CN116312958B - Anesthesia risk early warning system, emergency management system and method - Google Patents
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- 206010002091 Anaesthesia Diseases 0.000 title claims abstract description 142
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Abstract
The application relates to the technical field of anesthesia risk monitoring, and provides an anesthesia risk early warning system, an emergency management system and a method, wherein the anesthesia risk early warning system comprises: monitoring a terminal; a monitoring terminal; the monitoring terminal acquires monitoring data of a patient during anesthesia, stores the monitoring data, encrypts and transmits the monitoring data to the monitoring terminal; the monitoring terminal receives and verifies the monitoring data, predicts the monitoring data of the patient at the target moment based on the monitoring data at the current moment, and carries out anesthesia risk early warning according to the monitoring data at the current moment and the monitoring data at the target moment. According to the application, the monitoring data of the patient is safely transmitted through the monitoring terminal and the monitoring terminal, the anesthesia risk of the patient is accurately predicted, the on-duty state of the on-duty anesthesiologist is monitored, the system design from wireless monitoring data acquisition and anesthesia risk prediction to the emergency treatment whole process is sequentially realized, and comprehensive, safe and efficient anesthesia service is provided for the anesthesiologist.
Description
Technical Field
The application relates to the technical field of anesthesia risk monitoring, in particular to an anesthesia risk early warning system, an emergency management system and an anesthesia risk early warning method.
Background
Current anesthesia risk early warning systems and emergency management system research have focused mainly on data acquisition and processing, predictive models, system design and implementation. However, in particular applications, there are still some problems, including:
first, the data quality and security are low. Although vital signs and anesthesia depth monitoring technology based on wireless transmission technology brings convenience, when the vital signs and anesthesia depth monitoring technology coexist with other medical equipment, electromagnetic interference can be generated by wireless transmission, and accuracy and stability can be possibly affected. Meanwhile, wireless transmission data is easy to hack, so that patient data is tampered or forged, and medical accidents occur.
Second, model mobility is poor. Training data for anesthesia risk prediction models is typically derived from the clinical experience of a physician, whose subjectivity and limitation may lead to errors in the algorithm. In addition, most of the current anesthesia risk prediction models are trained and predicted based on historical data, and the situation of a patient cannot be predicted in real time.
Third, emergency treatment is not timely. Although there are many mature anesthesia emergency management methods at present, the current state of the attended anesthesiologist is not considered, and serious medical accidents are easily caused when the attended anesthesiologist is tired and the emergency is not handled in time.
Disclosure of Invention
In order to solve the problems in the prior art, the application provides an anesthesia risk early warning system, an emergency management system and an anesthesia risk early warning method, which aim to safely transmit monitoring data of a patient through a monitoring terminal and a monitoring terminal, accurately predict the anesthesia risk of the patient, monitor the on-duty state of an on-duty anesthesiologist and provide an anesthesia risk emergency scheme.
In a first aspect of the present application, there is provided an anesthesia risk warning system, the system having:
monitoring a terminal;
a monitoring terminal;
the monitoring terminal acquires monitoring data of a patient during anesthesia, stores the monitoring data, and encrypts and transmits the stored monitoring data to the monitoring terminal;
the monitoring terminal receives and verifies the monitoring data, predicts the monitoring data of the patient at the target moment based on the monitoring data at the current moment, and performs anesthesia risk early warning according to the monitoring data at the current moment and the monitoring data at the target moment.
Optionally, the monitoring data includes one or more of vital sign data, anesthesia depth data, and drug metabolism data; the monitoring terminal is configured with: one or more of a vital sign data acquisition component, an anesthesia depth data acquisition component, and a drug metabolism data acquisition component.
Optionally, a plurality of transmission paths are established between the monitoring terminal and the monitoring terminal; the monitoring terminal further comprises:
a target transmission path determination module;
a monitoring data multipath transmission module;
the target transmission path determining module determines a target transmission path among a plurality of transmission paths between the monitoring terminal and the monitoring terminal;
the monitoring data multipath transmission module transmits the monitoring data to the monitoring terminal through the target transmission path.
Optionally, the target transmission path determining module determines the target transmission path according to the data transmission quality of each transmission path.
Optionally, the data transmission quality includes a distortion rate and/or a delay rate.
Optionally, the monitoring data multipath transmission module includes:
a monitoring data first transmission unit;
the monitoring data first transmission unit selects a transmission path with the best data transmission quality to transmit all the monitoring data to the monitoring terminal.
Optionally, the monitoring data multipath transmission module includes:
a path transmission quality sorting unit;
a monitoring data importance level determining unit;
a monitoring data second transmission unit;
the path transmission quality sorting unit sorts the data transmission quality of each transmission path to obtain a priority list of the transmission paths;
the monitoring data importance level determining unit determines the data importance levels of different monitoring data according to the categories of the monitoring data;
the second transmission unit of the monitoring data matches the target transmission path for each monitoring data according to the priority list of the transmission paths and the data importance of the monitoring data.
Optionally, the monitoring terminal has: an encryption module; the monitoring terminal has: a verification module;
the encryption module is used for generating a data signature and/or a time stamp for the acquired monitoring data;
the verification module is used for carrying out data verification on the received monitoring data according to the data signature and/or the time stamp.
Optionally, the monitoring terminal has:
the anesthetic administration reference data acquisition module;
the anesthetic administration information generation module;
the anesthetic reference data acquisition module acquires anesthetic reference data;
the anesthesia medication information generation module establishes an anesthesia medication decision tree model according to preset anesthesia medication logic and anesthesia medication reference data, and generates anesthesia medication information by utilizing the anesthesia medication decision tree model.
Optionally, the monitoring terminal further has:
a monitoring data prediction module;
an anesthesia risk early warning module;
the monitoring data prediction module predicts the monitoring data of the patient at the target moment according to the monitoring data of the patient at the current moment and the anesthesia medication information;
the anesthesia risk early warning module judges whether a patient has anesthesia risk at the current moment according to the monitoring data at the current moment and the monitoring data at the target moment.
Optionally, the anesthesia risk early warning module determines that the patient has anesthesia risk at the current moment when the absolute value of the difference value between the monitoring data at the current moment and the monitoring data at the target moment is greater than a preset threshold value.
In a second aspect of the present application, there is provided an anesthesia risk emergency management system, the system comprising: a monitoring terminal and a monitoring terminal as described above; the monitoring terminal further comprises:
the on-duty state data acquisition module;
a duty state recognition module;
the on-duty state data acquisition module acquires on-duty state data of a current on-duty anesthesiologist;
the on-duty state identification module is used for identifying the on-duty state of the current on-duty anesthesiologist according to the on-duty state data of the current on-duty anesthesiologist.
Optionally, the on-duty status data includes physiological index information and/or facial expression information; the monitoring terminal is configured with: a physiological index information acquisition component and/or a facial expression information acquisition component.
Optionally, the monitoring terminal further has:
an emergency management module;
the emergency management module judges whether the on-duty state of the current on-duty anesthesiologist is abnormal, and if so, the emergency management module sends the received monitoring data to a monitoring terminal corresponding to the target anesthesiologist.
Optionally, the monitoring terminal further has:
a duty mode management module;
a duty mode switching module;
the on-duty mode management module distributes corresponding on-duty modes for each anesthesiologist according to on-duty scheduling rules; the attended mode comprises attended, unattended and to-be-attended, and the target anesthesiologist is an anesthesiologist to be attended;
when the emergency management module sends the received monitoring data to the monitoring terminal corresponding to the target anesthesiologist, the duty mode switching module generates a duty mode switching instruction, switches the duty mode of the current duty anesthesiologist from duty to non-duty, and switches the duty mode of the target duty anesthesiologist from duty to duty.
In a third aspect of the present application, there is provided a method of anesthesia risk emergency management, the method comprising:
the monitoring terminal acquires monitoring data of a patient during anesthesia, performs data storage on the monitoring data, and encrypts and transmits the stored monitoring data to the monitoring terminal;
the monitoring terminal receives and verifies the monitoring data, predicts the monitoring data of the patient at the target moment based on the monitoring data at the current moment, and carries out anesthesia risk early warning according to the monitoring data at the current moment and the monitoring data at the target moment;
the monitoring terminal acquires the on-duty state data of the current on-duty anesthesiologist, and recognizes the on-duty state of the current on-duty anesthesiologist according to the on-duty state data of the current on-duty anesthesiologist.
Optionally, the method further comprises:
the monitoring terminal judges whether the on-duty state of the current on-duty anesthesiologist is abnormal, if so, the monitoring terminal sends the received monitoring data to the monitoring terminal corresponding to the target anesthesiologist.
Optionally, the method further comprises:
the monitoring terminal distributes a corresponding on-duty mode for each anesthesiologist according to the on-duty scheduling rules; the attended mode comprises attended, unattended and to-be-attended, and the target anesthesiologist is an anesthesiologist to be attended;
when the monitoring terminal sends the received monitoring data to the monitoring terminal corresponding to the target anesthesiologist, a duty mode switching instruction is generated, the duty mode of the current duty anesthesiologist is switched from duty to non-duty, and the duty mode of the target duty anesthesiologist is switched from duty to duty.
The application has the beneficial effects that: the anesthesia risk early warning system, the emergency management system and the method are provided, the monitoring data of the patient are safely transmitted through the monitoring terminal and the monitoring terminal, the anesthesia risk of the patient is accurately predicted, the on-duty state of an on-duty anesthesiologist is monitored, an anesthesia risk emergency scheme is provided, the system design of the whole process of wireless monitoring data acquisition and anesthesia risk prediction to emergency treatment is sequentially realized, and comprehensive, safe and efficient anesthesia service is provided for the anesthesiologist.
Drawings
FIG. 1 is a schematic diagram of an anesthesia risk early warning system according to the present application;
FIG. 2 is a schematic diagram of a hybrid predictive model based on decision trees and stacked LSTM provided by the present application;
FIG. 3 is a schematic diagram of an anesthesia risk emergency management system according to the present application;
FIG. 4 is a schematic diagram of a video anomaly state recognition model for merging physical attention provided by the application;
fig. 5 is a schematic flow chart of an anesthesia risk emergency management method provided by the application.
Reference numerals:
10-monitoring a terminal; 20-monitoring terminal.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Example 1:
referring to fig. 1, fig. 1 is a schematic structural diagram of an anesthesia risk early warning system according to an embodiment of the present application.
As shown in fig. 1, an anesthesia risk warning system has: a monitoring terminal 10 and a monitoring terminal 20.
It should be noted that, the monitoring terminal 10 is a terminal for monitoring the status of a patient to obtain monitoring during anesthesia; the monitoring terminal 20 is typically a terminal for acquiring monitoring data of the monitoring terminal 10 and analyzing and visualizing the monitoring data during the anesthesia process; in practical applications, each monitoring terminal 20 is configured with a corresponding anesthesiologist who monitors the patient's status according to the analysis and visualization of the monitoring terminal 20, and adjusts the anesthesia scheme during surgery or treatment accordingly.
The monitoring terminal 10 acquires monitoring data of a patient during anesthesia, stores the monitoring data, and encrypts and transmits the stored monitoring data to the monitoring terminal 20; the monitoring terminal 20 receives and verifies the monitoring data, predicts the monitoring data of the patient at the target time based on the monitoring data at the current time, and performs anesthesia risk early warning according to the monitoring data at the current time and the monitoring data at the target time.
In this embodiment, the monitoring terminal 10 acquires the monitoring data of the patient during anesthesia, and the monitoring data is stored in the monitoring terminal 10 by using the storage device, so as to record the monitoring data of the patient locally, and prevent the data loss possibly caused in the process of monitoring data transmission in the conventional scheme.
Meanwhile, when the monitoring terminal 10 transmits the data, the monitoring data needs to be encrypted first, and when the monitoring terminal 20 receives the monitoring data, the monitoring data needs to be verified, so that the safety of the monitoring data is improved, and the monitoring data is prevented from being tampered.
In addition, after receiving the monitoring data, the monitoring terminal 20 can utilize the monitoring data at the current time to perform monitoring data prediction and anesthesia risk early warning at the target time, and perform anesthesia risk prediction by predicting the state of the patient at the future time.
In practical applications, the monitoring data includes one or more of vital sign data, anesthesia depth data, and drug metabolism data; the monitoring terminal 10 is provided with: one or more of a vital sign data acquisition component, an anesthesia depth data acquisition component, and a drug metabolism data acquisition component.
In this embodiment, the monitoring terminal 10 is configured with several different monitoring data acquisition devices, which can acquire one or more of vital sign data, anesthesia depth data and drug metabolism data, and provide more comprehensive anesthesia process monitoring for the patient.
Example 2:
in a preferred embodiment, a plurality of transmission paths are established between the monitoring terminal 10 and the monitoring terminal 20; the monitoring terminal 10 further includes: the target transmission path determining module and the monitoring data multipath transmission module.
Wherein the target transmission path determining module determines a target transmission path among a plurality of transmission paths between the monitoring terminal 10 and the monitoring terminal 20; the monitoring data multi-path transmission module transmits the monitoring data to the monitoring terminal 20 through the target transmission path.
In this embodiment, a plurality of transmission paths are established between the monitoring terminal 10 and the monitoring terminal 20, specifically, a plurality of transmission paths including different transmission modes, different relay forwarding devices or different transmission networks may be established, when the monitoring data collected by the monitoring terminal 10 is transmitted to the monitoring terminal 20, an optimal target transmission path is selected from the plurality of transmission paths, and then the target transmission path is utilized to transmit the monitoring data. The influence of signal interference can be reduced by the multipath transmission technology, and the stability and reliability of transmission are improved.
In some embodiments, the target transmission path determination module determines the target transmission path based on the data transmission quality of each transmission path. In practical applications, the data transmission quality includes a distortion rate and/or a delay rate.
On this basis, the monitoring data multipath transmission module is provided with: a monitoring data first transmission unit; the monitoring data first transmission unit selects a transmission path with the best data transmission quality to transmit all the monitoring data to the monitoring terminal 20.
The monitoring terminal 10 determines a target transmission path according to the data transmission quality when monitoring data by multipath transmission. In this embodiment, when monitoring data transmission is performed, a transmission path with the best transmission quality can be selected to transmit all monitoring data to the monitoring terminal 20, so as to ensure that the distortion rate and/or the delay rate of the overall data transmission are the lowest, and improve the quality and efficiency of the overall data transmission.
On this basis, the monitoring data multipath transmission module is provided with: the system comprises a path transmission quality sorting unit, a monitoring data importance level determining unit and a monitoring data second transmission unit.
The path transmission quality sorting unit sorts the data transmission quality of each transmission path to obtain a priority list of the transmission paths; the monitoring data importance level determining unit determines the data importance levels of different monitoring data according to the categories of the monitoring data; the monitoring data second transmission unit matches the target transmission path for each monitoring data according to the priority list of the transmission paths and the data importance of the monitoring data.
The monitoring terminal 10 determines a target transmission path according to the data transmission quality when monitoring data by multipath transmission. In this embodiment, when monitoring data transmission is performed, the priority of transmission quality of the transmission paths is adopted, and then according to the data importance of different types of monitoring data, the monitoring data transmission is performed for the transmission paths corresponding to the matching of different types of monitoring data.
Example 3:
in a preferred embodiment, the monitoring terminal 10 has: an encryption module; the monitoring terminal 20 includes: and a verification module.
The encryption module is used for generating a data signature and/or a time stamp for the acquired monitoring data; and the verification module is used for carrying out data verification on the received monitoring data according to the data signature and/or the time stamp.
In this embodiment, the encryption module configured at the monitoring terminal 10 and the verification module configured at the monitoring terminal 20 can perform tamper-proof protection on the monitoring data sent by the monitoring terminal 10. Specifically, when the monitoring data is transmitted, a data signature and/or a timestamp is generated for the monitoring data, when the monitoring terminal 20 receives the monitoring data, the monitoring data needs to be verified by the received data signature and/or timestamp, and the monitoring data is analyzed and visualized only when the verification passes, so that the accuracy and the integrity of the monitoring data can be ensured, the monitoring and the anesthesia risk early warning of the patient anesthesia process are prevented from being influenced by the falsification or forging of the data, and further the medical accident is caused. Compared with the existing wireless anesthesia depth monitoring method, the wireless anesthesia depth monitoring method has the advantages that the accuracy and the safety of wireless monitoring data are guaranteed from the aspects of data acquisition, transmission and receiving, and better robustness is achieved.
Example 4:
in a preferred embodiment, the monitoring terminal 20 has: the device comprises an anesthetic reference data acquisition module and an anesthetic information generation module.
The anesthetic reference data acquisition module acquires anesthetic reference data; the anesthesia medication information generation module establishes an anesthesia medication decision tree model according to preset anesthesia medication logic and anesthesia medication reference data, and generates anesthesia medication information by utilizing the anesthesia medication decision tree model.
In this embodiment, the monitoring terminal 20 can generate the most appropriate anesthetic information for the patient by acquiring the anesthetic reference data. In practical applications, the anesthetic drug reference data includes: one or more of medical history, physical condition (height, weight, sex, age), allergy history, medication history, and operation history. And establishing an anesthesia medication decision tree model according to preset anesthesia medication logic generated by clinical experience of doctors, so as to determine the medicine and the dosage which are most suitable for patients.
In a preferred embodiment, the monitoring terminal 20 further has: the monitoring data prediction module and the anesthesia risk early warning module.
The monitoring data prediction module predicts the monitoring data of the patient at the target moment according to the monitoring data of the patient at the current moment and the anesthesia medication information; the anesthesia risk early warning module judges whether the patient has anesthesia risk at the current moment according to the monitoring data at the current moment and the monitoring data at the target moment. And when the absolute value of the difference value between the monitoring data at the current moment and the monitoring data at the target moment is larger than a preset threshold value, the anesthesia risk early warning module judges that the patient has anesthesia risk at the current moment.
It should be noted that, the monitoring terminal 20 may also predict the monitoring data of the patient at the target moment according to the anesthesia medication information generated by the anesthesia medication decision tree model and the monitoring data collected by the monitoring terminal 10. In practical application, the drug and the dosage determined by the anesthesia medication decision tree model and the real-time monitoring data (including heart rate, blood pressure, anesthesia depth and drug metabolism condition) are used as model input by constructing an LSTM-based anesthesia risk early warning model, so as to predict the monitoring data at the next moment. For risk early warning, in practical application, if the absolute value of the difference value of the monitoring data at two moments is larger than a set threshold value, the risk of the anesthesia scheme is indicated and early warning is carried out.
As shown in fig. 2, the present embodiment is directed to the problem of poor mobility of the existing anesthesia risk prediction model, and builds a hybrid prediction model based on decision trees and stacked LSTM. Compared with a single type of prediction model, the hybrid prediction model can more comprehensively integrate clinical experience of doctors, self conditions of patients and real-time monitoring data in the anesthesia process, and generalization of the prediction model is enhanced.
Example 5:
referring to fig. 3, fig. 3 is a schematic structural diagram of an anesthesia risk emergency management system according to an embodiment of the present application.
As shown in fig. 3, an anesthesia risk emergency management system, comprising: the monitoring terminal 10 and the monitoring terminal 20 described in the foregoing embodiments. On the basis, the monitoring terminal 20 further has: the system comprises an on-duty state data acquisition module and an on-duty state identification module.
The on-duty state data acquisition module acquires on-duty state data of a current on-duty anesthesiologist; the on-duty state identification module identifies the on-duty state of the current on-duty anesthesiologist according to the on-duty state data of the current on-duty anesthesiologist.
It should be noted that, the monitoring terminal 20 may also identify the on-duty status of the current on-duty anesthesiologist by acquiring on-duty status data of the current on-duty anesthesiologist. In practical application, the on-duty status data includes physiological index information and/or facial expression information; the monitoring terminal 20 is configured with: a physiological index information acquisition component and/or a facial expression information acquisition component.
In this embodiment, as shown in fig. 4, after the attended state data of the current attended anesthesiologist is obtained, the emotional state of the current attended doctor can be predicted by constructing a video abnormal state recognition model integrating the physical sign attention. In practical applications, the advantages of CNN networks in terms of high-dimensional image data feature extraction and the ability of LSTM to be good at capturing time series dependencies over time can be exploited. Compared with the existing abnormal state identification model based on videos, the model is added with the abnormal identification module of the sign data, the sign information at the corresponding moment is input for training while the images are input, and the attention features of the abnormal state identification of the sign data are fused with the abnormal state identification of the images, so that the accurate prediction of the state of the on-duty doctor is realized.
Example 6:
in a preferred embodiment, the monitoring terminal 20 further has: an emergency management module; the emergency management module determines whether the current on-duty state of the anesthesiologist is abnormal, if so, the received monitoring data is sent to the monitoring terminal 20 corresponding to the target anesthesiologist.
It should be noted that, after identifying and judging the on-duty status of the current on-duty anesthesiologist according to the on-duty status data, the monitoring terminal 20 further includes: the system comprises a duty mode management module and a duty mode switching module.
The on-duty mode management module distributes corresponding on-duty modes for each anesthesiologist according to on-duty scheduling rules; the attended mode comprises attended, unattended and to-be-attended, and the target anesthesiologist is an anesthesiologist to be attended; the duty mode switching module generates a duty mode switching instruction when the emergency management module sends the received monitoring data to the monitoring terminal 20 corresponding to the target anesthesiologist, switches the duty mode of the current duty anesthesiologist from duty to non-duty, and switches the duty mode of the target duty anesthesiologist from duty to duty.
In this embodiment, a corresponding duty mode may be allocated to each anesthesiologist by acquiring a duty scheduling rule in advance, for example: the unattended operation mode allocated to the unattended anesthesiologist is unattended operation, the attended operation mode allocated to the anesthesiologist on duty and having an anesthesia task is attended operation, and the attended operation mode allocated to the anesthesiologist on duty but not having an anesthesia task is attended operation. On the basis, the device is identified and judged according to the on-duty state of the current on-duty anesthesiologist, when the on-duty state is abnormal (for example, the current anesthesiologist is identified to be in a doctor's bus or doze), the on-duty mode is switched from on duty to non-duty, the on-duty mode of the anesthesiologist who is on duty but has no anesthesia task is switched from on duty to on duty, and then the forced replacement of the anesthesiologist is carried out, so that the safety of the patient is ensured.
In practical application, when the state of the current on-duty anesthesiologist is identified to be abnormal, the system can also send an alarm to the anesthesiologist and provide a prompt for the anesthesiologist to replace, and the monitoring data is automatically transmitted to the monitoring terminal 20 corresponding to the target anesthesiologist for remote monitoring and processing. Therefore, compared with the existing video-based state abnormality recognition model, the model combines sign monitoring data, introduces a sign attention mechanism to a video abnormality state recognition module, more accurately recognizes the state of the doctor on duty and completes the switching of the mode on duty by recognizing and judging the state of the doctor on duty and switching the mode on duty. Therefore, the system design from wireless monitoring data acquisition to anesthesia risk prediction to emergency treatment complete flow can be sequentially realized, and comprehensive, safe and efficient anesthesia service is provided for anesthesiologists.
Referring to fig. 5, fig. 5 is a schematic flow chart of an anesthesia risk emergency management method according to an embodiment of the present application.
As shown in fig. 5, an anesthesia risk emergency management method includes the following steps:
s1: the monitoring terminal acquires monitoring data of a patient during anesthesia, performs data storage on the monitoring data, and encrypts and transmits the stored monitoring data to the monitoring terminal;
s2: the monitoring terminal receives and verifies the monitoring data, predicts the monitoring data of the patient at the target moment based on the monitoring data at the current moment, and carries out anesthesia risk early warning according to the monitoring data at the current moment and the monitoring data at the target moment;
s3: the monitoring terminal acquires the on-duty state data of the current on-duty anesthesiologist, and recognizes the on-duty state of the current on-duty anesthesiologist according to the on-duty state data of the current on-duty anesthesiologist.
In a preferred embodiment, the method further comprises:
s4: the monitoring terminal judges whether the on-duty state of the current on-duty anesthesiologist is abnormal, if so, the monitoring terminal sends the received monitoring data to the monitoring terminal corresponding to the target anesthesiologist.
In a preferred embodiment, the method further comprises:
s5: the monitoring terminal distributes a corresponding on-duty mode for each anesthesiologist according to the on-duty scheduling rules; the attended mode comprises attended, unattended and to-be-attended, and the target anesthesiologist is an anesthesiologist to be attended;
s6: when the monitoring terminal sends the received monitoring data to the monitoring terminal corresponding to the target anesthesiologist, a duty mode switching instruction is generated, the duty mode of the current duty anesthesiologist is switched from duty to non-duty, and the duty mode of the target duty anesthesiologist is switched from duty to duty.
In this embodiment, the monitoring terminal and the monitoring terminal are used for carrying out safe transmission on the monitoring data of the patient, accurately predicting the anesthesia risk of the patient, monitoring the on-duty state of the on-duty anesthesiologist, providing an anesthesia risk emergency scheme, and sequentially realizing wireless monitoring data acquisition and the system design from anesthesia risk prediction to emergency treatment complete flow, thereby providing comprehensive, safe and efficient anesthesia service for the anesthesiologist.
The specific implementation manner of the anesthesia risk emergency management method of the present application is basically the same as that of each embodiment of the anesthesia risk emergency management system, and will not be described herein.
In describing embodiments of the present application, it should be understood that the terms "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "center", "top", "bottom", "inner", "outer", "inside", "outside", etc. indicate orientations or positional relationships based on the drawings are merely for convenience in describing the present application and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present application. Wherein "inside" refers to an interior or enclosed area or space. "peripheral" refers to the area surrounding a particular component or region.
In the description of embodiments of the present application, the terms "first," "second," "third," "fourth" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first", "a second", "a third" and a fourth "may explicitly or implicitly include one or more such feature. In the description of the present application, unless otherwise indicated, the meaning of "a plurality" is two or more.
In describing embodiments of the present application, it should be noted that the terms "mounted," "connected," and "assembled" are to be construed broadly, as they may be fixedly connected, detachably connected, or integrally connected, unless otherwise specifically indicated and defined; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present application will be understood in specific cases by those of ordinary skill in the art.
In the description of embodiments of the application, a particular feature, structure, material, or characteristic may be combined in any suitable manner in one or more embodiments or examples.
In describing embodiments of the present application, it will be understood that the terms "-" and "-" are intended to be inclusive of the two numerical ranges, and that the ranges include the endpoints. For example: "A-B" means a range greater than or equal to A and less than or equal to B. "A-B" means a range of greater than or equal to A and less than or equal to B.
In the description of embodiments of the present application, the term "and/or" is merely an association relationship describing an association object, meaning that three relationships may exist, e.g., a and/or B, may represent: a exists alone, A and B exist together, and B exists alone. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship.
Although embodiments of the present application have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the application, the scope of which is defined in the appended claims and their equivalents.
Claims (11)
1. An anesthesia risk warning system, the system comprising:
monitoring a terminal;
a monitoring terminal;
the monitoring terminal acquires monitoring data of a patient during anesthesia, stores the monitoring data, and encrypts and transmits the stored monitoring data to the monitoring terminal;
the monitoring terminal receives and verifies monitoring data, predicts the monitoring data of a patient at a target moment by utilizing a mixed prediction model of an anesthesia medication decision tree and a stacked LSTM based on the monitoring data at the current moment and anesthesia medication information, and performs anesthesia risk early warning according to the monitoring data at the current moment and the monitoring data at the target moment, and judges that the patient has anesthesia risk at the current moment when the absolute value of the difference value between the monitoring data at the current moment and the monitoring data at the target moment is larger than a preset threshold value;
the monitoring terminal acquires the on-duty state data of the current on-duty anesthesiologist, and based on the on-duty state data, the on-duty state of the current on-duty anesthesiologist is identified by utilizing a mixed identification model of the CNN network and the multi-layer LSTM, and when the on-duty state is abnormal, the on-duty mode of the on-duty anesthesiologist is switched.
2. The anesthesia risk early warning system of claim 1 wherein the monitoring data comprises one or more of vital sign data, anesthesia depth data, and drug metabolism data; the monitoring terminal is configured with: one or more of a vital sign data acquisition component, an anesthesia depth data acquisition component, and a drug metabolism data acquisition component.
3. The anesthesia risk warning system of claim 1 wherein a plurality of transmission paths are established between the monitoring terminal and the monitoring terminal; the monitoring terminal has:
a target transmission path determination module;
a monitoring data multipath transmission module;
the target transmission path determining module determines a target transmission path among a plurality of transmission paths between the monitoring terminal and the monitoring terminal;
the monitoring data multipath transmission module transmits the monitoring data to the monitoring terminal through the target transmission path.
4. The anesthesia risk warning system according to claim 1, wherein the monitoring terminal has: an encryption module; the monitoring terminal has: a verification module;
the encryption module is used for generating a data signature and/or a time stamp for the monitoring data;
the verification module is used for carrying out data verification on the received monitoring data according to the data signature and/or the time stamp.
5. The anesthesia risk warning system of claim 1 wherein the monitoring terminal has:
the anesthetic administration reference data acquisition module;
the anesthetic administration information generation module;
the anesthetic reference data acquisition module acquires anesthetic reference data;
the anesthesia medication information generation module establishes an anesthesia medication decision tree model according to preset anesthesia medication logic and anesthesia medication reference data, and generates anesthesia medication information by utilizing the anesthesia medication decision tree model.
6. The anesthesia risk warning system of claim 5 wherein the monitoring terminal further comprises:
a monitoring data prediction module;
an anesthesia risk early warning module;
the monitoring data prediction module predicts the monitoring data of the patient at the target moment according to the monitoring data of the patient at the current moment and the anesthesia medication information;
the anesthesia risk early warning module judges whether a patient has anesthesia risk at the current moment according to the monitoring data at the current moment and the monitoring data at the target moment.
7. An anesthesia risk emergency management system, the system comprising:
the monitoring terminal and the monitoring terminal according to any one of claims 1-6; the monitoring terminal further comprises:
the on-duty state data acquisition module;
a duty state recognition module;
the on-duty state data acquisition module acquires on-duty state data of a current on-duty anesthesiologist;
the on-duty state identification module is used for identifying the on-duty state of the current on-duty anesthesiologist according to the on-duty state data of the current on-duty anesthesiologist.
8. The anesthesia risk emergency management system according to claim 7 wherein the attended state data comprises physiological index information and/or facial expression information; the monitoring terminal is configured with: a physiological index information acquisition component and/or a facial expression information acquisition component.
9. The anesthesia risk emergency management system according to claim 7, wherein the monitoring terminal further has:
an emergency management module;
the emergency management module judges whether the on-duty state of the current on-duty anesthesiologist is abnormal, and if so, the emergency management module sends the received monitoring data to a monitoring terminal corresponding to the target anesthesiologist.
10. The anesthesia risk emergency management system of claim 9 wherein the monitoring terminal further has:
a duty mode management module;
a duty mode switching module;
the on-duty mode management module distributes corresponding on-duty modes for each anesthesiologist according to on-duty scheduling rules; the attended mode comprises attended, unattended and to-be-attended, and the target anesthesiologist is an anesthesiologist to be attended;
when the emergency management module sends the received monitoring data to the monitoring terminal corresponding to the target anesthesiologist, the duty mode switching module generates a duty mode switching instruction, switches the duty mode of the current duty anesthesiologist from duty to non-duty, and switches the duty mode of the target duty anesthesiologist from duty to duty.
11. A method of anesthesia risk emergency management, the method comprising: the monitoring terminal acquires monitoring data of a patient during anesthesia, performs data storage on the monitoring data, and encrypts and transmits the stored monitoring data to the monitoring terminal;
the monitoring terminal receives and verifies the monitoring data, predicts the monitoring data of the patient at the target moment by utilizing a mixed prediction model of an anesthesia medication decision tree and a stacked LSTM (least squares) based on the monitoring data at the current moment and anesthesia medication information, carries out anesthesia risk early warning according to the monitoring data at the current moment and the monitoring data at the target moment, and judges that the patient has anesthesia risk at the current moment when the absolute value of the difference value between the monitoring data at the current moment and the monitoring data at the target moment is larger than a preset threshold value; the monitoring terminal acquires the on-duty state data of the current on-duty anesthesiologist, and based on the on-duty state data, the on-duty state of the current on-duty anesthesiologist is identified by utilizing a mixed identification model of the CNN network and the multi-layer LSTM, and when the on-duty state is abnormal, the on-duty mode of the on-duty anesthesiologist is switched.
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Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH10171967A (en) * | 1996-12-06 | 1998-06-26 | Hitachi Ltd | Remote medical treatment supporting method and remote medical treatment support system |
| CN107658016A (en) * | 2016-10-03 | 2018-02-02 | 朴植 | The Nounou intelligent guarding systems accompanied for health care for the aged |
| CN108615557A (en) * | 2018-03-22 | 2018-10-02 | 柳州市妇幼保健院 | A kind of intelligent anesthesia monitoring system |
| CN109741826A (en) * | 2018-12-13 | 2019-05-10 | 华中科技大学鄂州工业技术研究院 | Anaesthetize evaluation decision tree constructing method and equipment |
| CN111083443A (en) * | 2019-12-25 | 2020-04-28 | 中山大学 | Monitoring center auxiliary system and method based on deep learning |
| CN111180059A (en) * | 2019-12-30 | 2020-05-19 | 中国人民解放军陆军军医大学第一附属医院 | Remote medical monitoring system based on 5G network |
| CN112331284A (en) * | 2020-10-27 | 2021-02-05 | 复旦大学附属儿科医院 | Admission medical history auxiliary acquisition system for hereditary metabolic disease |
| CN112509678A (en) * | 2020-12-01 | 2021-03-16 | 郭文军 | Anesthesia information management system and courtyard comprehensive information management system |
| CN113974900A (en) * | 2021-09-27 | 2022-01-28 | 遵义医科大学 | Laboratory anesthesia information processing system and method based on big data |
| CN114366030A (en) * | 2021-12-31 | 2022-04-19 | 中国科学院苏州生物医学工程技术研究所 | Intelligent auxiliary system and method for anesthesia operation |
| CN114582510A (en) * | 2022-03-14 | 2022-06-03 | 梅英锋 | AI health monitoring management system based on big data |
| CN114611879A (en) * | 2022-02-17 | 2022-06-10 | 浙江大学 | Clinical risk prediction system based on multitask learning |
| CN114864094A (en) * | 2022-07-06 | 2022-08-05 | 湖南尚医康医疗科技有限公司 | Intensive care unit intelligent control method, equipment and medium |
| CN115299881A (en) * | 2022-08-08 | 2022-11-08 | 中科院成都信息技术股份有限公司 | Monitoring and regulating system and method for anesthesia maintenance period |
| CN115346680A (en) * | 2022-08-15 | 2022-11-15 | 中科院成都信息技术股份有限公司 | Anesthesia data processing method, system, medium, device and information processing terminal |
| CN115866442A (en) * | 2022-11-04 | 2023-03-28 | 苏州德品医疗科技股份有限公司 | Integrated intelligent monitoring system |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20200253820A1 (en) * | 2017-01-20 | 2020-08-13 | Physio-Control, Inc. | Systems and methods of managing and evaluating emergency medical procedures |
| US20230043921A1 (en) * | 2020-01-30 | 2023-02-09 | Evidation Health, Inc. | Sensor-based machine learning in a health prediction environment |
-
2023
- 2023-05-24 CN CN202310589951.6A patent/CN116312958B/en active Active
Patent Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH10171967A (en) * | 1996-12-06 | 1998-06-26 | Hitachi Ltd | Remote medical treatment supporting method and remote medical treatment support system |
| CN107658016A (en) * | 2016-10-03 | 2018-02-02 | 朴植 | The Nounou intelligent guarding systems accompanied for health care for the aged |
| CN108615557A (en) * | 2018-03-22 | 2018-10-02 | 柳州市妇幼保健院 | A kind of intelligent anesthesia monitoring system |
| CN109741826A (en) * | 2018-12-13 | 2019-05-10 | 华中科技大学鄂州工业技术研究院 | Anaesthetize evaluation decision tree constructing method and equipment |
| CN111083443A (en) * | 2019-12-25 | 2020-04-28 | 中山大学 | Monitoring center auxiliary system and method based on deep learning |
| CN111180059A (en) * | 2019-12-30 | 2020-05-19 | 中国人民解放军陆军军医大学第一附属医院 | Remote medical monitoring system based on 5G network |
| CN112331284A (en) * | 2020-10-27 | 2021-02-05 | 复旦大学附属儿科医院 | Admission medical history auxiliary acquisition system for hereditary metabolic disease |
| CN112509678A (en) * | 2020-12-01 | 2021-03-16 | 郭文军 | Anesthesia information management system and courtyard comprehensive information management system |
| CN113974900A (en) * | 2021-09-27 | 2022-01-28 | 遵义医科大学 | Laboratory anesthesia information processing system and method based on big data |
| CN114366030A (en) * | 2021-12-31 | 2022-04-19 | 中国科学院苏州生物医学工程技术研究所 | Intelligent auxiliary system and method for anesthesia operation |
| CN114611879A (en) * | 2022-02-17 | 2022-06-10 | 浙江大学 | Clinical risk prediction system based on multitask learning |
| CN114582510A (en) * | 2022-03-14 | 2022-06-03 | 梅英锋 | AI health monitoring management system based on big data |
| CN114864094A (en) * | 2022-07-06 | 2022-08-05 | 湖南尚医康医疗科技有限公司 | Intensive care unit intelligent control method, equipment and medium |
| CN115299881A (en) * | 2022-08-08 | 2022-11-08 | 中科院成都信息技术股份有限公司 | Monitoring and regulating system and method for anesthesia maintenance period |
| CN115346680A (en) * | 2022-08-15 | 2022-11-15 | 中科院成都信息技术股份有限公司 | Anesthesia data processing method, system, medium, device and information processing terminal |
| CN115866442A (en) * | 2022-11-04 | 2023-03-28 | 苏州德品医疗科技股份有限公司 | Integrated intelligent monitoring system |
Non-Patent Citations (4)
| Title |
|---|
| Artificial intelligence-based multimodal risk assessment model for surgical site infection (AMRAMS): development and validation study;Chen Weijia 等;《JMIR medical informatics》;第8卷(第6期);1-18 * |
| Remote mobile health monitoring system based on smart phone and browser/server structure;Zhang Yunzhou 等;《Journal of healthcare engineering》;第6卷;717-738 * |
| 基于HIS数据的住院人群癫痫发作自动监测模块的建立与优化;卢京川 等;《中国药物应用与监测》;第19卷(第4期);248-253 * |
| 基于用户体验的家庭助老服务机器人交互设计研究;庞广风;《中国优秀硕士学位论文全文数据库信息科技辑》(第01期);I140-1193 * |
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