform for the infusion patient;

[0107] Combining the real-time monitoring network, the multi-modal parameter controller, the abnormal alarm mechanism, the blockchain data storage unit, and the data sharing platform, configuring the liquid medicine monitor for the infusion patient.

[0108] Among them, the infusion liquid medicine refers to the liquid medicine infused to the patient through the infusion container and the infusion tube. The liquid level sensing unit refers to a sensor device for detecting the remaining amount of the liquid medicine in the infusion container. The flow rate sensing unit refers to a sensor device for detecting the flow rate of the liquid medicine through the infusion tube. The pressure sensing unit refers to a sensor device for detecting the pressure change in the infusion tube. The real-time monitoring network refers to a monitoring system composed of the liquid level sensing unit, the flow rate sensing unit, and the pressure sensing unit, which is used to collect and transmit the liquid level, flow rate, and pressure data of the liquid medicine in real time. The multi-modal signal parameters refer to the signal data collected by the liquid level sensing unit, the flow rate sensing unit, and the pressure sensing unit, including the liquid level, flow rate, and pressure, etc. The multi-modal parameter controller refers to a control device for processing and analyzing the multi-modal signal parameters, which can adopt a microprocessor or an embedded system, and analyze and process the collected liquid level, flow rate, and pressure signals through algorithms. The abnormal alarm mechanism refers to a system for detecting and reporting abnormal situations during the infusion process. The blockchain data storage unit refers to a data storage system based on blockchain technology, which is used to securely and immutably store the data during the infusion process. The data sharing platform refers to a network platform based on blockchain technology, which is used to share the monitoring data of the infusion patients within the medical system.

[0109] Optionally, based on the infusion container and the infusion tube, the liquid level sensing unit of the infusion liquid medicine can be set up by using an optical sensor. The flow rate sensing unit of the infusion liquid medicine can be set up by using a flow sensor. The pressure sensing unit of the infusion liquid medicine can be set up by using a pressure sensor. Based on the multi-modal signal parameters, the multi-modal parameter controller of the infusion liquid medicine can be created by using a microcontroller unit, such as the ARM Cortex - M series microcontroller. According to the multi-modal parameter controller, the abnormal alarm mechanism of the infusion liquid medicine can be set up by using a multi-layer perceptron (MLP) neural network. According to the multi-modal parameter controller, the blockchain data storage unit of the infusion liquid medicine can be set up by using the distributed ledger technology of the blockchain. Based on the blockchain data storage unit, the data sharing platform of the infusion patient can be constructed by using a relational database, such as the Oracle database.

[0110] By collecting the historical infusion data of the infusion patient in the embodiment of the present invention, the parameter settings of the liquid medicine monitor can be optimized according to the patient's previous infusion situation. The historical infusion data refers to various relevant data records generated by the patient during previous infusion processes, such as liquid medicine information, monitoring data, medical staff operation records, patient physiological response data, etc.

[0111] Optionally, the historical infusion data of the infusion patient can be collected through the electronic medical record system.

[0112] Furthermore, in the embodiments of the present invention, by performing error compensation processing on the liquid medicine monitor according to the historical infusion data, an optimized liquid medicine monitor can be obtained, which can reduce errors caused by equipment precision limitations, environmental interference, or patient individual differences, and improve the stability of monitoring. The error compensation processing refers to the process of analyzing historical infusion data, identifying possible errors during the infusion process, and then correcting and compensating for these errors. The errors include environmental interference, sensor precision limitations, operation errors, etc.

[0113] As an embodiment of the present invention, the step of performing error compensation processing on the liquid medicine monitor according to the historical infusion data to obtain an optimized liquid medicine monitor includes:

[0114] Identifying the error influencing factors of the liquid medicine monitor according to the historical infusion data;

[0115] Determining the measurement error of the liquid medicine monitor based on the error influencing factors;

[0116] Calculating the influence degree of the error influencing factors on the measurement error;

[0117] Constructing a real-time output confidence scoring mechanism for the liquid medicine monitor according to the influence degree and the historical infusion data;

[0118] Extracting the sensor measurement data from the historical infusion data and performing time series analysis on the sensor measurement data to obtain a time series analysis result;

[0119] Identifying the long-term drift trend of the sensor of the liquid medicine monitor based on the time series analysis result;

[0120] Combining the influence degree, the real-time output confidence scoring mechanism, and the long-term drift trend of the sensor, and performing error compensation processing on the liquid medicine monitor to obtain an optimized liquid medicine monitor.

[0121] Among them, the error influencing factors refer to various factors that cause errors in the measurement results of the liquid medicine monitor during the infusion process, such as environmental interference. The measurement error refers to the difference between the actual measurement value and the true value of the liquid medicine monitor. The influence degree refers to the specific contribution size of each error influencing factor to the measurement error. The real-time output confidence scoring mechanism refers to a system for evaluating the reliability of the real-time output data of the liquid medicine monitor. The sensor measurement data refers to the original data collected from the sensor of the liquid medicine monitor. The time series analysis processing refers to the process of analyzing the sensor measurement data in chronological order. The long-term drift trend of the sensor refers to the long-term change direction and degree of the sensor measurement value over time.

[0122] Optionally, based on the historical infusion data, the error impact factor of the liquid medicine monitor can be identified through a decision tree model. Based on the error impact factor, the measurement error of the liquid medicine monitor can be determined using a multiple linear regression model. According to the degree of impact and the historical infusion data, the real-time output confidence score mechanism of the liquid medicine monitor can be constructed through a Bayesian algorithm. For example, based on Bayes' theorem, the distribution of measurement errors in different temperature ranges is statistically obtained from historical data to obtain the prior probability. When the current ambient temperature is monitored in real time, combined with this prior information, the posterior probability of the current measurement result being accurate is calculated and used as the confidence score. The time series analysis of the sensor measurement data can be implemented using the exponential smoothing method. The degree of influence of the error impact factor on the measurement error can be calculated through a linear regression model. Based on the time series analysis results, the long-term drift trend of the sensor of the liquid medicine monitor can be identified using a polynomial trend model.

[0123] The abnormal association analysis module 102 is configured to identify the real-time infusion status of the infusion patient according to the optimized liquid medicine monitor, set up a multi-dimensional physiological parameter detection network for the infusion patient based on the real-time infusion status and the liquid medicine monitor, and construct an abnormal association analysis unit for the infusion patient according to the optimized liquid medicine monitor and the multi-dimensional physiological parameter detection network.

[0124] In the embodiment of the present invention, by identifying the real-time infusion status of the infusion patient according to the optimized liquid medicine monitor, potential infusion problems can be discovered in advance, effectively reducing medical risks and ensuring the infusion safety of patients. The real-time infusion status refers to the dynamic information about the patient's infusion situation obtained through the optimized liquid medicine monitor during the infusion process, such as the infusion speed and the remaining amount of medicine.

[0125] Furthermore, in the embodiment of the present invention, by setting up a multi-dimensional physiological parameter detection network for the infusion patient based on the real-time infusion status and the liquid medicine monitor, it can help medical staff more comprehensively and accurately understand the overall physical condition of the patient during the infusion process, and formulate personalized alarm thresholds and nursing plans. The multi-dimensional physiological parameter monitoring network refers to a system architecture for real-time and continuous monitoring of multiple physiological parameters of infusion patients, such as blood pressure parameters and heart rate parameters.

[0126] As an embodiment of the present invention, setting up a multi-dimensional physiological parameter detection network for the infusion patient based on the real-time infusion status and the liquid medicine monitor includes:

[0127] Identifying the physiological reaction of the infusion patient based on the real-time infusion status;

[0128] Determine the type of monitored physiological signals of the infusion patient according to the physiological reaction, and collect the liquid medicine monitoring parameters of the liquid medicine monitor;

[0129] Construct the physiological signal correlation matrix of the infusion patient based on the type of monitored physiological signals and the liquid medicine monitoring parameters;

[0130] Divide the infusion stage of the infusion patient according to the liquid medicine monitoring parameters and the physiological reaction;

[0131] Create the stage perception network of the infusion patient based on the infusion stage;

[0132] Extract the misperception signals of the stage perception network according to the type of monitored physiological signals;

[0133] Construct the fault tolerance processing unit of the stage perception network based on the misperception signals;

[0134] Set the multi-dimensional physiological parameter detection network of the infusion patient by combining the physiological signal correlation matrix, the stage perception network and the fault tolerance processing unit.

[0135] Wherein, the physiological reaction refers to the change in the physiological state of the patient during the infusion process caused by the action of the drug, the change in the infusion speed or other physiological factors. The type of monitored signal refers to the types of physiological signals that need to be monitored during the infusion process, such as heart rate and blood oxygen saturation. The liquid medicine monitoring parameters refer to the data related to the infusion process obtained through the liquid medicine monitor, such as the infusion speed. The physiological signal correlation matrix refers to a data structure that integrates various different types of physiological signals and liquid medicine monitoring parameters. The infusion stage refers to different stages divided according to the time and physiological state during the infusion process, including the infusion start stage, the infusion middle stage, and the infusion end stage. The stage perception network refers to a network structure that can adjust its behavior and decision-making according to the information of a specific stage. For example, at the infusion start stage, the network may pay more attention to the infusion speed and the initial physiological reaction; at the infusion end stage, the network may pay more attention to the remaining liquid volume and the change in the physiological state of the patient. The misperception signal refers to an abnormal signal caused by sensor failure, signal interference or other reasons during the monitoring process. The fault tolerance processing unit refers to a system that can automatically detect and correct misperception signals.

[0136] Optionally, based on the infusion stage, the stage perception network of the infusion patient can be created using the Internet of Medical Things (IoMT) architecture. For example, using the IoMT architecture, a stage perception network including a perception layer, a network layer, a platform layer, and an application layer can be constructed. The perception layer collects physiological signals related to infusion and liquid medicine monitoring parameters through medical and health perception devices. The network layer transmits the data to the platform layer using wireless or wired communication technologies. The platform layer performs data management and analysis, and the application layer provides specific business applications such as intelligent infusion monitoring. According to the type of monitored physiological signals, the error perception signals of the stage perception network can be extracted through the Dynamic Discrete Period Transform (DPT) algorithm. Based on the error perception signals, the fault tolerance processing unit of the stage perception network can be constructed using the Resilio elastic fault tolerance system.

[0137] As another embodiment of the present invention, constructing the physiological signal correlation matrix of the infusion patient based on the type of monitored physiological signals and the liquid medicine monitoring parameters includes:

[0138] Calculating the correlation coefficient of the type of monitored physiological signals and the liquid medicine monitoring parameters;

[0139] Based on the correlation coefficient, constructing a signal topology graph of the type of monitored physiological signals and the liquid medicine monitoring parameters;

[0140] According to the signal topology graph, extracting the non-linear signal features of the type of monitored physiological signals and the liquid medicine monitoring parameters;

[0141] Based on the correlation coefficient, extracting the correlation index parameters of the type of monitored physiological signals and the liquid medicine monitoring parameters;

[0142] According to the correlation coefficient and the correlation index parameters, identifying the interaction intensity of the type of monitored physiological signals and the liquid medicine monitoring parameters;

[0143] Based on the non-linear features and the interaction intensity, constructing the physiological signal correlation matrix of the infusion patient.

[0144] Among them, the correlation coefficient refers to a value used to quantify the correlation between the monitored physiological signal type and the liquid medicine monitoring parameter, which can be determined by the Pearson correlation coefficient, and its value range is between -1 and 1. The closer the value is to 1 or -1, the stronger the linear correlation between the two signals. The signal topology diagram refers to a graphical representation used to display the relationship network between multiple signals or parameters. The non-linear signal feature refers to the signal characteristic that cannot be described by a simple linear method. For example, the arrhythmia characteristic in an electrocardiogram can be extracted by the fractal dimension method. The correlation index parameter refers to the parameter that has a significant correlation between the monitored physiological signal type and the liquid medicine monitoring parameter. For example, the infusion speed affects blood pressure or heart rate. The interaction strength refers to the degree of mutual influence between the monitored physiological signal type and the liquid medicine monitoring parameter.

[0145] Exemplarily, based on the correlation coefficient, the specific steps for constructing the signal topology diagram of the monitored physiological signal type and the liquid medicine monitoring parameter are as follows: Using different physiological signals in the monitored physiological signal type and different monitoring parameters in the liquid medicine monitoring parameter as nodes, and using the correlation coefficient between the monitored physiological signal type and the liquid medicine monitoring parameter as edges, construct the signal topology diagram of the monitored physiological signal type and the liquid medicine monitoring parameter.

[0146] In an alternative embodiment of the present invention, according to the correlation coefficient and the correlation index parameter, the following formula is used to identify the interaction strength between the monitored physiological signal type and the liquid medicine monitoring parameter: ;

[0147] Wherein, represents the interaction strength between the monitored physiological signal type and the liquid medicine monitoring parameter, B represents the monitored physiological signal type, represents the liquid medicine monitoring parameter, k represents the index of the liquid medicine monitoring parameter, and the value range can be 1 ≤ k ≤ n. t represents the time point when the monitored physiological signal type and the liquid medicine monitoring parameter change, T represents the total number of time points, represents the derivative symbol, represents the change rate of the monitored physiological signal type at time point t, represents the change rate of the liquid medicine monitoring parameter at time point t, represents the inverse of the covariance matrix.

[0148] It should be noted that in this application, includes multiple different parameters. For example, can represent the infusion speed, can represent the liquid medicine concentration. The formula By weighting the rates of change of physiological signals and liquid medicine parameters with the inverse of the covariance matrix, the correlation degree between the two can be highlighted, making the calculated interaction strength more accurately reflect the actual interaction relationship.

[0149] In the embodiment of the present invention, by constructing an abnormal correlation analysis unit for the infusion patient according to the optimized liquid medicine monitor and the multi-dimensional physiological parameter detection network, potential risks and abnormal conditions can be identified more accurately, so as to provide more targeted alarm prompts. The abnormal correlation analysis unit refers to a functional module for analyzing the abnormal correlation between liquid medicine monitoring data and multi-dimensional physiological parameters.

[0150] As an embodiment of the present invention, the construction of the abnormal correlation analysis unit for the infusion patient according to the optimized liquid medicine monitor and the multi-dimensional physiological parameter detection network includes:

[0151] Collect the infusion parameters and physiological index parameters corresponding to the optimized liquid medicine monitor and the multi-dimensional physiological parameter detection network, and identify the normal parameter ranges of the infusion parameters and the physiological index parameters;

[0152] Analyze the causal relationship between the infusion parameters and the physiological index parameters;

[0153] Based on the causal relationship, create a causal relationship diagram of the optimized liquid medicine monitor and the multi-dimensional physiological parameter detection network;

[0154] According to the normal parameter ranges, identify the abnormal data points in the optimized liquid medicine monitor and the multi-dimensional physiological parameter detection network;

[0155] Perform the relationship association processing between the abnormal data points and the causal relationship diagram to obtain an associated causal relationship diagram;

[0156] Based on the associated causal relationship diagram, identify the abnormal correlation patterns of the infusion parameters and the physiological index parameters;

[0157] According to the abnormal correlation patterns and the associated causal relationship diagram, construct the abnormal correlation analysis unit for the infusion patient.

[0158] Among them, the infusion parameters refer to various data related to the infusion process collected by the optimized liquid medicine monitor, including but not limited to the flow rate, concentration, temperature, total infusion volume, remaining volume, etc. of the liquid medicine. The physiological index parameters refer to various data on the physical physiological conditions of the infusion patient obtained through the multi-dimensional physiological parameter detection network, such as body temperature and respiratory rate. The normal parameter range refers to the reasonable numerical interval determined for the infusion parameters and physiological index parameters based on medical knowledge, clinical experience, and a large amount of statistical data. For example, the normal infusion speed range is 20 - 60 drops per minute. The causal relationship refers to the relationship of mutual influence and interaction between the infusion parameters and physiological index parameters. For example, too fast infusion speed will cause the patient's heart rate to increase and blood pressure to rise. The abnormal data point refers to the data value and its corresponding time point that exceed the normal parameter range among the infusion parameters or physiological index parameters collected from the optimized liquid medicine monitor and the multi-dimensional physiological parameter detection network. For example, if the patient's heart rate suddenly rises above 120 beats per minute and lasts for a period of time, this heart rate value can be regarded as an abnormal data point. The associated causal relationship graph refers to the graph obtained by associating and annotating the identified abnormal data points with the relevant nodes and edges in the causal relationship graph created based on the causal relationship between the infusion parameters and physiological index parameters. The abnormal association pattern refers to the abnormal phenomenon discovered by analyzing the abnormal data points and their associated causal relationships in the associated causal relationship graph. For example, when the liquid medicine concentration rises abnormally, the patient's heart rate and blood pressure will both show abnormal changes.

[0159] Optionally, the causal relationship between the infusion parameters and the physiological index parameters can be analyzed through a Bayesian network model. Based on the causal relationship, the causal relationship graph of the optimized liquid medicine monitor and the multi-dimensional physiological parameter detection network can be created using a drawing tool, such as the Visio tool. According to the normal parameter range, the abnormal data points in the optimized liquid medicine monitor and the multi-dimensional physiological parameter detection network can be identified through a clustering algorithm, such as the DBSCAN algorithm. The relationship association processing between the abnormal data points and the causal relationship graph can be realized using a community discovery algorithm, such as the Louvain algorithm. Based on the associated causal relationship graph, the abnormal association pattern between the infusion parameters and the physiological index parameters can be identified through a frequent pattern mining algorithm. For example, in the associated causal relationship graph, the parameter combinations of the infusion parameters and the physiological index parameters are regarded as item sets, and the support and confidence thresholds are set using the Apriori algorithm to find the frequent abnormal association patterns that meet the conditions from the item sets.

[0160] The alarm threshold setting module 103 is configured to identify the real-time liquid medicine flow rate and the remaining liquid medicine volume of the infusion tube based on the optimized liquid medicine monitor, determine the optimal infusion speed of the infusion patient according to the real-time liquid medicine flow rate and the real-time infusion state, and set the infusion alarm threshold of the infusion patient based on the optimal infusion speed, the remaining liquid medicine volume and the real-time infusion state.

[0161] In an embodiment of the present invention, by identifying the real-time liquid medicine flow rate and the remaining liquid medicine volume of the infusion tube based on the optimized liquid medicine monitor, it can help medical staff accurately understand the progress of the infusion. The real-time liquid medicine flow rate refers to the volume of liquid medicine passing through a cross-section of the infusion tube per unit time during the infusion process. For example, if the flow rate set by an infusion pump is 50 ml / h, it means that 50 milliliters of liquid medicine will enter the patient's body through the infusion tube per hour. The remaining liquid medicine volume refers to the volume of liquid medicine in the infusion bottle (bag) that has not been infused into the patient's body at a certain moment during the infusion process.

[0162] Furthermore, in an embodiment of the present invention, by determining the optimal infusion speed of the infusion patient according to the real-time liquid medicine flow rate and the real-time infusion state, it can more accurately identify the real infusion abnormalities, reduce the occurrence of false alarms and missed alarms, and enable medical staff to handle problems during the infusion process more timely and accurately. The optimal infusion speed refers to the infusion speed that is most suitable for the patient after comprehensive evaluation based on factors such as the patient's individual conditions and the characteristics of the infused drugs.

[0163] As an embodiment of the present invention, determining the optimal infusion speed of the infusion patient according to the real-time liquid medicine flow rate and the real-time infusion state includes:

[0164] Based on the real-time infusion state, collect the basic medical parameters of the infusion patient and extract the drip drugs of the infusion patient and their corresponding drug components;

[0165] According to the drug components, identify the pharmacokinetic characteristics of the drip drugs;

[0166] Based on the real-time liquid medicine flow rate and the pharmacokinetic characteristics, calculate the blood drug concentration in the infusion patient's body;

[0167] According to the basic medical parameters, identify the disease characteristics of the infusion patient;

[0168] Based on the disease characteristics, determine the infusion goal of the infusion patient;

[0169] According to the disease characteristics and the infusion goal, set the target concentration range of the blood drug concentration;

[0170] Analyze the drug clearance ability of the infusion patient based on the basic medical parameters and the blood drug concentration;

[0171] Analyze the concentration change trend of the blood drug concentration according to the pharmacokinetic characteristics;

[0172] Identify the physical condition of the infusion patient according to the disease characteristics and the drug clearance ability;

[0173] Combine the target concentration range, the concentration change trend and the physical condition to determine the optimal infusion rate for the infusion patient.

[0174] Among them, the basic medical parameters refer to the data that can reflect the basic physical condition and physiological characteristics of the infusion patient, including but not limited to age, gender, weight, height, liver and kidney function indexes. The drip drug refers to the pharmaceutical preparation given to the patient by intravenous infusion. The drug component refers to the pharmacologically active chemical component contained in the drip drug. For example, the main component of penicillin drugs is β-lactam compounds, which play an antibacterial role by inhibiting the synthesis of bacterial cell walls. The pharmacokinetic characteristics refer to the dynamic change rules of the processes of drug absorption, distribution, metabolism, excretion, etc. in the patient's body, such as the absorption rate and degree of the drug. The disease characteristics refer to the characteristics and attributes used to describe and identify specific diseases, such as the severity of the disease. The blood drug concentration refers to the concentration of the drug in the blood. The infusion target refers to the treatment effect expected to be achieved through infusion treatment determined according to the disease characteristics and treatment needs of the patient. The target concentration range refers to the specific concentration interval reached by the drug in the body to ensure that the drug exerts its therapeutic effect while avoiding toxicity or side effects. This range is determined according to the pharmacokinetic characteristics, pharmacodynamic characteristics of the drug and the specific situation of the patient. The drug clearance ability refers to the ability of the body to metabolize and excrete the drug input into the body. The concentration change trend refers to the change of the blood drug concentration over time, including whether the blood drug concentration rises, falls or remains stable, and the rate of change. The physical condition refers to the physical and mental health status of the patient during the infusion process, such as adverse reactions to the drug.

[0175] Optionally, according to the drug component, the pharmacokinetic characteristics of the drip drug can be identified through in vitro experiments. According to the basic medical parameters, the disease characteristics of the infusion patient can be identified through a logistic regression model. Based on the basic medical parameters and the blood drug concentration, the drug clearance ability of the infusion patient can be analyzed using liver and kidney function index detection techniques. For example, by detecting indicators such as liver enzymes (such as alanine aminotransferase, aspartate aminotransferase, etc.), creatinine, and blood urea nitrogen in the serum, abnormal function indicators of the liver and kidneys can be extracted, and the drug clearance ability of the patient can be judged using the abnormal function indicators.

[0176] In an alternative embodiment of the present invention, based on the real-time liquid medicine flow rate and the pharmacokinetic characteristics, the following formula is used to calculate the blood drug concentration in the infusion patient: ;

[0177] wherein, represents the blood drug concentration in the infusion patient, represents the real-time liquid medicine flow rate, represents the volume of drug distribution in the pharmacokinetic characteristics, represents the elimination rate constant of the drug, represents the initial drug concentration, represents up to the time point the total dose of the liquid medicine, represents the time from when the drug starts to enter the body to when the drug concentration is measured.

[0178] In the embodiment of the present invention, by setting the infusion alarm threshold for the infusion patient based on the optimal infusion speed, the remaining liquid medicine volume, and the real-time infusion state, abnormal situations during the infusion process can be detected in a timely manner, the safety of nursing work can be improved, and it can help nursing staff more accurately meet the infusion needs of each patient, thereby enhancing the overall nursing quality. The infusion alarm threshold refers to a critical value for triggering an abnormal alarm set during the infusion process according to the optimal infusion speed, the remaining liquid medicine volume of the patient, and the individual differences of the patient.

[0179] Optionally, based on the optimal infusion speed, the remaining liquid medicine volume, and the real-time infusion state, the infusion alarm threshold for the infusion patient can be set using an individualized threshold algorithm. For example, for children or the elderly, since their tolerance to the infusion speed is relatively poor, the deviation range of the infusion speed alarm threshold can be reduced to ±10%; for patients suffering from diseases of important organs such as the heart, liver, and kidneys, the alarm thresholds of the optimal infusion speed and the remaining liquid medicine volume are adjusted according to the severity of the disease.

[0180] The hierarchical early warning module 104 is configured to identify the risk level of the infusion patient according to the abnormal correlation analysis unit, create a hierarchical early warning system for the infusion patient based on the risk level and the infusion alarm threshold, and generate personalized intervention suggestions for the infusion patient according to the real-time infusion state and the hierarchical early warning system.

[0181] In the embodiment of the present invention, by identifying the risk level of the infusion patient according to the abnormal correlation analysis unit, personalized risk assessment can be performed according to the characteristics of the infusion and physiological data of the patient individual, thereby improving the safety of infusion nursing. The risk level refers to dividing the infusion risk into different risk levels according to the size and impact degree of the infusion risk.

[0182] As an embodiment of the present invention, the risk level of the infusion patient is identified by the abnormal correlation analysis unit, including:

[0183] Extract the abnormal correlation parameters of the infusion patient according to the abnormal correlation analysis unit;

[0184] Set the risk evaluation index of the infusion patient according to the abnormal correlation parameters;

[0185] Identify the risk status of the infusion patient based on the risk evaluation index;

[0186] Analyze the evolution trend of the risk status according to the abnormal correlation analysis unit;

[0187] Monitor the real-time condition changes of the infusion patient based on the evolution trend;

[0188] Set the adaptive evaluation threshold of the risk evaluation index according to the real-time condition changes;

[0189] Identify the risk level of the infusion patient based on the adaptive evaluation threshold and the risk evaluation index.

[0190] Among them, the abnormal correlation parameter refers to the parameter extracted from various relevant data of the infusion patient, which can reflect the abnormal situation occurring during the infusion process and is mutually related, such as the infusion speed, the frequency of the occurrence of drug adverse reaction symptoms. The risk evaluation index refers to the quantitative index established based on the abnormal correlation parameter for evaluating the risk degree of the infusion patient, such as the number of abnormal correlation parameters, the severity of the abnormality, the strength of the causal relationship of the association, etc. The risk status refers to different degrees of risk situations faced by the infusion patient. For example, when multiple risk evaluation indexes show abnormalities, the patient is in a high-risk state; if only a few indexes are mildly abnormal, the patient is in a low-risk state. The evolution trend refers to the trend of the risk status changing over time, such as the low-risk state evolving into the high-risk state. The real-time condition changes refer to the immediate change information of the patient's physical condition during the infusion process, including symptoms, signs, and real-time data of various physiological parameters, such as the change of vital signs. The adaptive evaluation threshold refers to the boundary value dynamically adjusted according to the real-time condition changes for judging whether the risk evaluation index reaches the dangerous level.

[0191] Optionally, based on the risk assessment indicators, the risk status of the infusion patient can be identified using the support vector machine algorithm. According to the abnormal association analysis unit, the evolution trend of the risk status can be analyzed by the moving average method. Based on the evolution trend, the real-time condition changes of the infusion patient can be monitored using the infusion monitoring system. According to the real-time condition changes, the adaptive evaluation threshold of the risk assessment indicators can be set through a neural network model. For example, based on a neural network with multiple hidden layers, the input layer receives various physiological index data of the patient. After complex calculations in the hidden layers, the output layer gives the risk status of the patient and the threshold range corresponding to each index.

[0192] Furthermore, in the embodiment of the present invention, by creating a hierarchical early warning system for the infusion patient based on the risk level and the infusion alarm threshold, the patients can be clearly divided into different risk levels, enabling nursing staff to quickly identify patients who need key attention, avoiding waste of nursing resources, and improving the efficiency of nursing work. The hierarchical early warning system refers to a system for classifying and managing and warning infusion patients according to the risk level of the patient and the infusion alarm threshold.

[0193] As an embodiment of the present invention, creating the hierarchical early warning system for the infusion patient based on the risk level and the infusion alarm threshold includes:

[0194] Based on the risk level and the infusion alarm threshold, set the hierarchical early warning trigger conditions for the infusion patient;

[0195] According to the infusion alarm threshold, analyze the reasons for the infusion abnormality of the infusion patient;

[0196] Based on the reasons for the infusion abnormality, identify the severity of the abnormality of the infusion patient;

[0197] According to the severity of the abnormality, construct a threshold dynamic adjustment mechanism for the infusion alarm threshold;

[0198] According to the risk level and the severity of the abnormality, set the hierarchical alarm mode for the infusion patient;

[0199] Combining the hierarchical early warning trigger conditions, the threshold dynamic adjustment mechanism and the hierarchical alarm mode, create the hierarchical early warning system for the infusion patient.

[0200] Among them, the hierarchical warning trigger condition refers to the rule for initiating different levels of warnings based on the patient's risk level and the infusion alarm threshold. For example, for patients with a high risk level, when the infusion rate exceeds 10% of the normal threshold, a first-level warning is triggered; while for patients with a low risk level, a second-level warning is triggered only when the infusion rate exceeds 20% of the normal threshold. The reason for infusion abnormality refers to various factors that cause relevant parameters during the infusion process to exceed or fall below the infusion alarm threshold. For example, a blocked infusion tube may cause the infusion pressure to increase and exceed the alarm threshold; a patient's allergy to the drug may trigger a series of physical reactions, resulting in abnormal vital sign parameters, which in turn affect the infusion-related parameters. The severity of the abnormality refers to the severity level of the abnormal situation that occurs during the infusion process. For example, an infusion abnormality caused by drug anaphylactic shock seriously threatens the patient's life and belongs to a severe abnormality. The threshold dynamic adjustment mechanism refers to a mechanism for flexibly adjusting the infusion alarm threshold according to the severity of the infusion abnormality. The hierarchical alarm method refers to a method for conveying the urgency of the patient's infusion status to medical staff according to the risk level of the infusion patient and the severity of the infusion abnormality. For example, for a situation with a low risk level and a relatively mild abnormality, the patient information and abnormal content can be displayed on the computer screen at the nurse station in a specific color or icon, accompanied by a soft reminder sound; while for a situation with a high risk level and a severe abnormality, a high-decibel alarm sound, a flashing warning light, and emergency notifications can be sent to relevant medical staff through multiple channels such as text messages and instant messaging software.

[0201] Optionally, according to the infusion alarm threshold, the reason for the infusion abnormality of the infusion patient can be analyzed using a fault tree analysis model. According to the severity of the abnormality, the threshold dynamic adjustment mechanism of the infusion alarm threshold can be constructed through a fuzzy logic algorithm.

[0202] By generating personalized intervention suggestions for the infusion patient according to the real-time infusion status and the hierarchical warning system in the embodiments of the present invention, the individual needs of the patient can be better met, the nursing deficiency or over-treatment caused by the limitations of the alarm prompt can be avoided, and the probability of adverse events can be reduced. The personalized intervention suggestion refers to targeted nursing suggestions formulated for different patients based on multiple factors such as the real-time infusion status, physical condition, and disease risk level of the infusion patient. For example, if the patient's puncture site shows redness, swelling, pain, etc., it is recommended to apply local hot compress or change the puncture site.

[0203] Optionally, according to the real-time infusion status and the hierarchical early warning system, the personalized intervention suggestions for the infusion patient can be generated using a long short-term memory network model. For example, the long short-term memory network model can learn the fluctuations of the patient's vital signs and the changes in infusion-related parameters over a period of time, so as to predict possible risks in advance and give corresponding prevention and intervention measures.

[0204] The data display module 105 is configured to construct a user interaction interface for the infusion patient based on the real-time liquid medicine flow rate, the remaining liquid medicine volume, the personalized intervention suggestions, and the hierarchical early warning system.

[0205] By constructing a user interaction interface for the infusion patient based on the real-time liquid medicine flow rate, the remaining liquid medicine volume, the personalized intervention suggestions, and the hierarchical early warning system in the embodiment of the present invention, it can help medical staff quickly formulate and implement accurate nursing decisions, avoid the inapplicability brought by empirical or general decisions, and improve the nursing effect. The user interaction interface refers to a platform for information interaction between medical staff, patients, and their families in the context of infusion care.

[0206] As an embodiment of the present invention, constructing the user interaction interface for the infusion patient based on the real-time liquid medicine flow rate, the remaining liquid medicine volume, the personalized intervention suggestions, and the hierarchical early warning system includes:

[0207] Extracting the hierarchical early warning information of the infusion patient based on the hierarchical early warning system;

[0208] Combining the hierarchical early warning information, the real-time liquid medicine flow rate, the remaining liquid medicine volume, and the personalized intervention suggestions to generate a data display unit for the infusion patient;

[0209] Setting an information query entry for the infusion patient according to the data display unit;

[0210] Extracting a user access instruction of the information query entry, and setting a user access permission for the data display unit based on the user access instruction;

[0211] Creating an interactive information feedback mechanism for the infusion patient according to the user access permission;

[0212] Combining the data display unit, the information query entry, and the interactive information feedback mechanism to construct the user interaction interface for the infusion patient.

[0213] Among them, the hierarchical warning information refers to the risk prompt information obtained by comprehensively evaluating the relevant data of the infusion patient (such as the liquid medicine flow rate, the remaining liquid medicine volume, the physiological indicators of the patient, etc.) according to a pre-set hierarchical warning system. The data display unit refers to a module that integrates various relevant information of the infusion patient and displays it in a specific manner. The information query entry refers to the channel through which users (such as medical staff) query the detailed information of the infusion patient. The user access instruction refers to the request operation information sent by the user to the system through the information query entry. For example, the user enters "View the infusion flow rate record of a certain patient yesterday" at the information query entry. The user access permission refers to the access and operation permissions for different information in the data display unit of the infusion patient set according to the user's role (such as doctor, nurse, pharmacist, etc.) and responsibilities. The interactive information feedback mechanism refers to a mechanism used to achieve two-way information transmission and feedback during the information interaction process between the user and the user interface. It allows users (such as medical staff) to operate on the information displayed by the system (such as confirming warning information, adjusting infusion parameters, etc.) and feedback the operation results to the system for recording and processing. For example, when the medical staff adjusts the infusion flow rate, the system will feedback "The information that the flow rate has been successfully adjusted."

[0214] Optionally, combining the hierarchical warning information, the real-time liquid medicine flow rate, the remaining liquid medicine volume, and the personalized intervention suggestions, the data display unit of the infusion patient can be generated using a data visualization library. According to the data display unit, the information query entry of the infusion patient can be set through HTML. For example, create a <input> label as an input box to allow the user to enter the patient ID or other query keywords, and then use a <button>The label serves as a query button. Based on the user access instruction, the user access permission of the data display unit can be set using OAuth integration technology. For example, a medical institution can perform OAuth integration with a specific medical platform and grant a doctor the permission to access detailed infusion data of patients according to the doctor's authentication information on the platform.

[0215] The alarm prompt module 106 is configured to output an alarm prompt result for the infusion patient in combination with the abnormal correlation analysis unit, the hierarchical early warning system, and the user interaction interface.

[0216] In the embodiment of the present invention, by combining the abnormal correlation analysis unit, the hierarchical early warning system, and the user interaction interface to output the alarm prompt result for the infusion patient, potential risks can be detected in advance, response measures can be taken quickly, work efficiency can be improved, and medical staff can be helped to accurately grasp the risk level, handle it in a timely manner, avoid over - warning or delayed warning, ensure patient safety. The alarm prompt result refers to a notification or warning message automatically generated by the system when an abnormal situation is detected during the infusion process in a monitoring system, device, or software, such as informing that the current infusion rate is 80 drops per minute, exceeding the normal range (40 - 60 drops per minute).

[0217] In the embodiments of the present invention, by configuring the liquid medicine monitor for the infusion patient, information such as the infusion speed, infusion volume, and remaining liquid medicine volume of the liquid medicine can be monitored in real time, realizing precise liquid medicine infusion management. And according to the historical infusion data, error compensation processing of the liquid medicine monitor is performed to obtain an optimized liquid medicine monitor, which can reduce errors caused by equipment precision limitations, environmental interference, or patient individual differences, and improve the stability of monitoring. Further, in the embodiments of the present invention, by setting up a multi-dimensional physiological parameter detection network for the infusion patient based on the real-time infusion status and the liquid medicine monitor, it can help medical staff understand the overall physical condition of the patient during the infusion process more comprehensively and accurately, and formulate personalized alarm thresholds and nursing plans. In the embodiments of the present invention, by constructing an abnormal correlation analysis unit for the infusion patient according to the optimized liquid medicine monitor and the multi-dimensional physiological parameter detection network, potential risks and abnormal situations can be identified more accurately, thereby providing more targeted alarm prompts. Further, in the embodiments of the present invention, by setting the infusion alarm threshold for the infusion patient based on the optimal infusion speed, the remaining liquid medicine volume, and the real-time infusion status, abnormal situations during the infusion process can be detected in a timely manner, improving the safety of nursing work, and helping nursing staff meet the infusion needs of each patient more precisely, and enhancing the overall nursing quality. In the embodiments of the present invention, by creating a hierarchical early warning system for the infusion patient based on the risk level and the infusion alarm threshold, patients can be clearly divided into different risk levels, enabling nursing staff to quickly identify patients who need key attention, avoiding waste of nursing resources, and improving the efficiency of nursing work. Further, in the embodiments of the present invention, by generating personalized intervention suggestions for the infusion patient according to the real-time infusion status and the hierarchical early warning system, the individual needs of the patient can be better met, avoiding nursing deficiencies or over-treatment caused by the limitations of alarm prompts, and reducing the probability of adverse events. In the embodiments of the present invention, by constructing a user interaction interface for the infusion patient based on the real-time liquid medicine flow rate, the remaining liquid medicine volume, the personalized intervention suggestions, and the hierarchical early warning system, it can help medical staff quickly formulate and implement precise nursing decisions, avoid the inapplicability caused by empirical or general decisions, and enhance the nursing effect. Finally, in the embodiments of the present invention, by combining the abnormal correlation analysis unit, the hierarchical early warning system, and the user interaction interface, the alarm prompt result for the infusion patient is output, which can detect potential risks in advance, quickly make response measures, improve work efficiency, and help medical staff accurately grasp the risk level, handle it in a timely manner, avoid over-warning or delayed warning, and ensure patient safety. Therefore, an alarm prompt system for infusion nursing provided by the embodiments of the present invention can improve the efficiency and safety of infusion nursing.

[0218] Such as Figure 2 As shown, it is a schematic flowchart of an alarm prompt method for infusion care provided by an embodiment of the present invention. In this embodiment, the alarm prompt method for infusion care includes:

[0219] Obtain the infusion container and infusion tube of the infusion patient. Based on the infusion container and the infusion tube, configure the liquid medicine monitor for the infusion patient, collect the historical infusion data of the infusion patient, and perform error compensation processing on the liquid medicine monitor according to the historical infusion data to obtain an optimized liquid medicine monitor;

[0220] According to the optimized liquid medicine monitor, identify the real-time infusion status of the infusion patient. Based on the real-time infusion status and the liquid medicine monitor, set up a multi-dimensional physiological parameter detection network for the infusion patient. According to the optimized liquid medicine monitor and the multi-dimensional physiological parameter detection network, construct an abnormal correlation analysis unit for the infusion patient;

[0221] Based on the optimized liquid medicine monitor, identify the real-time liquid medicine flow rate and remaining liquid medicine volume in the infusion tube. According to the real-time liquid medicine flow rate and the real-time infusion status, determine the optimal infusion speed of the infusion patient. Based on the optimal infusion speed, the remaining liquid medicine volume and the real-time infusion status, set the infusion alarm threshold for the infusion patient;

[0222] According to the abnormal correlation analysis unit, identify the risk level of the infusion patient. Based on the risk level and the infusion alarm threshold, create a hierarchical early warning system for the infusion patient. According to the real-time infusion status and the hierarchical early warning system, generate personalized intervention suggestions for the infusion patient;

[0223] Based on the real-time liquid medicine flow rate, the remaining liquid medicine volume, the personalized intervention suggestions and the hierarchical early warning system, construct a user interaction interface for the infusion patient;

[0224] Combine the abnormal correlation analysis unit, the hierarchical early warning system and the user interaction interface to output the alarm prompt result of the infusion patient.

[0225] In several embodiments provided by the present invention, it should be understood that the provided systems and methods can be implemented in other ways. For example, the system embodiments described above are only illustrative. For example, the division of the modules is only a logical function division, and there can be other division methods in actual implementation.

[0226] In addition, in each embodiment of the present invention, each functional module can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware, or can be implemented in the form of a combination of hardware and software functional modules by those skilled in the art.

[0227] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.< / button>

Claims

1. An alarm prompt system for infusion care, characterized in that, The described alarm and prompt system for infusion nursing includes: a liquid medicine monitoring module, an abnormal correlation analysis module, an alarm threshold setting module, a hierarchical early warning module, a data display module, and an alarm prompt module; The liquid medicine monitoring module is used to obtain the infusion container and infusion tube of the infusion patient, configure the liquid medicine monitor for the infusion patient based on the infusion container and the infusion tube, collect the historical infusion data of the infusion patient, and perform error compensation processing on the liquid medicine monitor according to the historical infusion data to obtain an optimized liquid medicine monitor; The abnormal correlation analysis module is used to identify the real-time infusion status of the infusion patient according to the optimized liquid medicine monitor, set up a multi-dimensional physiological parameter detection network for the infusion patient based on the real-time infusion status and the liquid medicine monitor, and construct an abnormal correlation analysis unit for the infusion patient according to the optimized liquid medicine monitor and the multi-dimensional physiological parameter detection network; The alarm threshold setting module is used to identify the real-time liquid medicine flow rate and remaining liquid medicine volume in the infusion tube based on the optimized liquid medicine monitor, determine the optimal infusion speed of the infusion patient according to the real-time liquid medicine flow rate and the real-time infusion status, and set the infusion alarm threshold for the infusion patient based on the optimal infusion speed, the remaining liquid medicine volume, and the real-time infusion status; The hierarchical early warning module is used to identify the risk level of the infusion patient according to the abnormal correlation analysis unit, create a hierarchical early warning system for the infusion patient based on the risk level and the infusion alarm threshold, and generate personalized intervention suggestions for the infusion patient according to the real-time infusion status and the hierarchical early warning system; The data display module is used to construct a user interaction interface for the infusion patient based on the real-time liquid medicine flow rate, the remaining liquid medicine volume, the personalized intervention suggestions, and the hierarchical early warning system; The alarm prompt module is used to output the alarm prompt result of the infusion patient by combining the abnormal correlation analysis unit, the hierarchical early warning system, and the user interaction interface.

2. The alarm prompt system for infusion care according to claim 1, characterized in that, Configuring the liquid medicine monitor for the infusion patient based on the infusion container and the infusion tube includes: Identifying the infused liquid medicine in the infusion container and setting up a liquid level sensing unit, a flow rate sensing unit, and a pressure sensing unit for the infused liquid medicine based on the infusion container and the infusion tube; Constructing a real-time monitoring network for the infused liquid medicine based on the liquid level sensing unit, the flow rate sensing unit, and the pressure sensing unit; Collecting multi-modal signal parameters of the infused liquid medicine according to the real-time monitoring network; Creating a multi-modal parameter controller for the infused liquid medicine based on the multi-modal signal parameters; Setting up an abnormal alarm mechanism and a blockchain data storage unit for the infused liquid medicine according to the multi-modal parameter controller; Constructing a data sharing platform for the infusion patient based on the blockchain data storage unit; Configuring the liquid medicine monitor for the infusion patient by combining the real-time monitoring network, the multi-modal parameter controller, the abnormal alarm mechanism, the blockchain data storage unit, and the data sharing platform.

3. The alarm and prompt system for infusion care according to claim 1, wherein Performing error compensation processing on the liquid medicine monitor according to the historical infusion data to obtain an optimized liquid medicine monitor, including: Identifying the error impact factors of the liquid medicine monitor according to the historical infusion data; Determining the measurement error of the liquid medicine monitor based on the error impact factors; Calculating the influence degree of the error impact factors on the measurement error; Constructing a real-time output confidence scoring mechanism for the liquid medicine monitor according to the influence degree and the historical infusion data; Extracting the sensor measurement data from the historical infusion data and performing time series analysis on the sensor measurement data to obtain a time series analysis result; Identifying the long-term drift trend of the sensor of the liquid medicine monitor based on the time series analysis result; Combining the influence degree, the real-time output confidence scoring mechanism and the long-term drift trend of the sensor, performing error compensation processing on the liquid medicine monitor to obtain an optimized liquid medicine monitor.

4. The alarm prompt system for infusion care according to claim 1, wherein Setting a multi-dimensional physiological parameter detection network for the infusion patient based on the real-time infusion state and the liquid medicine monitor, including: Identifying the physiological reactions of the infusion patient based on the real-time infusion state; Determining the types of monitored physiological signals of the infusion patient according to the physiological reactions and collecting the liquid medicine monitoring parameters of the liquid medicine monitor; Constructing a physiological signal correlation matrix for the infusion patient based on the types of monitored physiological signals and the liquid medicine monitoring parameters; Dividing the infusion stages of the infusion patient according to the liquid medicine monitoring parameters and the physiological reactions; Creating a stage perception network for the infusion patient based on the infusion stages; Extracting the misperception signals of the stage perception network according to the types of monitored physiological signals; Constructing a fault tolerance processing unit for the stage perception network based on the misperception signals; Setting a multi-dimensional physiological parameter detection network for the infusion patient by combining the physiological signal correlation matrix, the stage perception network and the fault tolerance processing unit.

5. The alarm and reminder system for infusion care according to claim 4, wherein, Constructing a physiological signal correlation matrix for the infusion patient based on the types of monitored physiological signals and the liquid medicine monitoring parameters, including: Calculating the correlation coefficients between the types of monitored physiological signals and the liquid medicine monitoring parameters; Constructing a signal topology graph of the types of monitored physiological signals and the liquid medicine monitoring parameters based on the correlation coefficients; Extracting the non-linear signal features of the types of monitored physiological signals and the liquid medicine monitoring parameters according to the signal topology graph; Extracting the correlation index parameters of the types of monitored physiological signals and the liquid medicine monitoring parameters based on the correlation coefficients; Identifying the interaction intensity between the types of monitored physiological signals and the liquid medicine monitoring parameters according to the correlation coefficients and the correlation index parameters; Constructing a physiological signal correlation matrix for the infusion patient based on the non-linear features and the interaction intensity.

6. The alarm and reminder system for infusion care according to claim 1, wherein, Constructing an abnormal correlation analysis unit for the infusion patient according to the optimized liquid medicine monitor and the multi-dimensional physiological parameter detection network, including: Collect the infusion parameters and physiological index parameters corresponding to the optimized liquid medicine monitor and the multi-dimensional physiological parameter detection network, and identify the normal parameter ranges of the infusion parameters and the physiological index parameters; Analyze the causal relationship between the infusion parameters and the physiological index parameters; Based on the causal relationship, create a causal relationship diagram of the optimized liquid medicine monitor and the multi-dimensional physiological parameter detection network; According to the normal parameter ranges, identify the abnormal data points in the optimized liquid medicine monitor and the multi-dimensional physiological parameter detection network; Perform relationship association processing on the abnormal data points and the causal relationship diagram to obtain an associated causal relationship diagram; Based on the associated causal relationship diagram, identify the abnormal association patterns of the infusion parameters and the physiological index parameters; According to the abnormal association patterns and the associated causal relationship diagram, construct an abnormal association analysis unit for the infusion patient; 7. The alarm prompt system for infusion care according to claim 1, wherein, The determining the optimal infusion rate of the infusion patient according to the real-time liquid medicine flow rate and the real-time infusion status includes: Based on the real-time infusion status, collect the basic medical parameters of the infusion patient, and extract the drip medicine of the infusion patient and its corresponding drug components; According to the drug components, identify the pharmacokinetic characteristics of the drip medicine; Based on the real-time liquid medicine flow rate and the pharmacokinetic characteristics, calculate the blood drug concentration in the body of the infusion patient; According to the basic medical parameters, identify the disease characteristics of the infusion patient; Based on the disease characteristics, determine the infusion target of the infusion patient; According to the disease characteristics and the infusion target, set the target concentration range of the blood drug concentration; Based on the basic medical parameters and the blood drug concentration, analyze the liquid medicine clearance ability of the infusion patient; According to the pharmacokinetic characteristics, analyze the concentration change trend of the blood drug concentration; According to the disease characteristics and the liquid medicine clearance ability, identify the physical condition of the infusion patient; Combining the target concentration range, the concentration change trend and the physical condition, determine the optimal infusion rate of the infusion patient.

8. The alarm prompt system for infusion care according to claim 1, characterized in that, The identifying the risk level of the infusion patient according to the abnormal association analysis unit includes: According to the abnormal association analysis unit, extract the abnormal association parameters of the infusion patient; According to the abnormal association parameters, set the risk evaluation index of the infusion patient; Based on the risk evaluation index, identify the risk status of the infusion patient; According to the abnormal association analysis unit, analyze the evolution trend of the risk status; Based on the evolution trend, monitor the real-time condition change of the infusion patient; According to the real-time condition change, set the adaptive evaluation threshold of the risk evaluation index; Based on the adaptive evaluation threshold and the risk evaluation index, identify the risk level of the infusion patient.

9. The alarm prompt system for infusion care according to claim 1, wherein The creating a hierarchical early warning system for the infusion patient based on the risk level and the infusion alarm threshold includes: Based on the risk level and the infusion alarm threshold, set the hierarchical early warning trigger conditions for the infusion patient; According to the infusion alarm threshold, analyze the reasons for infusion abnormalities of the infusion patient; Identify the severity of the abnormality of the infusion patient based on the reasons for the infusion abnormality; Construct a threshold dynamic adjustment mechanism for the infusion alarm threshold according to the severity of the abnormality; Set the hierarchical alarm mode for the infusion patient according to the risk level and the severity of the abnormality; Create a hierarchical early warning system for the infusion patient by combining the hierarchical early warning trigger conditions, the threshold dynamic adjustment mechanism and the hierarchical alarm mode.

10. The alarm prompt system for infusion care according to claim 1, wherein, Based on the real-time liquid medicine flow rate, the remaining liquid medicine volume, the personalized intervention suggestion and the hierarchical early warning system, construct a user interaction interface for the infusion patient, including: Extract the hierarchical early warning information of the infusion patient based on the hierarchical early warning system; Generate a data display unit for the infusion patient by combining the hierarchical early warning information, the real-time liquid medicine flow rate, the remaining liquid medicine volume and the personalized intervention suggestion; Set an information query entry for the infusion patient according to the data display unit; Extract the user access instruction of the information query entry, and set the user access permission of the data display unit based on the user access instruction; Create an interactive information feedback mechanism for the infusion patient according to the user access permission; Construct a user interaction interface for the infusion patient by combining the data display unit, the information query entry and the interactive information feedback mechanism.

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