Patent ID: 11929159
Assignee: SHANGHAI SIXTH PEOPLE'S HOSPITAL
Field: Medical technology (Instruments)
Classification: CPC G  A | IPC A  G

Claim 2:
3. One or more non-transitory storage medium storing computer-executable instructions that, when executed by one or more processors, cause the one or more processors to perform a method comprising:
obtaining characteristic information and a blood glucose content at a current time of a target user through a computer apparatus, the characteristic information comprising clinical information and dietary information, wherein the clinical information comprises height, weight, fasting blood glucose content, and connecting peptide content;
determining an insulin injection amount at each time of a plurality of times after the current time of the target user through the computer apparatus and based on the characteristic information of the target user, the blood glucose content at the current time of the target user, a predetermined blood glucose prediction model, and a predetermined insulin injection amount prediction model; and
outputting the determined insulin injection amount to administer to the target user without the target user wearing a continuous blood glucose monitoring device at each time of the plurality of times of the target user through the computer apparatus,
wherein the determining the insulin injection amount at each time of the target user comprises:
inputting the characteristic information of the target user and the blood glucose content at the current time of the target user into the predetermined insulin injection amount prediction model to predict the insulin injection amount and determining the insulin injection amount at the current time;
inputting the insulin injection amount at the current time, the characteristic information of the target user, and the blood glucose content at the current time of the target user into the predetermined blood glucose prediction model to perform blood glucose prediction, and determining a blood glucose content of the target user at a next time corresponding to the current time; and
regarding the next time as a current time and returning to the inputting the characteristic information of the target user and the blood glucose content at the current time of the target user into the predetermined insulin injection amount prediction model to predict the insulin injection amount and determining the insulin injection amount at the current time; until the insulin injection amount at each time is determined, and obtaining the respective corresponding insulin injection amount at each time of the target user;

wherein the computer-executable instructions, when executed by one or more processors, cause the one or more processors to perform:
obtaining a marked training set and a unmarked training set, the marked training set comprising the characteristic information, the blood glucose content of each first-sample user at each sampling time, and the insulin injection amount of each first-sample user at each sampling time; the unmarked training set comprising characteristic information and a blood glucose content of each second-sample user at each sampling time;
initializing and obtaining an initial insulin injection amount prediction model; and
training the initial insulin injection amount prediction model based on the marked training set and the unmarked training set and determining the predetermined insulin injection amount prediction model;

wherein the initial insulin injection amount prediction model comprises first initial insulin injection amount prediction models;
wherein the determining the predetermined insulin injection amount prediction model comprises:
sampling the marked training set and determining mark training subsets, wherein the number of the mark training subsets is the same as the number of the first initial insulin injection amount prediction models, the mark training subset comprising the characteristic information, the blood glucose content at each sampling time, and the insulin injection amount at each sampling time of some sample users of the first-sample users;
training the corresponding first initial insulin injection amount prediction model based on the unmarked training set and the mark training subsets to obtain trained initial insulin injection amount prediction models;
performing weighted summation and averaging on each of the trained initial insulin injection amount prediction models based on weights, and obtaining an integrated insulin injection amount prediction model; and
determining the predetermined insulin injection amount prediction model according to the integrated insulin injection amount prediction model;

wherein the determining the predetermined insulin injection amount prediction model according to the integrated insulin injection amount prediction model comprises adjusting each of the weights in the integrated insulin injection amount prediction model based on the marked training set and determining the predetermined insulin injection amount prediction model; and
wherein the determining the predetermined blood glucose prediction model comprises:
obtaining a sample marked training set; the sample marked training set comprising characteristic information, a blood glucose content at each sampling time, and an insulin injection amount at each sampling time of each third sample user;
initializing and obtaining an initial blood glucose prediction model; and
training the initial blood glucose prediction model based on the marked training set and determining the predetermined blood glucose prediction model.