CN113679348A - Blood glucose prediction method, blood glucose prediction device, blood glucose prediction apparatus, and storage medium - Google Patents
Blood glucose prediction method, blood glucose prediction device, blood glucose prediction apparatus, and storage medium Download PDFInfo
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Abstract
The application relates to the field of artificial intelligence, in particular to a blood sugar prediction method, a blood sugar prediction device, blood sugar prediction equipment and a storage medium, wherein the method comprises the following steps: acquiring medical record information, blood sugar data and medication data of a user; screening the medical record information to obtain medical record information corresponding to an abnormal blood sugar value, and taking the medical record information corresponding to the abnormal blood sugar value as target medical record information; inputting the target medical record information, the blood glucose data and the medication data into a pre-trained blood glucose prediction model to obtain blood glucose trend data; acquiring state characteristic data of a user, and predicting diseases according to the blood glucose trend data and the state characteristic data to obtain expected diseases; and determining corresponding disease preventive measures according to the state characteristic data and the expected diseases, and pushing the disease preventive measures to a target terminal. Therefore, the user can be reminded to prevent possible diseases, and the use experience of the user is improved.
Description
Technical Field
The present application relates to the field of artificial intelligence, and in particular, to a blood glucose prediction method, a blood glucose prediction apparatus, a computer device, and a storage medium.
Background
Diabetes is a high-incidence disease at present, and is particularly suitable for the elderly, so that the health and the life of people are threatened all the time, although the diabetes treatment technology is rapidly developed, many people cannot detect the blood sugar regularly, so that many people cannot perceive that the people suffer from the diabetes, and meanwhile, the people cannot treat the diabetes in time, so that the disease condition is easily worsened, and the people are in a coma and even die.
Nowadays, users generally go to hospitals actively to perform review or spontaneous blood sugar measurement, and often find that the users may have diabetes when the measured blood sugar value is high. The existing blood sugar prediction model only predicts through real-time blood sugar data of a user and does not consider the influence of factors such as insulin injection, blood sugar reducing medicine taking or past disease record on the future blood sugar trend, so that the blood sugar prediction result is not accurate, diseases such as diabetes and the like cannot be prevented in advance, and the user misses the optimal treatment opportunity.
Disclosure of Invention
The application provides a blood sugar prediction method, a blood sugar prediction device, computer equipment and a storage medium, and aims to solve the problems that a user cannot perceive own illness condition, so that timely treatment is not carried out, the condition of an illness is easily deteriorated, and the user is in coma or even dies.
To achieve the above object, the present application provides a blood glucose prediction method, comprising:
acquiring medical record information, blood sugar data and medication data of a user;
screening the medical record information to obtain medical record information corresponding to an abnormal blood sugar value, and taking the medical record information corresponding to the abnormal blood sugar value as target medical record information;
inputting the target medical record information, the blood glucose data and the medication data into a pre-trained blood glucose prediction model to obtain blood glucose trend data;
acquiring state characteristic data of a user, and predicting diseases according to the blood glucose trend data and the state characteristic data to obtain expected diseases;
and determining corresponding disease preventive measures according to the state characteristic data and the expected diseases, and pushing the disease preventive measures to a target terminal.
To achieve the above object, the present application also provides a blood glucose prediction device including:
the data acquisition module is used for acquiring medical record information, blood sugar data and medication data of a user;
the medical record information screening module is used for screening the medical record information to obtain medical record information corresponding to the abnormal blood sugar value, and taking the medical record information corresponding to the abnormal blood sugar value as target medical record information;
the blood sugar prediction module is used for inputting the target medical record information, the blood sugar data and the medication data into a pre-trained blood sugar prediction model to obtain blood sugar trend data;
the disease prediction acquisition module is used for acquiring state characteristic data of a user and predicting diseases according to the blood glucose trend data and the state characteristic data to obtain expected diseases;
and the preventive measure generation module is used for determining corresponding preventive measures of diseases according to the state characteristic data and the expected diseases and pushing the preventive measures of diseases to a target terminal.
In addition, to achieve the above object, the present application also provides a computer device comprising a memory and a processor; the memory for storing a computer program; the processor is configured to execute the computer program and implement the blood glucose prediction method according to any one of the embodiments of the present application when executing the computer program.
In addition, to achieve the above object, the present application further provides a computer-readable storage medium storing a computer program, which when executed by a processor causes the processor to implement any one of the blood glucose prediction methods provided in the embodiments of the present application.
The blood sugar prediction method, the blood sugar prediction device, the blood sugar prediction equipment and the storage medium disclosed by the embodiment of the application monitor the blood sugar value of the user according to the medical record data and the real-time blood sugar value data of the user and predict the future blood sugar trend, so that the user is reminded to prevent possible diseases, and the use experience of the user is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic view of a scenario of a blood glucose prediction method provided in an embodiment of the present application;
fig. 2 is a schematic flow chart of a blood glucose prediction method according to an embodiment of the present disclosure;
FIG. 3 is a schematic block diagram of a blood glucose prediction device provided by an embodiment of the present application;
fig. 4 is a schematic block diagram of a computer device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some, but not all, embodiments of the present application. 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 application.
The flow diagrams depicted in the figures are merely illustrative and do not necessarily include all of the elements and operations/steps, nor do they necessarily have to be performed in the order depicted. For example, some operations/steps may be decomposed, combined or partially combined, so that the actual execution sequence may be changed according to the actual situation. In addition, although the division of the functional blocks is made in the device diagram, in some cases, it may be divided in blocks different from those in the device diagram.
The term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
The sugar in the blood is called blood glucose, and is mostly glucose. Most of the energy required for the cellular activities of tissues in the body comes from glucose, so blood glucose must be kept at a certain level to maintain the needs of organs and tissues in the body. The concentration of blood sugar in normal persons is 3.9-6.1mmol/L in fasting state. Fasting plasma glucose concentrations above 7.0mmol/L are termed hyperglycemic.
Diabetes often also occurs once hyperglycemia is detected. Diabetes mellitus is a group of metabolic diseases characterized by chronic hyperglycemia due to multiple causes, and is caused by defects in insulin secretion and/or utilization. The long-term carbohydrate, fat and protein metabolism disorder can cause multi-system damage, and lead to chronic progressive lesion, hypofunction and failure of tissues and organs such as eyes, kidneys, nerves, hearts, blood vessels and the like. Monitoring and prediction of blood glucose levels is therefore particularly important for the prevention of diabetes.
Generally, hyperglycemia easily causes diabetes and also causes complications such as diabetic nephropathy, diabetic peripheral neuropathy, diabetic retinopathy and the like; since hypoglycemia can easily cause cardiovascular and cerebrovascular diseases, it is necessary to control the blood sugar of the user to a good range, so that complications are not likely to occur to the user.
In order to solve the problems, the application provides a blood sugar prediction method which is applied to a server, so that the blood sugar value of a user can be monitored and the future blood sugar trend can be predicted according to medical record data and real-time blood sugar value data of the user, the user is reminded of preventing diseases which may occur, and the use experience of the user is improved.
The terminal device may include a fixed terminal such as a mobile phone, a tablet computer, a Personal Digital Assistant (PDA), and the like. The servers may be, for example, individual servers or clusters of servers. However, for the sake of understanding, the following embodiments will be described in detail with reference to a blood glucose prediction method applied to a server.
Some embodiments of the present application will be described in detail below with reference to the accompanying drawings. The embodiments described below and the features of the embodiments can be combined with each other without conflict.
As shown in fig. 1, the blood glucose prediction method provided in the embodiment of the present application may be applied to the application environment shown in fig. 1. The application environment includes a terminal device 110 and a server 120, wherein the terminal device 110 can communicate with the server 120 through a network. Specifically, the server 120 obtains medical record information, blood glucose data and medication data at the current time and state feature data sent by the terminal device 110, and the server 120 determines a future blood glucose trend, an expected disease and a disease prevention measure according to the obtained data and sends the future blood glucose trend, the expected disease and the disease prevention measure to the terminal device 110 to remind a user of disease prevention. The server 120 may be an independent server, or may be a cloud server that provides basic cloud computing services such as cloud service, a cloud database, cloud computing, cloud functions, cloud storage, Network service, cloud communication, middleware service, domain name service, security service, Content Delivery Network (CDN), big data and an artificial intelligence platform. The terminal device 110 may be, but is not limited to, a smart phone, a tablet computer, a notebook computer, a desktop computer, a smart speaker, a smart watch, and the like. The terminal and the server may be directly or indirectly connected through wired or wireless communication, and the application is not limited herein.
Referring to fig. 2, fig. 2 is a schematic flow chart of a blood glucose prediction method according to an embodiment of the present application. The blood sugar prediction method can be applied to a server, so that the blood sugar value of a user can be monitored according to medical record data and real-time blood sugar value data of the user, and the future blood sugar trend can be predicted, so that the user is reminded to prevent possible diseases, and the use experience of the user is improved.
As shown in fig. 2, the blood glucose prediction method includes steps S101 to S105.
S101, acquiring medical record information, blood sugar data and medication data of a user.
The medical record information of the user is the disease information recorded when the user watches a doctor, and specifically can be electronic medical record information including various diseases of the user and corresponding medical records. The server can be in communication connection with the hospital system, so that medical record information of the user can be acquired from the hospital system, and the medical record information can also be input by the user from the terminal equipment. The blood glucose data is a blood glucose value that is measured by the user last time, and may specifically include a fasting blood glucose value of the user, a blood glucose value for two hours after a meal, a random blood glucose value, and a historical blood glucose value, where the fasting blood glucose value is a blood glucose value measured in a state where the user has not eaten any food containing calories for at least 8 hours or more. The random blood glucose level is a blood glucose level at any time irrespective of the meal time, and can be measured at any time before or after a meal. The historical blood glucose values are fasting blood glucose values of the user for the last few days (e.g., the last 3 days), blood glucose values for two hours after a meal, and random blood glucose values. The medication data is blood sugar reducing medicine or insulin which is taken by the user last time.
The embodiment of the application can acquire and process related data based on an artificial intelligence technology. Among them, Artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.
The artificial intelligence infrastructure generally includes technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and the like.
In some embodiments, after obtaining the blood glucose data, cleaning the blood glucose data of the user to obtain the cleaned blood glucose data; and carrying out smooth denoising processing on the cleaned data to obtain processed blood sugar data, and taking the processed blood sugar data as the blood sugar data input into the pre-trained blood sugar prediction model. From this, standard blood glucose data can be obtained and used as input for a pre-trained blood glucose prediction model.
Specifically, when data cleaning is performed on blood glucose data of a user, abnormal detection can be performed on the blood glucose data of the user to obtain corresponding abnormal blood glucose data, and the abnormal blood glucose data is cleaned to obtain cleaned blood glucose data. Smooth denoising is carried out on the cleaned blood glucose data by using Kalman filtering, and smooth denoising processing is carried out on the cleaned data set by using Kalman filtering, so that smaller time delay can be ensured, and meanwhile, high-frequency noise is obviously reduced.
In some embodiments, the cleaning of the blood glucose data of the user is performed by performing anomaly detection on each piece of the blood glucose data, and if an abnormal blood glucose value exists in the blood glucose data, performing cleaning on the abnormal blood glucose value to obtain cleaned blood glucose data.
Specifically, abnormality detection is performed on each of the blood glucose data; if the blood sugar data has abnormal blood sugar values, cleaning the abnormal blood sugar values to obtain cleaned blood sugar data; and if the blood sugar data has no abnormal blood sugar value, the blood sugar data is considered as the cleaned blood sugar data.
The abnormal detection of each blood sugar data is determined by detecting whether the blood sugar difference value of any two data points in the same group of blood sugar data exceeds a preset blood sugar difference value threshold value; if the blood sugar difference value of any two data points in the same group of blood sugar data exceeds a preset blood sugar difference value threshold, the group of blood sugar data is considered to be abnormal; and if the blood sugar difference value of any two data points in the same group of blood sugar data does not exceed the preset blood sugar difference value threshold, determining that the group of blood sugar data is not abnormal. Wherein, the same group of blood sugar data can be fasting blood sugar value, blood sugar value after two hours and random blood sugar value collected on the same day. The preset blood glucose difference threshold value can be any value.
Illustratively, if the fasting blood glucose value is 3.4mmol/L and the blood glucose value is 11.5mmol/L at two hours after meal in the same group of blood glucose data, and the normoglycemia standard is that the fasting blood glucose value is 3.9-6.1mmol/L and the blood glucose value is less than 7.8mmol/L at 2 hours after meal, if the preset blood glucose difference threshold value is 4mmol/L, the group of blood glucose data has abnormality. At this time, the fasting blood glucose level indicates hypoglycemia, and the blood glucose level indicates hyperglycemia two hours after a meal, and therefore, it is not considered that the group of blood glucose data is abnormal.
S102, screening the medical record information to obtain medical record information corresponding to the abnormal blood sugar value, and taking the medical record information corresponding to the abnormal blood sugar value as target medical record information.
The target medical record information is related medical record information corresponding to abnormal blood glucose values, for example, if symptoms such as frequent urination, easy hunger, anxiety and tremor occur, the measured blood glucose values are abnormal at that time, the medical record information corresponding to the symptoms is the target medical record information, or the medical record information corresponding to the abnormal blood glucose values is directly acquired from the medical record information and is used as the target medical record information.
The medical record information of the user comprises a lot of information irrelevant to complications caused by hyperglycemia or hypoglycemia, for example, the user goes to a hospital for seeing a doctor after a certain fever, symptoms including dizziness, limb weakness and the like are recorded in the medical record, but obviously, the symptoms are caused by the fever, the relevance of the symptoms and the target medical record information required by the blood sugar prediction method is not large, or the medical record information comprises a lot of diseases, but the blood sugar value is not abnormal, so the information needs to be screened.
Meanwhile, the medical record information of the user may further include blood glucose data during illness and medication data during illness, the blood glucose data during illness is fasting blood glucose value, blood glucose value after two hours and random blood glucose value during illness of the user, and the medication data during illness is blood glucose lowering medicine or insulin taken by the user during illness. Therefore, the data can be obtained by screening medical record information of the user and input into a pre-trained blood sugar prediction model for blood sugar prediction.
In some embodiments, based on a word segmentation algorithm, performing word segmentation processing on the medical record information to obtain a word segmentation result corresponding to the medical record information; and screening the word segmentation result to obtain target medical record information. The word segmentation algorithm can be a hidden Markov model-based word segmentation algorithm, a conditional random field-based word segmentation algorithm and the like. Therefore, the medical record information can be subjected to word segmentation processing, and then the word segmentation result is screened to obtain the relevant medical record information causing the blood sugar to rise or fall, and the relevant medical record information is used as the input of the blood sugar prediction model.
Specifically, based on a preset medical knowledge base, performing word segmentation on the medical record information to obtain a plurality of word segmentation results; the preset medical knowledge base comprises standard names, similar meaning words, associated symptom words and the like.
Illustratively, the medical record information may be segmented based on a knowledge base (a preset medical knowledge base) corresponding to the blood glucose complications, where the knowledge base includes standard names, near-meaning words, associated symptom words, and the like corresponding to the blood glucose complications, so as to obtain a segmentation result.
Illustratively, for example, word segmentation is performed on one medical record information to obtain corresponding associated symptom words including frequent micturition, hungry tendency, anxiety, tremor, foot pain, alopecia and the like, and then the word segmentation result is screened, so that it is obvious that the relationship between the two symptoms of foot pain and alopecia and complications corresponding to blood sugar abnormality is not large, and therefore the two symptoms are screened to obtain target medical record information.
In some embodiments, the step of screening the word segmentation results to obtain the target medical record information includes performing word meaning prediction on each word in the word segmentation results based on a medical word meaning prediction model to obtain a word meaning prediction result corresponding to each word, and the step of screening the word segmentation results based on the word meaning prediction results to obtain the target medical record information. Therefore, medical record information related to complications caused by blood glucose abnormality can be rapidly screened and obtained.
The medical word meaning prediction model is used for predicting the similarity degree of word segmentation results and standard medical word segmentation, the medical word meaning prediction model is obtained by training a semantic matching model and a standard medical word segmentation database, the semantic prediction model can comprise models such as an LSTM matching model, an MV-DSSM model and an ESIM model, the standard medical word segmentation database is used for storing the standard medical word segmentation database, the standard medical word segmentation is a medical word segmentation related to complications caused by blood sugar abnormality, and the word meaning prediction result is the similarity degree of each word segmentation and the standard medical word segmentation in the standard medical word segmentation database.
Specifically, medical word segmentation matching can be performed on a standard medical word segmentation database corresponding to the blood glucose complication through a medical word meaning prediction model, the similarity between each segmentation and a standard medical word in the standard medical word segmentation database is calculated, and each word meaning prediction result is sequenced according to the similarity to obtain a sequencing result; and screening the word segmentation result based on the sequencing result to obtain standard medical data.
Illustratively, if the participles in the participle result include frequent micturition, hungry, anxiety, tremor, foot pain, alopecia, etc., since frequent micturition, hungry, anxiety and tremor belong to common symptoms of blood sugar abnormality, the similarities corresponding to the several symptoms of frequent micturition, hungry, anxiety and tremor are higher, and the similarities corresponding to the two symptoms of anxiety and tremor are lower, it is possible to screen out the participles with lower similarities in subsequent screening, obtain the participles which are more in line with the blood sugar complication, and use the medical record information including the participles as the target medical record information.
S103, inputting the target medical record information, the blood sugar data and the medication data into a pre-trained blood sugar prediction model to obtain blood sugar trend data.
The pre-trained blood sugar prediction model is used for predicting the future blood sugar trend of the user and laying a cushion for subsequent disease diagnosis.
In some embodiments, the illness time, the measurement time of the blood glucose data and the medication time of the medication data in the target medical record information are respectively determined; determining the weight proportion corresponding to the target medical record information, the blood glucose data and the medication data according to the illness time, the measurement time and the medication time; and inputting the target medical record information, the blood sugar data, the medication data and the weight proportion corresponding to the target medical record information, the blood sugar data, the medication data and the weight proportion to a pre-trained blood sugar prediction model to obtain blood sugar trend data. The illness time is the occurrence time corresponding to symptoms in target medical record information, the measurement time is the acquisition time corresponding to the measurement of each blood sugar value, and the medication time is the time corresponding to the taking of medicines or the injection of insulin. Therefore, a higher weight proportion can be distributed to the blood sugar data with the close date, and the accuracy of blood sugar prediction is improved.
For example, if it is determined that the time of illness in the target medical record information is 7 months 21 days to 7 months 27 days, the measurement time of the blood glucose data is 8 months 3 days, and the medication time of the medication data is 8 months 1 days; since the reliability of data is lower as the time interval is longer, a lower weight proportion is assigned to data with longer time intervals, and a higher weight proportion is assigned to data with shorter time intervals. Therefore, the target medical record information, the blood sugar data and the medication data can be determined to correspond to the weight proportion of 20%, 50% and 30% respectively.
In some embodiments, the difference values between the illness time, the measurement time, and the medication time and the data acquisition time are respectively determined to obtain a corresponding first time difference, a corresponding second time difference, and a corresponding third time difference; determining a weight proportion corresponding to the target medical record information according to the first time difference; determining a weight proportion corresponding to the blood glucose data according to the second time difference; and determining the weight proportion corresponding to the medication data according to the third time difference. Therefore, the time interval of each data can be determined through the time difference, so that the reliability of the data is determined, a higher weight proportion is distributed to the blood sugar data close to the date, and the accuracy of blood sugar prediction is improved.
And the data acquisition time is the time corresponding to the medical record information of the user, the blood sugar data of the user and the medication data acquired by the server. The first time difference is a difference value between the affected time and the time corresponding to the medical record information of the user acquired by the server, the second time difference is a difference value between the measurement time and the time corresponding to the blood glucose data of the user acquired by the server, and the third time difference is a difference value between the medication time and the time corresponding to the medication data of the user acquired by the server.
For example, if the first time difference is 24h, the second time difference is 14h, and the third time difference is 18h, it can be seen that the second time difference is the shortest, that is, the confidence level of the blood glucose data is the highest, and the confidence level of the target medical record information is the lowest, so that it can be determined that the target medical record information, the blood glucose data, and the medication data correspond to weight proportions of 20%, 50%, and 30%, respectively.
In some embodiments, the difference values between the illness time, the measurement time, and the medication time and the data acquisition time are respectively determined to obtain a corresponding first time difference, a corresponding second time difference, and a corresponding third time difference; calculating to obtain a total time difference according to the first time difference, the second time difference and the third time difference; determining the ratio of the first time difference to the total time difference, and determining the weight proportion corresponding to the target medical record information according to the ratio of the first time difference to the total time difference; determining the proportion of the second time difference to the total time difference, and determining the weight proportion corresponding to the blood sugar data according to the proportion of the second time difference to the total time difference; and determining the ratio of the third time difference to the total time difference, and determining the weight proportion corresponding to the medication data according to the ratio of the third time difference to the total time difference.
For example, if the first time difference is 1h, the second time difference is 4h, and the third time difference is 5h, the total time difference is calculated to be 10h, the ratio of the first time difference to the total time difference is determined to be 10%, the ratio of the second time difference to the total time difference is determined to be 40%, and the ratio of the first time difference to the total time difference is determined to be 50%, so that the target medical record information, the blood glucose data, and the medication data may be determined to have weight ratios of 10%, 40%, and 50%, respectively.
In some embodiments, medical record information, blood glucose data and medication data of a target user are acquired, and the medical record information, the blood glucose data and the medication data of the target user are used as a training set; and training a preset blood sugar prediction model through the training set to obtain a pre-trained blood sugar prediction model. The preset blood sugar prediction model is an untrained blood sugar prediction model, and the target user is a user suffering from diabetes or a user corresponding to complications caused by overhigh and overlow blood sugar. Specifically, the preset blood glucose prediction model comprises a recurrent neural network based on a long-term and short-term memory model, the neural network can learn long-term dependency, and meanwhile, a storage unit in the long-term and short-term memory model can help the neural network to combine stored memory and data, so that the prediction accuracy is improved. And because a bidirectional long-short term memory model is used, the neural network can refer to outdated data and infer future data, so that the training time is accelerated.
Specifically, medical history information, blood glucose data and medication data of a target user are obtained and are divided into a training set, a verification set and a test set, a preset blood glucose prediction model is trained through the training set, so that the converted medical history information, blood glucose data and medication data are learned, parameters of the blood glucose prediction model are adjusted through the verification set, and for example, hidden unit numbers are selected in a neural network and parameters of a network structure or a control model complexity degree are determined. And finally, testing the resolving power, such as the recognition rate and other performances of the trained model through the test set so as to achieve the performance of the test, and selecting the optimal model as a target detection model. Therefore, the neural network can be trained through the open source data set, and a corresponding trained blood sugar prediction model is obtained.
In some embodiments, the target medical record information, the blood glucose data and the medication data are input into a first long-short term memory network in a pre-trained blood glucose prediction model for operation, so as to obtain a hidden state vector sequence in the first long-short term memory network, wherein the blood glucose prediction model comprises a first long-short term memory network for encoding and a second long-short term memory network for decoding; and inputting the hidden state vector sequence into the second long-short term memory network for operation to obtain blood sugar trend data.
Wherein the second long-short term memory model is a recurrent neural network model based on an attention mechanism. The first long-short term memory model differs from the second long-short term memory model in that a mechanism of attention is added to the second long-short term model. The attention mechanism refers to the visual attention mechanism of human, which is a brain signal processing mechanism specific to human vision.
Specifically, determining an influence factor corresponding to a future trend of blood glucose according to the target medical record information, the blood glucose data and the medication data; and inputting the influence factors corresponding to the future blood sugar trend into a first long-short term memory network in a blood sugar prediction model for operation to obtain a hidden state vector sequence in the first long-short term memory network. Inputting the hidden state vector sequence into the second long-short term memory network for operation, thereby obtaining a high-dimensional vector sequence output by the second long-short term memory network; and reading the high-dimensional vector sequence according to the corresponding relation between the preset component vectors and the meanings of the prediction results, thereby obtaining the blood glucose trend data in different future time periods.
And the target medical record information, the blood sugar data, the medication data and the influence factors corresponding to the future blood sugar trend are used as the input of a long-term and short-term memory model together. The hidden state vector sequence can be used as a decoding basis of a second long-short term memory network, and the second long-short term memory network outputs a high-dimensional vector sequence which represents the prediction results of different time periods, wherein the component of the high-dimensional vector represents the blood glucose trend data. And acquiring the blood glucose trend data in different time periods in the future according to the corresponding relation between the preset component vectors and the meanings of the prediction results.
And S104, acquiring state characteristic data of the user, and predicting diseases according to the blood glucose trend data and the state characteristic data to obtain expected diseases.
The state characteristic data comprises physiological data, diet data and motion data, the physiological data can comprise height information, weight information and the like of the user, the diet data can be lunch and dinner food which are nearest to the user, and the motion data is body building or running duration which is nearest to the user. The expected disease is a disease that may appear in the future for the user.
In some embodiments, based on a pre-trained disease classification prediction model, and based on the blood glucose trend data, the physiological data, the diet data and the exercise data, performing disease prediction, determining an expected disease and a corresponding probability; wherein the disease classification prediction model comprises a fully connected layer and a softmax layer.
Specifically, the blood glucose trend data, the physiological data, the diet data and the exercise data are input into a pre-trained disease classification model, and finally the disease classification model can output the expected illness and the corresponding probability of the user.
The full-connection layer multiplies a weight matrix and an input vector by adding offset, maps n disease types into k real numbers, and simultaneously maps the k real numbers into k real numbers (probability) in (0,1) by the softmax layer, and simultaneously ensures that the sum of the k real numbers is 1. Illustratively, a probability of diabetes of 70%, a probability of cardiovascular and cerebrovascular diseases of 20%, and a probability of cerebral thrombosis of 10% can be finally obtained, respectively.
The specific probability calculation formula is as follows:
γ＝softmax(z)＝softmax(WTx+b)
wherein x is the input of the full link layer, and specifically, the blood glucose trend data, the physiological data, the diet data and the exercise data can be used as the input of the full link layer, W is the weightTx is the inner product of the weight and the input of the fully connected layer, b is the bias term, and γ is the probability of the Softmax output, from which each expected disease and corresponding probability can be calculated. Obtaining the scores of K disease categories in the range of (-infinity, + ∞) through a full connection layer, and obtaining the probability corresponding to each disease category by first dividing the scores
Mapped to (0, + ∞), and then normalized to (0,1) to obtain the corresponding probability, and the specific softmax calculation formula is:
wherein z isjCan representIs the corresponding score of the jth diseased species, in particular, zj＝wj*x+bjWherein b isjBias term corresponding to the jth disease class, wjAnd obtaining the score of each category by weighting and summing the features for the data weight corresponding to the jth diseased category, namely the importance degree of each data and the influence degree on the final score, and mapping the score into the probability through a Softmax function.
In some embodiments, feature extraction is performed on the blood glucose trend data, the physiological data, the diet data and the exercise data respectively to obtain feature information corresponding to each data; and performing characteristic matching in a preset disease library according to the characteristic information to obtain expected diseases and corresponding probability. The preset disease library comprises a series of diseases caused by hypo-or over-high blood sugar, and each disease corresponds to characteristic information of various data.
Specifically, the characteristic extraction of the blood glucose trend data, the physiological data, the diet data and the exercise data can be performed through a pre-trained decoder such as a ResNet decoder, a Glove decoder and the like, so as to obtain the characteristic information corresponding to each data. And comparing the similarity item by utilizing the extracted feature information and the feature information corresponding to each disease in a preset disease library to obtain comprehensive similarity, wherein the comprehensive similarity can also be regarded as the probability corresponding to each disease, and the disease with the highest comprehensive similarity can be regarded as the expected disease.
Illustratively, if matching with diabetes in the disease library, the similarity between the blood glucose characteristic information and the blood glucose characteristic information corresponding to the diabetes is compared to obtain a similarity of 80%, and then the physiological characteristic information, the diet characteristic information and the exercise characteristic information are sequentially compared to obtain similarities of 70%, 60% and 90%, respectively. By averaging the individual similarity measures, a combined similarity measure of 75% can be obtained, and the expected disease is considered to be diabetes and the corresponding probability is 75%.
In some embodiments, after determining the expected prevalence and corresponding probability, a predictive complete prompt may also be output.
The prompting information may specifically include application program (APP) or Email, short message, chat tool such as WeChat, qq, and other means.
For example, after determining the disease category and the corresponding probability, the Application (APP) may transmit a prompt to remind the user that the disease prediction has been completed, and the user may also view the predicted expected disease and the corresponding probability on the Application (APP).
S105, determining corresponding disease preventive measures according to the state characteristic data and the expected diseases, and pushing the disease preventive measures to a target terminal.
The disease prevention measure is a measure for preventing a desired disease, and may specifically include a diet plan and an exercise plan, where the target terminal is a terminal device of a user.
In some embodiments, a demand for nutritional elements and a target amount of exercise is determined based on the physiological data, the dietary data, the exercise data, and the expected disease; and determining corresponding disease preventive measures according to the demand of the nutrient elements and the target exercise amount, wherein the disease preventive measures comprise a diet plan and an exercise plan.
Specifically, the required amount of nutrient elements is determined according to the physiological data, the diet data and the expected diseases, and a corresponding diet plan is determined according to the required amount of the nutrient elements. And determining the demand of nutrient elements according to the physiological data, the motion data and the expected diseases, and determining a corresponding disease preventive measure motion plan according to the target motion amount.
Illustratively, if the user is obese, analyzing the dietary data for protein that has not recently been ingested in sufficient quantities, analyzing the exercise data for exercise for a 20 minute week period, the highest probability of disease being expected is diabetes.
Since the highest probability of disease is expected to be diabetes and not a sufficient amount of protein has recently been ingested, the corresponding dietary plan can be confirmed as follows: breakfast an egg, 150 ml milk and a small steamed stuffed bun can be eaten. The Chinese food can be cooked rice, and the cold jelly fungus, the fried bitter gourd, the cucumber and the like can be eaten when the Chinese food is fried. The dinner can eat a small amount of rice, and the vegetables can eat celery and fried spinach. Since the highest probability of disease is expected to be diabetes, the user is fat and not enough exercise, the corresponding exercise plan can be confirmed as follows: run for 15 minutes daily, muscle relax for 5 minutes, strength train for 10 minutes.
Referring to fig. 3, fig. 3 is a schematic block diagram of a blood glucose prediction apparatus according to an embodiment of the present application, which can be configured in a server for executing the blood glucose prediction method.
As shown in fig. 3, the blood glucose prediction apparatus 200 includes: the system comprises a data acquisition module 201, a medical record information screening module 202, a blood sugar prediction module 203, a disease prediction acquisition module 204 and a preventive measure generation module 205.
The data acquisition module 201 is used for acquiring medical record information, blood sugar data and medication data of a user;
a medical record information screening module 202, configured to screen the medical record information to obtain medical record information corresponding to an abnormal blood glucose value, and use the medical record information corresponding to the abnormal blood glucose value as target medical record information;
the blood sugar prediction module 203 is used for inputting the target medical record information, the blood sugar data and the medication data into a pre-trained blood sugar prediction model to obtain blood sugar trend data;
a disease prediction obtaining module 204, configured to obtain state feature data of a user, and perform disease prediction according to the blood glucose trend data and the state feature data to obtain an expected disease;
and the preventive measure generation module 205 is configured to determine a corresponding disease preventive measure according to the state characteristic data and the expected disease, and push the disease preventive measure to a target terminal.
It should be noted that, as will be clear to those skilled in the art, for convenience and brevity of description, the specific working processes of the apparatus, the modules and the units described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The methods, apparatus, and devices of the present application are operational with numerous general purpose or special purpose computing system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet-type devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.
For example, the method and apparatus described above may be implemented in the form of a computer program that can be run on a computer device as shown in fig. 4.
Referring to fig. 4, fig. 4 is a schematic diagram of a computer device according to an embodiment of the present disclosure. The computer device may be a server.
As shown in fig. 4, the computer device includes a processor, a memory, and a network interface connected by a system bus, wherein the memory may include a nonvolatile storage medium and an internal memory.
The non-volatile storage medium may store an operating system and a computer program. The computer program includes program instructions that, when executed, cause a processor to perform any of the blood glucose prediction methods.
The processor is used for providing calculation and control capability and supporting the operation of the whole computer equipment.
The internal memory provides an environment for the execution of a computer program on a non-volatile storage medium, which when executed by the processor, causes the processor to perform any of the methods for blood glucose prediction.
The network interface is used for network communication, such as sending assigned tasks and the like. Those skilled in the art will appreciate that the configuration of the computer apparatus is merely a block diagram of a portion of the configuration associated with aspects of the present application and is not intended to limit the computer apparatus to which aspects of the present application may be applied, and that a particular computer apparatus may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
It should be understood that the Processor may be a Central Processing Unit (CPU), and the Processor may be other general purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, etc. Wherein a general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
Wherein, in some embodiments, the processor is configured to execute a computer program stored in the memory to implement the steps of: acquiring medical record information, blood sugar data and medication data of a user; screening the medical record information to obtain medical record information corresponding to an abnormal blood sugar value, and taking the medical record information corresponding to the abnormal blood sugar value as target medical record information; inputting the target medical record information, the blood glucose data and the medication data into a pre-trained blood glucose prediction model to obtain blood glucose trend data; acquiring state characteristic data of a user, and predicting diseases according to the blood glucose trend data and the state characteristic data to obtain expected diseases; and determining corresponding disease preventive measures according to the state characteristic data and the expected diseases, and pushing the disease preventive measures to a target terminal.
In some embodiments, the processor is further configured to: performing word segmentation processing on the medical record information based on a word segmentation algorithm to obtain a word segmentation result corresponding to the medical record information; performing word sense prediction on each word in the word segmentation result based on a medical word sense prediction model to obtain a word sense prediction result corresponding to each word; and screening the word segmentation result based on the word meaning prediction result to obtain target medical record information.
In some embodiments, the processor is further configured to: respectively determining the illness time, the measurement time of the blood sugar data and the medication time of the medication data in the target medical record information; determining the weight proportion corresponding to the target medical record information, the blood glucose data and the medication data according to the illness time, the measurement time and the medication time; and inputting the target medical record information, the blood sugar data, the medication data and the weight proportion corresponding to the target medical record information, the blood sugar data, the medication data and the weight proportion to a pre-trained blood sugar prediction model to obtain blood sugar trend data.
In some embodiments, the processor is further configured to: respectively determining the difference values of the illness time, the measurement time and the medication time with the data acquisition time to obtain a corresponding first time difference, a corresponding second time difference and a corresponding third time difference; calculating to obtain a total time difference according to the first time difference, the second time difference and the third time difference; determining the ratio of the first time difference to the total time difference, and determining the weight proportion corresponding to the target medical record information according to the ratio of the first time difference to the total time difference; determining the proportion of the second time difference to the total time difference, and determining the weight proportion corresponding to the blood sugar data according to the proportion of the second time difference to the total time difference; and determining the ratio of the third time difference to the total time difference, and determining the weight proportion corresponding to the medication data according to the ratio of the third time difference to the total time difference.
In some embodiments, the processor is further configured to: inputting the target medical record information, the blood sugar data, the medication data and the weight proportion corresponding to the target medical record information, the blood sugar data, the medication data and the weight proportion into a first long-short term memory network in a pre-trained blood sugar prediction model for operation to obtain a hidden state vector sequence in the first long-short term memory network; and inputting the hidden state vector sequence into the second long-short term memory network for operation to obtain blood sugar trend data.
In some embodiments, the processor is further configured to: based on a pre-trained disease classification prediction model, predicting diseases according to the blood sugar trend data, the physiological data, the diet data and the exercise data to obtain expected diseases and corresponding probabilities; wherein the disease classification prediction model comprises a fully connected layer and a softmax layer.
In some embodiments, the processor is further configured to: determining the demand amount of nutrient elements and the target exercise amount according to the state characteristic data and the expected diseases; and determining corresponding disease preventive measures according to the required quantity of the nutrient elements and the target quantity of motion, wherein the disease preventive measures comprise a diet plan and an exercise plan.
The embodiment of the present application further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, where the computer program includes program instructions, and the program instructions, when executed, implement any one of the blood glucose prediction methods provided in the embodiment of the present application.
The computer-readable storage medium may be an internal storage unit of the computer device described in the foregoing embodiment, for example, a hard disk or a memory of the computer device. The computer readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like provided on the computer device.
Further, the computer-readable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to the use of the blockchain node, and the like.
The invention relates to a novel application mode of computer technologies such as storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like of a block chain language model. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
While the invention has been described with reference to specific embodiments, the scope of the invention is not limited thereto, and those skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the invention. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A method of blood glucose prediction, the method comprising:
acquiring medical record information, blood sugar data and medication data of a user;
screening the medical record information to obtain medical record information corresponding to an abnormal blood sugar value, and taking the medical record information corresponding to the abnormal blood sugar value as target medical record information;
inputting the target medical record information, the blood glucose data and the medication data into a pre-trained blood glucose prediction model to obtain blood glucose trend data;
acquiring state characteristic data of a user, and predicting diseases according to the blood glucose trend data and the state characteristic data to obtain expected diseases;
and determining corresponding disease preventive measures according to the state characteristic data and the expected diseases, and pushing the disease preventive measures to a target terminal.
2. The method according to claim 1, wherein the screening the medical record information to obtain medical record information corresponding to an abnormal blood glucose value and using the medical record information corresponding to the abnormal blood glucose value as target medical record information comprises:
performing word segmentation processing on the medical record information based on a word segmentation algorithm to obtain a word segmentation result corresponding to the medical record information;
performing word sense prediction on each word in the word segmentation result based on a medical word sense prediction model to obtain a word sense prediction result corresponding to each word;
and screening the word segmentation result based on the word meaning prediction result to obtain target medical record information.
3. The method of claim 1, wherein inputting the target medical record information, the blood glucose data, and the medication data into a pre-trained blood glucose prediction model to obtain blood glucose trend data comprises:
respectively determining the illness time, the measurement time of the blood sugar data and the medication time of the medication data in the target medical record information;
determining the weight proportion corresponding to the target medical record information, the blood glucose data and the medication data according to the illness time, the measurement time and the medication time;
and inputting the target medical record information, the blood sugar data, the medication data and the weight proportion corresponding to the target medical record information, the blood sugar data, the medication data and the weight proportion to a pre-trained blood sugar prediction model to obtain blood sugar trend data.
4. The method of claim 3, wherein determining the respective weight ratios of the target medical record information, the blood glucose data and the medication data according to the illness time, the measurement time and the medication time comprises:
respectively determining the difference values of the illness time, the measurement time and the medication time with the data acquisition time to obtain a corresponding first time difference, a corresponding second time difference and a corresponding third time difference;
calculating to obtain a total time difference according to the first time difference, the second time difference and the third time difference;
determining the ratio of the first time difference to the total time difference, and determining the weight proportion corresponding to the target medical record information according to the ratio of the first time difference to the total time difference;
determining the proportion of the second time difference to the total time difference, and determining the weight proportion corresponding to the blood sugar data according to the proportion of the second time difference to the total time difference;
and determining the ratio of the third time difference to the total time difference, and determining the weight proportion corresponding to the medication data according to the ratio of the third time difference to the total time difference.
5. The method of claim 3, wherein the blood glucose prediction model comprises a first long-short term memory network and a second long-short term memory network, and the inputting the target medical record information, the blood glucose data and the medication data and corresponding weight ratios into a pre-trained blood glucose prediction model to obtain blood glucose trend data comprises:
inputting the target medical record information, the blood sugar data, the medication data and the weight proportion corresponding to the target medical record information, the blood sugar data, the medication data and the weight proportion into a first long-short term memory network in a pre-trained blood sugar prediction model for operation to obtain a hidden state vector sequence in the first long-short term memory network;
and inputting the hidden state vector sequence into the second long-short term memory network for operation to obtain blood sugar trend data.
6. The method of claim 1, wherein the state characteristic data comprises physiological data, dietary data, and exercise data, and wherein predicting a disease based on the state characteristic data to obtain an expected disease and corresponding probability comprises:
based on a pre-trained disease classification prediction model, predicting diseases according to the blood sugar trend data, the physiological data, the diet data and the exercise data to obtain expected diseases and corresponding probabilities; wherein the disease classification prediction model comprises a fully connected layer and a softmax layer.
7. The method of claim 1, wherein determining corresponding disease prevention measures according to the status characteristic data and the expected disease and pushing the disease prevention measures to a target terminal comprises:
determining the demand amount of nutrient elements and the target exercise amount according to the state characteristic data and the expected diseases;
and determining corresponding disease preventive measures according to the required quantity of the nutrient elements and the target quantity of motion, wherein the disease preventive measures comprise a diet plan and an exercise plan.
8. A blood glucose prediction device, comprising:
the data acquisition module is used for acquiring medical record information, blood sugar data and medication data of a user;
the medical record information screening module is used for screening the medical record information to obtain medical record information corresponding to the abnormal blood sugar value, and taking the medical record information corresponding to the abnormal blood sugar value as target medical record information;
the blood sugar prediction module is used for inputting the target medical record information, the blood sugar data and the medication data into a pre-trained blood sugar prediction model to obtain blood sugar trend data;
the disease prediction acquisition module is used for acquiring state characteristic data of a user and predicting diseases according to the blood glucose trend data and the state characteristic data to obtain expected diseases;
and the preventive measure generation module is used for determining corresponding preventive measures of diseases according to the state characteristic data and the expected diseases and pushing the preventive measures of diseases to a target terminal.
9. A computer device, wherein the computer device comprises a memory and a processor;
the memory for storing a computer program;
the processor is used for executing the computer program and realizing the following when the computer program is executed:
the method of claim 1 to 7 for predicting blood glucose.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which, when executed by a processor, causes the processor to implement the blood glucose prediction method according to any one of claims 1 to 7.
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