Intelligent updating method, device, system and medium for anesthetic medication database

Abstract

The present invention belongs to the field of intelligent medical assistance technology, solves the technical problem that the prior art relies on the professional experience of medical staff or a limited sample database to dispense anesthetics, resulting in inaccurate anesthetic dosage, and provides an intelligent update method, device, system and medium for an anesthetic medication database. The method includes: traversing the database according to the physical index parameters of the current user, obtaining the patient's anesthetic information and the corresponding predicted anesthetic effect curve, comparing the difference between the predicted anesthetic effect curve and the actual anesthetic effect curve, thereby optimizing the anesthetic information, and updating the database with the current patient and the newly added patient corresponding to the optimized anesthetic information. The present invention also includes a device, system and medium for executing the above method. The present invention optimizes the dosage information and predicts the anesthetic effect through the actual anesthetic effect corresponding to the anesthetic dosage information, improves the anesthetic medication database, and realizes accurate medication based on the patient's physical indicators.

Description

Technical Field

[0001] The present invention relates to the field of intelligent medical assistance technology, and in particular to an intelligent updating method, device, system and medium for an anesthetic medication database. Background Art

[0002] As we all know, when we have surgery or treat certain diseases, doctors will use anesthetic drugs to temporarily make the body or part of the body lose consciousness and pain, so as to save the human body from the pain of surgery and promote the smoother operation. Anesthetic drugs can be divided into three categories: inhaled anesthetic drugs, intravenous anesthetic drugs and local anesthetic drugs.

[0003] Narcotic drugs and psychotropic drugs are used by doctors to anesthetize patients and facilitate the smooth progress of surgery. However, if they fall into the hands of criminals, they will be used as drugs and endanger society. To this end, the state has issued strict laws and regulations to regulate the use of narcotic drugs. Medical institutions shall implement the requirements of the "Regulations on the Administration of Narcotic Drugs and Psychotropic Drugs" in the procurement, storage and use of narcotic drugs. In the prior art, the storage and use of narcotic drugs are subject to relatively strict constraints, but the selection of the dosage of narcotic drugs depends on the doctor to determine the dosage of narcotic drugs for different patients based on the patient's weight, body fat percentage, etc. combined with his own professional experience.

[0004] However, the arbitrariness of different doctors in the use of anesthetic drugs will bring risks to anesthetic drug management on the one hand, and excessive dosage will cause patients to develop drug resistance on the other hand. Recently, with the rise of computer technology, especially deep learning and artificial intelligence technology, smart medical care has gradually become a direction of medical technology development. The prior art has also proposed some computer methods that do not rely on doctor experience, etc. for the use of anesthetic dosage. For example, the prior art CN112807542A proposes a method and device for anesthetic dosage control, which mainly includes: obtaining the first physiological state information of the first user; classifying the first physiological state information of the first user according to the first physiological state information of the first user to obtain the first category of physiological state information; obtaining the surgical information of the first user; inputting the first category of physiological state information and the surgical information of the first user into the preoperative intervention model to obtain the first preoperative intervention plan; according to the first preoperative intervention plan, preoperatively intervene on the first user to obtain the first preoperative intervention result; inputting the first preoperative intervention result and the surgical information into the anesthesia dosage estimation model to obtain the first anesthesia dosage information of the first user. The above method is based on each user's physiological status information and surgical information input into a pre-established preoperative intervention model to obtain a preoperative intervention plan, and then combines the preoperative intervention results and surgical information to obtain an anesthesia dosage estimation model. As a whole, it relies on a pre-established data model to estimate anesthesia dosage, but actually ignores the limitations of the model itself. If the data model collects fewer samples, and there is no deep learning of a large number of samples to continuously optimize the data model, or even if the accuracy of each data model reaches 90%, the accuracy of the two data models will be greatly reduced, resulting in inaccurate anesthetic dosage data obtained. Summary of the invention

[0005] In view of this, an embodiment of the present invention provides an intelligent update method, device, system and medium for an anesthetic medication database, so as to solve the technical problem in the prior art of inaccurate anesthetic dosage caused by relying on the professional experience of medical staff or a limited sample database to dispense anesthetics.

[0006] The technical solution adopted by the present invention is:

[0007] The present invention provides an intelligent updating method for an anesthetic medication database, the method comprising:

[0008] S1: Obtain the current user's physical index parameters;

[0009] S2: traversing the anesthetic medication database according to the physical indicator parameters, and outputting anesthetic information matching the physical indicator parameters of the current user, wherein the anesthetic information includes anesthetic type, dosage, usage method, and a predicted anesthetic effect curve of the anesthetic effect during the operation;

[0010] S3: obtaining an actual anesthetic effect curve of the current user during the operation under the action of the anesthetic of the anesthetic information;

[0011] S4: Compare the predicted anesthesia effect curve with the actual anesthesia effect curve to obtain medication effect difference information of the current user;

[0012] S5: Optimize the anesthetic information according to the medication effect difference information, obtain new case samples corresponding to the optimized anesthetic information and real case samples of the current user corresponding to the anesthetic information, and update the anesthetic medication database with the new case samples and the real case samples.

[0013] Preferably, S2 includes:

[0014] S21: traversing the anesthetic medication database according to the physical indicator parameter to obtain at least one reference case sample adapted to the physical sign information corresponding to the physical indicator parameter;

[0015] S22: obtaining the anesthetic information of the current user according to the anesthetic sample information of each reference case sample;

[0016] The physical sign information includes normal physical signs and obesity signs.

[0017] Preferably, the S21 includes:

[0018] S211: Obtaining physical information of the current user according to the height information and weight information of the physical indicator parameters;

[0019] S212: traversing the anesthetic medication database according to the physical indicator parameters to obtain all historical case samples corresponding to each of the physical sign information;

[0020] S213: Obtaining the reference case samples of the current user according to the physical sign information of the current user and the physical sign information corresponding to the historical case samples.

[0021] Preferably, the S211 includes:

[0022] S2111: Calculate the body mass index of the current user according to the height information and the weight information of the current user using the formula B=W / H²;

[0023] S2112: When the body mass index exceeds a preset physical sign threshold range, determining that the physical sign information of the current user is the obesity physical sign;

[0024] S2113: When the body mass index is less than or equal to the preset physical sign threshold range, determining that the physical sign information of the current user is the normal physical sign;

[0025] Where B is body mass index, W is patient weight, and H is patient height.

[0026] Preferably, the S22 includes:

[0027] S221: Obtaining a sample body fat percentage of a reference case sample and a target body fat percentage of the current user;

[0028] S222: Calculate the compensation dose of the anesthetic agent according to the sample body fat percentage and the target body fat percentage using the formula A=Wa(1-C);

[0029] S223: Obtaining the anesthetic dosage information of the current user according to the compensation dose and the sample medication information of the reference case sample;

[0030] Among them, A is the compensation dose of anesthetics used by the current user relative to the reference case sample, W is the patient's weight, a is the compensation dose of anesthetics used per unit weight, and C is the body fat percentage ratio of the current user to the reference case sample.

[0031] Preferably, if the current patient's physical sign information is an obesity sign, the S222 includes:

[0032] S2221: Obtain the gender and age information of the current user;

[0033] S2222: Calculate the target body fat percentage according to the gender information, the age information and the body mass index using the formula K=mB+nY-S+b;

[0034] S2223: Calculate the compensation dose of the anesthetic agent according to the target body fat percentage and the sample body fat percentage by A=Wa{1-(mB+nY-S+b) / Q};

[0035] Among them, Q is the sample body fat percentage of the reference case sample, K is the body fat percentage of the current user, B is the body mass index, Y is the patient's age, S is the patient's gender, and b, m, and n are constants.

[0036] Preferably, S3 includes:

[0037] S31: using the BIS anesthesia depth monitor to measure the EEG waveform of the current user during the operation in real time and collect corresponding external condition feedback information;

[0038] S32: Outputting the real-time sleep level of the current user through the BIS evaluation system according to the brain wave waveform and the external condition feedback information;

[0039] S33: establishing the current anesthesia effect curve according to the real-time sleep level of the current user;

[0040] The external condition feedback information includes at least one of the following: feedback information corresponding to swallowing reflex, eyelash reflex, and pupil size.

[0041] The present invention also provides an intelligent updating device for an anesthetic medication database, the device comprising:

[0042] Physical index detection module: obtain the physical index parameters of the current user;

[0043] Data processing module: traverses the anesthetic medication database according to the physical indicator parameters, and outputs anesthetic information matching the physical indicator parameters of the current user, wherein the anesthetic information includes anesthetic type, dosage, usage method and a predicted anesthetic effect curve of the anesthetic effect during surgery;

[0044] Current anesthesia effect module: obtains the actual anesthesia effect curve of the current user during the operation under the action of the anesthetic of the anesthetic information;

[0045] Anesthesia effect difference module: compares the predicted anesthesia effect curve with the actual anesthesia effect curve to obtain the medication effect difference information of the current user;

[0046] Database update module: optimizes the anesthetic information according to the medication effect difference information, obtains new case samples corresponding to the optimized anesthetic information and real case samples of the current user corresponding to the anesthetic information, and updates the anesthetic medication database with the new case samples and the real case samples.

[0047] The present invention also provides an intelligent update system for an anesthetic medication database, comprising: at least one processor, at least one memory, and computer program instructions stored in the memory, and when the computer program instructions are executed by the processor, any of the methods described above is implemented.

[0048] The present invention also provides a medium on which computer program instructions are stored, and when the computer program instructions are executed by a processor, any of the above-mentioned methods is implemented.

[0049] In summary, the beneficial effects of the present invention are as follows:

[0050] The present invention provides an intelligent updating method, device, system and medium for an anesthetic medication database, which obtains the body index parameters of the current user, and then searches the anesthetic medication database for reference case samples similar to the body index parameters, and determines the anesthetic dosage information and predicted anesthetic effect curve of the current user according to the anesthetic medication information of the reference case samples and the corresponding anesthetic effect curve during the operation, and then collects the real-time anesthetic effect during the operation of the current user to establish the actual current anesthetic effect curve; optimizes the anesthetic dosage information of the current user and its corresponding predicted anesthetic effect curve according to the difference between the predicted anesthetic effect curve and the current anesthetic effect curve, obtains a target corresponding to the body index parameters that are closer to the optimized anesthetic dosage information and anesthetic effect curve, updates the anesthetic medication database with the newly added samples corresponding to the current user and the optimized anesthetic information, enriches the reference samples of the anesthetic medication database, realizes accurate anesthetic medication according to the patient's body index parameters, and improves the accuracy of medication. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the drawings required for use in the embodiment of the present invention will be briefly introduced below. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work, and these are all within the protection scope of the present invention.

[0052] Figure 1 Schematic diagram of the process of the intelligent updating method of the anesthetic medication database in Example 1;

[0053] Figure 2 This is a schematic diagram of a process for obtaining a predicted anesthetic effect curve in Example 1;

[0054] Figure 3 This is a schematic diagram of the process of obtaining historical medical record samples in Example 1;

[0055] Figure 4 This is a schematic diagram of the process of obtaining the actual anesthesia effect curve in Example 1;

[0056] Figure 4-1 This is a schematic diagram of the structure of the BIS anesthesia depth monitor in Example 1;

[0057] Figure 5 This is a schematic diagram of the structure of the intelligent updating device of the anesthetic medication database in Example 2;

[0058] Figure 6 This is a schematic diagram of the structure of the intelligent update system of the anesthetic medication database in Example 3. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solution and advantages of the embodiment of the present invention clearer, the technical solution in the embodiment of the present invention will be clearly and completely described in conjunction with the drawings in the embodiment of the present invention. It should be noted that, in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. In the description of the present invention, it should be understood that the orientation or position relationship indicated by the terms "center", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc. is based on the orientation or position relationship shown in the drawings, which is only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the present invention. Moreover, the term "include", "comprise" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such a process, method, article or device. In the absence of further restrictions, the elements defined by the phrase "comprising..." do not exclude the existence of other identical elements in the process, method, article or device comprising the elements. If there is no conflict, the various features of the present invention and the embodiments can be combined with each other, all within the protection scope of the present invention.

[0060] Example 1

[0061] As mentioned in the background technology, the current anesthetic dosage is determined by the anesthesiologist based on the patient's weight and body fat percentage, combined with the anesthesiologist's professional experience. This method has large fluctuations and cannot accurately prescribe medication for each patient, thus affecting the anesthetic effect during surgery. Figure 1 , Figure 1The flowchart of the intelligent updating method of the anesthetic medication database in the first embodiment of the present invention is shown in FIG. The anesthetic medication database here can be a small database established by the hospital itself, or a special database constructed in the medical system of a region, and the hospitals in the region can share the anesthetic medication history of the users in the region, including the historical medication type, dosage, surgical information, etc. The anesthetic medication database continuously adds new case samples to establish samples for each user who has used anesthetic drugs. For new users who have not used anesthetic drugs, similar reference samples can be found in the database based on the user's physical index parameters, and then a reference anesthetic dosage is given. Of course, as the number of users (such as patients of different ages, genders, and physical index data) increases, the database is continuously updated. As the number of samples increases, more accurate values ​​will be given to the anesthetic medication information of new users. For example, when a user uses anesthetic drugs at the age of 20, his body is thin, but when he uses anesthetic drugs at the age of 30, he becomes fat, and when he uses anesthetic drugs at the age of 50, he becomes thin again. Case samples can be established for different periods of time. These case samples can be used as references for new users later. At the same time, data models can be established for the same patient at different age groups, and case samples for the entire life process of the same user can also be established (some medication contraindications noted according to the patient's previous experience in the anesthetic medication process during subsequent use), which can greatly improve the user's anesthetic medication experience. The intelligent update method of the anesthetic medication database in Example 1 of the present invention includes:

[0062] S1: Obtain the current user's physical index parameters;

[0063] Specifically, the patient undergoes a physical examination before surgery, and the physical examination items corresponding to the physical indicator parameters include at least one of the following: historical case examination, current physical physiological indicators and life phenomenon status, etc.; historical case examination includes but is not limited to: hypertension, diabetes, lung disease, heart disease, allergy history, etc., current physical physiological indicators include but are not limited to: heart rate, respiratory rate, blood pressure, blood routine, blood biochemistry, electrocardiogram, color ultrasound of various parts of the body, B-ultrasound and other imaging data; life phenomenon status includes but is not limited to: body temperature, sleep, endocrine, heart rate, emotional response and metabolism and other parameters. The patient's anesthetic dosage information is determined based on physical index parameters, and the anesthetic dosage information includes at least the following information: anesthetic category, anesthetic dosage, anesthetic use time and anesthetic type; wherein, anesthetic categories include but are not limited to local anesthesia such as lidocaine, tetracaine, ropivacaine, etc., and general anesthesia such as inhaled anesthetics (sevoflurane, isoflurane, enflurane, etc.), intravenous anesthetics (propofol, etomidate, etc.), anesthetic analgesics (maca, fentanyl, etc.) and muscle relaxants (atracurium, cisatracurium, etc.), and anesthetic types include but are not limited to local anesthesia, spinal anesthesia and general anesthesia, and each type of anesthesia can be further divided.

[0064] S2: traversing the anesthetic medication database according to the physical indicator parameters, and outputting anesthetic information matching the physical indicator parameters of the current user, wherein the anesthetic information includes anesthetic type, dosage, usage method, and a predicted anesthetic effect curve of the anesthetic effect during the operation;

[0065] Specifically, the anesthetic information includes anesthetic dosage information and anesthesia effect curves. The anesthetic dosage information includes anesthetic type, dosage and usage method. The usage method includes anesthesia type and usage time; collect patient case information that has completed the operation to establish an anesthetic medication database. The anesthetic medication database includes the physical index parameters of each surgical patient and their anesthetic dosage information, as well as the anesthetic effect curve established by the patient's anesthesia depth information during the operation. At the same time, it also includes the anesthetic dosage information and the corresponding anesthetic effect curve corresponding to the physical index parameters of virtual patients fitted with each physical index parameter when multiple surgical patients with similar physical index parameters undergo the same or similar operations; traverse the anesthetic medication database with the physical index parameters of the current user, and find at least one historical case as a reference sample for the current user, so as to obtain the anesthetic dosage information of the current user and the predicted anesthetic effect curve of the surgical process. The surgical process includes the pre-operative preparation stage, the surgical stage, and the post-operative awakening stage.

[0066] It should be noted that the anesthetic dosage information of the current user can be a comprehensive value of the anesthetic dosage information of multiple reference samples, or it can be a further comparison of physical indicator parameters, with the anesthetic dosage information of the sample with the highest approximation of important parameters. The predicted anesthetic effect curve is a relationship curve between time and sleepiness level corresponding to the anesthetic dosage information, and the curve can be obtained by curve fitting of multiple samples.

[0067] In one embodiment, see Figure 2 , said S2 comprises:

[0068] S21: traversing the anesthetic medication database according to the physical indicator parameter to obtain at least one reference case sample adapted to the physical sign information corresponding to the physical indicator parameter;

[0069] Specifically, the physical index parameters of the current user are compared with the physical index parameters of each sample in the database, and similar samples are output as reference samples to obtain reference case samples. The reference case samples include surgical information, anesthetic information, etc. of the sample patients.

[0070] In one embodiment, see Figure 3 , the S21 comprises:

[0071] S211: Obtaining physical information of the current user according to the height information and weight information of the physical indicator parameters;

[0072] Specifically, with respect to the use of anesthetics, physical sign information is selected as a method for screening samples to improve the reliability of reference samples; the physical sign information of the current user is determined using the height information and weight information in the body indicator parameters, and the physical sign types include obese physique and normal physique.

[0073] In one embodiment, the S211 includes:

[0074] S2111: Calculate the body mass index of the current user according to the height information and the weight information of the current user using the formula B=W / H²;

[0075] S2112: When the body mass index exceeds a preset physical sign threshold range, determining that the physical sign information of the current user is the obesity physical sign;

[0076] S2113: When the body mass index is less than or equal to the preset physical sign threshold range, determining that the physical sign information of the current user is the normal physical sign;

[0077] Where B is body mass index, W is patient weight, and H is patient height.

[0078] Specifically, the body mass index is an indicator parameter to measure the fatness or thinness of a human body. The patient's body mass index can be calculated according to the formula based on the patient's height and weight information. It should be noted that the normal value of the body mass index is between 20 and 25. A value exceeding 25 is considered a sign of overweight, and a value exceeding 30 is considered a sign of obesity. For signs of overweight, medication can be given based on actual body weight or a dose slightly lower than the dose corresponding to the actual body weight. However, for patients with obese physical signs, the drug dose corresponding to the actual body weight must be reduced to a certain extent, otherwise the drug dose will be too large, posing a danger to the patient. For example, for a person with a height of 1.75m and a weight of 75kg, his body mass parameter is B = 75 / 1.75² ≈24.49, which is between 20 and 25. This is a normal sign, and medication can be given based on the actual weight. However, for a person with a height of 1.75m and a weight of 98kg, his body mass parameter is B = 98 / 1.75² = 32, which is over 30. This is a sign of obesity, and the dosage of the drug used should be reduced to a certain extent to prevent excessive medication.

[0079] S212: traversing the anesthetic medication database according to the physical indicator parameters to obtain all historical case samples corresponding to each of the physical sign information;

[0080] Specifically, the anesthetic medication database is traversed using the body indicator parameters to obtain historical case samples of various physical sign information similar to the body indicator parameters, that is, the historical case samples include case samples with normal physical signs and obesity physical signs.

[0081] S213: Obtaining the reference case samples of the current user according to the physical sign information of the current user and the physical sign information corresponding to the historical case samples.

[0082] Specifically, case samples corresponding to physical sign information similar to that of the current user are screened out from historical case samples as reference case samples for the current user; using case samples with the same physical signs as reference samples can greatly ensure that the anesthetic effect of the anesthetic on the current sample is closer to the predicted effect, thereby improving the accuracy of medication.

[0083] S22: obtaining the anesthetic information of the current user according to the anesthetic sample information of each reference case sample;

[0084] The physical sign information includes normal physical signs and obesity signs.

[0085] Specifically, the anesthetic sample information of each reference sample is compared to obtain the anesthetic information of the current user; for example, when the body index parameters are similar but the corresponding anesthetic effects of different anesthetic types are compared, the anesthetic sample information whose anesthetic effect better meets the current surgical requirements is selected as the anesthetic information of the current user; for another example, when the body index parameters are similar but the anesthetic types are the same, the corresponding anesthetic effects are compared, and all the anesthetic sample information is comprehensively used to obtain the anesthetic information; therefore, the method of generating the anesthetic information is not specifically limited here.

[0086] In one embodiment, if the current patient's physical sign information is an obesity sign, S22 includes:

[0087] S221: Obtaining a sample body fat percentage of a reference case sample and a target body fat percentage of the current user;

[0088] S222: Calculate the compensation dose of the anesthetic agent according to the sample body fat percentage and the target body fat percentage using the formula A=Wa(1-C);

[0089] Specifically, the reason why patients with obese signs need a certain reduction in medication according to actual body weight compared to patients with normal signs is that their body fat percentages are different. Patients with obese signs have higher body fat content. If medication is given according to their actual body weight, it is bound to cause a falsely high dosage, which may cause damage to the patient's health. For this reason, in this embodiment, different methods are used to determine the dosage according to normal signs and obese signs. For patients with normal signs, if their weight is 75kg, the selected anesthetic is Liyuexi, and the compensation dose of the anesthetic dosage per unit body weight of Liyuexi is 0.003-0.005mg / kg. If their body mass index determines that they have obese signs, and it is known that their body fat percentage is 30%, and the body fat percentage of the reference sample is 25%, then the corresponding anesthetic compensation dose is A = 75kg × 0.003~0.005mg / kg × (1-30% / 25%) = -0.45~(-0.75)mg; that is, for current users with signs of obesity, the dosage of the reference sample should be reduced to ensure the safety of the dosage.

[0090] In one embodiment, the S222 includes:

[0091] S2221: Obtain the gender and age information of the current user;

[0092] S2222: Calculate the target body fat percentage according to the gender information, the age information and the body mass index using the formula K=mB+nY-S+b;

[0093] S2223: Calculate the compensation dose of the anesthetic agent according to the target body fat percentage and the sample body fat percentage by A=Wa{1-(mB+nY-S+b) / Q};

[0094] Among them, Q is the sample body fat percentage of the reference case sample, K is the body fat percentage of the current user, B is the body mass index, Y is the patient's age, S is the patient's gender, and b, m, and n are constants.

[0095] Specifically, in this embodiment, after calculating the body mass parameter based on the patient's weight information and height information and determining that the patient has obesity signs, before determining the medication dosage, the body fat percentage should also be calculated based on the patient's gender information and age information to facilitate the calculation of the corresponding medication dosage. For example, when the patient is male, S is 1, and when the patient is female, S is 0. Assuming m=1.2, n=0.23, b=5.4, then a 40-year-old adult male with a height of 1.75m and a weight of 98kg has a body mass parameter of B = 98 / 1.75 2 = 32 is more than 30, which is a sign of obesity, so calculate the body fat percentage K = 1.2 × 32 + 0.23 × 40 -1- 5.4 = 41.2, then the body fat percentage is 41.2%. When calculating, the dosage of the drug should be reduced according to the body fat percentage parameter of 41.2%.

[0096] S223: Obtaining the anesthetic dosage information of the current user according to the compensation dose and the sample medication information of the reference case sample;

[0097] Among them, A is the compensation dose of anesthetics used by the current user relative to the reference case sample, W is the patient's weight, a is the compensation dose of anesthetics used per unit weight, and K is the body fat percentage ratio of the current user to the reference case sample.

[0098] S3: obtaining an actual anesthesia effect curve of the current user during the operation under the action of the anesthetic of the anesthetic information;

[0099] Specifically, during the operation, the BIS (Bispectral Idex) anesthesia depth monitor is used to evaluate the sedation depth of the current user, so as to establish a time-current anesthesia effect curve, which is convenient for analyzing the anesthetic effect produced by the body index parameters corresponding to the current anesthetic dosage information during the operation; for example, when the BIS value is 85-100, it means that the current user is in an awake state, when the BIS value is 65-85, it means that the current user is in a sedated state, when the BIS value is 40-65, it means that the current user is in an anesthetized state, and when the BIS value is less than 40, it means that the current user may have burst suppression; the BIS evaluation system can intuitively and accurately obtain the anesthesia effect of the current user during the operation; targeted pre-operative intervention and post-operative recovery intervention can be carried out, please refer to Figure 4-1 As shown in the figure, the BIS (Bispectral Idex) anesthesia depth monitor includes a BIS module and a BIS sensor. The BIS sensor is attached to the patient's forehead, and the user collects EEG information. The BIS (Bispectral Idex) anesthesia depth monitor measures the linear components of the EEG (including frequency and power), and analyzes the nonlinear relationship between the component waves (phase and harmonics). The EEG signals that can represent different sedation levels are selected, standardized and digitized, and finally converted into a simple quantitative indicator, namely the BIS value.

[0100] In one embodiment, see Figure 4 , said S3 comprises:

[0101] S31: using the BIS anesthesia depth monitor to measure the EEG waveform of the current user during the operation in real time and collect corresponding external condition feedback information;

[0102] S32: Outputting the real-time sleep level of the current user through the BIS evaluation system according to the brain wave waveform and the external condition feedback information;

[0103] Specifically, BIS mainly reflects the excitation or inhibition state of the cerebral cortex. The size of the BIS value is highly correlated with sedation, consciousness, and memory. By collecting EEG signals in real time, the regular electrical activity of brain cell groups is described by the amplitude, frequency, and phase of the EEG waveform. Combined with the corresponding external condition feedback, the anesthetic effect of the current anesthetic information under external stimulation is determined. At the same time, in surgeries that require continuous anesthesia, the external condition stimulation and anesthetic dosage can also be adjusted in real time; external condition feedback includes but is not limited to language response tests, swallowing reflex, eyelash reflex, pupil size and other feedback information.

[0104] S33: establishing the current anesthesia effect curve according to the real-time sleep level of the current user;

[0105] The external condition feedback information includes at least one of the following: language response, swallowing reflex, eyelash reflex, and feedback information corresponding to pupil size.

[0106] S4: Compare the predicted anesthesia effect curve with the actual anesthesia effect curve to obtain medication effect difference information of the current user;

[0107] Specifically, when there are multiple historical samples of the current user's reference sample, the anesthetic dose of the anesthetic information is the comprehensive value of the anesthetic dosage corresponding to the multiple reference samples, preferably the average value, and the predicted anesthetic effect curve is the fitting curve of the anesthetic effect curve corresponding to each reference sample; the predicted anesthetic effect curve is compared with the current anesthetic effect curve, and the value of the difference position is determined as the medication effect difference information, and the medication difference information includes at least one of the following: the sleep-wake difference corresponding to the sedation depth of the current user detected by the BIS anesthesia depth monitor before, during and after surgery.

[0108] S5: Optimize the anesthetic information according to the medication effect difference information, obtain new case samples corresponding to the optimized anesthetic information and real case samples of the current user corresponding to the anesthetic information, and update the anesthetic medication database with the new case samples and the real case samples.

[0109] Specifically, the anesthetic information is optimized through the medication effect difference information, including the adjustment of the anesthetic dosage, the adjustment of the anesthetic use time and the intervention of the external condition stimulation during the corresponding operation, so as to improve the medication effect of the target object with the same or similar physical index parameters as the current user. At the same time, the medication effect difference information is used to optimize the predicted anesthetic effect curve to obtain a new anesthetic effect curve corresponding to the optimized anesthetic information. At the same time, based on the new anesthetic effect curve and the optimized anesthetic information, a case sample under an ideal state is generated as a new case sample, and more reference samples are added for the next case sample with similar physical index parameters, so that the next medication information and the corresponding anesthetic effect curve are closer to the real anesthetic effect, so as to achieve scientific and accurate medication. That is to say, after completing the operation of the current user, two new samples will be added to the anesthetic medication database, one sample is the real sample of the current user's physical index parameters and its corresponding anesthetic information, and the other is the new case sample corresponding to the physical index parameters under the ideal state represented by the anesthetic information optimized by the medication effect difference information.

[0110] The intelligent updating method, device, system and medium of the anesthetic medication database of the present embodiment are adopted to obtain the physical index parameters of the current user, and then search the anesthetic medication database for reference case samples similar to the physical index parameters, and determine the anesthetic dosage information and predicted anesthetic effect curve of the current user according to the anesthetic medication information of the reference case samples and the corresponding anesthetic effect curve during the operation, and then collect the real-time anesthetic effect of the current user during the operation to establish the actual current anesthetic effect curve; optimize the anesthetic dosage information of the current user and its corresponding predicted anesthetic effect curve according to the difference between the predicted anesthetic effect curve and the current anesthetic effect curve, and obtain the target corresponding to the physical index parameters that are closer to the optimized anesthetic dosage information and anesthetic effect curve, and update the anesthetic medication database with the newly added samples corresponding to the current user and the optimized anesthetic information, enrich the reference samples of the anesthetic medication database, realize the precise use of anesthetics according to the patient's physical index parameters, and improve the accuracy of medication.

[0111] The method provided by the embodiment of the present invention can also help the anesthetic drug management department to scientifically supervise abnormal anesthetic drug use, reduce unnecessary medical accidents, help ease the doctor-patient relationship, and improve the user's medical experience.

[0112] Example 2

[0113] Embodiment 2 of the present invention further provides an intelligent updating device for an anesthetic medication database based on the method of embodiment 1, see Figure 5 ,include:

[0114] Physical index detection module: obtain the physical index parameters of the current user;

[0115] Data processing module: traverses the anesthetic medication database according to the physical indicator parameters, and outputs anesthetic information matching the physical indicator parameters of the current user, wherein the anesthetic information includes anesthetic type, dosage, usage method and a predicted anesthetic effect curve of the anesthetic effect during surgery;

[0116] Current anesthesia effect module: obtains the actual anesthesia effect curve of the current user during the operation under the action of the anesthetic of the anesthetic information;

[0117] Anesthesia effect difference module: compares the predicted anesthesia effect curve with the actual anesthesia effect curve to obtain the medication effect difference information of the current user;

[0118] Database update module: optimizes the anesthetic information according to the medication effect difference information, obtains new case samples corresponding to the optimized anesthetic information and real case samples of the current user corresponding to the anesthetic information, and updates the anesthetic medication database with the new case samples and the real case samples.

[0119] The intelligent updating device, intelligent updating method, device, system and medium of the anesthetic medication database of the present embodiment are used to obtain the physical index parameters of the current user, and then search the anesthetic medication database for reference case samples similar to the physical index parameters, and determine the anesthetic dosage information and predicted anesthetic effect curve of the current user according to the anesthetic medication information of the reference case samples and the corresponding anesthetic effect curve during the operation, and then collect the real-time anesthetic effect of the current user during the operation to establish the actual current anesthetic effect curve; according to the difference between the predicted anesthetic effect curve and the current anesthetic effect curve, the anesthetic dosage information of the current user and its corresponding predicted anesthetic effect curve are optimized to obtain a target corresponding to the physical index parameters that are closer to the optimized anesthetic dosage information and anesthetic effect curve, and the anesthetic medication database is updated with the newly added samples corresponding to the current user and the optimized anesthetic information, enriching the reference samples of the anesthetic medication database, realizing accurate anesthetic medication according to the patient's physical index parameters, and improving the accuracy of medication.

[0120] In one embodiment, the data processing module includes:

[0121] Reference case unit: traverses the anesthetic medication database according to the physical indicator parameters to obtain at least one reference case sample that matches the physical indicator parameters;

[0122] Anesthesia effect curve unit: obtaining the anesthetic information of the current user according to the anesthetic sample information of each reference case sample;

[0123] The physical sign information includes normal physical signs and obesity signs.

[0124] In one embodiment, the reference case unit comprises:

[0125] Physical sign information unit: obtains the physical sign information of the current user according to the height information and weight information of the physical indicator parameters;

[0126] Historical case unit: traverse the anesthetic medication database according to the physical index parameters to obtain all historical case samples corresponding to each of the physical sign information;

[0127] Case screening unit: obtaining the reference case samples of the current user according to the physical sign information of the current user and the physical sign information corresponding to the historical case samples.

[0128] In one embodiment, the vital sign information unit includes:

[0129] Body mass unit: according to the height information and the weight information of the current user, the body mass index of the current user is calculated by the formula B=W / H²;

[0130] A first body sign recognition unit: when the body mass index exceeds a preset body sign threshold range, determining that the body sign information of the current user is the obesity sign;

[0131] A second body sign recognition unit: when the body mass index is less than or equal to the preset body sign threshold range, determining that the body sign information of the current user is the normal body sign;

[0132] Where B is body mass index, W is patient weight, and H is patient height.

[0133] In one embodiment, the anesthesia effect curve unit includes:

[0134] Body fat percentage acquisition unit: acquires the sample body fat percentage of the reference case sample and the target body fat percentage of the current user;

[0135] Compensation dosage unit: according to the body fat percentage of the sample and the target body fat percentage, the compensation dosage of the anesthetic is calculated by the formula A=Wa(1-C);

[0136] Anesthesia dosage unit: obtaining the anesthetic dosage information of the current user according to the compensation dose and the sample medication information of the reference case sample;

[0137] Among them, A is the compensation dose of anesthetics used by the current user relative to the reference case sample, W is the patient's weight, a is the compensation dose of anesthetics used per unit weight, and C is the body fat percentage ratio of the current user to the reference case sample.

[0138] In one embodiment, if the current patient's physical sign information is an obese sign, the compensation dosage unit includes:

[0139] Personal information acquisition unit: obtain the gender information and age information of the current user;

[0140] Target body fat percentage unit: according to the gender information, the age information and the body mass index, the target body fat percentage is calculated by the formula K=mB+nY-S+b;

[0141] A compensation dose calculation unit: according to the target body fat percentage and the sample body fat percentage, the compensation dose of the anesthetic is calculated by A=Wa{1-(mB+nY-S+b) / Q};

[0142] Among them, Q is the sample body fat percentage of the reference case sample, K is the body fat percentage of the current user, B is the body mass index, Y is the patient's age, S is the patient's gender, and b, m, and n are constants.

[0143] In one embodiment, the current anesthesia effect module includes:

[0144] Real-time data acquisition unit: Use the BIS anesthesia depth monitor to measure the current user's brain wave waveform during the operation and collect the corresponding external condition feedback information in real time;

[0145] Awakening evaluation unit: outputs the real-time sleep level of the current user through the BIS evaluation system according to the brain wave waveform and the external condition feedback information;

[0146] Anesthesia effect curve unit: establishes the current anesthesia effect curve according to the real-time sleep level of the current user;

[0147] The external condition feedback information includes at least one of the following: feedback information corresponding to swallowing reflex, eyelash reflex, and pupil size.

[0148] The intelligent updating device of the anesthetic medication database of the present embodiment is used to obtain the physical index parameters of the current user, and then search the anesthetic medication database for reference case samples with similar physical index parameters. The anesthetic dosage information and the predicted anesthetic effect curve of the current user are determined based on the anesthetic medication information of the reference case samples and the corresponding anesthetic effect curve during the operation. Then, the real-time anesthetic effect of the current user during the operation is collected to establish the actual current anesthetic effect curve. The anesthetic dosage information of the current user and its corresponding predicted anesthetic effect curve are optimized based on the difference between the predicted anesthetic effect curve and the current anesthetic effect curve, so that the optimized anesthetic dosage information and anesthetic effect curve can be applicable to the general population, so as to realize accurate anesthetic medication according to the patient's physical index parameters and improve the accuracy of medication.

[0149] Example 3

[0150] The present invention provides an intelligent updating system and storage medium for an anesthetic medication database, such as Figure 6 As shown, the system comprises at least one processor, at least one memory and computer program instructions stored in the memory.

[0151] Specifically, the processor may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiment of the present invention.

[0152] The memory may include a large capacity memory for data or instructions. By way of example and not limitation, the memory may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. Where appropriate, the memory may include a removable or non-removable (or fixed) medium. Where appropriate, the memory may be inside or outside the data processing device. In a particular embodiment, the memory is a non-volatile solid-state memory. In a particular embodiment, the memory includes a read-only memory (ROM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM) or a flash memory or a combination of two or more of these.

[0153] The processor implements any one of the intelligent updating methods for the anesthetic medication database in the first embodiment described above by reading and executing computer program instructions stored in the memory.

[0154] In one example, the electronic device may further include a communication interface and a bus, wherein the processor, the memory, and the communication interface are connected via the bus and communicate with each other.

[0155] The communication interface is mainly used to implement communication between the modules, devices, units and / or equipment in the embodiments of the present invention.

[0156] The bus includes hardware, software or both, and the components of the electronic device are coupled to each other. For example, but not limitation, the bus may include an accelerated graphics port (AGP) or other graphics bus, an enhanced industrial standard architecture (EISA) bus, a front-side bus (FSB), a hypertransport (HT) interconnection, an industrial standard architecture (ISA) bus, an infinite bandwidth interconnection, a low pin count (LPC) bus, a memory bus, a microchannel architecture (MCA) bus, a peripheral component interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a serial advanced technology attachment (SATA) bus, a video electronics standard association local (VLB) bus or other suitable buses or a combination of two or more of these. Where appropriate, the bus may include one or more buses. Although the embodiments of the present invention describe and illustrate a specific bus, the present invention contemplates any suitable bus or interconnection.

[0157] In summary, the embodiments of the present invention provide a method, device, system and medium for intelligently updating an anesthetic medication database.

[0158] It should be clear that the present invention is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present invention is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present invention.

[0159] The functional blocks shown in the structural block diagram described above can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present invention are programs or code segments used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier. "Machine-readable medium" may include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROMs, flash memories, erasable ROMs (EROMs), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0160] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent updating method for an anesthetic medication database, characterized in that: The method comprises: S1: Obtain the current user's physical index parameters; S2: traversing the anesthetic medication database according to the physical indicator parameters, and outputting anesthetic information matching the physical indicator parameters of the current user, wherein the anesthetic information includes anesthetic type, dosage, usage method, and a predicted anesthetic effect curve of the anesthetic effect during the operation; S3: obtaining an actual anesthesia effect curve of the current user during the operation under the action of the anesthetic of the anesthetic information; S4: Compare the predicted anesthesia effect curve with the actual anesthesia effect curve to obtain medication effect difference information of the current user; S5: optimizing the anesthetic information according to the medication effect difference information, obtaining a newly added case sample corresponding to the optimized anesthetic information and a real case sample of the current user corresponding to the anesthetic information, and updating the anesthetic medication database with the newly added case sample and the real case sample; The S2 includes: S21: traversing the anesthetic medication database according to the physical indicator parameter to obtain at least one reference case sample adapted to the physical sign information corresponding to the physical indicator parameter; S22: obtaining the anesthetic information of the current user according to the anesthetic sample information of each reference case sample; Wherein, the physical sign information includes normal physical signs and obesity physical signs; The S22 includes: S221: Obtaining a sample body fat percentage of a reference case sample and a target body fat percentage of the current user; S222: Calculate the compensation dose of the anesthetic agent according to the sample body fat percentage and the target body fat percentage using the formula A=Wa(1-C); S223: Obtaining anesthetic dosage information of the current user according to the compensation dosage and the sample medication information of the reference case sample; Wherein, A is the compensation dose of the anesthetic used by the current user relative to the reference case sample, W is the patient's weight, a is the compensation dose of the anesthetic used per unit weight, and C is the body fat percentage ratio of the current user to the reference case sample; If the current patient's physical sign information is an obesity sign, the S222 includes: S2221: Obtain the gender and age information of the current user; S2222: Calculate the target body fat percentage according to the gender information, the age information and the body mass index using the formula K=mB+nY-S+b; S2223: Calculate the compensation dose of the anesthetic agent according to the target body fat percentage and the sample body fat percentage by A=Wa{1-(mB+nY-S+b) / Q}; Among them, Q is the sample body fat percentage of the reference case sample, K is the body fat percentage of the current user, B is the body mass index, Y is the patient's age, S is the patient's gender, and b, m, and n are constants.

2. The intelligent updating method of the anesthetic medication database according to claim 1, characterized in that: The S21 includes: S211: Obtaining physical information of the current user according to the height information and weight information of the physical indicator parameters; S212: traversing the anesthetic medication database according to the physical indicator parameters to obtain all historical case samples corresponding to each of the physical sign information; S213: Obtaining the reference case samples of the current user according to the physical sign information of the current user and the physical sign information corresponding to the historical case samples.

3. The intelligent updating method of the anesthetic medication database according to claim 2, characterized in that: The S211 includes: S2111: Calculate the body mass index of the current user according to the height information and the weight information of the current user using the formula B=W / H²; S2112: When the body mass index exceeds a preset physical sign threshold range, determining that the physical sign information of the current user is the obesity physical sign; S2113: When the body mass index is less than or equal to the preset physical sign threshold range, determining that the physical sign information of the current user is the normal physical sign; Where B is body mass index, W is patient weight, and H is patient height.

4. The intelligent updating method of the anesthetic medication database according to any one of claims 1 to 3, characterized in that: The S3 includes: S31: using the BIS anesthesia depth monitor to measure the EEG waveform of the current user during the operation in real time and collect corresponding external condition feedback information; S32: Outputting the real-time sleep level of the current user through the BIS evaluation system according to the brain wave waveform and the external condition feedback information; S33: establishing the actual anesthesia effect curve according to the real-time sleep level of the current user; The external condition feedback information includes at least one of the following: feedback information corresponding to swallowing reflex, eyelash reflex, and pupil size.

5. An intelligent updating device for an anesthetic medication database, characterized in that: The device comprises: Physical index detection module: obtain the physical index parameters of the current user; Data processing module: traverses the anesthetic medication database according to the physical indicator parameters, and outputs anesthetic information matching the physical indicator parameters of the current user, wherein the anesthetic information includes anesthetic type, dosage, usage method and a predicted anesthetic effect curve of the anesthetic effect during surgery; Current anesthesia effect module: obtains the actual anesthesia effect curve of the current user during the operation under the action of the anesthetic of the anesthetic information; Anesthesia effect difference module: compares the predicted anesthesia effect curve with the actual anesthesia effect curve to obtain the medication effect difference information of the current user; Database update module: optimize the anesthetic information according to the medication effect difference information, obtain new case samples corresponding to the optimized anesthetic information and real case samples of the current user corresponding to the anesthetic information, and update the anesthetic medication database with the new case samples and the real case samples; The traversing of the anesthetic medication database according to the body indicator parameters and outputting the anesthetic information matching the body indicator parameters of the current user, wherein the anesthetic information includes the anesthetic type, dosage, usage method and the predicted anesthetic effect curve of the anesthetic effect during the operation includes: Traversing the anesthetic medication database according to the physical indicator parameters to obtain at least one reference case sample adapted to the physical sign information corresponding to the physical indicator parameters; According to the anesthetic sample information of each reference case sample, obtaining the anesthetic information of the current user; Wherein, the physical sign information includes normal physical signs and obesity physical signs; The obtaining of the anesthetic information of the current user according to the anesthetic sample information of each reference case sample includes: Obtain the sample body fat percentage of the reference case sample and the target body fat percentage of the current user; According to the body fat percentage of the sample and the target body fat percentage, a compensation dose of the anesthetic is calculated by the formula A=Wa(1-C); Obtaining anesthetic dosage information of the current user according to the compensation dose and the sample medication information of the reference case sample; Wherein, A is the compensation dose of the anesthetic used by the current user relative to the reference case sample, W is the patient's weight, a is the compensation dose of the anesthetic used per unit weight, and C is the body fat percentage ratio of the current user to the reference case sample; If the current patient's physical sign information is an obese sign, the compensation dose of the anesthetic dosage calculated by the formula A=Wa(1-C) based on the sample body fat percentage and the target body fat percentage includes: Get the current user's gender and age information; The target body fat percentage is calculated according to the gender information, the age information and the body mass index by the formula K=mB+nY-S+b; According to the target body fat percentage and the sample body fat percentage, the compensation dose of the anesthetic is calculated by A=Wa{1-(mB+nY-S+b) / Q}; Among them, Q is the sample body fat percentage of the reference case sample, K is the body fat percentage of the current user, B is the body mass index, Y is the patient's age, S is the patient's gender, and b, m, and n are constants.

6. An intelligent updating system for an anesthetic medication database, characterized in that: include: At least one processor, at least one memory and computer program instructions stored in the memory, when the computer program instructions are executed by the processor, implement the method according to any one of claims 1 to 4.

7. A medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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