Revival evaluation method for anesthetic patient and related equipment

Abstract

According to the awakening evaluation method for the anesthetic patient and the related equipment, uninterrupted intelligent monitoring, evaluation and communication can be carried out on the patient in the process from anesthesia to awakening, and professional nursing of anesthesia medical staff is linked. The method comprises the following steps: monitoring target vital sign data of a target patient, wherein the vital sign data comprises heart rate, body temperature, respiratory rate, pulse oxyhemoglobin saturation and blood pressure; determining target anesthesia information and metabolic capability corresponding to the target patient; preprocessing the target vital sign data to obtain vital sign features; inputting the vital sign characteristics, the target anesthesia information and the metabolic capability into a target awakening evaluation model corresponding to the target patient to determine a current awakening stage of the target patient; determining wakeup nursing information corresponding to the wakeup stage; and based on the awakening nursing information, performing awakening evaluation on the target patient.

Description

Technical Field

[0001] The present invention relates to the field of anesthesia, and in particular, to a method for evaluating the awakening of anesthetized patients and related devices. Background Art

[0002] The Anesthesia Recovery Unit (abbreviation: PACU) is an important place for closely monitoring the vital signs of patients after general anesthesia and ensuring that patients safely pass through the peri-anesthetic period. Affected by the original underlying diseases, surgical operations, and anesthesia, the recovery period after general anesthesia is a critical period for various postoperative complications in patients. Therefore, perioperative nursing is of great significance for the safety and recovery of patients. Currently, the PACU is routinely equipped with electrocardiogram monitors, ventilators, oxygen inhalation / negative pressure suction devices, etc. Anesthesia nurses must closely monitor postoperative patients at the bedside, strengthen rounds, and conduct awakening evaluations, and transfer patients back to the ward after reaching the awakening standard. Implementing standardized PACU discharge evaluations for patients can accurately and scientifically determine the timing of patient discharge, improve the turnover efficiency of the PACU, reduce complications, and is of great significance for ensuring the safety of postoperative patients.

[0003] There are many anesthesia nursing positions in China, with a wide distribution area and a shortage of human resources. Currently, for most PACUs, the nurse-patient ratio for routine general anesthesia patients is 1:2 - 3; for high-risk patients, those with a history of major diseases, those who have experienced serious complications during the operation, and those who are intubated and enter the recovery room, the ratio of anesthesia nurses to patients is 1:1. Although the monitor can real-time monitor the vital signs of postoperative recovery patients, due to the shortage of anesthesia nursing human resources, there may be events of interrupted anesthesia nursing, delayed awakening evaluation of patients, and lack of dynamic awakening evaluation monitoring results, posing safety hazards in clinical work. Summary of the Invention

[0004] Embodiments of the present invention provide a method for evaluating the awakening of anesthetized patients and related devices, which can perform uninterrupted intelligent monitoring, evaluation, and communication during the process from anesthesia to awakening of patients, and link the professional care of anesthesiologists and nurses.

[0005] The first aspect of the present invention provides a method for evaluating the awakening of anesthetized patients, including:

[0006] Monitoring the target vital sign data of the target patient, where the vital sign data includes heart rate, body temperature, respiratory rate, pulse oxygen saturation, and blood pressure;

[0007] Determining the target anesthesia information and metabolic capacity corresponding to the target patient;

[0008] Preprocessing the target vital sign data to obtain vital sign characteristics;

[0009] Input the vital sign characteristics, the target anesthesia information, and the metabolic capacity into the target awakening assessment model corresponding to the target patient to determine the current awakening stage of the target patient;

[0010] Determine the awakening care information corresponding to the awakening stage;

[0011] Based on the awakening care information, perform an awakening assessment on the target patient.

[0012] The second aspect of the present invention provides an awakening assessment device for anesthetized patients, including:

[0013] A monitoring module, configured to monitor the target vital sign data of the target patient, where the vital sign data includes heart rate, body temperature, respiratory rate, pulse oxygen saturation, and blood pressure;

[0014] A first determination module, configured to determine the target anesthesia information and metabolic capacity corresponding to the target patient;

[0015] A preprocessing module, configured to preprocess the target vital sign data to obtain vital sign characteristics;

[0016] A second determination module, configured to input the vital sign characteristics, the target anesthesia information, and the metabolic capacity into the target awakening assessment model corresponding to the target patient to determine the current awakening stage of the target patient;

[0017] A third determination module, configured to determine the awakening care information corresponding to the awakening stage;

[0018] An assessment module, configured to perform an awakening assessment on the target patient based on the awakening care information.

[0019] In a possible design, the preprocessing module is specifically configured to:

[0020] Preprocess the target vital sign data, where the preprocessing includes data cleaning and normalization processing;

[0021] Extract statistical characteristics from the preprocessed vital sign data based on a sliding window;

[0022] Determine the correlation between different vital signs in the preprocessed vital sign data;

[0023] Calculate the target entropy of the vital sign data in the preprocessed vital sign data;

[0024] Integrate the statistical characteristics, the correlation, and the target entropy to obtain the vital sign characteristics.

[0025] In a possible design, the evaluation module is further configured to:

[0026] Determine the physical state classification of the target patient at the current moment according to the target vital signs;

[0027] Generate nursing guidance information corresponding to the physical state classification, so that the nursing staff can monitor the target patient according to the nursing guidance information.

[0028] In a possible design, the second determination module is further configured to:

[0029] Collect the anesthesia awakening time series data, anesthesia dosage data, and metabolic capacity data corresponding to multiple patients;

[0030] Cluster the basic information of the multiple patients to obtain N categories, where N is an integer greater than or equal to 2;

[0031] Preprocess the anesthesia awakening time series data, anesthesia dosage data, and metabolic capacity data corresponding to the patients in each of the N categories to obtain a training sample set corresponding to each of the N categories;

[0032] Construct an initial awakening recognition model based on a deep neural network;

[0033] Train and validate the initial awakening recognition model based on the training sample set corresponding to each of the N categories to obtain an awakening evaluation model corresponding to each of the N categories.

[0034] In a possible design, the evaluation module is further configured to:

[0035] Receive the nursing request of the target patient;

[0036] Analyze the nursing request to obtain the nursing needs of the target patient, and feedback the nursing needs of the target patient, so that the nursing staff can care for the target patient according to the nursing needs of the target patient.

[0037] In a possible design, the evaluation module is further configured to:

[0038] Real-time monitor the electroencephalogram signal of the target patient;

[0039] Generate an anesthesia awakening report for the target patient according to the electroencephalogram signal, the vital sign data, and the awakening nursing information.

[0040] In a possible design, the first determination module is specifically configured to:

[0041] Obtain the physical examination data of the target patient before anesthesia;

[0042] Extract the metabolic characteristics from the physical examination data;

[0043] Evaluate the metabolic capacity corresponding to the target patient based on the metabolic characteristics.

[0044] The third aspect of the present invention provides an electronic device, including a memory and a processor. When the processor executes the computer management program stored in the memory, the steps of the awakening evaluation method for anesthetized patients as described in the first aspect above are implemented.

[0045] The fourth aspect of the present invention provides a computer-readable storage medium, on which a computer management program is stored. When the computer management program is executed by a processor, the steps of the awakening evaluation method for anesthetized patients as described in the first aspect above are implemented.

[0046] In summary, it can be seen that in the embodiments provided by the present application, an awakening evaluation model for the user evaluation awakening stage can be constructed in advance, and based on this awakening evaluation model, the current awakening stage of the target patient can be determined according to the patient's vital sign data, anesthesia information, and metabolic capacity, and then the awakening care information for this awakening stage can be determined, and the patient can be evaluated for awakening according to this awakening care information. Thus, intelligent monitoring, evaluation, and communication without interruption can be carried out during the process from the patient's anesthesia to awakening, linking the professional care of anesthesiologists and nurses. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a schematic flowchart of the awakening evaluation method for anesthetized patients provided by an embodiment of the present invention;

[0048] Figure 2 It is a schematic virtual structure diagram of the awakening evaluation device for anesthetized patients provided by an embodiment of the present invention;

[0049] Figure 3 It is a schematic hardware structure diagram of the awakening evaluation device for anesthetized patients provided by an embodiment of the present invention;

[0050] Figure 4 It is a schematic diagram of an embodiment of the electronic device provided by an embodiment of the present invention;

[0051] Figure 5 It is a schematic diagram of an embodiment of the computer-readable storage medium provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0052] 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. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0053] In the following description, specific embodiments of the present invention will be described with reference to steps and symbols performed by one or more computers, unless otherwise stated. Therefore, these steps and operations will be referred to several times as being performed by a computer. As used herein, computer execution includes operations of a computer processing unit that represents electronic signals in a structured form of data. This operation transforms the data or maintains it in a position in the computer's memory system, which can be reconfigured or otherwise changed in a manner well known to those skilled in the art to change the operation of the computer. The data structure in which the data is maintained is a physical location in the memory, which has specific characteristics defined by the data format. However, the principles of the present invention are described in the above text, which does not represent a limitation. Those skilled in the art will understand that the various steps and operations described below can also be implemented in hardware.

[0054] The principles of the present invention operate in many other general-purpose or specific-purpose computing, communication environments or configurations. Examples of well-known computing systems, environments, and configurations suitable for the present invention may include, but are not limited to, mobile phones, personal computers, servers, multi-processor systems, microcomputer-based systems, mainframe computers, and distributed computing environments, including any of the above systems or devices.

[0055] The terms "first", "second", "third", etc. in the present invention are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion.

[0056] The awakening evaluation method for anesthetized patients provided by the present invention will be described below from the perspective of an awakening evaluation device for anesthetized patients. The awakening evaluation device for anesthetized patients can be a server or a service unit in the server, and is not specifically limited.

[0057] Please refer to Figure 1 , Figure 1 which is a schematic flowchart of the awakening evaluation method for anesthetized patients provided by the embodiments of the present invention. The awakening evaluation method for anesthetized patients includes:

[0058] 101. Monitor the target vital sign data of the target patient.

[0059] In this embodiment, the awakening assessment device for anesthetized patients can monitor the target vital sign data of the target patient in real time. Among them, the vital sign data includes, but is not limited to, heart rate, body temperature, respiratory rate, pulse oxygen saturation, and blood pressure. Here, the method of monitoring the target vital sign data of the target patient is specifically defined. For example, the awakening assessment device for anesthetized patients can collect physical sign signals through vital sign sensors, and the vital sign sensors include, but are not limited to, temperature sensors, blood glucose sensors, heart rate sensors, blood oxygen sensors, uric acid sensors, and pulse sensors. At the same time, the electrocardiogram of the target patient can be collected through an electrocardiograph; then the physical sign signals are converted into electrical signals, and the collected electrical signals are processed, such as analog-to-digital conversion, noise filtering, signal amplification, etc.; and the processed electrical signals (such as mV, mA) are converted into relevant physical sign parameters (temperature, blood glucose, heart rate, blood oxygen, etc.). The following is a detailed description of the target vital sign data:

[0060] Monitor changes in heart rate and blood pressure to ensure they are within a safe range and avoid blood pressure drops or abnormal heart rates caused by residual anesthetic drugs.

[0061] Respiratory rate and depth: Check whether breathing has recovered spontaneously, whether the respiratory rate is normal, and whether the respiratory depth is sufficient to ensure that the patient can maintain sufficient oxygen exchange.

[0062] Oxygen saturation: Use a pulse oximeter to monitor oxygen saturation and ensure that it remains above 95%, indicating good respiratory function in the patient.

[0063] Assessment of consciousness state: Use a sedation scoring scale to evaluate the patient's level of consciousness and ensure that the patient can respond to stimuli, such as opening the eyes, speaking, or following simple instructions. Monitor whether the patient can breathe spontaneously, which is an important sign of the recovery of consciousness.

[0064] Pain assessment: Use a pain assessment scale, such as the Facial Expression Pain Scale or the Numerical Rating Scale, to assess whether the patient has pain and give analgesic treatment in a timely manner.

[0065] Monitoring of muscle relaxation: Monitor the degree of muscle relaxation to ensure that the effect of muscle relaxant drugs has subsided and the muscle strength has recovered to a level sufficient to support spontaneous breathing and limb movement.

[0066] It should be noted that the awakening assessment device for anesthetized patients can also monitor the electroencephalogram (EEG) signal of the target patient in real time and evaluate the depth of anesthesia of the target patient by analyzing the characteristics of the EEG. For example, using the BIS (bispectral index) or Narcotrend index, AI can identify changes in the state of consciousness to ensure the safety of the patient during the awakening process.

[0067] In one embodiment, after obtaining the target vital sign data, the awakening assessment device for anesthetized patients also performs the following operations:

[0068] Determine the physical state classification of the target patient at the current moment according to the target vital signs;

[0069] Generate nursing guidance information corresponding to the physical state classification, so that the nursing staff can monitor the target patient according to the nursing guidance information.

[0070] In this embodiment, classification can be performed according to the surgical condition of the target patient or the physical state of the target patient. For example, the physical state of the target patient can be divided into three levels. The classification situation of the target patient at the current moment is determined according to different vital signs. Then, corresponding nursing guidance information is generated according to the classification situation, and the nursing guidance information is notified to the nursing staff, so that the nursing staff can perform corresponding nursing on the target patient according to the nursing guidance information, such as using a ventilator or a defibrillator.

[0071] 102. Determine the target anesthesia information and metabolic capacity corresponding to the target patient.

[0072] In this embodiment, the anesthesia dosage and metabolic capacity used by the target patient are the most important factors affecting the patient's awakening time. Therefore, it is necessary to determine the target anesthesia information corresponding to the target patient. Specifically, the awakening evaluation device for anesthetized patients can send the hospitalization information or surgical information of the target patient to the anesthesiologist to obtain the target anesthesia information corresponding to the target patient. The target anesthesia information includes, but is not limited to, anesthesia time, anesthesia dosage, surgical duration, etc.; at the same time, the awakening evaluation device for anesthetized patients can obtain the physical examination data of the target patient before anesthesia, extract the metabolic characteristics from the physical examination data, and then evaluate the metabolic capacity corresponding to the target patient according to the metabolic characteristics (a model can be trained in advance, and the metabolic characteristics are evaluated based on this model to obtain the metabolic capacity corresponding to the target patient).

[0073] 103. Preprocess the target vital sign data to obtain vital sign features.

[0074] In this embodiment, after determining the target vital sign data, the target vital sign data can be preprocessed. Specifically: preprocess the target vital sign data, and the preprocessing includes data cleaning and normalization processing; extract statistical features from the preprocessed vital sign data based on a sliding window; determine the correlation between different vital signs in the preprocessed vital sign data; calculate the target entropy of the vital sign data in the preprocessed vital sign data; integrate the statistical features, the correlation, and the target entropy to obtain the vital sign features.

[0075] It should be noted that missing values, outliers, and incorrect data are removed or corrected through data cleaning to ensure data quality. The data is normalized or standardized to make features with different dimensions comparable. The time series features of vital sign data are analyzed to identify trends and seasonal patterns. Statistical features such as the mean, standard deviation, maximum, and minimum within a sliding window are calculated to reflect the short-term changes in vital signs. The variability of heart rate data, such as RMSSD (root mean square of successive NN interval differences) and SDNN (standard deviation of NN intervals), is analyzed to reflect the activity of the autonomic nervous system. The mean, maximum, minimum, etc. of respiratory rate are calculated, as well as the changes in respiratory depth. The correlations between different vital signs are analyzed, such as the relationships between heart rate and blood pressure, and between respiratory rate and blood oxygen saturation. The time series data is transformed into the frequency domain to analyze the frequency components of vital signs and identify the main frequency peaks. The entropy of vital sign data, such as sample entropy and approximate entropy, is calculated to reflect the complexity and randomness of the data. The types and doses of anesthetic drugs used are taken as features, considering their effects on vital signs. At the same time, age, gender, weight, underlying diseases, etc. are considered, as these information may affect the changes in vital signs.

[0076] 104. Input the vital sign features, target anesthetic information, and metabolic capacity into the target awakening assessment model corresponding to the target patient to determine the current awakening stage of the target patient.

[0077] In this embodiment, the awakening assessment device for anesthetized patients can pre-train multiple different types of awakening assessment models. After determining the vital sign features, target anesthetic information, and metabolic capacity corresponding to the target patient, the vital sign features, target anesthetic information, and metabolic capacity are input into the target awakening assessment model corresponding to the target patient to determine the current awakening stage of the target patient. Different awakening stages correspond to different awakening care information.

[0078] When training the awakening assessment model, the awakening assessment device for anesthetized patients can collect the anesthetic awakening time series data, anesthetic dosage data, and metabolic capacity data corresponding to multiple patients, and cluster the basic information of multiple patients to obtain N categories, where N is an integer greater than or equal to 2; preprocess the anesthetic awakening time series data, anesthetic dosage data, and metabolic capacity data corresponding to the patients in each of the N categories (the preprocessing has been described in detail in step 103 above, and will not be elaborated here specifically) to obtain the training sample set corresponding to each of the N categories; and divide the training sample set into a training set and a validation set, and the specific division ratio is not limited. For example, the data ratio between the training set and the validation set is 8:2;

[0079] After that, an initial wake-up recognition model is constructed based on a deep neural network, and the initial wake-up recognition model is trained and verified based on the training sample set corresponding to each of the N categories, so as to obtain a wake-up evaluation model corresponding to each of the N categories.

[0080] 105. Determine the wake-up care information corresponding to the wake-up stage.

[0081] In this embodiment, the period from when the patient is anesthetized to complete wake-up can be divided into several different wake-up stages. Different wake-up stages correspond to different vital sign information and different wake-up care information. After determining the wake-up stage, the wake-up care information corresponding to this wake-up stage can be determined. The wake-up care information can be wake-up audio, which can be voice, music, combined audio of voice and music, etc., including but not limited to the existing audio in the hospital, the audio provided by the family members of the target patient, and other audio.

[0082] It should be noted that the process from when the patient is anesthetized to complete wake-up can be divided into 5 wake-up cycles in advance. Each wake-up cycle corresponds to a wake-up stage. When waking up in the next wake-up cycle, the vital sign data of the target patient collected in the previous wake-up cycle and the body reaction state of the target patient corresponding to voice wake-up can be referred to.

[0083] 106. Perform a wake-up evaluation on the target patient based on the wake-up care information.

[0084] In this embodiment, after determining the wake-up care information, the wake-up evaluation device for anesthetized patients can perform a wake-up evaluation on the patient based on this wake-up care information. That is, after determining the wake-up care information, a wake-up operation is performed on the target patient based on this wake-up care information. Then, the body reaction state of the target patient is obtained, and the corresponding wake-up evaluation items are determined (the wake-up evaluation items include the evaluation content for evaluating the wake-up state of anesthetized patients), and the wake-up situation of the target patient is evaluated based on this body reaction state.

[0085] It should be noted that the wake-up evaluation device for anesthetized patients can also receive the care requests of the target patient; analyze the care requests to obtain the care needs of the target patient, and feedback the care needs of the target patient, so that the nursing staff can provide care to the target patient according to the care needs of the target patient.

[0086] It should also be noted that the wake-up evaluation device for anesthetized patients generates a corresponding anesthesia wake-up report based on the collected data and the care information in each stage (including wake-up care information, care information corresponding to care needs, and care guidance information).

[0087] In summary, it can be seen that in the embodiments provided by the present application, an awakening assessment model for the user assessment awakening stage can be constructed in advance, and based on this awakening assessment model, the current awakening stage of the target patient can be determined according to the patient's vital sign data, anesthesia information, and metabolic capacity. Furthermore, the awakening care information for this awakening stage can be determined, and the patient can be evaluated for awakening according to this awakening care information. Thus, intelligent monitoring, evaluation, and communication without interruption can be carried out during the process from the patient's anesthesia to awakening, linking the professional care of anesthesiologists and nurses.

[0088] The embodiments of the present invention have been described above from the perspective of the awakening assessment method for anesthetized patients. Next, the embodiments of the present invention will be described from the perspective of the awakening assessment device for anesthetized patients.

[0089] Please refer to Figure 2 , the schematic virtual structure diagram of the awakening assessment device for anesthetized patients in the embodiments of the present invention. The awakening assessment device 200 for anesthetized patients includes:

[0090] A monitoring module 201, configured to monitor the target vital sign data of the target patient, where the vital sign data includes heart rate, body temperature, respiratory rate, pulse oxygen saturation, and blood pressure;

[0091] A first determination module 202, configured to determine the target anesthesia information and metabolic capacity corresponding to the target patient;

[0092] A preprocessing module 203, configured to preprocess the target vital sign data to obtain vital sign features;

[0093] A second determination module 204, configured to input the vital sign features, the target anesthesia information, and the metabolic capacity into the target awakening assessment model corresponding to the target patient to determine the current awakening stage of the target patient;

[0094] A third determination module 205, configured to determine the awakening care information corresponding to the awakening stage;

[0095] An evaluation module 206, configured to evaluate the awakening of the target patient based on the awakening care information.

[0096] In a possible design, the preprocessing module 203 is specifically configured to:

[0097] Preprocess the target vital sign data, where the preprocessing includes data cleaning and standardization processing;

[0098] Extract statistical features from the preprocessed vital sign data based on a sliding window;

[0099] Determine the correlation between different vital signs in the preprocessed vital sign data;

[0100] Calculate the target entropy of the vital sign data in the preprocessed vital sign data;

[0101] Integrate the statistical features, the correlation, and the target entropy to obtain the vital sign features.

[0102] In a possible design, the evaluation module 206 is further configured to:

[0103] Determine the physical state grading of the target patient at the current moment according to the target vital signs;

[0104] Generate nursing guidance information corresponding to the physical state grading, so that the nursing staff can monitor the target patient according to the nursing guidance information.

[0105] In a possible design, the second determination module 204 is further configured to:

[0106] Collect the anesthesia awakening time series data, anesthesia dosage data, and metabolic capacity data corresponding to multiple patients;

[0107] Cluster the basic information of the multiple patients to obtain N categories, where N is an integer greater than or equal to 2;

[0108] Preprocess the anesthesia awakening time series data, anesthesia dosage data, and metabolic capacity data corresponding to the patients in each of the N categories to obtain a training sample set corresponding to each of the N categories;

[0109] Construct an initial awakening recognition model based on a deep neural network;

[0110] Train and verify the initial awakening recognition model based on the training sample set corresponding to each of the N categories to obtain an awakening evaluation model corresponding to each of the N categories.

[0111] In a possible design, the evaluation module 206 is further configured to:

[0112] Receive the nursing requests of the target patient;

[0113] Parse the nursing requests to obtain the nursing needs of the target patient, and feedback the nursing needs of the target patient, so that the nursing staff can care for the target patient according to the nursing needs of the target patient.

[0114] In a possible design, the evaluation module 206 is further configured to:

[0115] Real-time monitor the electroencephalogram signal of the target patient;

[0116] Generate an anesthesia awakening report for the target patient based on the electroencephalogram signal, the vital sign data, and the awakening care information.

[0117] In a possible design, the first determination module 202 is specifically configured to:

[0118] Obtain the physical examination data of the target patient before anesthesia;

[0119] Extract the metabolic characteristics from the physical examination data;

[0120] Evaluate the metabolic capacity corresponding to the target patient based on the metabolic characteristics.

[0121] Above Figure 2 The anesthesia patient awakening evaluation device in the embodiments of the present invention has been described from the perspective of modular functional entities. Next, the anesthesia patient awakening evaluation device in the embodiments of the present invention will be described in detail from the perspective of hardware processing. Please refer to FIG. 300, which is a schematic diagram of an embodiment of the anesthesia patient awakening evaluation device 300 in the embodiments of the present invention. The anesthesia patient awakening evaluation device 300 includes:

[0122] An input device 301, an output device 302, a processor 303, and a memory 304 (where the number of processors 303 can be one or more, Figure 3 Taking one processor 303 as an example). In some embodiments of the present invention, the input device 301, the output device 302, the processor 303, and the memory 304 can be connected through a communication bus or other means, where, Figure 3 Taking the connection through a communication bus as an example.

[0123] Among them, by calling the operation instructions stored in the memory 304, the processor 303 is configured to perform the following steps:

[0124] Monitor the target vital sign data of the target patient, where the vital sign data includes heart rate, body temperature, respiratory rate, pulse oxygen saturation, and blood pressure;

[0125] Determine the target anesthesia information and metabolic capacity corresponding to the target patient;

[0126] Preprocess the target vital sign data to obtain vital sign characteristics;

[0127] Input the vital sign characteristics, the target anesthesia information, and the metabolic capacity into the target awakening evaluation model corresponding to the target patient to determine the awakening stage where the target patient is currently located;

[0128] Determine the awakening care information corresponding to the awakening stage;

[0129] Perform a recovery assessment on the target patient based on the wake-up care information.

[0130] By invoking the operation instructions stored in the memory 304, the processor 303 is further configured to execute Figure 1 Any one of the corresponding embodiments.

[0131] Please refer to Figure 4 , Figure 4 which is a schematic diagram of an embodiment of the electronic device provided by the embodiment of the present invention.

[0132] As Figure 4 shown, the embodiment of the present invention provides an electronic device, including a memory 410, a processor 420, and a computer program 411 stored on the memory 410 and executable on the processor 420. When the processor 420 executes the computer program 411, the following steps are implemented:

[0133] Monitor the target vital sign data of the target patient, where the vital sign data includes heart rate, body temperature, respiratory rate, pulse oxygen saturation, and blood pressure;

[0134] Determine the target anesthesia information and metabolic capacity corresponding to the target patient;

[0135] Preprocess the target vital sign data to obtain vital sign features;

[0136] Input the vital sign features, the target anesthesia information, and the metabolic capacity into the target wake-up assessment model corresponding to the target patient to determine the wake-up stage at which the target patient is currently located;

[0137] Determine the wake-up care information corresponding to the wake-up stage;

[0138] Perform a recovery assessment on the target patient based on the wake-up care information.

[0139] In the specific implementation process, when the processor 420 executes the computer program 411, it can implement Figure 1 Any one of the corresponding embodiments.

[0140] Since the electronic device introduced in this embodiment is the device used to implement an anesthesia patient's wake-up assessment device in the embodiment of the present invention, based on the method introduced in the embodiment of the present invention, those skilled in the art can understand the specific implementation manners and various variations of the electronic device in this embodiment. Therefore, the specific implementation of how this electronic device implements the method in the embodiment of the present invention will not be described in detail here. As long as the device used by those skilled in the art to implement the method in the embodiment of the present invention belongs to the scope of protection of the present invention.

[0141] Please refer to FIG. 500, which is a schematic diagram of an embodiment of a computer-readable storage medium provided by an embodiment of the present invention.

[0142] As shown in FIG. 500, an embodiment of the present invention also provides a computer-readable storage medium 500, on which a computer program 511 is stored. When the computer program 511 is executed by a processor, the following steps are implemented:

[0143] Monitor the target vital sign data of the target patient, where the vital sign data includes heart rate, body temperature, respiratory rate, pulse oxygen saturation, and blood pressure;

[0144] Determine the target anesthesia information and metabolic capacity corresponding to the target patient;

[0145] Preprocess the target vital sign data to obtain vital sign features;

[0146] Input the vital sign features, the target anesthesia information, and the metabolic capacity into the target awakening assessment model corresponding to the target patient to determine the current awakening stage of the target patient;

[0147] Determine the awakening care information corresponding to the awakening stage;

[0148] Perform an awakening assessment on the target patient based on the awakening care information.

[0149] In the specific implementation process, the computer program 511 is executed by the processor to implement Figure 1 any one of the corresponding embodiments.

[0150] It should be noted that in the above embodiments, the descriptions of the various embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0151] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0152] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded computers, or other programmable data processing devices to generate a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices produce means for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0153] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including instruction means that implement the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0154] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0155] Embodiments of the present invention also provide a computer program product, which includes computer software instructions. When the computer software instructions run on a processing device, the processing device is caused to execute the processes in the Figure 1 corresponding embodiments.

[0156] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions according to the embodiments of the present invention are generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from a website, computer, server, or data center to another website, computer, server, or data center in a wired manner (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or a wireless manner (such as infrared, wireless, microwave, etc.). The computer-readable storage medium may be any available medium that can be stored by a computer or a data storage device such as a server or a data center that includes one or more available media integrated. The available medium may be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium (such as a solid state disk (SSD)), etc.

[0157] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0158] In several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and in actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of the devices or units can be in an electrical, mechanical, or other forms.

[0159] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0160] In addition, in each embodiment of the present invention, each functional unit may be integrated into a processing unit, may exist physically alone for each unit, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of a software functional unit.

[0161] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, may be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present invention. The foregoing storage medium includes: various media such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disc that can store program codes.

[0162] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of each embodiment of the present invention.

Claims

1. A method for evaluating the awakening of an anesthetized patient, characterized in that, including: monitoring the target vital sign data of a target patient, where the vital sign data includes heart rate, body temperature, respiratory rate, pulse oxygen saturation, and blood pressure; determining the target anesthesia information and metabolic capacity corresponding to the target patient; preprocessing the target vital sign data to obtain vital sign features; inputting the vital sign features, the target anesthesia information, and the metabolic capacity into a target awakening assessment model corresponding to the target patient to determine the current awakening stage of the target patient; determining the awakening care information corresponding to the awakening stage; performing an awakening assessment on the target patient based on the awakening care information.

2. The method according to claim 1, wherein The processing of the target vital sign data to obtain vital sign features includes: preprocessing the target vital sign data, where the preprocessing includes data cleaning and normalization processing; extracting statistical features from the preprocessed vital sign data based on a sliding window; determining the correlation between different vital signs in the preprocessed vital sign data; calculating the target entropy of the vital sign data in the preprocessed vital sign data; integrating the statistical features, the correlation, and the target entropy to obtain the vital sign features.

3. The method according to claim 1, characterized in that, The method further includes: determining the physical state classification of the target patient at the current moment according to the target vital signs; generating care guidance information corresponding to the physical state classification, so that nursing staff can monitor the target patient according to the care guidance information.

4. The method according to claim 1, wherein The method further includes: collecting the anesthesia awakening time series data, anesthesia dosage data, and metabolic capacity data corresponding to multiple patients; clustering the basic information of the multiple patients to obtain N categories, where N is an integer greater than or equal to 2; preprocessing the anesthesia awakening time series data, anesthesia dosage data, and metabolic capacity data corresponding to the patients in each of the N categories to obtain a training sample set corresponding to each of the N categories; constructing an initial awakening recognition model based on a deep neural network; training and validating the initial awakening recognition model based on the training sample set corresponding to each of the N categories to obtain an awakening assessment model corresponding to each of the N categories.

5. The method according to any one of claims 1 to 4, characterized in that The method further includes: receiving the care request of the target patient; analyzing the care request to obtain the care needs of the target patient, and feeding back the care needs of the target patient, so that nursing staff can care for the target patient according to the care needs of the target patient.

6. The method according to any one of claims 1 to 4, characterized in that The method further includes: real-time monitoring the electroencephalogram signal of the target patient; generating an anesthesia awakening report of the target patient according to the electroencephalogram signal, the vital sign data, and the awakening care information.

7. The method according to any one of claims 1 to 4, characterized in that, The determining of the metabolic capacity corresponding to the target patient includes: obtaining the physical examination data of the target patient before anesthesia; extracting the metabolic characteristics from the physical examination data; evaluating the metabolic capacity corresponding to the target patient based on the metabolic characteristics.

8. An awakening assessment device for anesthetized patients, characterized in that, including: A monitoring module for monitoring target vital sign data of a target patient, where the vital sign data includes heart rate, body temperature, respiratory rate, pulse oxygen saturation, and blood pressure; A first determination module for determining target anesthesia information and metabolic capacity corresponding to the target patient; A preprocessing module for preprocessing the target vital sign data to obtain vital sign features; A second determination module for inputting the vital sign features, the target anesthesia information, and the metabolic capacity into a target recovery assessment model corresponding to the target patient to determine the current recovery stage of the target patient; A third determination module for determining wake-up nursing information corresponding to the recovery stage; An assessment module for performing a recovery assessment on the target patient based on the wake-up nursing information.

9. An electronic device, characterized in that, It includes: A memory and a processor, where the processor is used to implement the recovery assessment method for an anesthetized patient as described in any one of claims 1 to 7 above when executing a computer management program stored in the memory.

10. A computer-readable storage medium, on which a computer management program is stored, characterized in that: The computer management program, when executed by the processor, implements the recovery assessment method for an anesthetized patient as described in any one of claims 1 to 7.

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