Clinical auxiliary smoking cessation management method based on artificial intelligence and related equipment

Abstract

According to the clinical auxiliary smoking cessation management method based on artificial intelligence and the related equipment, a scientific smoking cessation guidance scheme can be provided for a user, and the smoking cessation success rate is increased. The method comprises the following steps: collecting target smoking attribute information of a target user, wherein the target smoking attribute information comprises demographic information, smoking information, smoking cessation information and health information of the target user; selecting a target analysis model from a plurality of analysis models according to the target smoking attribute information; preprocessing the target smoking attribute information to obtain smoking attribute features; inputting the smoking attribute characteristics into the target analysis model to obtain a target smoking cessation guidance scheme of the target user; and outputting the target smoking cessation guidance scheme.

Description

Technical Field

[0001] The present invention relates to the field of smoking cessation management, and in particular to an artificial intelligence-based clinical assisted smoking cessation management method and related equipment. Background Art

[0002] Controlling smoking is the most important and feasible measure for disease prevention and individual health care. Most smokers will go through all or most of the smoking cessation stages before they can completely quit smoking. Some tobacco addicts cannot quit smoking even after they develop smoking-related diseases. Clinical medical staff should not only provide appropriate treatment methods based on the specific conditions of smokers, but also continue to pay attention to the smoking cessation process of smokers, provide smoking cessation assistance when smokers encounter problems at various stages of smoking cessation, and finally successfully quit smoking.

[0003] At present, most of the forms of follow-up for smoking cessation in my country are to require smokers to return to smoking cessation clinics for follow-up or to understand their smoking cessation status through telephone calls. Affected by multiple factors such as region, time, and family, the duration, frequency, and quality of smoking cessation follow-up are far from meeting the requirements of clinical smoking cessation guidelines. In addition, there are deficiencies in the existing information management methods for smoking cessation patients. Most continuous smoking cessation follow-up is implemented by specialists and nurses, without unified data information management, and lacks integrity and continuity. Summary of the invention

[0004] The embodiment of the present invention provides a clinical assisted smoking cessation management method and related equipment based on artificial intelligence, which can provide users with a scientific smoking cessation guidance program and improve the success rate of smoking cessation.

[0005] The first aspect of the present invention provides a clinical assisted smoking cessation management method based on artificial intelligence, comprising:

[0006] Collecting target smoking attribute information of the target user, wherein the target smoking attribute information includes demographic information, smoking information, smoking cessation information and health information of the target user;

[0007] selecting a target analysis model from a plurality of analysis models according to the target smoking attribute information;

[0008] Preprocessing the target smoking attribute information to obtain smoking attribute features;

[0009] Inputting the smoking attribute characteristics into the target analysis model to obtain a target smoking cessation guidance program for the target user;

[0010] Output the target smoking cessation guidance program.

[0011] The second aspect of the present invention provides a clinical auxiliary smoking cessation management device based on artificial intelligence, comprising:

[0012] A collection module, used for collecting target smoking attribute information of a target user, wherein the target smoking attribute information includes demographic information, smoking information, smoking cessation information and health information of the target user;

[0013] A selection module, configured to select a target analysis model from a plurality of analysis models according to the target smoking attribute information;

[0014] A preprocessing module, used for preprocessing the target smoking attribute information to obtain smoking attribute characteristics;

[0015] An analysis module, used for inputting the smoking attribute characteristics into the target analysis model to obtain a target smoking cessation guidance program for the target user;

[0016] An output module is used to output the target smoking cessation guidance program.

[0017] In a possible design, the analysis module is further used for:

[0018] Clustering is performed according to the smoking attribute information of each smoker in the smoker user set to obtain a clustered smoker user set, wherein the smoker user set includes a plurality of smokers, each of the plurality of smokers has successfully quit smoking, and the clustered smoker user set includes a subset of smokers corresponding to each cluster category in a plurality of cluster categories;

[0019] Preprocessing the smoking attribute information of each target smoking user in the target smoking user subset to obtain a smoking cessation training sample set corresponding to each cluster category in the multiple cluster categories, wherein the target smoking user subset is a subset of smoking users in any cluster category in the cluster smoking user set;

[0020] An initial recognition model is constructed, and the initial recognition model is iteratively trained based on the smoking cessation training sample set until a preset iteration termination condition is reached, thereby obtaining the multiple analysis models.

[0021] In a possible design, the output module is specifically used for:

[0022] Outputting the target smoking cessation guidance program to the smoking cessation guidance physician corresponding to the target user;

[0023] receiving an adjustment instruction returned by the smoking cessation guidance physician, and adjusting the smoking cessation guidance program according to the adjustment instruction to obtain a physician adjustment program;

[0024] The physician adjustment plan is sent to the terminal device corresponding to the target user.

[0025] In a possible design, the output module is further used for:

[0026] Monitoring the target smoking behavior of the target user in real time;

[0027] Determining whether the target smoking behavior exceeds the permitted smoking behavior of a target stage, wherein the target stage is included in a plurality of smoking cessation stages included in the target smoking cessation guidance program;

[0028] If so, a prompt message is generated to prompt the target user that the target smoking behavior exceeds the smoking plan.

[0029] In a possible design, the output module is further used for:

[0030] Acquire the current target health information of the target user;

[0031] The prompt information is generated based on the target health information.

[0032] In a possible design, the output module is further used for:

[0033] Determining a set of smoking cessation assistance users corresponding to the target user;

[0034] The prompt information is sent to the terminal device of each assisted smoking cessation user in the assisted smoking cessation user set, so that the assisted smoking cessation user can supervise the target user.

[0035] In a possible design, the output module is further used for:

[0036] During the process of the target user executing the target smoking cessation guidance program, monitoring the smoking data of the target user in the current cycle;

[0037] Determine the change in smoking data between the current period and the previous period;

[0038] The target smoking cessation guidance program is adjusted according to the changes, and the adjusted target smoking cessation guidance program is output.

[0039] The third aspect of the present invention provides an electronic device, including a memory and a processor, wherein the processor is used to implement the steps of the artificial intelligence-based clinical assisted smoking cessation management method as described in the first aspect when executing a computer management program stored in the memory.

[0040] The fourth aspect of the present invention provides a computer-readable storage medium on which a computer management program is stored. When the computer management program is executed by a processor, the steps of the clinical assisted smoking cessation management method based on artificial intelligence as described in the first aspect above are implemented.

[0041] In summary, it can be seen that in the embodiments provided by the present invention, an analysis model for generating a smoking cessation guidance program can be pre-trained, and then the smoking attribute data of the target user can be obtained, and the smoking cessation guidance program of the target user can be obtained according to the smoking attribute data of the target user and the analysis model, and the user can be guided to quit smoking. In this way, a scientific smoking cessation guidance program can be provided for the user to improve the success rate of smoking cessation. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A schematic diagram of a process flow of a clinical assisted smoking cessation management method based on artificial intelligence provided by an embodiment of the present invention;

[0043] Figure 2 A virtual structural diagram of an artificial intelligence-based clinical auxiliary smoking cessation management device provided in an embodiment of the present invention;

[0044] Figure 3 A schematic diagram of the hardware structure of an artificial intelligence-based clinical assisted smoking cessation management device provided in an embodiment of the present invention;

[0045] Figure 4 A schematic diagram of an electronic device according to an embodiment of the present invention;

[0046] Figure 5 A schematic diagram of an embodiment of a computer-readable storage medium provided for an embodiment of the present invention. DETAILED DESCRIPTION

[0047] 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0048] In the following description, specific embodiments of the present invention will be described with reference to steps and symbols performed by one or more computers, unless otherwise stated. Therefore, these steps and operations will be mentioned several times as being performed by a computer, and computer execution referred to herein includes operations by a computer processing unit of electronic signals representing data in a structured form. This operation converts the data or maintains it at a location in the computer's memory system, which can be reconfigured or otherwise change the operation of the computer in a manner familiar to testers in the field. The data structure maintained by the data is a physical location in the memory, which has specific characteristics defined by the data format. However, the principles of the present invention are described in the above text, which does not represent a limitation, and testers in the field will understand that the various steps and operations described below can also be implemented in hardware.

[0049] The principles of the present invention operate with many other general or special purpose computing, communication environments or configurations. Examples of well-known computing systems, environments and configurations suitable for use with the present invention may include, but are not limited to, handheld phones, personal computers, servers, multiprocessor systems, microcomputer-based systems, mainframe computers, and distributed computing environments, including any of the above systems or devices.

[0050] The terms "first", "second", and "third" in the present invention are used to distinguish different objects rather than to describe a specific order. In addition, the terms "include", "have", and any variations thereof are intended to cover non-exclusive inclusions.

[0051] The following describes the artificial intelligence-based clinical assisted smoking cessation management method provided by the present invention from the perspective of an artificial intelligence-based clinical assisted smoking cessation management device. The artificial intelligence-based clinical assisted smoking cessation management device can be a server or a service unit in a server, without specific limitation.

[0052] See also Figure 1 , Figure 1 A schematic diagram of a process flow of a clinical assisted smoking cessation management method based on artificial intelligence provided by an embodiment of the present invention, wherein the clinical assisted smoking cessation management method based on artificial intelligence comprises:

[0053] 101. Collect target smoking attribute information of the target user.

[0054] In this embodiment, the clinical smoking cessation management device based on artificial intelligence can collect target smoking attribute information of the target user, wherein the target smoking attribute information includes demographic information, smoking information, smoking cessation information and health information of the target user. The method of collecting the target smoking attribute information is not limited here. For example, the demographic information, smoking information and smoking cessation information can be obtained by questionnaire, and the health information can be obtained by testing. For ease of understanding, each information in the target smoking attribute information is described in detail below:

[0055] Demographic information includes the target user’s name, gender, age, contact number, home address, ethnicity, education, living conditions, occupation and other basic information;

[0056] The smoking information includes the years of smoking, the time interval between smoking, the time of smoking (i.e., under what circumstances the target user smokes, or at what time the target user smokes), the degree of desire for smoking, and the number of cigarettes smoked in a cycle. It may also include the target user's smoking start time (i.e., at what age did he start smoking), regular smoking time (i.e., at what age did he start smoking regularly), the number of years of regular smoking, and whether he smokes in special circumstances, such as whether he smokes when he is sick, whether he smokes when he is with his spouse or family members, whether he smokes at home, and whether he smokes in the car; of course, the smoking information may also include some other circumstances of the target user's smoking, such as under what circumstances the amount of smoking will increase and smoking habits (e.g., to relieve stress, to socialize, etc.), etc., which are not specifically limited;

[0057] Information on smoking cessation included whether or not you have tried to quit smoking, whether or not you have successfully quit smoking, what methods you used to quit smoking, whether or not there was a withdrawal reaction after quitting smoking, the longest time you have quit smoking, whether or not you have used medication to quit smoking, the reasons for your quitting failure, and the reasons for quitting smoking;

[0058] Health information includes carbon monoxide information (i.e. the carbon monoxide content in the target user's exhaled breath), lung function information (including but not limited to total lung capacity, vital capacity, and 1-second ventilation), blood pressure, weight, past medical history, and treatment medications.

[0059] 102. Select a target analysis model from multiple analysis models based on the target smoking attribute information.

[0060] In this embodiment, after collecting the target smoking attribute information, the clinical assisted smoking cessation management device based on artificial intelligence can select a target analysis model from multiple analysis models based on the target smoking attribute information. That is, the clinical assisted smoking cessation management device based on artificial intelligence can analyze and train the smoking attribute information of various types of users in advance to obtain analysis models corresponding to various types of users, thereby selecting a target analysis model corresponding to the target user from multiple analysis models based on the target smoking attribute information.

[0061] For ease of understanding, the following is an explanation of the training process of the analysis model:

[0062] Step 1: Clustering is performed according to the smoking attribute information of each smoking user in the smoking user set to obtain a clustered smoking user set.

[0063] In this step, a plurality of former smokers who have successfully quit smoking are selected as a set of smokers for training of the analysis model. Before the training, the smokers in the set of smokers are clustered according to the smoking attribute information of each smoker in the set of smokers to obtain a clustered set of smokers. That is, the smokers in the set of smokers are clustered according to the basic information, smoking information, smoking cessation information, carbon monoxide information and lung function information of each smoker in the set of smokers to reduce the training error. In this embodiment, the clustering algorithm k-means can be used to cluster the smokers in the set of smokers according to the basic information, smoking information, smoking cessation information, carbon monoxide information and lung function information of each smoker in the set of smokers, so as to obtain a clustered set of smokers corresponding to each of the multiple categories.

[0064] Step 2: pre-process the smoking attribute information of each target smoking user in the target smoking user subset to obtain a smoking cessation training sample set corresponding to each cluster category in the multiple cluster categories.

[0065] In this step, the artificial intelligence-based clinical assisted smoking cessation management device can receive manual annotations of the smoking attribute information of each target smoking user, and preprocess the labeled smoking attribute information of each target smoking user. The preprocessing includes but is not limited to data cleaning, missing value interpolation, and sensitive information removal, thereby obtaining a training sample set corresponding to each clustering category in multiple clustering categories.

[0066] It should be noted that manual labeling of smoking attribute information includes but is not limited to detailed analysis of multiple smoking cessation plans provided by users who have successfully quit smoking, obtaining the operations performed by the users at each stage of the entire smoking cessation process, and integrating the operations at each stage to generate a detailed smoking cessation guide plan.

[0067] Step 3: construct an initial recognition model, and iteratively train the initial recognition model based on the smoking cessation training sample set until a preset iteration termination condition is reached to obtain multiple analysis models.

[0068] In this step, the artificial intelligence-based clinical assisted smoking cessation management device can construct an initial recognition model, which is a recognition model constructed by a neural network, including a convolutional layer, a pooling layer, a fully connected layer and a softmax function, and iteratively train the initial recognition model according to the training sample set corresponding to each clustering category in the multiple clustering categories, and after each iteration is completed, determine whether the preset iteration termination condition is met, that is, after each iteration is completed, determine whether the number of iterations reaches a preset threshold, if so, determine that the preset iteration termination condition is met; or, determine whether the loss function of the initial recognition model converges, if so, determine that the preset iteration termination condition is met.

[0069] When it is determined that a preset iteration termination condition is reached, the initial recognition model that has reached the preset iteration termination condition is determined as a trained analysis model.

[0070] It should be noted that the artificial intelligence-based clinical assisted smoking cessation management device can also divide the training samples in the training sample set into training sample subsets, test subsets and verification subsets, perform model training through the training sample subsets, test the trained model through the test subsets, and verify the model through the verification subsets, thereby improving the prediction accuracy of the model.

[0071] 103. Preprocess the target smoking attribute information to obtain smoking attribute characteristics.

[0072] In this embodiment, the artificial intelligence-based clinical assisted smoking cessation management device can preprocess the target smoking attribute information to obtain the smoking attribute information, and the preprocessing method is consistent with the preprocessing method in the model training process.

[0073] It should be noted that the target analysis model can be selected through step 102, and the target smoking attribute information can be preprocessed through step 103. However, there is no restriction on the order of execution between these two steps. Step 102 can be executed first, or step 103 can be executed first, or they can be executed simultaneously. There is no specific limitation.

[0074] 104. Input the smoking attribute characteristics into the target analysis model to obtain a target smoking cessation guidance plan for the target user.

[0075] In this embodiment, after determining the smoking attribute characteristics, the clinical assisted smoking cessation management device based on artificial intelligence can input the smoking attribute characteristics into the target analysis model to obtain a target smoking cessation guidance plan for the target user. The target smoking cessation guidance plan is the best smoking cessation guidance plan obtained by the target analysis model based on the smoking situation of the target user. The smoking cessation guidance plan includes a smoking cessation plan, dietary guidance, and psychological support, etc. At the same time, the entire smoking cessation process can be divided into multiple different stages, and different smoking cessation plans are set for each stage, and corresponding smoking cessation learning information is determined. The most suitable action tasks can be determined according to the different stages of the target user's smoking cessation process, such as delaying smoking; taking deep breaths; engaging in some activities that one likes to replace oral and manual desires when smoking; seeking help and support from doctors, family, and friends, etc.

[0076] 105. Output target smoking cessation guidance plan.

[0077] In this embodiment, after generating the target smoking cessation guidance plan corresponding to the target user, the target smoking cessation guidance plan can be output. For example, it can be pushed to the target user or the auxiliary smoking cessation user corresponding to the target user according to stages and time nodes, and the output of the target smoking cessation guidance plan can be realized in various forms such as text, pictures, videos, questionnaires, text messages and WeChat.

[0078] It should be noted that since the target smoking cessation guidance plan is directly generated by the model, there may be a certain error between the target smoking cessation guidance plan and the actual situation of the target user. Therefore, when outputting the target smoking cessation guidance plan, the target smoking cessation guidance plan can also be output to the smoking cessation guidance physician corresponding to the target user, so that the smoking cessation guidance physician can make adjustments based on the generated target smoking cessation guidance plan. In this way, the adjustment instructions of the smoking cessation guidance physician for the smoking cessation guidance plan can be received, and the adjusted smoking cessation guidance plan can be obtained, and the adjusted smoking cessation guidance plan can be sent to the terminal device corresponding to the target user.

[0079] In one embodiment, the artificial intelligence-based clinical assisted smoking cessation management device further performs the following operations during the target user's smoking cessation process:

[0080] Monitoring the target smoking behavior of the target user in real time;

[0081] Determining whether the target smoking behavior exceeds the permitted smoking behavior of a target stage, wherein the target stage is included in a plurality of smoking cessation stages included in the target smoking cessation guidance program;

[0082] If so, a prompt message is generated to prompt the target user that the current target smoking behavior exceeds the smoking plan.

[0083] In this embodiment, since the target user cannot give up smoking at once and needs to continuously reduce the amount of smoking until he stops smoking completely, the amount of smoking of the target user will be limited in each smoking cessation stage in the target smoking cessation guidance program. The target smoking behavior of the target user is monitored in real time, and it is determined whether the target smoking behavior is the smoking behavior allowed in the target stage. If it is, it means that the target user's smoking amount exceeds the allowed amount in this stage, and a prompt message is generated to prompt the target user that the target smoking behavior exceeds the smoking plan. It can be understood that the prompt message can be generated based on the current target health information of the target user. For example, the prompt message can be the current carbon monoxide content of the target user, and the carbon monoxide content of normal non-smoking users is compared to timely detect the harm of smoking by the target user; of course, the prompt message can also be only through the current smoking cessation stage of the target user, the number of cigarettes allowed in the smoking cessation stage, the current number of cigarettes allowed has been exceeded, please pay attention, etc.

[0084] It should be noted that the artificial intelligence-based clinical assisted smoking cessation management device can be linked with a smart cigarette box or a smart lighter. When the allowed number of times is exceeded at this stage, the smart cigarette box is controlled to be closed and the smart lighter cannot be started.

[0085] In one embodiment, the artificial intelligence-based clinical assisted smoking cessation management device further performs the following operations:

[0086] Determining a set of smoking cessation assistance users corresponding to the target user;

[0087] The prompt information is sent to the terminal device of each assisted smoking cessation user in the assisted smoking cessation user set, so that the assisted smoking cessation user can supervise the target user.

[0088] In this embodiment, the clinical assisted smoking cessation management device based on artificial intelligence can determine the set of assisted smoking cessation users corresponding to the target user. The set of assisted smoking cessation users can be relatives of the target user or common "smoking cessation partners" to assist the target user in quitting smoking. After generating the prompt information, the prompt information can be sent to the terminal device of each assisted smoking cessation user in the set of assisted smoking cessation users, so that the users in the set of assisted smoking cessation users can supervise the target user and improve the success rate of smoking cessation.

[0089] It should also be noted that since each person's willpower and degree of dependence on nicotine are different, in the process of the target user executing the target smoking cessation guidance program, the target user's smoking data in the current cycle can also be detected, and the changes in the smoking data between the current cycle and the previous cycle can be determined. For example, the smoking data of the day can be obtained and compared with the smoking data of the previous day. If the change is large, it means that the target user has a strong willpower to nicotine and a low degree of dependence on nicotine. Therefore, the smoking cessation plan can be adjusted according to the changes to better suit the situation of the target user, and the adjusted target smoking cessation guidance program can be output.

[0090] In summary, it can be seen that in the embodiments provided by the present invention, an analysis model for generating a smoking cessation guidance program can be pre-trained, and then the smoking attribute data of the target user can be obtained, and the smoking cessation guidance program of the target user can be obtained according to the smoking attribute data of the target user and the analysis model, and the user can be guided to quit smoking. In this way, a scientific smoking cessation guidance program can be provided for the user to improve the success rate of smoking cessation.

[0091] The above describes the embodiment of the present invention from the perspective of a clinical assisted smoking cessation management method based on artificial intelligence. The following describes the embodiment of the present invention from the perspective of a clinical assisted smoking cessation management device based on artificial intelligence.

[0092] See also Figure 2 , a virtual structural diagram of a clinical assisted smoking cessation management device based on artificial intelligence in an embodiment of the present invention, the clinical assisted smoking cessation management device based on artificial intelligence 200 comprises:

[0093] The collection module 201 is used to collect target smoking attribute information of a target user, wherein the target smoking attribute information includes basic information, smoking information, smoking cessation information, carbon monoxide information and lung function information of the target user;

[0094] A selection module 202, configured to select a target analysis model from a plurality of analysis models according to the target smoking attribute information;

[0095] A preprocessing module 203, used for preprocessing the target smoking attribute information to obtain smoking attribute features;

[0096] An analysis module 204, configured to input the smoking attribute characteristics into the target analysis model to obtain a target smoking cessation guidance program for the target user;

[0097] The output module 205 is used to output the target smoking cessation guidance program.

[0098] In a possible design, the analysis module 204 is further used for:

[0099] Clustering is performed according to the smoking attribute information of each smoker in the smoker user set to obtain a clustered smoker user set, wherein the smoker user set includes a plurality of smokers, each of the plurality of smokers has successfully quit smoking, and the clustered smoker user set includes a subset of smokers corresponding to each cluster category in a plurality of cluster categories;

[0100] Preprocessing the smoking attribute information of each target smoking user in the target smoking user subset to obtain a smoking cessation training sample set corresponding to each cluster category in the multiple cluster categories, wherein the target smoking user subset is a subset of smoking users in any cluster category in the cluster smoking user set;

[0101] An initial recognition model is constructed, and the initial recognition model is iteratively trained based on the smoking cessation training sample set until a preset iteration termination condition is reached, thereby obtaining the multiple analysis models.

[0102] In a possible design, the output module 205 is specifically used for:

[0103] Outputting the target smoking cessation guidance program to the smoking cessation guidance physician corresponding to the target user;

[0104] receiving an adjustment instruction returned by the smoking cessation guidance physician, and adjusting the smoking cessation guidance program according to the adjustment instruction to obtain a physician adjustment program;

[0105] The physician adjustment plan is sent to the terminal device corresponding to the target user.

[0106] In a possible design, the output module 205 is further used for:

[0107] Monitoring the target smoking behavior of the target user in real time;

[0108] Determining whether the target smoking behavior exceeds the permitted smoking behavior of a target stage, wherein the target stage is included in a plurality of smoking cessation stages included in the target smoking cessation guidance program;

[0109] If so, a prompt message is generated to prompt the target user that the target smoking behavior exceeds the smoking plan.

[0110] In a possible design, the output module 205 is further used for:

[0111] Acquire the current target carbon monoxide information and target lung function information of the target user;

[0112] The prompt information is generated based on the target carbon monoxide information and the target lung function information.

[0113] In a possible design, the output module 205 is further used for:

[0114] Determining a set of smoking cessation assistance users corresponding to the target user;

[0115] The prompt information is sent to the terminal device of each assisted smoking cessation user in the assisted smoking cessation user set, so that the assisted smoking cessation user can supervise the target user.

[0116] In a possible design, the output module 205 is further used for:

[0117] During the process of the target user executing the target smoking cessation guidance program, monitoring the smoking data of the target user in the current cycle;

[0118] Determine the change in smoking data between the current period and the previous period;

[0119] The target smoking cessation guidance program is adjusted according to the changes, and the adjusted target smoking cessation guidance program is output.

[0120] above Figure 2 The clinical assisted smoking cessation management device based on artificial intelligence in the embodiment of the present invention is described from the perspective of modular functional entities. The clinical assisted smoking cessation management device based on artificial intelligence in the embodiment of the present invention is described in detail from the perspective of hardware processing. Please refer to Figure 3 , a schematic diagram of an embodiment of an artificial intelligence-based clinical assisted smoking cessation management device 300 in an embodiment of the present invention, the artificial intelligence-based clinical assisted smoking cessation management device 300 comprises:

[0121] An input device 301, an output device 302, a processor 303 and a memory 304 (the number of the processor 303 can be one or more, Figure 3 In some embodiments of the present invention, the input device 301, the output device 302, the processor 303 and the memory 304 may be connected via a communication bus or other means, wherein: Figure 3 The communication bus connection is taken as an example.

[0122] Wherein, by calling the operation instruction stored in the memory 304, the processor 303 is used to perform the following steps:

[0123] Collecting target smoking attribute information of the target user, wherein the target smoking attribute information includes basic information, smoking information, smoking cessation information, carbon monoxide information, and lung function information of the target user;

[0124] selecting a target analysis model from a plurality of analysis models according to the target smoking attribute information;

[0125] Preprocessing the target smoking attribute information to obtain smoking attribute characteristics;

[0126] Inputting the smoking attribute characteristics into the target analysis model to obtain a target smoking cessation guidance program for the target user;

[0127] Output the target smoking cessation guidance program.

[0128] By calling the operation instructions stored in the memory 304, the processor 303 is also used to execute Figure 1 Any method in the corresponding embodiment.

[0129] See also Figure 4 , Figure 4 A schematic diagram of an electronic device according to an embodiment of the present invention.

[0130] like Figure 4 As shown, an embodiment of the present invention provides an electronic device, including a memory 410, a processor 420, and a computer program 411 stored in the memory 410 and executable on the processor 420. When the processor 420 executes the computer program 411, the following steps are implemented:

[0131] Collecting target smoking attribute information of the target user, wherein the target smoking attribute information includes basic information, smoking information, smoking cessation information, carbon monoxide information, and lung function information of the target user;

[0132] selecting a target analysis model from a plurality of analysis models according to the target smoking attribute information;

[0133] Preprocessing the target smoking attribute information to obtain smoking attribute characteristics;

[0134] Inputting the smoking attribute characteristics into the target analysis model to obtain a target smoking cessation guidance program for the target user;

[0135] Output the target smoking cessation guidance program.

[0136] In the specific implementation process, when the processor 420 executes the computer program 411, it can achieve Figure 1 Any implementation manner in the corresponding embodiments.

[0137] Since the electronic device introduced in this embodiment is a device used to implement an artificial intelligence-based clinical assisted smoking cessation management device in the embodiment of the present invention, based on the method introduced in the embodiment of the present invention, technical personnel in this field can understand the specific implementation method of the electronic device of this embodiment and its various variations. Therefore, how the electronic device implements the method in the embodiment of the present invention will not be introduced in detail here. As long as the equipment used by technical personnel in this field to implement the method in the embodiment of the present invention falls within the scope of protection of the present invention.

[0138] See also Figure 5 , Figure 5 A schematic diagram of an embodiment of a computer-readable storage medium provided in an embodiment of the present invention.

[0139] like Figure 5 As shown, the embodiment of the present invention further provides a computer-readable storage medium 500, on which a computer program 511 is stored. When the computer program 511 is executed by a processor, the following steps are implemented:

[0140] Collecting target smoking attribute information of the target user, wherein the target smoking attribute information includes basic information, smoking information, smoking cessation information, carbon monoxide information, and lung function information of the target user;

[0141] selecting a target analysis model from a plurality of analysis models according to the target smoking attribute information;

[0142] Preprocessing the target smoking attribute information to obtain smoking attribute features;

[0143] Inputting the smoking attribute characteristics into the target analysis model to obtain a target smoking cessation guidance program for the target user;

[0144] Output the target smoking cessation guidance program.

[0145] In the specific implementation process, the computer program 511 is executed by the processor to achieve Figure 1 Any implementation manner in the corresponding embodiments.

[0146] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0147] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0148] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0149] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0150] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0151] The embodiment of the present invention also provides a computer program product, which includes computer software instructions. When the computer software instructions are executed on a processing device, the processing device executes the following Figure 1 The process in the corresponding embodiment.

[0152] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state disk (SSD)), etc.

[0153] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0154] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There are other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0155] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0156] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0157] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store program codes.

[0158] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A clinical assisted smoking cessation management method based on artificial intelligence, characterized in that: include: Collecting target smoking attribute information of the target user, wherein the target smoking attribute information includes demographic information, smoking information, smoking cessation information, and health information of the target user; selecting a target analysis model from a plurality of analysis models according to the target smoking attribute information; Preprocessing the target smoking attribute information to obtain smoking attribute characteristics; Inputting the smoking attribute characteristics into the target analysis model to obtain a target smoking cessation guidance program for the target user; Output the target smoking cessation guidance program.

2. The method according to claim 1, characterized in that The method further comprises: Clustering is performed according to the smoking attribute information of each smoker in the smoker user set to obtain a clustered smoker user set, wherein the smoker user set includes a plurality of smokers, each of the plurality of smokers has successfully quit smoking, and the clustered smoker user set includes a subset of smokers corresponding to each cluster category in a plurality of cluster categories; Preprocessing the smoking attribute information of each target smoking user in the target smoking user subset to obtain a smoking cessation training sample set corresponding to each cluster category in the multiple cluster categories, wherein the target smoking user subset is a subset of smoking users in any cluster category in the cluster smoking user set; An initial recognition model is constructed, and the initial recognition model is iteratively trained based on the smoking cessation training sample set until a preset iteration termination condition is reached, thereby obtaining the multiple analysis models.

3. The method according to claim 1, characterized in that The output target smoking cessation guidance program includes: Outputting the target smoking cessation guidance program to the smoking cessation guidance physician corresponding to the target user; receiving an adjustment instruction returned by the smoking cessation guidance physician, and adjusting the smoking cessation guidance program according to the adjustment instruction to obtain a physician adjustment program; The physician adjustment plan is sent to the terminal device corresponding to the target user.

4. The method according to any one of claims 1 to 3, characterized in that The method further comprises: Monitoring the target smoking behavior of the target user in real time; Determining whether the target smoking behavior exceeds the permitted smoking behavior of a target stage, wherein the target stage is included in a plurality of smoking cessation stages included in the target smoking cessation guidance program; If so, a prompt message is generated to prompt the target user that the target smoking behavior exceeds the smoking plan.

5. The method according to claim 4, characterized in that The generation prompt information includes: Acquire the current target health information of the target user; The prompt information is generated based on the target health information.

6. The method according to claim 4, characterized in that The outputting of the prompt information includes: Determining a set of smoking cessation assistance users corresponding to the target user; The prompt information is sent to the terminal device of each assisted smoking cessation user in the assisted smoking cessation user set, so that the assisted smoking cessation user can supervise the target user.

7. The method according to any one of claims 1 to 3, 5 to 6, characterized in that: The method further comprises: During the process of the target user executing the target smoking cessation guidance program, monitoring the smoking data of the target user in the current cycle; Determine the change in smoking data between the current period and the previous period; The target smoking cessation guidance program is adjusted according to the changes, and the adjusted target smoking cessation guidance program is output.

8. A clinical assisted smoking cessation management device based on artificial intelligence, characterized in that: include: A collection module, used for collecting target smoking attribute information of a target user, wherein the target smoking attribute information includes demographic information, smoking information, smoking cessation information and health information of the target user; A selection module, configured to select a target analysis model from a plurality of analysis models according to the target smoking attribute information; A preprocessing module, used for preprocessing the target smoking attribute information to obtain smoking attribute characteristics; An analysis module, used for inputting the smoking attribute characteristics into the target analysis model to obtain a target smoking cessation guidance program for the target user; An output module is used to output the target smoking cessation guidance program.

9. An electronic device, characterized in that: include: A memory and a processor, wherein the processor is used to implement the artificial intelligence-based clinical assisted smoking cessation management method as described in any one of claims 1 to 7 when executing the computer management program stored in the memory.

10. A computer-readable storage medium having a computer management program stored thereon, characterized in that: When the computer management program is executed by the processor, the artificial intelligence-based clinical assisted smoking cessation management method as described in any one of claims 1 to 7 is implemented.

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