CN113936771B - Iterative generation method and device of health index target - Google Patents
Iterative generation method and device of health index target Download PDFInfo
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Abstract
The invention discloses a health index target iteration generation method and device, and relates to the technical field of information processing. One embodiment of the method comprises: acquiring the latest value of a body index to be detected, body basic data of a user, the current value of the body index to be detected and the execution result of the guidance scheme of the user for the first period in the first period, wherein the latest value of the body index to be detected is generated when the user executes the guidance scheme for the first period in the first period; carrying out prediction processing on the current value, the body basic data and the execution result by using the model to obtain a predicted change value of the body index to be measured of the user in the second period; and determining an index target of the body index to be measured of the user in the second period based on the latest value and the predicted change value. Therefore, the index target can be dynamically adjusted based on the latest value of the body index to be measured in the real execution process of the user, so that the target formulation is more consistent with the real body condition of the user, and the user can effectively manage the body index to be measured.
Description
Technical Field
The invention relates to the technical field of information processing, in particular to a health index target iteration generation method and device.
Background
Type 2 diabetes, also known as non-insulin dependent diabetes mellitus, is characterized in that the human body can produce insulin, but cells cannot respond to the insulin, so that the effect of the insulin is greatly reduced. Generally, diseases caused by insulin resistance combined with relative insulin hyposecretion can occur at any age, but are frequently seen in adults, often become ill after the age of 40, become hidden, have relatively mild symptoms and are easily ignored by patients.
Blood glucose monitoring is part of the daily life of type 2 diabetic patients, and the accuracy of blood glucose monitoring has a very important impact on the diabetic patients and ultimately on their quality of life. A diabetic patient may measure blood glucose concentrations multiple times per day and make the measurements as part of the diabetes self-management process. If a diabetic patient is unable to control blood glucose levels at the target concentration, serious diabetes-related complications, such as cardiovascular disease, renal disease, nerve damage, blindness, and the like, may develop.
Disclosure of Invention
In view of this, embodiments of the present invention provide an iterative generation method and apparatus for a health index target, which can dynamically adjust an index target based on a latest value of a body index to be measured in a user real execution process, so that the target formulation better conforms to a user real body condition, and a user can effectively manage the body index to be measured.
To achieve the above object, according to a first aspect of the embodiments of the present invention, there is provided a method for iteratively generating a health indicator target, the method including: acquiring the latest value of a body index to be detected generated by a user executing a guidance scheme aiming at a first period in the first period; acquiring basic body data of a user, a current value of a body index to be measured and an execution result of a guidance scheme of the user for a first period in the first period; predicting the current value, the body basic data and the execution result by using a model to obtain a predicted change value of the body index to be detected of the user in a second period; and determining an index target of the body index to be measured of the user in the second period based on the latest value and the predicted change value.
Optionally, the determining, based on the latest value and the predicted change value, an index target of a body index to be measured of the user at a second period includes: determining an estimated value of a body index to be measured in a second period based on the latest value and the predicted change value; judging whether the estimated value is larger than the upper limit of the control threshold corresponding to the body index to be detected; and determining an index target of the body index to be measured of the user in the second period based on the judgment result.
Optionally, the determining, based on the determination result, an index target of the body index to be measured of the user at the second period includes: if the judgment result indicates that the estimation value is larger than the upper limit of the control threshold, determining that the estimation value is used as an upper limit value of an index target, and using a lower limit of a normal range corresponding to the body index to be detected as a lower limit value of the index target to obtain the index target of the body index to be detected of the user in a second period; and if the judgment result indicates that the estimated value is not greater than the upper limit of the control threshold, taking the normal range of the body index to be detected as the index target of the body index to be detected of the user in the second period.
Optionally, the method further includes: determining a baseline value and a control threshold upper limit of a body index to be detected based on body basic data of a user; judging whether the baseline value is larger than the upper limit of the control threshold value; and determining an initial target of the body index to be measured of the user in the initial period based on the judgment result.
Optionally, the determining, based on the determination result, an initial target of the body index to be measured of the user in the initial period includes: if the baseline value is larger than the upper limit of the control threshold, taking the upper limit of the control threshold as the upper limit value of the initial target, and taking the lower limit of the normal range corresponding to the body index to be detected as the lower limit value of the initial target to obtain the initial target; and if the baseline value is not larger than the upper limit of the control threshold, taking the normal range of the body index to be detected as an initial target.
Optionally, the instructional regimen comprises a dietary regimen and/or an exercise regimen.
To achieve the above object, according to a second aspect of the embodiments of the present invention, there is also provided a health indicator target iteration generation apparatus, including: the first acquisition module is used for acquiring the latest value of the body index to be detected generated by the user executing the guidance scheme aiming at the first period in the first period; the second acquisition module is used for acquiring body basic data of the user, a current value of a body index to be detected and an execution result of a guidance scheme of the user aiming at a first period in the first period; the prediction module is used for carrying out prediction processing on the current value, the body basic data and the execution result by using a model to obtain a predicted change value of the body index to be detected of the user in a second period; and the first determination module is used for determining the index target of the body index to be measured of the user in the second period based on the latest value and the predicted change value.
In order to achieve the above object, according to a third aspect of the embodiments of the present invention, there is further provided a system for iteratively generating a health indicator target, where the system includes a client and a server; the client acquires the latest value of a body index to be detected generated by a user executing a guidance scheme aiming at a first period in the first period, body basic data of the user, the current value of the body index to be detected and an execution result of the guidance scheme aiming at the first period in the first period; sending the latest value, the current value, the body basic data and the execution result to the server; the server carries out prediction processing on the current value, the body basic data and the execution result by using a model to obtain a predicted change value of the body index to be measured of the user in a second period; determining an index target of the body index to be measured of the user in a second period based on the latest value and the predicted change value; and the server sends the index target to the client.
To achieve the above object, according to a fourth aspect of the embodiments of the present invention, there is also provided an apparatus/terminal/server, the apparatus including: one or more processors; a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement a method as described in the first aspect.
To achieve the above object, according to a fifth aspect of the embodiments of the present invention, there is also provided a computer-readable storage medium on which a computer program is stored, the program implementing the method according to the first aspect when executed by a processor.
According to the embodiment of the invention, the latest value of the body index to be measured generated by executing the guidance scheme aiming at the first period by the user in the first period is obtained; acquiring basic body data of the user, a current value of a body index to be measured and an execution result of a guidance scheme of the user for a first period in the first period; then, carrying out prediction processing on the current value, the body basic data and the execution result by using a model to obtain a predicted change value of the body index to be detected of the user in a second period; and determining an index target of the body index to be measured of the user in the second period based on the latest value and the predicted change value. Therefore, the target is split into the initial target and the index target, so that the user has a clear target when entering the group, and the target feeling of the user is enhanced; and moreover, the index target can be dynamically adjusted based on the latest value of the body index to be detected in the real execution process of the user, so that the target formulation is more in line with the real body condition of the user, and the body index to be detected can be effectively managed.
Further effects of the above-described non-conventional alternatives will be described below in connection with specific embodiments.
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The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein like or corresponding reference numerals designate like or corresponding parts throughout the several views.
FIG. 1 is a flow chart of a method for iterative generation of a health index target in accordance with an embodiment of the present invention;
FIG. 2 is a flow chart of a method for iterative generation of a health index target according to another embodiment of the present invention;
FIG. 3 is a flow chart of a method for determining a user's initial goal in one embodiment of the invention;
FIG. 4 is a diagram of an apparatus for generating an index target according to an embodiment of the present invention;
FIG. 5 is an exemplary system architecture diagram in which embodiments of the present invention may be employed;
fig. 6 is a schematic block diagram of a computer system suitable for use in implementing a terminal device or server of an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention are described below with reference to the accompanying drawings, in which various details of embodiments of the invention are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
The utility model provides a generating system of periodic behavior report, comprising a client and a server; the client acquires behavior data and health index parameters of a user in a preset period, wherein the behavior data comprises at least one of exercise behaviors, diet behaviors and medication behaviors; sending the behavior data and the health index parameters to a server; the server generates periodic behavior reports of a predetermined period for the user based on the behavior data and the health index parameters at least in part using a machine learning model; the periodic behavior report at least comprises an evaluation result of a preset period, a behavior suggestion of the next period and/or an index target of the body index to be detected; the server updates the behavior labels of the users based on the periodic behavior reports and acquires the education contents matched with the behavior labels; the server sends the periodic behavior report and the education content to the client.
The present application is described below with respect to an index target generation method of a body index to be measured in a periodic behavior report. As shown in fig. 1, a flowchart of an iterative generation method of a health index target according to an embodiment of the present invention is shown. An iterative generation method of a health index target at least comprises the following operation flows: s101, acquiring the latest value of a body index to be measured generated by a user executing a guidance scheme aiming at a first period in the first period; s102, acquiring basic body data of a user, a current value of a body index to be measured and an execution result of a guidance scheme of the user for a first period in the first period; s103, carrying out prediction processing on the current value, the body basic data and the execution result by using the model to obtain a predicted change value of the body index to be measured of the user in the second period; and S104, determining an index target of the body index to be measured of the user in the second period based on the latest value and the predicted change value.
In S101, an initial target of the body index to be measured generated when the user executes the guidance program in the first period is determined. The initial target can be obtained based on presetting, or can be obtained by processing basic data of the user by using a model, or can be obtained by analyzing and processing the basic data of the user; and are not to be so limited herein. The body index to be measured can be determined according to the actual application scene of the index target generation method; the body index to be measured, such as fasting blood glucose, postprandial blood glucose, body weight or blood pressure, is not limited to a few.
The guideline may be a pre-set guideline or may be a guideline determined based on initial goals and user body basic data. The guidance program is a diet program; or the guidance program is a movement program; or the instruction plan is a diet plan and a sport plan.
The guidance schedule may vary with the target of the body metric being measured by the user, e.g., a first period guidance schedule for a first period, a second period guidance schedule corresponding to a second period metric target, and a third period guidance schedule corresponding to a third period metric target. The diet exercise regimen can be scheduled by the physician or adjusted by AI calculations for each cycle.
In S102, the body basic data includes, but is not limited to, age, sex, height, weight, body fat, waist circumference, pancreatic islet function, blood glucose or blood pressure, etc. The current value of the body index to be measured may be the latest value of the body index to be measured at the end of the first period, or may be the latest value of the body index to be measured at the beginning of the second period.
In S103, the basic body data, the current value of the body index to be measured, and the execution result of the user for the guidance program in the first period may be analyzed to obtain the predicted change value of the body index to be measured in the second period; and predicting the predicted change value of the body index to be detected in the second period by using a machine learning method.
Specifically, the training phase: taking the basic body data of the user, the current value of the body index to be measured and the execution result of the user aiming at the guidance scheme in the previous period as training samples, and taking the predicted change value of the body index to be measured when the previous period is finished as a training target; performing model training on the training sample by using a shallow neural network to obtain a training result; and adjusting parameters of the model based on a plurality of training results to obtain a prediction model. A prediction stage: and preprocessing the sample to be detected of the user in the first period by using the prediction model to obtain the predicted change value of the body index to be detected of the user in the second period. The sample to be tested of the first period comprises body basic data, the current value of the body index to be tested and the execution result of the user aiming at the guidance scheme in the first period. For example: when the body index to be detected is height, the height prediction change value is obtained after the body index is preprocessed by using the prediction model; when the body index to be detected is fasting blood glucose, the fasting blood glucose prediction change value is obtained after pretreatment by using a prediction model; and when the body index to be detected is postprandial blood sugar, obtaining a postprandial blood sugar prediction change value after preprocessing by using the prediction model.
It should be noted that a standard guidance scheme may also be added to the training samples in the training phase and the samples to be tested in the prediction phase, and the standard guidance scheme is different from the first period guidance scheme. The standard guidance scheme is a guidance scheme in which the body of the user is in an ideal state; the first periodic tutorial scheme is a tutorial scheme that comprehensively considers the physical condition of the user and is adaptive to the initial goal. For example, when the body index to be measured is fasting blood glucose or postprandial blood glucose, the standard guidance program is a blood glucose standard range, a body weight standard range, a body fat standard range and a muscle mass standard range. When the body index to be detected is the body weight, the standard guidance scheme comprises a standard diet scheme and a standard exercise scheme; standard dietary regimens such as balanced nutritional ratios of the diet and the user can strictly follow the dietary regimen; the standard exercise scheme such as exercise heart rate and exercise duration selects a heart rate lower limit and a exercise duration lower limit on the premise of meeting exercise requirements, and generally requires that the heart rate of a user is kept in a certain heart rate range and exercise is carried out for at least a certain duration when the user exercises.
It should be noted that the predicted change value is used to indicate the increase or decrease of the body index to be measured from the beginning of a period to the end of the period.
In S104, an index target of the body index to be measured of the user in the second period is determined based on the latest value of the body index to be measured in the first period and the predicted change value of the body index to be measured in the second period; and then acquiring the latest value of the body index to be measured generated by the user executing the guidance scheme corresponding to the index target in the second period, and determining the index target of the body index expected to be measured by the user in the third period based on the latest value of the body index in the second period and the predicted change value of the body index expected to be measured in the third period. Similarly, the fourth cycle index target determination process is similar to the third cycle index target determination process, and repeated description is omitted here.
It should be noted that the user of the present invention may be a general user, or a patient suffering from a chronic disease, such as a type 2 diabetic patient, or a hypertensive patient.
Here, the second period is a next period adjacent to the first period; the first cycle may be an initial cycle or any cycle in which the user is performing a dietary exercise regimen.
According to the embodiment of the invention, the latest value of the body index to be tested, the body basic data of the user, the current value of the body index to be tested and the execution result of the guidance scheme of the user aiming at the first period in the first period are obtained by executing the guidance scheme aiming at the first period by the user in the first period; then, the current value, the body basic data and the execution result are subjected to prediction processing by using the model, and the prediction change value of the body index to be measured of the user in the second period is obtained; and determining an index target of the body index to be measured of the user in the second period based on the latest value and the predicted change value. Therefore, the index target of the next period can be dynamically adjusted according to the real execution result, the body basic data and the current value of the body index to be detected of the user in the previous period, so that the user can make the corresponding target formulation of each body index to be detected in the whole blood sugar reduction process more accord with the real body condition of the user, the execution is easier, the effective management of the user on the target is realized, and the target sense of the blood sugar management of the user is enhanced.
As shown in fig. 2, a flowchart of an iterative generation method of a health index target according to another embodiment of the present invention; the method is obtained by optimization on the basis of the previous embodiment, and at least comprises the following operation flows: s201, acquiring the latest value of a body index to be measured generated by a user executing a guidance scheme aiming at a first period in the first period; s202, acquiring basic body data of a user, a current value of a body index to be measured and an execution result of a guidance scheme of the user for a first period in the first period; s203, carrying out prediction processing on the current value, the body basic data and the execution result by using the model to obtain a predicted change value of the body index to be measured of the user in the second period; s204, determining an estimated value of the body index to be measured in the second period based on the latest value and the variation value; s205, judging whether the estimated value is larger than the upper limit of the control threshold corresponding to the body index to be detected; and S206, determining the index target of the body index to be measured of the user in the second period based on the judgment result.
The specific implementation processes of S201, S202, and S203 are similar to those of S101, S102, and S103, and are not repeated here.
In S204 to S206, specifically, if the determination result indicates that the estimated value is greater than the upper limit of the control threshold, the estimated value is used as an upper limit value of the index target, and a lower limit of a normal range corresponding to the body index to be measured is used as a lower limit value of the index target, so as to obtain the index target of the body index to be measured of the user in the second period; and if the characteristic estimated value of the judgment result is not larger than the upper limit of the control threshold, taking the normal range of the body index to be detected as the index target of the body index to be detected of the user in the second period. The upper limit of the control threshold corresponding to the body index to be measured is determined according to the body basic data of the user.
For example; when the generation method of the embodiment is applied to blood sugar control of the diabetic and the body index to be detected is fasting blood sugar; determining the upper limit of the control threshold of the fasting blood glucose of the user based on basic body data such as the age, the sex, the height, the weight, the body fat, the waist circumference, the pancreatic islet function, the blood glucose and the like of the user; making a difference value between the latest fasting blood glucose value and the predicted fasting blood glucose change value, and determining the difference value as an estimated fasting blood glucose value of the user in a second period; and judging whether the estimation value is larger than the upper limit of the control threshold corresponding to the fasting blood glucose, if the judgment result represents that the estimation value is larger than the upper limit of the control threshold corresponding to the fasting blood glucose, taking the lower limit of the normal range corresponding to the fasting blood glucose as the lower limit of the index target, and taking the estimation value as the upper limit of the index target, thereby obtaining the index target of the fasting blood glucose of the user in the second period. And if the characteristic estimated value of the judgment result is not larger than the upper limit of the control threshold, taking the normal range of the fasting blood glucose as the index target of the fasting blood glucose of the user in the second period. Similarly, the determination of the third cycle fasting glucose index target is similar to the determination of the second cycle fasting glucose index target, except that the third cycle fasting glucose index target is obtained based on the second cycle data.
The method comprises the steps of carrying out prediction processing on a current value of a body index to be measured, body basic data and an execution result of a corresponding guidance scheme of a previous period by using a trained prediction model to obtain a prediction change value of the body index to be measured of a next period adjacent to the previous period; therefore, the real situation of the body of the user and the execution situation of the guidance scheme can be comprehensively considered, and the accuracy of predicting the next cycle variation value is improved; then determining the estimated value of the body index to be measured in the next period based on the latest value of the body index to be measured in the previous period and the change value of the next period; and finally, judging whether the estimated value is larger than the upper limit of the control threshold corresponding to the body index to be detected, and determining the index target of the body index to be detected in the next period of the user based on the judgment result. Therefore, the index target of the body index to be measured can be dynamically adjusted based on the latest value of the body index to be measured in the real execution process of the user, so that the formulation of the index target is more consistent with the real body condition of the user, the target feeling of the user is enhanced, the execution of the target by the user is facilitated, and the effective management of the body index to be measured by the user is facilitated.
FIG. 3 is a flow chart of a method for determining an initial target of a user according to an embodiment of the present invention; the method for determining the initial target of the user is optimized on the basis of the foregoing embodiments, and the method at least includes the following operation flows: s301, determining a baseline value and a control threshold upper limit of a body index to be detected based on body basic data of a user; s302, judging whether the baseline value is larger than the upper limit of the control threshold value; and S303, determining an initial target of the body index to be measured of the user in the initial period based on the judgment result.
Specifically, if the baseline value is greater than the upper limit of the control threshold, taking the upper limit of the control threshold as the upper limit value of the initial target, and taking the lower limit of the normal range corresponding to the body index to be measured as the lower limit value of the initial target to obtain the initial target; and if the baseline value is not larger than the upper limit of the control threshold, taking the normal range of the body index to be detected as an initial target.
Taking the body index to be measured as fasting blood glucose as an example for explanation, the baseline value of fasting blood glucose refers to the blood glucose value of the user just before executing the initial period guidance scheme.
The method and the device can determine the initial target of the body index to be detected in the initial period by combining the body condition of the user, so that the user has a clear target when the user starts to execute the guidance scheme, the target feeling of the user is enhanced, and the experience of the user is improved.
It should be understood that, in various embodiments of the present invention, the size of the sequence number of each process described above does not mean the execution sequence, and the execution sequence of each process should be determined by its function and the inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.
The method for iteratively generating the health index target in conjunction with type 2 diabetes will be described in detail below.
The body indexes to be tested of the type 2 diabetes patients comprise fasting blood sugar, postprandial blood sugar and body weight. Since the methods of iterative generation of the fasting blood glucose indicator target and the postprandial blood glucose health indicator target are similar, the generation of the fasting blood glucose indicator target and the weight indicator target will be described as an example.
The iterative generation method of the fasting blood glucose health index target is as follows:
first, a baseline fasting glucose value and an upper fasting glucose control threshold are determined based on patient body basic data, which includes patient body composition data (e.g., gender, age, height, weight, etc.) and body index data (e.g., blood glucose, blood pressure, etc.). And judging whether the baseline value of the fasting blood glucose is larger than the upper limit of the fasting blood glucose control threshold. And if the judgment result indicates that the fasting blood glucose baseline value is greater than the upper limit of the fasting blood glucose control threshold value, taking the upper limit of the fasting blood glucose control threshold value as the upper limit value of the initial target, and taking the lower limit of the normal range corresponding to the fasting blood glucose as the lower limit value of the initial target to obtain the initial target of the fasting blood glucose of the user in the first period. If the baseline fasting glucose value is not greater than the upper fasting glucose control threshold, the normal range of fasting glucose is used as the initial target for fasting glucose in the first period. For example, with a period of 7 days, a type 2 diabetic patient is more than 65 years old, the diabetic course is more than 10 years, the risk of hypoglycemia is high, the fasting blood glucose is 8.2mmol, the body weight is 60kg, and after analyzing the basic body data of the patient, the fasting blood glucose baseline value is 8.2mmol, and the upper limit of the fasting blood glucose control threshold value is 6.9 mmol. Since the fasting blood glucose baseline value is greater than the fasting blood glucose control threshold upper limit, the user has an initial target of 4-8.2mmol/L for fasting blood glucose in the first 7 skis.
Secondly, acquiring the latest value of fasting blood glucose generated by the patient executing the guidance scheme aiming at the first period in the first period; obtaining a patient's physical basic data, a current value of fasting glucose, a standard guidance program or a guidance program, and/or a result of a user's performance of a guidance program in a first cycle; the standard guidance scheme comprises a blood sugar standard range, a body weight standard range, a body fat standard range and a muscle mass standard range; and predicting the basic body data, the current value of the fasting blood glucose, the guidance scheme or the standard guidance scheme and the execution result by using the trained prediction model to obtain the fasting blood glucose predicted change value of the user in the second period. For example, the fasting blood glucose decrease of the next cycle is predicted by using a machine learning mode. Taking the body basic data, the current value of fasting blood glucose, the blood glucose standard range, the weight standard range, the body fat standard range, the muscle mass standard range of the patient and the execution result of the patient in the previous period aiming at the guidance scheme as training samples, taking the fasting blood glucose change difference value (for example, one period is 7 days, and the fasting blood glucose value in the previous period is the highest blood glucose within 7 days to the lowest blood glucose within 7 days) in the previous period of the patient as a training target, and performing model training on the training samples by using a superficial neural network to obtain a training result; and adjusting parameters of the model based on a plurality of training results to obtain a prediction model. A prediction stage: inputting basic data of the body of the patient, the current value of fasting blood glucose, the blood glucose standard range, the weight standard range, the body fat standard range, the muscle mass standard range and the execution result of the user aiming at the guidance scheme in the first period into the prediction model to obtain the change value of fasting blood glucose of the user in the second period. Therefore, the body basic data, the living habits and the current fasting blood glucose of the user are combined to be used as the input of the prediction model, the prediction change value of the fasting blood glucose is predicted, and the accuracy of fasting blood glucose prediction is improved.
Finally, determining a second period fasting blood glucose estimation value based on the latest value of fasting blood glucose and the fasting blood glucose prediction change value; judging whether the fasting blood glucose estimated value is larger than the upper limit of the fasting blood glucose control threshold value; if the judgment result indicates that the fasting blood glucose estimation value is larger than the upper limit of the fasting blood glucose control threshold value, taking the fasting blood glucose estimation value as the upper limit value of the index target, and taking the lower limit of the normal range corresponding to the fasting blood glucose as the lower limit value of the index target to obtain the fasting blood glucose index target of the user in the second period; and if the judgment result indicates that the fasting blood glucose estimation value is not larger than the upper limit of the fasting blood glucose control threshold, taking the normal range of the fasting blood glucose as the fasting blood glucose index target of the user in the second period. For example: the latest value of the fasting blood sugar is 7.3mmol, the predicted change value of the fasting blood sugar is 0.1mmol, and the upper limit of the fasting blood sugar control threshold value is 6.9 mmol; the fasting blood glucose estimated value is 7.2mmol, and the fasting blood glucose estimated value is larger than the upper limit of the fasting blood glucose control threshold value, so that the target of the fasting blood glucose index of the user in the second period is 4.4-7.2 mmol.
The iterative generation method of the weight health index target is as follows:
first, a weight baseline value and an upper weight control threshold are determined based on patient body basic data, including patient body composition data (e.g., gender, age, height, weight, etc.) and body index data (e.g., blood glucose, blood pressure, etc.). And judging whether the weight baseline value is larger than the upper limit of the weight control threshold value. And if the judgment result represents that the weight baseline value is larger than the weight control threshold upper limit, taking the weight control threshold upper limit as the upper limit value of the initial target, and taking the lower limit value of the normal range corresponding to the weight as the lower limit value of the initial target to obtain the weight initial target of the user in the first period. And if the weight baseline value is not larger than the upper limit of the weight control threshold, taking the weight normal range as the initial target of the weight of the patient in the first period. For example, with a cycle of 7 days, a type 2 diabetic patient is older than 65 years, the diabetic course is more than 10 years, the risk of hypoglycemia is high, the fasting blood glucose is 8.2mmol, and the body weight is 60kg, and after analyzing the basic body data of the patient, the basic body weight value is 60kg, and the upper limit of the body weight control threshold is 56 kg. Since the baseline weight value is greater than the upper weight control threshold, the user has an initial weight target of 44.7-60kg on the first 7 days.
Secondly, a machine learning mode is used for predicting the weight prediction change value of the next period. Obtaining a latest weight value generated by a patient executing a diet exercise program for a first cycle in the first cycle; acquiring body basic data, a current value of weight, a diet exercise scheme or a standard diet exercise scheme of a patient and/or an execution result of the user for the diet exercise scheme in a first period; and (3) carrying out prediction processing on the body basic data, the current value of the weight, the diet exercise scheme or the standard diet exercise scheme and/or the execution result by using the trained prediction model to obtain a predicted change value of the weight of the user in a second period, for example, predicting the weight reduction amplitude of the type 2 diabetes in the next period by using a machine learning mode. Taking the body basic data, the current value of the weight, the diet exercise scheme or the standard diet exercise scheme of the patient and/or the execution result of the patient aiming at the diet exercise scheme in the previous period as training samples, taking the weight difference value of the user within 7 days (the highest weight within 7 days to the lowest weight within 7 days) as a training target, and performing model training on the training samples by utilizing a superficial neural network to obtain a training result; and adjusting parameters of the model based on a plurality of training results to obtain a prediction model. A prediction stage: and inputting basic data of the body of the patient, the current value of the weight, the diet exercise scheme or the standard diet exercise scheme and/or the result of the user executed on the diet exercise scheme in the first 7 days into a prediction model to obtain the predicted change value of the weight of the user in the second 7 days. Therefore, the body basic data, the living habits and the weight current value of the user are combined to be used as model input, the weight prediction change value is predicted, and the accuracy of weight prediction is improved.
Finally, determining a weight estimated value in the second 7 days based on the latest weight value and the predicted weight change value; judging whether the weight estimation value is larger than the upper limit of a weight control threshold value or not; if the judgment result represents that the weight estimation value is larger than the upper limit of the weight control threshold, taking the weight estimation value as the upper limit value of the index target, and taking the lower limit of the normal range corresponding to the weight as the lower limit value of the index target to obtain the index target of the weight of the user in the second 7 days; and if the judgment result represents that the weight estimation value is not larger than the upper limit of the weight control threshold, taking the weight normal range as the weight index target of the user in the second 7 days. For example: the latest weight value is 58kg, the weight prediction change value output by the prediction model is 1kg, and the upper limit of the weight control threshold is 56 kg; the weight estimate was 57kg, and since the weight estimate was greater than the upper weight control threshold, the user targeted the weight index at 44.7-57kg on the second 7 days.
By repeatedly executing the guidance scheme, the target of fasting blood glucose index after reaching the standard in the nth period is 4.4-6.0mmol/L, and the target of body weight index is 47.4-55 kg.
The current value of the body weight may be the latest value of the body weight of the first 7 days, or may be the latest value of the body weight of the first day when the second 7 days start. The current value of fasting glucose may be the latest value of fasting glucose on the first 7 days, or may be the latest value of fasting glucose on the first day at the beginning of the second 7 days.
Therefore, the iterative generation method of the health index target is beneficial to the management of the type 2 diabetes mellitus patients on the blood sugar, so that the type 2 diabetes mellitus patients can effectively control the blood sugar according to physical conditions.
FIG. 4 is a schematic diagram of an apparatus for determining a motion pattern according to an embodiment of the present invention; the apparatus 400 comprises: a first obtaining module 401, configured to obtain a latest value of a body index to be measured, where the body index is generated by a user executing a guidance scheme for a first period in the first period; a second obtaining module 402, configured to obtain body basic data of the user, a current value of a body index to be measured, and an execution result of a guidance scheme of the user for a first period in the first period; the prediction module 403 is configured to perform prediction processing on the current value, the basic body data, and the execution result by using a model to obtain a predicted change value of a body index to be measured of the user at a second period; a first determining module 404, configured to determine an index target of the body index to be measured of the user at a second period based on the latest value and the predicted change value.
In an optional embodiment, the apparatus further comprises: the second determination module is used for determining a baseline value and a control threshold upper limit of the body index to be detected based on the body basic data of the user; the judging module is used for judging whether the baseline value is larger than the upper limit of the control threshold; and the third determining module is used for determining an initial target of the body index to be measured of the user in the initial period based on the judgment result.
In an alternative embodiment, the third determining module includes: the first determining subunit is configured to, if the baseline value is greater than the upper limit of the control threshold, take the upper limit of the control threshold as an upper limit value of the initial target, and take a lower limit of a normal range corresponding to the body index to be measured as a lower limit value of the initial target, so as to obtain the initial target; and the second determining subunit is used for taking the normal range of the body index to be measured as the initial target if the baseline value is not greater than the upper limit of the control threshold.
In alternative embodiments, the instructional plan comprises a dietary plan and/or an exercise plan.
In an alternative embodiment, the first determining module comprises: the first determination unit is used for determining the estimated value of the body index to be measured in the second period based on the latest value and the change value; the judging unit is used for judging whether the estimated value is larger than the upper limit of the control threshold corresponding to the body index to be detected; and the second determination unit is used for determining the index target of the body index to be measured of the user in the second period based on the judgment result.
In an alternative embodiment, the second determination unit includes: the first determining subunit is configured to determine, if the determination result indicates that the estimated value is greater than the upper limit of the control threshold, to use the estimated value as an upper limit value of an index target, and to use a lower limit of a normal range corresponding to the body index to be measured as a lower limit value of the index target, so as to obtain an index target of the body index to be measured of the user in a second period; and the second determining subunit is configured to, if the determination result indicates that the estimated value is not greater than the upper limit of the control threshold, use the normal range of the body index to be measured as the index target of the body index to be measured of the user in the second period.
The device can execute the iterative generation method of the health index target provided by the embodiment of the invention, and has the corresponding functional modules and beneficial effects of the iterative generation method of the health index target. For technical details that are not described in detail in this embodiment, reference may be made to the iterative generation method of the health index target provided by the embodiment of the present invention.
As shown in fig. 5, the system architecture 500 may include terminal devices 501, 502, 503, a network 504, and a server 505, which are exemplary system architecture diagrams to which embodiments of the present invention may be applied. The network 504 serves to provide a medium for communication links between the terminal devices 501, 502, 503 and the server 505. Network 504 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 501, 502, 503 to interact with a server 505 over a network 504 to receive or send messages or the like. The terminal devices 501, 502, 503 may have installed thereon various communication client applications, such as shopping-like applications, web browser applications, search-like applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only).
The terminal devices 501, 502, 503 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 505 may be a server providing various services, such as a background management server (for example only) providing support for click events generated by users using the terminal devices 501, 502, 503. The background management server may analyze and perform other processing on the received click data, text content, and other data, and feed back a processing result (for example, target push information, product information — just an example) to the terminal device.
It should be noted that the iterative generation method of the health index target provided in the embodiment of the present application is generally executed by the server 505, and accordingly, the generation device of the index target is generally disposed in the server 505.
It should be understood that the number of terminal devices, networks, and servers in fig. 5 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Referring now to FIG. 6, shown is a block diagram of a computer system suitable for use in implementing a terminal device or server of an embodiment. The terminal device shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 6, the computer system 600 includes a Central Processing Unit (CPU) 601 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. In the RAM603, various programs and data necessary for the operation of the computer system 600 are also stored. The CPU601, ROM602, and RAM603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604. The following components are connected to the I/O interface 605: an input portion 606 including a keyboard, a mouse, and the like; an output portion 607 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The driver 610 is also connected to the I/O interface 605 as needed. A removable medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 610 as necessary, so that a computer program read out therefrom is mounted in the storage section 608 as necessary.
In particular, according to the embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 609, and/or installed from the removable medium 611. The computer program performs the above-described functions defined in the system of the present invention when executed by the Central Processing Unit (CPU) 601.
It should be noted that the computer readable medium shown in the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, or device. In the present invention, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present invention may be implemented by software or hardware. The described modules may also be provided in a processor, which may be described as: a processor includes a sending module, an obtaining module, a determining module, and a first processing module. The names of these modules do not in some cases constitute a limitation on the unit itself, and for example, the sending module may also be described as a "module that sends a picture acquisition request to a connected server".
As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be separate and not incorporated into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to comprise: s101, acquiring the latest value of a body index to be measured generated by a user executing a guidance scheme aiming at a first period in the first period; s102, acquiring basic body data of a user, a current value of a body index to be measured and an execution result of a guidance scheme of the user for a first period in the first period; s103, carrying out prediction processing on the current value, the body basic data and the execution result by using the model to obtain a predicted change value of the body index to be measured of the user in the second period; and S104, determining an index target of the body index to be measured of the user in the second period based on the latest value and the predicted change value.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (7)
1. An iterative generation method of a health index target, comprising:
acquiring the latest value of a body index to be detected generated by a user executing a guidance scheme aiming at a first period in the first period; wherein the instructional regimen comprises a dietary regimen and a sports regimen;
acquiring body basic data of a user, a current value of a body index to be detected and an execution result of a guidance scheme of the user for a first period in the first period, wherein the current value of the body index to be detected is the latest value of the body index to be detected at the end of the first period or the latest value of the body index to be detected at the beginning of a second period, and the second period is the next period adjacent to the first period;
predicting the current value, the body basic data and the execution result by using a machine learning prediction model to obtain a predicted change value of the body index to be measured of the user in a second period, wherein the predicted change value is used for indicating the increase or decrease of the body index to be measured from the beginning of one period to the end of the period;
determining an estimated value of a body index to be measured in a second period based on the latest value and the predicted change value;
judging whether the estimated value is larger than the upper limit of the control threshold corresponding to the body index to be detected, wherein the upper limit of the control threshold corresponding to the body index to be detected is determined according to basic data of the body of the user;
if the judgment result indicates that the estimation value is larger than the upper limit of the control threshold, determining that the estimation value is used as an upper limit value of an index target, and using a lower limit of a normal range corresponding to the body index to be detected as a lower limit value of the index target to obtain the index target of the body index to be detected of the user in a second period;
and if the judgment result indicates that the estimated value is not greater than the upper limit of the control threshold, taking the normal range of the body index to be detected as the index target of the body index to be detected of the user in the second period.
2. The method of claim 1, further comprising:
determining a baseline value and a control threshold upper limit of a body index to be detected based on body basic data of a user;
judging whether the baseline value is larger than the upper limit of the control threshold value;
and determining an initial target of the body index to be measured of the user in the initial period based on the judgment result.
3. The method of claim 2, wherein determining the initial target of the physical metric of the user to be measured in the initial period based on the determination result comprises:
if the baseline value is larger than the upper limit of the control threshold, taking the upper limit of the control threshold as the upper limit value of the initial target, and taking the lower limit of the normal range corresponding to the body index to be detected as the lower limit value of the initial target to obtain the initial target;
and if the baseline value is not larger than the upper limit of the control threshold, taking the normal range of the body index to be detected as an initial target.
4. An apparatus for iterative generation of a health index target, comprising:
the first acquisition module is used for acquiring the latest value of the body index to be detected generated by the user executing the guidance scheme aiming at the first period in the first period; wherein the instructional regimen comprises a dietary regimen and a sports regimen;
the second acquisition module is used for acquiring body basic data of a user, a current value of a body index to be detected and an execution result of a guidance scheme of the user for a first period in the first period, wherein the current value of the body index to be detected is the latest value of the body index to be detected at the end of the first period or the latest value of the body index to be detected at the beginning of a second period, and the second period is the next period adjacent to the first period;
the prediction module is used for carrying out prediction processing on the current value, the body basic data and the execution result by using a prediction model using machine learning to obtain a predicted change value of the body index to be detected of the user in a second period; the predicted change value is used for indicating the increase or decrease of the body index to be measured from the beginning of a period to the end of the period;
the first determination module is used for determining the estimated value of the body index to be measured in the second period based on the latest value and the predicted change value; judging whether the estimated value is larger than the upper limit of the control threshold corresponding to the body index to be detected, wherein the upper limit of the control threshold corresponding to the body index to be detected is determined according to basic data of the body of the user; if the judgment result indicates that the estimation value is larger than the upper limit of the control threshold, determining that the estimation value is used as an upper limit value of an index target, and using a lower limit of a normal range corresponding to the body index to be detected as a lower limit value of the index target to obtain the index target of the body index to be detected of the user in a second period; and if the judgment result indicates that the estimated value is not greater than the upper limit of the control threshold, taking the normal range of the body index to be detected as the index target of the body index to be detected of the user in the second period.
5. An iterative generation system of a health index target is characterized by comprising a client and a server;
the client acquires the latest value of a body index to be detected generated by a user executing a guidance scheme aiming at a first period in the first period, body basic data of the user, the current value of the body index to be detected and an execution result of the guidance scheme aiming at the first period in the first period; the current value of the body index to be measured is the latest value of the body index to be measured at the end of the first period or the latest value of the body index to be measured at the beginning of the second period, and the second period is the next period adjacent to the first period; sending the latest value, the current value, the body basic data and the execution result to the server;
the server carries out prediction processing on the current value, the body basic data and the execution result by utilizing a prediction model of machine learning to obtain a predicted change value of the body index to be measured of the user in a second period; the predicted change value is used for indicating the increase or decrease of the body index to be measured from the beginning of a period to the end of the period; determining an estimated value of the body index to be measured in the second period based on the latest value and the predicted change value; judging whether the estimated value is larger than the upper limit of the control threshold corresponding to the body index to be detected, wherein the upper limit of the control threshold corresponding to the body index to be detected is determined according to basic data of the body of the user; if the judgment result indicates that the estimation value is larger than the upper limit of the control threshold, determining that the estimation value is used as an upper limit value of an index target, and using a lower limit of a normal range corresponding to the body index to be detected as a lower limit value of the index target to obtain the index target of the body index to be detected of the user in a second period; if the judgment result indicates that the estimated value is not larger than the upper limit of the control threshold, taking the normal range of the body index to be detected as an index target of the body index to be detected of the user in a second period;
and the server sends the index target to the client.
6. A device/terminal/server, characterized in that the device comprises:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-3.
7. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-3.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105078474A (en) * | 2015-08-21 | 2015-11-25 | 武汉苏酷科技有限公司 | Blood glucose and blood pressure monitoring and control system |
| CN105426670A (en) * | 2015-11-10 | 2016-03-23 | 中国科学院合肥物质科学研究院 | Chronic disease track management system based on mobile terminal, and closed loop guide method of chronic disease track management system |
| CN107731275A (en) * | 2017-09-25 | 2018-02-23 | 上海斐讯数据通信技术有限公司 | A kind of method and system of dynamic adjustment health exercising plan |
| CN111445978A (en) * | 2020-03-26 | 2020-07-24 | 北京动亮健康科技有限公司 | Motion scheme reactivity prediction model, modeling method and electronic equipment |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP7073074B2 (en) * | 2017-10-26 | 2022-05-23 | オムロンヘルスケア株式会社 | Goal management system, goal management server, and goal management program |
| US11183291B2 (en) * | 2018-02-12 | 2021-11-23 | Zoe Limited | Generating personalized nutritional recommendations using predicted values of biomarkers |
| US11101028B2 (en) * | 2019-10-03 | 2021-08-24 | Rom Technologies, Inc. | Method and system using artificial intelligence to monitor user characteristics during a telemedicine session |
| CN111798963A (en) * | 2020-07-07 | 2020-10-20 | 上海交通大学医学院附属瑞金医院 | Individual health management method based on energy metabolism and weight change |
-
2021
- 2021-12-17 CN CN202111545121.0A patent/CN113936771B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105078474A (en) * | 2015-08-21 | 2015-11-25 | 武汉苏酷科技有限公司 | Blood glucose and blood pressure monitoring and control system |
| CN105426670A (en) * | 2015-11-10 | 2016-03-23 | 中国科学院合肥物质科学研究院 | Chronic disease track management system based on mobile terminal, and closed loop guide method of chronic disease track management system |
| CN107731275A (en) * | 2017-09-25 | 2018-02-23 | 上海斐讯数据通信技术有限公司 | A kind of method and system of dynamic adjustment health exercising plan |
| CN111445978A (en) * | 2020-03-26 | 2020-07-24 | 北京动亮健康科技有限公司 | Motion scheme reactivity prediction model, modeling method and electronic equipment |
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