Intelligent medical drug management method and drug registration platform

Abstract

The invention discloses an intelligent medical drug management method and a drug registration platform, relates to the technical field of drug management platforms, and breaks through the limitation that drug adjustment is judged only according to symptom representation traditionally by comprehensively considering environmental factors and emotion factors to evaluate the necessity of drug adjustment. Multi-element potential factors influencing the health state and the drug effect of patients are deeply mined, patient groups really needing drug adjustment are accurately positioned, highly personalized medical intervention is achieved, burden and potential risks caused by unnecessary drug adjustment to the patients are avoided, priorities are divided based on disease risk prediction values, and the accuracy of drug adjustment is improved. And the patient information is pushed to the on-duty doctor working machine in order, so that the vigor, the time, the medicine reserve and the like of the medical resource doctor are preferentially inclined to the patient with high risk and urgent need of medicine adjustment, the treatment delay of the patient with high demand caused by average force of medical service is avoided, the overall medical service efficiency is improved, and the maximum treatment value of limited resources is brought into play.

Description

Technical Field

[0001] The present invention relates to the technical field of drug management platforms, and in particular to a smart medical drug management method and a drug registration platform. Background Art

[0002] Modern medical concepts have gradually shifted from simple disease treatment to disease prevention and patient health management throughout their life cycle. For drug management, it is no longer just about the symptoms and diseases that patients have already experienced, but more about discovering potential risk factors in advance and preventing the occurrence and development of diseases by adjusting drugs and other means. This change in concept has prompted the medical industry to seek a drug management method that can comprehensively consider multiple factors such as patients' lifestyles, environments, and emotions to achieve full management of patients' health. Therefore, a smart medical drug management method and drug registration platform came into being.

[0003] Prior art, such as the invention application patent with announcement number: CN118866399A, discloses a method for risk assessment and prevention and control of high-alert drugs in hospitals. Step five: Formulate risk prevention and control strategies: Formulate corresponding risk prevention and control strategies for different risk levels, including strengthening medication guidance, increasing monitoring frequency, adjusting medication dosage, and replacing drugs; Step six: Real-time risk assessment and prevention and control: During the use of drugs, collect patient information and drug use information in real time, conduct real-time risk assessment through risk prediction models, and automatically trigger corresponding risk prevention and control strategies based on the assessment results; determine the factors affecting the risk of high-alert drugs by constructing a risk assessment model, and assign corresponding weights to each factor, and then determine the calculation method of the risk value for each factor by collecting historical data and expert experience, and then use machine learning algorithms to train and optimize the weights and risk values ​​to improve the accuracy and reliability of risk assessment.

[0004] In view of the above scheme, the inventor of the present application found that the above technology has at least the following technical problems: 1. The existing technology can often only be based on the patient's self-reported symptoms or changes in physiological indicators during regular examinations. However, the patient's symptom description may be subjective and inaccurate, and changes in physiological indicators may sometimes be temporary or interfered with by other factors that are not considered. For example, a patient gives feedback to the doctor after feeling unwell, and the doctor may directly consider adjusting the medicine, but if the discomfort is a short-term reaction caused by environmental changes, rather than changes in the disease itself, then such drug adjustment is unnecessary, and at the same time, the impact of the environment and emotions on the patient's condition and drug effect is not fully considered. Emotional fluctuations may affect the patient's physiological indicators such as blood pressure and heart rate, and then affect the metabolism and efficacy of the drug. If emotional factors are not considered, it may cause the doctor to misjudge the disease as worsening and adjust the drug dosage when the patient's emotional changes cause fluctuations in physiological indicators, increasing the risk of adverse drug reactions in the patient. Similarly, changes in environmental factors such as temperature, humidity, and air pressure will also affect the patient's body. For example, patients may experience symptoms of heat stroke in a high temperature environment. If the drug is adjusted simply because it is caused by the disease, it may not really solve the patient's problem.

[0005] In the absence of existing technologies that consider life-related indicators to predict disease risks, existing disease risk predictions often only focus on the diagnosis of the disease itself and the monitoring of physiological indicators. This single-dimensional assessment cannot fully understand the patient's health status and disease development trends. For patients with cardiovascular diseases, only focusing on physiological indicators such as blood pressure and blood lipids, while ignoring the impact of patients' circadian rhythm disorders, such as staying up late for a long time, irregular eating, such as frequent overeating and other living habits on the disease, it is difficult to accurately predict whether the disease will worsen and when complications may occur. Since there is no comprehensive living habit and other factors to build a disease risk prediction model, the existing risk warning is usually issued only after the patient's physiological indicators are obviously abnormal or symptoms of the disease have appeared. This leads to medical intervention often lagging behind, and it is not possible to adjust drugs in time for prevention in the process of gradual accumulation of disease risks. The patient's exercise frequency gradually decreases and the body function declines, which may cause some chronic diseases such as diabetes and hypertension to gradually get out of control, but it is difficult for existing technologies to detect this trend in the early stage and intervene in advance through drug adjustments.

[0006] Existing technologies lack the ability to analyze drug adjustment priorities based on disease risk prediction values, and the allocation of medical resources, including doctors' time, energy, and drug resources, may be chaotic. In a busy medical environment, doctors may treat all patients who need drug adjustments equally, resulting in the inability to promptly treat some patients with more urgent conditions and higher risks, while spending too much time on some patients with relatively stable conditions and lower risks. This will not only delay the treatment of high-risk patients, but may also cause a waste of medical resources, such as unnecessary and frequent adjustments to drugs for low-risk patients, which increases medical costs and the burden on patients. In terms of pushing patient information to doctors, if there is no effective prompt according to the risk level, doctors may need to spend a lot of time screening and identifying which patients need urgent treatment and which can be delayed. When faced with a lot of patient information, doctors need to check medical records and examination reports one by one to determine the urgency of the condition, which may cause delays in the treatment of key patients in an emergency, affecting the overall efficiency and quality of medical services. Summary of the invention

[0007] In view of the above-mentioned technical deficiencies, the purpose of the present invention is to provide a smart medical drug management method and a drug registration platform.

[0008] In order to solve the above technical problems, the present invention adopts the following technical solutions: In the first aspect, the present invention provides a smart medical drug management method, including: Step 1, evaluation of the necessity of drug adjustment: obtaining the environmental factors and emotional factors corresponding to each patient in the medical platform at the current moment, and then evaluating the necessity of drug adjustment corresponding to each patient, and recording each patient who needs drug adjustment as a patient to be adjusted.

[0009] Step 2: Analysis of disease risk prediction values: Obtain the life-related indicators corresponding to each historical period of each patient to be adjusted, and analyze and obtain the disease risk prediction value corresponding to each patient to be adjusted based on the necessity value of drug adjustment corresponding to each patient to be adjusted.

[0010] Step 3: Analysis of drug adjustment priority: According to the disease risk prediction value corresponding to each patient to be adjusted, the drug adjustment priority corresponding to each patient to be adjusted is analyzed, and each patient to be adjusted is pushed to the on-duty doctor's work machine according to the drug adjustment priority, and risk warnings are given.

[0011] Preferably, the environmental factors and emotional factors corresponding to each patient in the medical platform at the current moment are obtained, and the specific acquisition process is as follows: A1. Each patient carries a smart wearable device with him / her. The smart wearable device has an environmental sensor and basic physiological monitoring functions, and a built-in temperature and humidity sensor to measure the temperature and humidity of the surrounding environment, as well as the skin temperature, sweat rate, and metabolic rate of the human body. At the same time, each patient's smart wearable device is connected to a smart phone. The smart wearable device synchronously obtains the temperature, humidity, and wind speed in the environment through communication with the mobile phone, and substitutes them into the thermal comfort equation to calculate the human thermal comfort index corresponding to each patient in the medical platform at the current moment. At the same time, the smart wearable device is equipped with an air pressure sensor, and the air pressure change rate is obtained through the air pressure sensor. Combined with the positioning system, the air pressure values ​​and corresponding location information at different time points are obtained, and the relationship formula between air pressure and altitude is used to deduce the altitude change rate, and the data are transmitted to the medical platform through Bluetooth or wireless communication through communication with the mobile phone to achieve data synchronization.

[0012] A2. Each patient's smart wearable device has an ECG monitoring function, which can obtain the ECG signal of each patient and record the RR interval sequence data. The fast Fourier transform analysis method is used to convert the RR interval sequence from the time domain to the frequency domain, thereby obtaining a power spectrum density estimate. In the power spectrum density diagram, the power of the LF and HF frequency bands is determined, and the low- and high-frequency power ratio corresponding to each patient is calculated by the LF / HF ratio. Each patient takes a selfie and uploads it to a mobile phone. The facial expression recognition software is used to obtain the expression category probability value corresponding to each patient. Each patient's smart wearable device has a blood pressure monitoring function, thereby obtaining the blood pressure change rate corresponding to each patient.

[0013] Preferably, the evaluation of the necessity of drug adjustment corresponding to each patient is carried out as follows: B1. Obtaining the environmental adaptation value and emotional adaptation value corresponding to each patient, and inputting the environmental adaptation value and emotional adaptation value corresponding to each patient into the drug adjustment necessity evaluation model, and outputting the drug adjustment necessity value result corresponding to each patient.

[0014] B2. The result of the necessity value of drug adjustment includes the values ​​of 1 and -1. When the necessity value of drug adjustment corresponding to a patient is 1, it indicates that the necessity of drug adjustment for the patient is high and drug adjustment is required. Conversely, when the necessity value of drug adjustment corresponding to a patient is -1, it indicates that the necessity of drug adjustment for the patient is low and drug adjustment is not required. In this way, the necessity of drug adjustment for each patient is evaluated.

[0015] Preferably, the environmental adaptation value and emotional adaptation value corresponding to each patient are obtained, and the specific acquisition process is as follows: C1, the human thermal comfort index, air pressure change rate and altitude change rate corresponding to each patient are input into the environmental adaptation value evaluation model, and the environmental adaptation value corresponding to each patient is output, and the environmental adaptation value corresponding to each patient is recorded as , f represents the number corresponding to each patient, and f is a positive integer.

[0016] C2. Input the low-high frequency power ratio, expression category probability value and blood pressure change rate corresponding to each patient into the emotion adaptation value evaluation model, output the emotion adaptation value corresponding to each patient, and record the emotion adaptation value corresponding to each patient as .

[0017] Preferably, the expression of the drug adjustment necessity assessment model is: , where It represents the result of the necessity value of drug adjustment corresponding to the fth patient. Adjust the necessity value threshold for the set drug, where: , They are the standard environment adaptation value and standard emotion adaptation value corresponding to the set patients, , They are the weight factors corresponding to the set patient environment adaptation value and the weight factors corresponding to the emotion adaptation value, , They are the adjustment factors corresponding to the set patient environment adaptation value and the adjustment factors corresponding to the emotional adaptation value. Represents a natural constant.

[0018] Preferably, the analysis obtains the disease risk prediction value corresponding to each patient to be adjusted, and the specific analysis process is as follows: obtain the life-related indicators corresponding to each historical period of each patient to be adjusted, and the life-related indicators include circadian rhythm compliance, eating regularity compliance, exercise frequency compliance and social frequency compliance, and record the circadian rhythm compliance, eating regularity compliance, exercise frequency compliance and social frequency compliance corresponding to each historical period of each patient to be adjusted as , , and , where q represents the number of each patient to be adjusted, q is a positive integer, and w represents the number of each historical period, w is a positive integer. Substitute into the calculation formula: The disease risk prediction value corresponding to each patient to be adjusted is obtained ,in, , , , They are the standard circadian rhythm compliance, standard eating pattern compliance, standard exercise frequency compliance, and standard social frequency compliance corresponding to the set patients. , , , The weight factors corresponding to the patient's circadian rhythm compliance, eating pattern compliance, exercise frequency compliance, and social frequency compliance are set respectively. It is represented by the necessity value of drug adjustment corresponding to the qth patient to be adjusted, Represents a natural constant.

[0019] Preferably, the analysis of the drug adjustment priority corresponding to each patient to be adjusted is specifically carried out as follows: the disease risk prediction value corresponding to each patient to be adjusted is arranged in order from large to small, and the drug adjustment priority corresponding to each patient to be adjusted is set according to the arrangement order.

[0020] Preferably, each patient to be adjusted is pushed to the duty doctor's work machine according to the drug adjustment priority, and a risk prompt is given. The specific prompt process is as follows: D1. Obtain the disease risk prediction level corresponding to each patient to be adjusted. If the disease risk prediction level corresponding to a patient to be adjusted is a low risk level, the patient information is displayed in a pop-up window with a light blue background on the system interface of the duty doctor's work machine. The pop-up window title displays "Low-risk patient drug adjustment prompt", the font color is dark gray, and the patient's name, age, and symptom description are clearly listed in the pop-up window, as well as the corresponding disease risk prediction value, with a brief text description: "The patient's disease is currently at a low risk level, life-related indicators are in good agreement with the standards, and the condition is stable. It is recommended to routinely review the medication situation.

[0021] D2. If the disease risk prediction level of a patient to be adjusted is a medium risk level, the patient information will be displayed in a pop-up window with an orange background on the system interface of the on-duty doctor's work computer. The pop-up window title will display "Emergency review of drugs for medium-risk patients". The pop-up window lists the patient's key information in detail, including past medical history, recent symptom change log, complete disease risk prediction value and analysis chart, with a brief text description: "The patient is in a medium-risk state, some lifestyle indicators deviate from the condition, causing fluctuations in the condition, and the efficacy of existing drugs is unstable. The rationality of medication needs to be evaluated as soon as possible, and the monitoring frequency should be increased to once a week.

[0022] D3. If the disease risk prediction level of a patient to be adjusted is a high-risk level, the patient information will be displayed in a pop-up window with a red background on the system interface of the on-duty doctor's work computer. The pop-up window title will display "High-risk patient drug crisis intervention reminder". The pop-up window contains the patient's full-scale identity, condition, and medication information, and also embeds real-time vital signs data, with a brief text description: "The patient's condition is critical and at high risk. The serious disorder of life exacerbates the risk of disease deterioration. Complications may occur at any time. Immediately re-evaluate all medications, activate emergency drug plans, monitor closely 24 hours a day, and organize consultations.

[0023] Preferably, the disease risk prediction level corresponding to each patient to be adjusted is obtained, and the specific acquisition process is as follows: the disease risk prediction value corresponding to each patient to be adjusted is compared with the disease risk prediction value interval corresponding to each disease risk prediction level in the database; if the disease risk prediction value corresponding to a patient to be adjusted is within the disease risk prediction value interval corresponding to a disease risk prediction level in the database, the disease risk prediction level in the database is recorded as the disease risk prediction level corresponding to the patient to be adjusted.

[0024] In a second aspect, the present invention provides a smart medical drug registration platform, including: a drug adjustment necessity assessment module: used to obtain the environmental factors and emotional factors corresponding to each patient in the medical platform at the current moment, and then assess the necessity of drug adjustment corresponding to each patient, and record each patient who needs drug adjustment as a patient to be adjusted.

[0025] Disease risk prediction value analysis module: used to obtain the life-related indicators corresponding to each historical period of each patient to be adjusted, and according to the necessity value of drug adjustment corresponding to each patient to be adjusted, the disease risk prediction value corresponding to each patient to be adjusted is analyzed.

[0026] Drug adjustment priority analysis module: used to analyze the drug adjustment priority of each patient to be adjusted according to the disease risk prediction value corresponding to each patient to be adjusted, and push each patient to be adjusted to the on-duty doctor's work machine according to the drug adjustment priority, and provide risk warnings.

[0027] The beneficial effects of the present invention are: 1. The embodiments of the present invention evaluate the necessity of drug adjustment by comprehensively considering environmental factors and emotional factors, break through the limitation of traditional judgment of drug adjustment based only on symptom manifestations, deeply explore the multiple potential factors affecting patients' health status and drug effects, accurately locate the patient groups who really need drug adjustment, realize highly personalized medical intervention, avoid the burden and potential risks of unnecessary medication adjustment on patients, introduce life-related indicators and combine the necessity value of drug adjustment to analyze the disease risk prediction value, start from the dual dimensions of patients' long-term living habits and current medical needs, comprehensively outline the patient's disease risk portrait, and gain insight into the potential fluctuation trend of the disease in advance, change the previous "passive response to disease changes" to "active prevention and control of risk upgrades", lay a solid foundation for the timing and strategy formulation of subsequent drug adjustments, and improve the foresight of medical management.

[0028] 2. In the embodiment of the present invention, priorities are divided based on the disease risk prediction value, and patient information is pushed to the duty doctor's work machine in an orderly manner, ensuring that medical resources, doctors' energy, time, and drug reserves are prioritized to high-risk patients who urgently need to adjust their drugs, avoiding the "average effort" of medical services that leads to delays in the treatment of high-demand patients, improving the overall efficiency of medical services, and maximizing the treatment value of limited resources. Especially in scenarios with large patient flow and tight medical staff, efficient and smooth medical operations are guaranteed, and detailed and intuitive patient prompt information is provided to the duty doctor. Different risk levels correspond to differentiated pop-up window displays, text descriptions, and supporting operation suggestions, so that the doctor can instantly grasp the patient's core condition and urgency, and facilitate the rapid formulation of accurate medication adjustment plans. At the same time, this clear communication mechanism is conducive to the subsequent collaboration of the medical team, and can also explain the reasons for the medication change to the patient in a reasonable and well-founded manner, enhance the patient's trust and cooperation, and improve the doctor-patient interaction relationship. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0030] Figure 1 The present invention is a flowchart of the steps for implementing the method.

[0031] Figure 2 It is a schematic diagram of the connection of the system modules of the present invention. DETAILED DESCRIPTION

[0032] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0033] Embodiments of the present invention Figure 1 As shown, a smart medical drug management method includes: Step 1, evaluation of the necessity of drug adjustment: obtaining the environmental factors and emotional factors corresponding to each patient in the medical platform at the current moment, and then evaluating the necessity of drug adjustment corresponding to each patient, and recording each patient who needs drug adjustment as a patient to be adjusted.

[0034] In a specific embodiment, the environmental factors and emotional factors corresponding to each patient in the medical platform at the current moment are obtained, and the specific acquisition process is as follows: A1. Each patient carries a smart wearable device with him / her. The smart wearable device has an environmental sensor and basic physiological monitoring functions, and a built-in temperature and humidity sensor to measure the temperature and humidity of the surrounding environment, as well as the skin temperature, sweat rate, and metabolic rate of the human body. At the same time, each patient's smart wearable device is connected to a smart phone. The smart wearable device synchronously obtains the temperature, humidity, and wind speed in the environment through communication with the mobile phone, and substitutes them into the thermal comfort equation to calculate the human thermal comfort index corresponding to each patient in the medical platform at the current moment. At the same time, the smart wearable device is equipped with an air pressure sensor, and the air pressure change rate is obtained through the air pressure sensor. Combined with the positioning system, the air pressure values ​​and corresponding location information at different time points are obtained, and the relationship formula between air pressure and altitude is used to deduce the altitude change rate, and through communication with the mobile phone, these data are transmitted to the medical platform via Bluetooth or wireless communication to achieve data synchronization.

[0035] It should be noted that, first of all, the metabolic heat production value, sensible heat exchange value and latent heat exchange value are estimated by smart wearable devices. The thermal comfort index can be regarded as a comprehensive reflection of the balance of human heat. Assume that the calculation equation of the thermal comfort index (HTC) is: HTC = (metabolic heat production - sensible heat exchange value - latent heat exchange value) / human body surface area. Calculate the value of HTC. The size of this value represents the thermal comfort level of the human body. The air pressure sensor of the smart wearable device is used to record the air pressure values ​​at different time points, and these time points must be recorded. First, the change in air pressure is calculated, that is, the air pressure value at the later time point is subtracted from the air pressure value at the previous time point, and then the interval between the two time points is calculated. The air pressure change is divided by the time interval, and the result is the air pressure change rate. The air pressure values ​​at different time points are obtained through the air pressure sensor of the smart wearable device, and the positioning system is used to obtain the altitude part of the location information corresponding to these time points. First, the change in altitude is calculated, the altitude at the later time point is subtracted from the altitude at the previous time point, and the interval between the two time points is calculated at the same time. The altitude change is divided by the time interval, and the result is the altitude change rate.

[0036] A2. Each patient's smart wearable device has an ECG monitoring function, which can obtain the ECG signal of each patient and record the RR interval sequence data. The fast Fourier transform analysis method is used to convert the RR interval sequence from the time domain to the frequency domain, thereby obtaining a power spectrum density estimate. In the power spectrum density diagram, the power of the LF and HF frequency bands is determined, and the low- and high-frequency power ratio corresponding to each patient is calculated by the LF / HF ratio. Each patient takes a selfie and uploads it to a mobile phone. The facial expression recognition software is used to obtain the expression category probability value corresponding to each patient. Each patient's smart wearable device has a blood pressure monitoring function, thereby obtaining the blood pressure change rate corresponding to each patient.

[0037] In another specific embodiment, the evaluation of the necessity of drug adjustment corresponding to each patient is carried out as follows: B1. Obtaining the environmental adaptation value and emotional adaptation value corresponding to each patient, and inputting the environmental adaptation value and emotional adaptation value corresponding to each patient into the drug adjustment necessity evaluation model, and outputting the drug adjustment necessity value result corresponding to each patient.

[0038] B2. The result of the necessity value of drug adjustment includes the values ​​of 1 and -1. When the necessity value of drug adjustment corresponding to a patient is 1, it indicates that the necessity of drug adjustment for the patient is high and drug adjustment is required. Conversely, when the necessity value of drug adjustment corresponding to a patient is -1, it indicates that the necessity of drug adjustment for the patient is low and drug adjustment is not required. In this way, the necessity of drug adjustment for each patient is evaluated.

[0039] In another specific embodiment, the environmental adaptation value and emotional adaptation value corresponding to each patient are obtained, and the specific acquisition process is as follows: C1, the human thermal comfort index, air pressure change rate and altitude change rate corresponding to each patient are input into the environmental adaptation value evaluation model, and the environmental adaptation value corresponding to each patient is output, and the environmental adaptation value corresponding to each patient is recorded as , f represents the number corresponding to each patient, and f is a positive integer; It should be noted that the analysis process of the environmental adaptation value corresponding to each patient is as follows: the human thermal comfort index, air pressure change rate and altitude change rate corresponding to each patient are input through normalization processing, and the human thermal comfort index, air pressure change rate and altitude change rate corresponding to each patient after processing are recorded as , and , substitute into the analytical formula , get the environmental adaptation value corresponding to each patient , , , They are respectively the weight coefficient corresponding to the patient's thermal comfort index, the weight coefficient corresponding to the air pressure change rate, and the weight coefficient corresponding to the altitude change rate.

[0040] It should be noted that , , Both are greater than 0 and less than 1.

[0041] It should also be noted that, based on the professional knowledge and research of experts in the field, and after discussion and confirmation with industry organizations or professional institutions, the experts set the weight coefficients corresponding to the patient's thermal comfort index, the air pressure change rate, and the altitude change rate according to their own experience and knowledge.

[0042] C2. Input the low-high frequency power ratio, expression category probability value and blood pressure change rate corresponding to each patient into the emotion adaptation value evaluation model, output the emotion adaptation value corresponding to each patient, and record the emotion adaptation value corresponding to each patient as .

[0043] It should be noted that the emotion adaptation value corresponding to each patient is obtained by analyzing according to the above-mentioned analysis process of the environment adaptation value corresponding to each patient.

[0044] In another specific embodiment, the expression of the drug adjustment necessity assessment model is: , where It represents the result of the necessity value of drug adjustment corresponding to the fth patient. Adjust the necessity value threshold for the set drug, where: , They are the standard environment adaptation value and standard emotion adaptation value corresponding to the set patients, , They are the weight factors corresponding to the set patient environment adaptation value and the weight factors corresponding to the emotion adaptation value, , They are the adjustment factors corresponding to the set patient environment adaptation value and the adjustment factors corresponding to the emotional adaptation value. Represents a natural constant.

[0045] It should be noted that , Both are greater than 0 and less than 1.

[0046] It should also be noted that through the summary of a large amount of research data and experimental data. According to professional institutions and research institutions, the standard environmental adaptation values ​​and standard emotional adaptation values ​​corresponding to patients are set. At the same time, based on the professional knowledge and research basis of experts in the field, they are discussed and confirmed with industry organizations or professional institutions. Experts set the weight factors corresponding to the patient's environmental adaptation value and the weight factors corresponding to the emotional adaptation value based on their own experience and knowledge, as well as set the adjustment factors corresponding to the patient's environmental adaptation value and the adjustment factors corresponding to the emotional adaptation value.

[0047] Step 2: Analysis of disease risk prediction values: Obtain the life-related indicators corresponding to each historical period of each patient to be adjusted, and analyze and obtain the disease risk prediction value corresponding to each patient to be adjusted based on the necessity value of drug adjustment corresponding to each patient to be adjusted.

[0048] In a specific embodiment, the analysis obtains the disease risk prediction value corresponding to each patient to be adjusted, and the specific analysis process is as follows: obtain the life-related indicators corresponding to each historical period of each patient to be adjusted, and the life-related indicators include circadian rhythm compliance, eating regularity compliance, exercise frequency compliance and social frequency compliance, and record the circadian rhythm compliance, eating regularity compliance, exercise frequency compliance and social frequency compliance corresponding to each historical period of each patient to be adjusted as , , and , where q represents the number of each patient to be adjusted, q is a positive integer, and w represents the number of each historical period, w is a positive integer. Substitute into the calculation formula: The disease risk prediction value corresponding to each patient to be adjusted is obtained ,in, , , , They are the standard circadian rhythm compliance, standard eating pattern compliance, standard exercise frequency compliance, and standard social frequency compliance corresponding to the set patients. , , , The weight factors corresponding to the patient's circadian rhythm compliance, eating pattern compliance, exercise frequency compliance, and social frequency compliance are set respectively. It is represented by the necessity value of drug adjustment corresponding to the qth patient to be adjusted, Represents a natural constant.

[0049] It should be noted that the degree of match between the actual sleep-wake time and activity pattern of the patient and the reference standard in each historical cycle is counted. For example, the difference between the actual bedtime and the recommended bedtime, as well as the difference between the actual wake-up time and the recommended wake-up time, are calculated, and these differences are combined through a weighted algorithm to obtain a value to indicate the degree of deviation from the circadian rhythm. Then, this degree of deviation is subtracted from 1 to obtain the circadian rhythm compliance; for each historical cycle, the patient's actual meal time is counted to match the reference meal time. For example, the sum of the absolute values ​​of the difference between the actual meal time and the recommended meal time for breakfast, lunch and dinner is calculated, while considering whether the amount of food is within a reasonable range. If the amount of food deviates greatly from the reasonable range, it can be compared with the reasonable amount of food per meal estimated based on individual circumstances, which will also affect the compliance. Combining these factors through a weighted algorithm, a value between 0 and 1 is obtained as the compliance of the eating pattern; in each historical cycle, the actual number of social activities of the patient is counted, and the actual number of social activities is compared with the reference number of social activities to obtain the social frequency compliance. If the actual number of social activities reaches or exceeds the reference number of social activities, the compliance is 1; if it is lower than the reference number of social activities, the compliance is calculated in the range of 0-1 based on the ratio of the actual number of social activities to the reference number of social activities.

[0050] It should be noted that , , , Both are greater than 0 and less than 1.

[0051] It should also be noted that through the summary of a large amount of research data and experimental data. Professional institutions and research institutions set the corresponding standard circadian rhythm compliance, standard eating pattern compliance, standard exercise frequency compliance, and standard social frequency compliance for patients. At the same time, based on the professional knowledge and research basis of experts in the field, they discuss and confirm with industry organizations or professional institutions. Experts set the weight factors corresponding to the patient's circadian rhythm compliance, the weight factors corresponding to the eating pattern compliance, the weight factors corresponding to the exercise frequency compliance, and the weight factors corresponding to the social frequency compliance based on their own experience and knowledge.

[0052] The embodiments of the present invention evaluate the necessity of drug adjustment by comprehensively considering environmental factors and emotional factors, break through the limitation of traditional judgment of drug adjustment based only on symptom manifestations, deeply explore the multiple potential factors that affect the patient's health status and drug effect, accurately locate the patient group who really needs drug adjustment, realize highly personalized medical intervention, avoid the burden and potential risks of unnecessary medication adjustment on patients, introduce life-related indicators and combine the drug adjustment necessity value to analyze the disease risk prediction value, start from the dual dimensions of the patient's long-term living habits and current medical needs, comprehensively outline the patient's disease risk portrait, and gain insight into the potential fluctuation trend of the disease in advance, change the previous "passive response to disease changes" to "active prevention and control of risk upgrades", lay a solid foundation for the timing and strategy formulation of subsequent drug adjustments, and improve the foresight of medical management.

[0053] Step 3: Analysis of drug adjustment priority: According to the disease risk prediction value corresponding to each patient to be adjusted, the drug adjustment priority corresponding to each patient to be adjusted is analyzed, and each patient to be adjusted is pushed to the on-duty doctor's work machine according to the drug adjustment priority, and risk warnings are given.

[0054] In a specific embodiment, each patient to be adjusted is pushed to the duty doctor's work machine according to the drug adjustment priority, and a risk prompt is given. The specific prompt process is as follows: D1. Obtain the disease risk prediction level corresponding to each patient to be adjusted. If the disease risk prediction level corresponding to a patient to be adjusted is a low risk level, the patient information is displayed in a pop-up window with a light blue background on the system interface of the duty doctor's work machine. The pop-up window title displays "Low-risk patient drug adjustment prompt", the font color is dark gray, and the patient's name, age, brief description of the symptoms, and the corresponding disease risk prediction value are clearly listed in the pop-up window, with a brief text description attached: "The patient's disease is currently at a low risk level, the life-related indicators are in good agreement with the standards, the condition is stable, and it is recommended to routinely review the medication situation.

[0055] D2. If the disease risk prediction level of a patient to be adjusted is a medium risk level, the patient information will be displayed in a pop-up window with an orange background on the system interface of the on-duty doctor's work computer. The pop-up window title will display "Emergency review of drugs for medium-risk patients". The pop-up window lists the patient's key information in detail, including past medical history, recent symptom change log, complete disease risk prediction value and analysis chart, with a brief text description: "The patient is in a medium-risk state, some lifestyle indicators deviate from the condition, causing fluctuations in the condition, and the efficacy of existing drugs is unstable. The rationality of medication needs to be evaluated as soon as possible, and the monitoring frequency should be increased to once a week.

[0056] D3. If the disease risk prediction level of a patient to be adjusted is a high-risk level, the patient information will be displayed in a pop-up window with a red background on the system interface of the on-duty doctor's work computer. The pop-up window title will display "High-risk patient drug crisis intervention reminder". The pop-up window contains the patient's full-scale identity, condition, and medication information, and also embeds real-time vital signs data, with a brief text description: "The patient's condition is critical and at high risk. The serious disorder of life exacerbates the risk of disease deterioration. Complications may occur at any time. Immediately re-evaluate all medications, activate emergency drug plans, monitor closely 24 hours a day, and organize consultations.

[0057] In another specific embodiment, the disease risk prediction level corresponding to each patient to be adjusted is obtained, and the specific acquisition process is as follows: the disease risk prediction value corresponding to each patient to be adjusted is compared with the disease risk prediction value interval corresponding to each disease risk prediction level in the database; if the disease risk prediction value corresponding to a patient to be adjusted is within the disease risk prediction value interval corresponding to a disease risk prediction level in the database, the disease risk prediction level in the database is recorded as the disease risk prediction level corresponding to the patient to be adjusted.

[0058] In the embodiment of the present invention, priorities are divided based on the disease risk prediction value, and patient information is pushed to the duty doctor's work machine in an orderly manner, ensuring that medical resources, doctors' energy, time, and drug reserves are prioritized to high-risk patients who urgently need to adjust their drugs, avoiding the "average effort" of medical services that leads to delays in the treatment of high-demand patients, improving the overall efficiency of medical services, and maximizing the treatment value of limited resources. Especially in scenarios with large patient flow and tight medical staff, efficient and smooth medical operations are guaranteed, and detailed and intuitive patient prompt information is provided to the duty doctor. Different risk levels correspond to differentiated pop-up displays, text descriptions, and supporting operation suggestions, so that the doctor can instantly grasp the patient's core condition and urgency, and facilitate the rapid formulation of accurate medication adjustment plans. At the same time, this clear communication mechanism is conducive to the subsequent collaboration of the medical team, and can also explain the reasons for the medication change to the patient in a reasonable and well-founded manner, enhance the patient's trust and cooperation, and improve the doctor-patient interaction relationship.

[0059] Embodiments of the present invention Figure 2As shown, a smart medical drug registration platform includes: a drug adjustment necessity assessment module: used to obtain the environmental factors and emotional factors corresponding to each patient in the medical platform at the current moment, and then assess the necessity of drug adjustment corresponding to each patient, and record each patient who needs drug adjustment as a patient to be adjusted.

[0060] Disease risk prediction value analysis module: used to obtain the life-related indicators corresponding to each historical period of each patient to be adjusted, and according to the necessity value of drug adjustment corresponding to each patient to be adjusted, the disease risk prediction value corresponding to each patient to be adjusted is analyzed.

[0061] Drug adjustment priority analysis module: used to analyze the drug adjustment priority of each patient to be adjusted according to the disease risk prediction value corresponding to each patient to be adjusted, and push each patient to be adjusted to the on-duty doctor's work machine according to the drug adjustment priority, and provide risk warnings.

[0062] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined in this specification, they shall all fall within the protection scope of the present invention.

Claims

1. A smart medical drug management method, characterized in that: include: Step 1: Evaluation of the necessity of drug adjustment: Obtain the environmental factors and emotional factors corresponding to each patient in the current medical platform, and then evaluate the necessity of drug adjustment corresponding to each patient, and record each patient who needs drug adjustment as each patient to be adjusted; Step 2: Analysis of disease risk prediction values: Obtain the life-related indicators corresponding to each historical period of each patient to be adjusted, and analyze and obtain the disease risk prediction value corresponding to each patient to be adjusted based on the drug adjustment necessity value corresponding to each patient to be adjusted; Step 3: Analysis of drug adjustment priority: According to the disease risk prediction value corresponding to each patient to be adjusted, the drug adjustment priority corresponding to each patient to be adjusted is analyzed, and each patient to be adjusted is pushed to the on-duty doctor's work machine according to the drug adjustment priority, and risk warnings are given.

2. A smart medical drug management method as claimed in claim 1, characterized in that: The environmental factors and emotional factors corresponding to each patient in the current medical platform are obtained, and the specific acquisition process is as follows: A1. Each patient carries a smart wearable device with him / her. The smart wearable device has environmental sensors and basic physiological monitoring functions, and has a built-in temperature and humidity sensor to measure the temperature and humidity of the surrounding environment, as well as the skin temperature, sweat rate, and metabolic rate of the human body. At the same time, each patient's smart wearable device is connected to a smart phone. The smart wearable device synchronously obtains the temperature, humidity, and wind speed in the environment through communication with the mobile phone, and substitutes them into the thermal comfort equation to calculate the human thermal comfort index corresponding to each patient in the medical platform at the current moment. At the same time, the smart wearable device is equipped with an air pressure sensor, which obtains the air pressure change rate through the air pressure sensor, and then combines with the positioning system to obtain the air pressure values ​​and corresponding location information at different time points. The relationship formula between air pressure and altitude is used to deduce the altitude change rate, and the data is transmitted to the medical platform through Bluetooth or wireless communication through communication with the mobile phone to achieve data synchronization; A2. Each patient's smart wearable device has an ECG monitoring function, which can obtain the ECG signal of each patient and record the RR interval sequence data. The fast Fourier transform analysis method is used to convert the RR interval sequence from the time domain to the frequency domain, thereby obtaining a power spectrum density estimate. In the power spectrum density diagram, the power of the LF and HF frequency bands is determined, and the low- and high-frequency power ratio corresponding to each patient is calculated by the LF / HF ratio. Each patient takes a selfie and uploads it to a mobile phone. The facial expression recognition software is used to obtain the expression category probability value corresponding to each patient. Each patient's smart wearable device has a blood pressure monitoring function, thereby obtaining the blood pressure change rate corresponding to each patient.

3. A smart medical drug management method as claimed in claim 2, characterized in that: The specific evaluation process of evaluating the necessity of drug adjustment for each patient is as follows: B1. Obtaining the environmental adaptation value and emotional adaptation value corresponding to each patient, and inputting the environmental adaptation value and emotional adaptation value corresponding to each patient into the drug adjustment necessity assessment model, and outputting the drug adjustment necessity value result corresponding to each patient; B2. The result of the necessity value of drug adjustment includes the values ​​of 1 and -1. When the necessity value of drug adjustment corresponding to a patient is 1, it indicates that the necessity of drug adjustment for the patient is high and drug adjustment is required. Conversely, when the necessity value of drug adjustment corresponding to a patient is -1, it indicates that the necessity of drug adjustment for the patient is low and drug adjustment is not required. In this way, the necessity of drug adjustment for each patient is evaluated.

4. A smart medical drug management method as claimed in claim 3, characterized in that: The environmental adaptation value and emotional adaptation value corresponding to each patient are obtained, and the specific acquisition process is as follows: C1. Input the human thermal comfort index, air pressure change rate and altitude change rate corresponding to each patient into the environmental adaptation value evaluation model, output the environmental adaptation value corresponding to each patient, and record the environmental adaptation value corresponding to each patient as , f represents the number corresponding to each patient, and f is a positive integer; C2. Input the low-high frequency power ratio, expression category probability value and blood pressure change rate corresponding to each patient into the emotion adaptation value evaluation model, output the emotion adaptation value corresponding to each patient, and record the emotion adaptation value corresponding to each patient as .

5. A smart medical drug management method as claimed in claim 4, characterized in that: The expression of the drug adjustment necessity assessment model is: , where It represents the result of the necessity value of drug adjustment corresponding to the fth patient. Adjust the necessity value threshold for the set drug, where: , They are the standard environment adaptation value and standard emotion adaptation value corresponding to the set patients, , They are the weight factors corresponding to the set patient environment adaptation value and the weight factors corresponding to the emotion adaptation value, , They are the adjustment factors corresponding to the set patient environment adaptation value and the adjustment factors corresponding to the emotional adaptation value. Represents a natural constant.

6. The smart medical drug management method according to claim 1, characterized in that: The analysis obtains the disease risk prediction value corresponding to each patient to be adjusted. The specific analysis process is as follows: Obtain the life-related indicators corresponding to each historical cycle of each patient to be adjusted, including circadian rhythm compliance, eating regularity compliance, exercise frequency compliance, and social frequency compliance, and record the circadian rhythm compliance, eating regularity compliance, exercise frequency compliance, and social frequency compliance of each historical cycle of each patient to be adjusted as , , and , where q represents the number of each patient to be adjusted, q is a positive integer, and w represents the number of each historical period, w is a positive integer. Substitute into the calculation formula: The disease risk prediction value corresponding to each patient to be adjusted is obtained ,in, , , , They are the standard circadian rhythm compliance, standard eating pattern compliance, standard exercise frequency compliance, and standard social frequency compliance corresponding to the set patients. , , , The weight factors corresponding to the patient's circadian rhythm compliance, eating pattern compliance, exercise frequency compliance, and social frequency compliance are set respectively. It is represented by the necessity value of drug adjustment corresponding to the qth patient to be adjusted, Represents a natural constant.

7. A smart medical drug management method as claimed in claim 6, characterized in that: The analysis of the drug adjustment priority corresponding to each patient to be adjusted is as follows: The disease risk prediction values ​​corresponding to each patient to be adjusted are arranged in order from large to small, and the drug adjustment priority corresponding to each patient to be adjusted is set according to the arrangement order.

8. A smart medical drug management method as claimed in claim 7, characterized in that: The patient to be adjusted is pushed to the on-duty doctor's work machine according to the drug adjustment priority, and a risk prompt is given. The specific prompt process is as follows: D1. Obtain the disease risk prediction level corresponding to each patient to be adjusted. If the disease risk prediction level corresponding to a patient to be adjusted is a low risk level, the patient information is displayed in a pop-up window with a light blue background on the system interface of the on-duty doctor's work computer. The pop-up window title displays "Low-risk patient drug adjustment reminder" with a dark gray font color. The pop-up window clearly lists the patient's name, age, brief description of symptoms, and the corresponding disease risk prediction value, with a brief text description: "The patient's disease is currently at a low risk level, the life-related indicators are in good agreement with the standards, and the condition is stable. It is recommended to routinely review the medication situation; D2. If the disease risk prediction level of a patient to be adjusted is a medium risk level, the patient information will be displayed in an orange pop-up window on the system interface of the on-duty doctor's work computer. The pop-up window title will display "Emergency review of drugs for medium-risk patients". The pop-up window lists the patient's key information in detail, including past medical history, recent symptom change log, complete disease risk prediction value and analysis chart, with a short text description: "The patient is in a medium-risk state, some lifestyle indicators deviate from the condition and the condition fluctuates, and the efficacy of existing drugs is unstable. The rationality of medication needs to be evaluated as soon as possible, and the monitoring frequency should be increased to once a week; D3. If the disease risk prediction level of a patient to be adjusted is a high-risk level, the patient information will be displayed in a pop-up window with a red background on the system interface of the on-duty doctor's work computer. The pop-up window title will display "High-risk patient drug crisis intervention reminder". The pop-up window contains the patient's full identity, condition, and medication information, and also embeds real-time vital signs data, with a short text description: "The patient's condition is critical and at high risk. The serious disorder of life exacerbates the risk of disease deterioration. Complications may occur at any time. Immediately re-evaluate all medications, activate emergency drug plans, monitor closely 24 hours a day, and organize consultations.

9. A smart medical drug management method as claimed in claim 8, characterized in that: The specific process of obtaining the disease risk prediction level corresponding to each patient to be adjusted is as follows: The disease risk prediction value corresponding to each patient to be adjusted is compared with the disease risk prediction value interval corresponding to each disease risk prediction level in the database. If the disease risk prediction value corresponding to a patient to be adjusted is within the disease risk prediction value interval corresponding to a disease risk prediction level in the database, the disease risk prediction level in the database is recorded as the disease risk prediction level corresponding to the patient to be adjusted.

10. A smart medical drug registration platform for executing the smart medical drug management method according to any one of claims 1 to 9, characterized in that: include: Drug adjustment necessity assessment module: used to obtain the environmental factors and emotional factors corresponding to each patient in the current medical platform, and then assess the necessity of drug adjustment for each patient, and record each patient who needs drug adjustment as a patient to be adjusted; Disease risk prediction value analysis module: used to obtain the life-related indicators corresponding to each historical period of each patient to be adjusted, and according to the necessity value of drug adjustment corresponding to each patient to be adjusted, thereby analyzing and obtaining the disease risk prediction value corresponding to each patient to be adjusted; Drug adjustment priority analysis module: used to analyze the drug adjustment priority of each patient to be adjusted according to the disease risk prediction value corresponding to each patient to be adjusted, and push each patient to be adjusted to the on-duty doctor's work machine according to the drug adjustment priority, and provide risk warnings.

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