CN115363571A - Machine learning blood glucose monitoring system and method based on CGM sensor - Google Patents
Machine learning blood glucose monitoring system and method based on CGM sensor Download PDFInfo
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Abstract
The invention discloses a CGM sensor-based machine learning blood glucose monitoring system and a method, wherein the system comprises a server terminal, a medical care APP terminal, a CGM sensor and a patient APP terminal; the invention relates to a blood sugar monitoring method, which comprises the steps of continuously collecting blood sugar data from the beginning of taking a medicine, highly synchronizing the blood sugar sampling time and the drug effect time in real time, realizing real-time data interaction, being more credible and reliable than the traditional blood sugar data monitoring, and having causal relation related to the medicine taking, diet and exercise of a patient under the continuous collection of the data, thereby being convenient for corresponding the causal relation from the time dimension, further being convenient for evaluating and monitoring the blood sugar data of the patient.
Description
Technical Field
The invention relates to the technical field of blood glucose monitoring, in particular to a machine learning blood glucose monitoring system and method based on a CGM sensor.
Background
Glucose in blood is called blood sugar (Glu), which is an important component of a human body and also an important source of energy, and a normal human body needs a lot of sugar every day to provide energy and provide power for the normal operation of various tissues and organs, so that the blood sugar can maintain the needs of various organs and tissues in the body only by keeping a certain level.
The Oral Glucose Tolerance Test (OGTT) is a glucose load test for understanding the function of pancreatic β cells and the ability of the body to regulate blood glucose, is a diagnostic test for diagnosing diabetes, and is widely used in clinical practice.
Most of the existing blood glucose monitoring methods monitor blood glucose of a patient in a fingertip blood or venous blood collecting mode, draw a blood glucose trend line according to collected data of the patient and have a blood glucose level reminding function, but the monitoring method needs to collect fingertip blood or venous blood, is painful in process, cannot acquire real-time data, only can acquire stage time data, has less data amount, cannot completely reflect blood glucose fluctuation of the patient, and is difficult to form a personalized data model for the patient, so that the monitoring result is not accurate enough.
Disclosure of Invention
In view of the above problems, the present invention aims to provide a CGM sensor-based machine learning blood glucose monitoring system and method, which solve the problems that the existing blood glucose monitoring method is painful in process, cannot acquire real-time data, only can acquire periodic time data, has a small data amount, cannot completely reflect blood glucose fluctuation of a patient, and is difficult to form a personalized data model for the patient, so that the monitoring result is inaccurate.
In order to achieve the purpose of the invention, the invention is realized by the following technical scheme: the utility model provides a machine learning blood sugar monitoring method based on CGM sensor, includes server terminal, medical care APP terminal, CGM sensor and patient APP terminal all with server terminal wireless connection, the CGM sensor is worn and real-time detection patient's blood sugar data by the patient, patient APP terminal is including the data acquisition module that is used for gathering CGM sensor detected data, the state that is used for the patient to set up state data sets up the module, is used for the patient to carry out OGTI self-test module of OGTI automatic test and the blood sugar monitoring module that is used for monitoring blood sugar data and state data, the server terminal gathers including the classifier that is used for carrying out unsupervised study to the data set, the preprocessing module that is used for carrying out the preprocessing to the data collection, the classification algorithm module that is used for carrying out the feature extraction to the preprocessing data and the data acquisition module that is used for gathering the data collection.
The further improvement lies in that: medical APP terminal is including using medicine prescription module and patient monitoring module, medical APP terminal monitors the patient state through patient monitoring module to prescription medicine is provided for the patient through using medicine prescription module.
The further improvement is that: the state data of the state setting module is set by the patient, and the set state comprises the medication state of the patient, the diet state of the patient and the exercise state of the patient.
The further improvement lies in that: the blood sugar monitoring module comprises an integral blood sugar monitoring unit, a medication monitoring unit, a diet correlation monitoring unit and an exercise monitoring unit, wherein the integral blood sugar monitoring unit monitors blood sugar data of a patient according to a data acquisition module, and the medication monitoring unit, the diet correlation monitoring unit and the exercise monitoring unit respectively monitor medication conditions, diet conditions and exercise conditions of the patient through a state setting module.
A machine learning blood sugar monitoring method based on a CGM sensor comprises the following steps:
the method comprises the following steps: monitoring blood glucose data of a patient by using a CGM (China general microbiological culture collection) sensor, collecting blood glucose monitoring data of the patient, collecting medication data, diet data and exercise data of the patient by using an APP (application program) terminal of the patient, sending the collected blood glucose monitoring data, medication data, diet data and exercise data to a preprocessing module of a server terminal, and pushing the blood glucose monitoring data to a classifier of the server terminal in real time;
step two: the method comprises the steps that firstly, received blood sugar monitoring data, medication data, diet data and exercise data are preprocessed through a preprocessing module, then the preprocessed data are subjected to feature extraction through a classification algorithm module, a data model base is formed, then a classifier is used for carrying out unsupervised learning on the data model base through a clustering algorithm and according to real-time blood sugar monitoring data, the blood sugar influence factors of patients are sorted and clustered, and diet factor clustering, medication factor clustering and exercise factor clustering are obtained;
step three: the method comprises the steps that a user starts an OGTI self-testing module through a patient APP terminal, the OGTI self-testing module automatically starts timing, the patient is enabled to take glucose water in an alarm mode for 4 times at intervals of 0.5 hour, CGM sensor data monitoring data are automatically extracted at the same time, an OGTT self-testing report is automatically formed, and finally a data collecting and summarizing module summarizes the OGTT self-testing report, diet factor clustering, medication factor clustering and exercise factor clustering and comprehensively evaluates and monitors the blood glucose condition of the patient.
The further improvement lies in that: in the first step, the blood glucose monitoring data is monitored after the CGM sensor is worn by the patient, and the medication data, the diet data and the exercise data are preset on the APP terminal of the patient by the user.
The further improvement is that: in the second step, the pretreatment specifically comprises the following steps: and the preprocessing module of the server terminal enters a learning mode and performs multidimensional learning based on the received blood sugar monitoring data, the medication data, the diet data and the exercise data.
The further improvement lies in that: in the second step, the unsupervised learning comprises the following specific steps: through learning of unmarked training samples in the data model base, potential structures and rules of a data set in the data model base are discovered and revealed, and then the blood sugar influence factors of patients are sorted and clustered according to the obtained structures and rules.
The invention has the beneficial effects that: the invention realizes real-time interaction of data by real-time interactive data, continuously acquiring blood sugar data from the beginning of taking a medicine, and highly synchronizing the blood sugar sampling time and the drug effect time in real time, compared with the traditional blood sugar data monitoring, the invention is more reliable and reliable, and the data is related to the administration, diet and exercise of patients under continuous acquisition and has causal relationship, thereby facilitating the correspondence of the causal relationship from time dimension, and further facilitating the evaluation and monitoring of the blood sugar data of the patients.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic diagram of a system configuration according to a first embodiment of the present invention;
FIG. 2 is a schematic diagram of an automatic OGTT testing process according to a second embodiment of the present invention;
fig. 3 is a schematic view of an unsupervised learning process in the second embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example one
Referring to fig. 1, the embodiment provides a CGM sensor-based machine learning blood glucose monitoring method, comprising a server terminal, a medical care APP terminal, a CGM sensor and a patient APP terminal, wherein the medical care APP terminal, the CGM sensor and the patient APP terminal are all wirelessly connected with the server terminal, the CGM sensor is worn by a patient and detects blood glucose data of the patient in real time, the patient APP terminal comprises a data acquisition module for acquiring detection data of the CGM sensor, a state setting module for setting state data of the patient, an OGTI self-testing module for performing OGTI automatic testing on the patient, and a blood glucose monitoring module for monitoring the blood glucose data and the state data, through the setting of medication, exercise and diet on the APP terminal of the patient, the system collects the blood sugar data of the patient in real time through Bluetooth, collects the data according to minutes, hours and days, taking into consideration factors which may affect the blood sugar of patients, through a real-time interactive processing mechanism of collected data and behavior data, the three-dimensional blood sugar monitoring data of a patient can be displayed externally, so that the subsequent machine learning, diagnosis, early warning and the like can be conveniently carried out, the OGTT test is automatically carried out through the OGTI self-testing module, only the OGTT test in the APP terminal of the patient needs to be clicked, the APP automatically starts timing, the patients can take glucose water by alarm clock at intervals of 0.5, 1.0, 1.5 and 2.0 hours, and CGM sensor data are automatically extracted at the same time, a patient does not need to pay attention to blood sugar measurement and corresponding time any more, an OGTT report is automatically formed after all the flow is finished, and the condition of the patient is evaluated.
Medical APP terminal is including using medicine prescription module and patient monitoring module, and medical APP terminal monitors the patient state through patient monitoring module to prescribe prescription medicine for the patient through using medicine prescription module.
The state data of the state setting module is set by the patient, and the set state comprises the medication state of the patient, the diet state of the patient and the exercise state of the patient.
The blood sugar monitoring module comprises an integral blood sugar monitoring unit, a medication monitoring unit, a diet correlation monitoring unit and an exercise monitoring unit, the integral blood sugar monitoring unit monitors blood sugar data of a patient according to the data acquisition module, and the medication monitoring unit, the diet correlation monitoring unit and the exercise monitoring unit respectively monitor medication conditions, diet conditions and exercise conditions of the patient through the state setting module.
Example two
Referring to fig. 2 and 3, the present embodiment provides a CGM sensor-based machine learning blood glucose monitoring method, including the following steps:
the method comprises the following steps: monitoring blood glucose data of a patient by using a CGM sensor and collecting blood glucose monitoring data of the patient, collecting medication data, diet data and exercise data of the patient by using a patient APP terminal, sending the collected blood glucose monitoring data, medication data, diet data and exercise data to a preprocessing module of a server terminal, pushing the blood glucose monitoring data to a classifier of the server terminal in real time, monitoring the blood glucose monitoring data after the patient wears the CGM sensor, and presetting the medication data, diet data and exercise data on the patient APP terminal by a user;
step two: the method comprises the following steps of preprocessing received blood sugar monitoring data, medication data, diet data and exercise data through a preprocessing module, wherein the preprocessing comprises the following specific steps: a preprocessing module of the server terminal enters a learning mode, and multidimensional learning is carried out based on the received blood sugar monitoring data, the received medication data, the received diet data and the received exercise data;
and then, carrying out feature extraction on the preprocessed data through a classification algorithm module to form a data model base, carrying out unsupervised learning on the data model base by using a clustering algorithm and according to real-time blood glucose monitoring data by using a classifier to realize sorting and clustering of blood glucose influencing factors of patients to obtain diet factor clustering, medication factor clustering and exercise factor clustering, wherein the unsupervised learning comprises the following specific steps: through learning of unmarked training samples in the data model base, the potential structure and rule of a data set in the data model base are discovered and disclosed, and then the blood sugar influence factors of patients are sorted and clustered according to the obtained structure and rule;
the machine learning is to collect multidimensional data and use a clustering algorithm to carry out an unsupervised learning method, the unsupervised learning aims to learn unmarked training samples, discover and disclose potential structures and rules of a data set, and finally realize clustering arrangement of patient influence factors to obtain diet factor clustering, medication factor clustering and exercise factor clustering, so that patients and medical staff can visually check what reason causes blood sugar change, and influence factors are large, pertinence of patient data is greatly improved, and compared with simple statistics of traditional blood sugar data, problems found intuitively are easier to correct;
step three: the user starts the OGTI self-testing module through a patient APP terminal, the OGTI self-testing module automatically starts timing, the patient is enabled to take glucose water in an alarm mode for 4 times at intervals of 0.5 hour, the CGM sensor data monitoring data are automatically extracted at the same time, an OGTT self-testing report is automatically formed, and finally the data acquisition and collection module collects the OGTT self-testing report, the diet factor cluster, the medication factor cluster and the exercise factor cluster and comprehensively evaluates and monitors the blood glucose condition of the patient.
When the system is used, after a patient wears the CGM sensor and sets the self medication state, the exercise state and the diet state, the system can enter a learning mode firstly when modeling is not completed, multidimensional learning is carried out by combining data acquired by a real-time interactive data processing mechanism based on actual data and setting of the user, an individualized feature library is finally formed after feature extraction, the feature library only aims at the patient, after the feature library is formed, the influence factors are provided with a judgment basis, at the moment, the system enters a data processing mode by depending on the feature library, a plurality of pieces of data which are beneficial to subsequent diagnosis are finally extracted and clustered, and the data can also be used as early warning data.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (8)
1. A machine learning blood glucose monitoring system based on CGM sensor, characterized in that: including server terminal, medical care APP terminal, CGM sensor and patient APP terminal all with server terminal wireless connection, the CGM sensor is worn and real-time detection patient's blood sugar data by the patient, patient APP terminal is including the data acquisition module that is used for gathering CGM sensor detected data, the state that is used for the patient to set up state data sets up the module, is used for the patient to carry out OGTI of OGTI automatic test from the testing module and the blood sugar monitoring module that is used for monitoring blood sugar data and state data, the server terminal is including the classifier that is used for carrying out unsupervised study to the data set, the preprocessing module that is used for carrying out the preliminary treatment to the data collection, the classification algorithm module that is used for carrying out the feature extraction to the preliminary treatment data and the data collection module that is used for gathering the data.
2. The CGM sensor-based machine-learned blood glucose monitoring system of claim 1, wherein: medical APP terminal is including using medicine prescription module and patient monitoring module, medical APP terminal monitors the patient state through patient monitoring module to prescription medicine is provided for the patient through using medicine prescription module.
3. The CGM sensor-based machine-learned blood glucose monitoring system of claim 1, wherein: the state data of the state setting module is set by the patient, and the set state comprises the medication state of the patient, the diet state of the patient and the exercise state of the patient.
4. The CGM sensor-based machine-learned blood glucose monitoring system of claim 1, wherein: the blood sugar monitoring module comprises an integral blood sugar monitoring unit, a medication monitoring unit, a diet correlation monitoring unit and an exercise monitoring unit, wherein the integral blood sugar monitoring unit monitors blood sugar data of a patient according to a data acquisition module, and the medication monitoring unit, the diet correlation monitoring unit and the exercise monitoring unit respectively monitor medication conditions, diet conditions and exercise conditions of the patient through a state setting module.
5. A machine learning blood glucose monitoring method based on a CGM sensor is characterized by comprising the following steps:
the method comprises the following steps: monitoring blood glucose data of a patient by using a CGM (China general microbiological culture collection) sensor, collecting blood glucose monitoring data of the patient, collecting medication data, diet data and exercise data of the patient by using an APP (application program) terminal of the patient, sending the collected blood glucose monitoring data, medication data, diet data and exercise data to a preprocessing module of a server terminal, and pushing the blood glucose monitoring data to a classifier of the server terminal in real time;
step two: the method comprises the steps that firstly, received blood sugar monitoring data, medication data, diet data and exercise data are preprocessed through a preprocessing module, then the preprocessed data are subjected to feature extraction through a classification algorithm module, a data model base is formed, then a classifier is used for carrying out unsupervised learning on the data model base through a clustering algorithm and according to real-time blood sugar monitoring data, the blood sugar influence factors of patients are sorted and clustered, and diet factor clustering, medication factor clustering and exercise factor clustering are obtained;
step three: the user starts the OGTI self-testing module through a patient APP terminal, the OGTI self-testing module automatically starts timing, the patient is enabled to take glucose water in an alarm mode for 4 times at intervals of 0.5 hour, the CGM sensor data monitoring data are automatically extracted at the same time, an OGTT self-testing report is automatically formed, and finally the data acquisition and collection module collects the OGTT self-testing report, the diet factor cluster, the medication factor cluster and the exercise factor cluster and comprehensively evaluates and monitors the blood glucose condition of the patient.
6. The CGM sensor-based machine-learned blood glucose monitoring method of claim 5, wherein: in the first step, the blood glucose monitoring data is monitored after the CGM sensor is worn by the patient, and the medication data, the diet data and the exercise data are preset on the APP terminal of the patient by the user.
7. The CGM sensor-based machine-learned blood glucose monitoring method of claim 5, wherein: in the second step, the pretreatment comprises the following specific steps: and a preprocessing module of the server terminal enters a learning mode, and performs multidimensional learning based on the received blood sugar monitoring data, the received medicine data, the received diet data and the received exercise data.
8. The CGM sensor-based machine-learned blood glucose monitoring method of claim 5, wherein: in the second step, the unsupervised learning comprises the following specific steps: through learning of unmarked training samples in the data model base, potential structures and rules of a data set in the data model base are discovered and revealed, and then the blood sugar influence factors of patients are sorted and clustered according to the obtained structures and rules.
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