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Continuous blood sugar monitoring method and device

A blood sugar monitoring and blood sugar technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve problems such as short time, small amount of blood sugar data, and no solution proposed

Active Publication Date: 2017-09-26
TSINGHUA BERKELEY SHENZHEN INST +1
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, in practical applications, because many patients use CGM equipment for a short period of time, the amount of collected blood glucose data is small, so the above machine learning algorithm cannot be used to predict their blood glucose concentration
[0004] For the above problems, no effective solution has been proposed

Method used

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  • Continuous blood sugar monitoring method and device

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Embodiment 1

[0043] Figure 1a It is a flow chart of a method based on continuous blood glucose monitoring provided by Embodiment 1 of the present invention. This embodiment is applicable to using group blood glucose data to predict individual blood glucose concentration. This method can be performed by a device based on continuous blood glucose monitoring. Specifically Including the following steps:

[0044] Step S110: Obtain the blood glucose data of at least two users, form a first blood glucose data matrix according to a preset data splicing method, and obtain a plurality of first blood glucose data input vectors and each first blood glucose data input vector from the first blood glucose data matrix corresponding output data.

[0045]Among them, the user's blood glucose data can be collected by using CGM equipment, and the CGM equipment can include a continuous blood glucose meter. The blood glucose data collected by CGM is generally set to be collected at intervals. Therefore, the blo...

Embodiment 2

[0065] Figure 2a It is a structural block diagram of a device based on continuous blood glucose monitoring provided by Embodiment 3 of the present invention. This embodiment is applicable to predicting individual blood glucose concentration by using group blood glucose data. The device specifically includes:

[0066] The blood glucose data generation module 210 in the training phase is used to obtain the blood glucose data of at least two users, form a first blood glucose data matrix according to a preset data splicing method, and obtain a plurality of first blood glucose data input vectors and Each first blood glucose data input vector corresponds to output data respectively.

[0067] The blood glucose data prediction model generation module 220 is used to use machine learning algorithms to train the preset mathematical model according to the multiple sets of first blood glucose data input vectors and the output data corresponding to each first blood glucose data input vecto...

Embodiment 3

[0079] This embodiment is a preferred embodiment, and this embodiment provides an LSTM algorithm based on continuous blood glucose monitoring to realize prediction of individual blood glucose data from group blood glucose data. Figure 3a , Figure 3b They are respectively the flow charts of the training phase and the prediction phase of an LSTM network algorithm based on continuous blood glucose monitoring provided by Embodiment 4 of the present invention. The method can be performed by a device based on continuous blood glucose monitoring, and the method specifically includes the following steps:

[0080] Step S310, arrange the blood glucose data of multiple users collected by the CGM device into a matrix D by day.

[0081] Step S320, obtain the input matrix D according to the preset data splicing method in and the output matrix D out .

[0082] Among them, the CGM device can collect the blood glucose data of the user in real time, and output a blood glucose value every ...

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Abstract

The invention discloses a continuous blood sugar monitoring method and device. The continuous blood sugar monitoring method comprises the steps that blood sugar data of at least two users is obtained, a first blood sugar data matrix is formed according to a preset data splicing method, and multiple first blood sugar data input vectors and output data corresponding to the first blood sugar data input vectors respectively are obtained from the first blood sugar data matrix; a machine learning algorithm is adopted to train a preset mathematical model according to the multiple first blood sugar data input vectors and the output data corresponding to the first blood sugar data input vectors respectively to obtain a blood sugar data prediction model; existing blood sugar data of one user to be predicted is obtained, and second blood sugar data input vectors are formed by adopting the preset data splicing method; the second blood sugar data input vectors are input into the blood sugar data prediction model, a blood sugar data prediction value of the user to be predicted after the current moment is obtained, and it is achieved that the blood sugar concentration is predicted by using population blood sugar data.

Description

technical field [0001] Embodiments of the present invention relate to the field of blood glucose monitoring, and in particular to a method and device based on continuous blood glucose monitoring. Background technique [0002] Diabetes is a metabolic pathological state in which long-term fluctuations in blood glucose levels exceed the normal range (90-120mmol / L). If not managed properly, it will lead to serious complications. At present, there is no effective method for the treatment of diabetes. The existing blood glucose measurement methods require frequent acupuncture blood collection for self-monitoring of blood glucose. Frequent acupuncture blood collection will cause physical pain and psychological pain to the patient. Fear and resistance on the Internet can even lead to infection, which limits the frequency of blood sugar testing. Due to the lack of blood glucose data collected, many data analysis methods cannot be used. [0003] The emergence of CGM (Continuous Gluc...

Claims

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Application Information

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IPC IPC(8): G06F19/00
Inventor 董宇涵李征唐圆圆米雪龙张林
Owner TSINGHUA BERKELEY SHENZHEN INST
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