WO2019072141A1 - 基于云端大数据的胰岛素泵个体化配置优化系统和方法 - Google Patents

基于云端大数据的胰岛素泵个体化配置优化系统和方法 Download PDF

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WO2019072141A1
WO2019072141A1 PCT/CN2018/109282 CN2018109282W WO2019072141A1 WO 2019072141 A1 WO2019072141 A1 WO 2019072141A1 CN 2018109282 W CN2018109282 W CN 2018109282W WO 2019072141 A1 WO2019072141 A1 WO 2019072141A1
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time
insulin
data
injection
blood glucose
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PCT/CN2018/109282
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English (en)
French (fr)
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于非
陈志彦
吕剑峰
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微泰医疗器械(杭州)有限公司
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Priority to EP18866271.2A priority Critical patent/EP3721921B1/en
Priority to US16/967,647 priority patent/US11786656B2/en
Publication of WO2019072141A1 publication Critical patent/WO2019072141A1/zh

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
    • A61M5/00—Devices for bringing media into the body in a subcutaneous, intra-vascular or intramuscular way; Accessories therefor, e.g. filling or cleaning devices, arm-rests
    • A61M5/14—Infusion devices, e.g. infusing by gravity; Blood infusion; Accessories therefor
    • A61M5/142—Pressure infusion, e.g. using pumps
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
    • G16H20/17—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients delivered via infusion or injection
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    • A61M5/14—Infusion devices, e.g. infusing by gravity; Blood infusion; Accessories therefor
    • A61M5/168—Means for controlling media flow to the body or for metering media to the body, e.g. drip meters, counters ; Monitoring media flow to the body
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    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
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    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
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Definitions

  • the invention relates to the technical field of smart medical devices, in particular to a cloud-based big data-based insulin pump individualized configuration optimization system and method.
  • Diabetes is a disease caused by a patient's own insulin deficiency (type 1 diabetes) or resistance to insulin or a decrease in insulin secretion rate (type 2 diabetes), resulting in high levels of sugar in the blood, causing various health problems. All type 1 diabetes patients and most patients with advanced type 2 diabetes require insulin injection from outside to control blood sugar.
  • the continuous subcutaneous insulin injection device also known as the subcutaneous insulin injection pump, is an insulin input device controlled by artificial intelligence, which controls the physiological secretion mode of insulin to control hyperglycemia by continuously injecting insulin by subcutaneous injection. Treatment equipment.
  • insulin secretion can be roughly divided into two parts according to the relationship with the meal: one is the continuous micro-distribution that does not depend on the meal, that is, the basal insulin secretion; the other is the large amount of insulin secretion caused by the hyperglycemia stimulation after the meal.
  • the insulin pump is controlled by artificial intelligence, and the basal rate insulin injection is performed by an adjustable pulse subcutaneous infusion method.
  • the patient At the time of eating, the patient himself sets the pre-meal according to the type and total amount of the food. Dosage insulin and infusion mode to control postprandial blood glucose. Clinical studies have demonstrated that insulin pumps can more effectively control the level of glycated hemoglobin compared to multiple insulin injections, while also improving the quality of life of patients.
  • the insulin syringe pump has the following features:
  • the use of insulin pump can improve the patient's compliance with treatment, reduce the pain and inconvenience caused by multiple subcutaneous injections of insulin to diabetic patients; increase the freedom of eating and exercising of diabetic patients; improve the self-glycemic management ability of patients; reduce the psychological state of diabetic patients burden.
  • the dose and infusion rate of the CSII insulin injection pump can be adjusted at any time by the patient.
  • blood glucose and insulin levels in the human body are in a dynamic process of constant dynamics, and will be affected by many factors. How to determine the dose of insulin injection pump has always been one of the research priorities of diabetes treatment.
  • the recommended infusion protocol for the 2009 Chinese insulin pump treatment guidelines can be summarized as follows: First, the initial dose should be determined according to the patient's diabetes type, blood glucose level and body weight, and distributed to the basic infusion volume and pre-meal high dose, and then according to the actual patient The situation is determined to supplement the large dose and correct the large dose to comprehensively control the patient's blood glucose.
  • the time period taken for the basic infusion volume, pre-meal, supplementation and correction of the large dose, and the basic infusion rate is often set according to specifications or experience.
  • the basic infusion amount refers to the amount of insulin required to maintain the body's basic blood glucose metabolism, and the effect of the basic infusion amount on the patient can be adjusted by adjusting the basic infusion rate and the corresponding time period.
  • the basic infusion rate refers to the speed at which the insulin pump provides basal insulin. It is generally expressed in units of insulin (U)/h. It has more setting modes and can be set to one or more time periods according to the needs of blood sugar control. It is divided into 3 to 6 time periods.
  • T2DM In patients with T2DM, more T1DM patients require more segmentation. In T2DM patients, the segmentation method of so-called fragile DM patients with large blood glucose fluctuation values is often different from ordinary patients. Pre-meal, supplement, and correction of high-dose infusions are set by the patient based on current carbohydrate intake and their own physiological parameters, such as insulin-carbohydrate metabolism ratio and insulin sensitivity.
  • the real-time dynamic blood glucose monitoring system generally called CGMS, continuously records the glucose level of the intercellular fluid through a glucose sensor embedded subcutaneously, thereby reflecting the change in blood glucose.
  • the continuous blood glucose information obtained from CGMS can make a blood glucose change chart for the doctor to carry out clinical analysis and diagnosis, and comprehensively understand the type of blood sugar fluctuation of the patient, which is of great significance for blood sugar control and diabetes treatment.
  • CGMS and CSII insulin pump systems supported by artificial intelligence algorithms can assist doctors and patients. It is necessary to complete some simple medical decisions.
  • Chinese patent CN101254322A discloses a high-dose insulin automatic intelligent infusion method and device based on model predictive control, which is supported by a real-time dynamic blood glucose monitoring system (CGMS), and the feed is detected online by a strong tracking filter.
  • CGMS real-time dynamic blood glucose monitoring system
  • High-dose insulin so its main focus is on large doses before meals.
  • the automatically calculated high dose insulin dose is likely to be unsafe for the user.
  • Chinese patent CN103418053B discloses an individualized insulin injection pump system that assists in optimizing the basic infusion rate by modeling and simulating data from a real-time dynamic blood glucose monitoring system (CGMS), but it does not involve high dose insulin. Injection has clinical guiding significance.
  • CGMS real-time dynamic blood glucose monitoring system
  • Chinese Patent No. CN102500013A discloses a portable intelligent insulin pump and a control model thereof for treating diabetes by dynamically monitoring a user's blood sugar level and tracking blood sugar changes by infusion of insulin. This closed-loop infusion pump control model does not take into account the severe blood sugar fluctuations that patients may have after a meal, and has a safety hazard in use.
  • the object of the present invention is to provide a cloud-based big data-based insulin pump individualized configuration optimization system and method to solve the deficiencies of the prior art.
  • a cloud-based big data-based insulin pump individualized configuration optimization system including an insulin pump, a real-time dynamic blood glucose monitoring system, a smart phone, a blood glucose monitoring application software installed in a smart phone, and a cloud big data server;
  • the insulin pump comprises a syringe pump body with a control module and a wireless transmission module, a replaceable drug reservoir and a subcutaneous indwelling needle; the wireless transmission module of the insulin pump is wirelessly coupled with the smartphone and transmits data to and from the blood glucose monitoring application software;
  • the real-time dynamic blood glucose monitoring system includes a replaceable implantable glucose sensor probe, a reusable signal collector and a signal transmitter; the signal transmitter of the real-time dynamic blood glucose monitoring system is wirelessly coupled to the smartphone and is used with the blood glucose monitoring application. Transfer data to each other;
  • the smartphone and the blood glucose monitoring application installed in the smartphone have the functions of transmitting data through wireless transmission technology and real-time dynamic blood glucose monitoring system and insulin pump, and uploading and downloading data through the mobile data network or wireless network and the cloud big data server. ;
  • the cloud big data server has the functions of storing, updating, calculating and transmitting user personal information and historical data;
  • the cloud big data server calculates the individualized parameters related to diabetes according to the stored user historical data, and automatically corrects and outputs the parameter output data of the insulin pump and the implanted glucose sensor to the smart phone, and the parameters include 1 The number of unit carbohydrates per unit of insulin conversion CR, insulin sensitivity index IS, insulin retention time TA, the rate at which the user releases glucose into the blood by metabolism during fasting, the single large dose BOLUS, the basic infusion rate BASAL ;
  • the insulin pump can download the latest user parameters from the cloud big data server through the smartphone, and then calculate the recommended insulin high-dose injection plan based on the user's input of carbohydrate intake, and recommend the user according to the user's time segmentation of the basic infusion rate. Updated basic infusion rate plan.
  • the user personal information and historical data stored by the cloud big data server include the user name, gender, age, contact number, insulin pump product serial number used, insulin pump infusion dose, time and infusion rate record, blood glucose output value.
  • BG and corresponding data measure time and date Ts, the individual's recorded carbohydrate intake, sleep and exercise.
  • GR The rate at which the body releases glucose into the blood through metabolism during fasting.
  • BASAL Basic infusion rate, usually counted in insulin units per hour (U/h)
  • BGcurrent is the blood glucose value before the high-dose injection of CGMS
  • BGtarget is the target blood glucose level
  • BOLUSprev is the last large-dose injection
  • CARBS is the current carbohydrate intake entered by the user
  • TI is the largest The distance from the midpoint of the last high-dose injection, min (TI, TA) takes the smaller value in TI, TA, so that when TI is greater than or equal to TA, the last large dose of residual Is 0;
  • the BASAL infusion rate is calculated during the fasting period of time t:
  • BGstart is the average blood glucose for a period of time at the beginning of the fasting period of the CGMS reading
  • TI is the time from the midpoint of the last high-dose injection at the beginning of the period.
  • the cloud big data server can optimize its physiological parameters CR, IS, TA by real-time collecting real-time data obtained by users using CGMS and insulin pump for high-dose injection.
  • the specific steps are as follows:
  • Step A establish a regression equation
  • BGbefore is the blood glucose level before high-dose injection, the same as BGcurrent in the formula
  • BGafter is the measured blood glucose value after a large dose injection for a period of time
  • step B the following data near the high-injection injection time Tstart is obtained from the insulin pump and the CGMS through the smartphone:
  • the data in the last three to six months is used for regression, and the subscript number n of the historical data variable is arranged in reverse order according to Tstart, that is, the closer to the current historical data, the smaller the serial number is;
  • Step C build a sample matrix
  • TAu is the upper limit allowed by the TA
  • TAl is the lower limit allowed by the TA
  • Step F reject the abnormal data: calculate the residual: The data items whose residuals are greater than the threshold are eliminated, and then the regression algorithms A to F are repeated until there is no data item whose residual is greater than the threshold;
  • Step G calculating the updated physiological parameters IS, CR, TA according to the regression algorithm result:
  • Step H finally, get with Correcting the currently set IS, CR, and TA with a certain correction ratio ⁇ , the value range of ⁇ is 0 ⁇ 1,
  • TAl: TA ⁇ %, where 0 ⁇ 100;
  • TAu: TA ⁇ %, where 100 ⁇ 150;
  • the cloud big data server can optimize the value of the physiological parameter GR in different time periods and the corresponding basic infusion rate BASAL by collecting real-time data obtained by the user using the CGMS and the insulin pump in real time, and the specific steps are as follows:
  • Step A firstly segment the 24 hours a day according to the basic infusion rate set by the user with reference to the doctor's suggestion and his own situation, and the GR and BASAL values in each time period need to be independently set and calculated, for each time period and time period.
  • the data of 2 hours after the occurrence of eating or high-dose injection needs to be excluded from this time period, and the data of this time period is updated to only Data containing a longer continuous time remaining after 2 hours of eating/high-dose injections in the time period;
  • Step B collecting sample data in each valid time period:
  • Tstart the start time of the period
  • BGstart the average blood glucose level in the previous period of the time period
  • BGend Average blood glucose in the last short period of time
  • the data of the same period in the last three to six months is used for regression, and the subscript number n of the historical data variable is arranged in reverse order according to Tstart, that is, the closer to the current historical data, the smaller the serial number is;
  • Step C for each effective time period, consider the effect of ingesting insulin, during which the body releases the total amount of glucose ⁇ BG into the blood through metabolism:
  • Step D using the regression method to calculate the updated value of the GR for each valid time period
  • T' n Tcurrent-Tstart n and Tcurrent is the current time, that is, the time Tstart 1 of the most recent historical data;
  • Step E will get Correct the currently set GR with a certain correction ratio ⁇
  • ⁇ ranges from 0 ⁇ 1;
  • Step F using the modified GR and the historical sample packet of the simultaneous segment
  • Step G all calculated Perform time weighting to calculate the current BASAL correction value
  • w'(T') is a time-dependent weight, and the closer the sample is to the current time, the greater the weight
  • Step H if calculated If the difference from the current BASAL value exceeds the threshold, it will be obtained Correct the currently set BASAL with a certain correction ratio ⁇ :
  • ⁇ ranges from 0 ⁇ 1;
  • BASAL as a setting parameter for the insulin pump base injection rate, and store it with the physiological parameter GR to the cloud big data server and push it to the mobile phone application and insulin pump.
  • the cloud big data server can optimize the basic infusion rate BASAL for different time periods by collecting real-time data obtained by the user using the CGMS and the insulin pump in real time, and the specific steps are as follows:
  • Step A firstly segment the 24 hours a day according to the basic infusion rate set by the user with reference to the doctor's suggestion and his own situation, and the BASAL value in each time period needs to be independently set and calculated, for each time period and before the time period.
  • the data for 2 hours after the occurrence of a meal or high-dose injection needs to be excluded from this time period, and the data of this time period is updated to include only time. Data for the longer continuous time remaining after 2 hours of eating/dilution injection in the segment;
  • Step B collecting sample data in each valid time period:
  • Tstart the start time of the period
  • BGstart the average blood glucose level in the previous period of the time period
  • BGend Average blood glucose in the last short period of time
  • Step C for the nth time period, using the historical sample data packet of the time period to calculate the corrected time period according to the formula value:
  • Step D all calculated Perform time weighting to calculate the current BASAL correction value
  • w'(T') is a time-dependent weight, and the closer the sample is to the current time, the greater the weight
  • Step E if calculated If the difference from the current BASAL value exceeds the threshold, it will be obtained Correct the currently set BASAL with a certain correction ratio ⁇ :
  • ⁇ ranges from 0 ⁇ 1.
  • An optimization method for individualized configuration of insulin pump based on cloud big data comprising the following steps:
  • Step 1 After the system is started, the smart phone application obtains the cloud big data server data to determine whether the user is using the insulin injection system for the first time, and if so, prompts the user to set the parameters according to the doctor's order, IS, CR, TA, GR, time division Segment and base injection rate or continue with the default settings; if not, download the updated above parameters from the cloud big data server;
  • Step 2 if the user manually inputs the high-dose injection command, enter the high-dose mode, otherwise the insulin pump is in the basic injection mode;
  • Step 3 In the basic injection mode, the insulin injection is performed according to the preset current time period basis rate, and the blood glucose data monitored by the CGMS is periodically uploaded to the cloud server; after the time period is finished or the user performs the operation of the insulin pump Whether there is an update of the cloud big data server GR and the base rate, if yes, update the local storage parameters, and then repeat step two, if not, repeat step two directly;
  • Step 4 In the high-dose mode, the insulin pump prompts the user to manually input the amount of carbohydrates to be ingested by the smartphone application and confirms the target blood glucose level to be reached, and obtains the current blood glucose value BGcurrent measured by the CGMS;
  • Step 5 Calculate the required high-dose injection volume using the previously set or obtained parameter values:
  • BOLUS CARBS/CR+(BGcurrent-BGtarget)/IS-BOLUSprev[1-min(TI,TA)/TA];
  • Step 6 prompting the patient to confirm the infusion volume and the large dose infusion time, calculate the injection stop time
  • T BOLUS infusion volume / bolus-rate, bolus-rate is user-defined insulin high-dose infusion rate
  • Step 7 Upload the insulin injection information Tstart, Tend, BOLUS, CARBS and CGMS blood glucose monitoring data to the cloud big data server;
  • Step 8 Perform a high dose injection until the Tend time is reached
  • Step IX detecting whether there is an update of the physiological parameter in the cloud, if yes, updating the local storage parameter, and then repeating step two, if not, repeating step two directly.
  • the present invention proposes an insulin pump individualized configuration optimization system including a smart phone, a cloud big data server, a real-time dynamic blood glucose monitoring system and an insulin pump.
  • the invention also establishes a set of algorithms to establish a regression equation between blood glucose fluctuation, carbohydrate intake and historical data of insulin injection records and calculate physiological parameters related to diabetes of the user, and correct in real time according to the update of the data. These parameters are recalculated for the required insulin injection rate. Because the physiological characteristics and course of disease development of each diabetic patient are different, the individual differences are huge, so the insulin injection method required by each person is also very different.
  • the system constructed by the present invention can effectively calculate the optimal insulin injection amount and injection rate of each user by the blood glucose measurement history data of the user stored in the cloud, and assist the doctor and the patient to formulate a more effective diabetes treatment plan.
  • the invention realizes the automatic updating of the insulin pump setting according to the user historical data, so that the patient user can know his or her condition in time, and can complete the adjustment of the diabetes treatment plan and provide real-time feedback information without registering the medical treatment.
  • the present invention alleviates the psychological anxiety of the patient's poor control of the condition and the need to manage the life of the pump in a timely manner, while saving the time and expense for the patient to frequently visit.
  • the cloud big data server of the present invention can archive individualized parameters related to diabetes and blood glucose historical data, insulin infusion historical data, and form an analysis report for medical reference, so as to plan the patient to continue the treatment plan after insulin pump therapy. .
  • FIG. 1 is a schematic structural view of a system of the present invention.
  • FIG. 2 is a schematic flow chart of the method of the present invention.
  • a cloud-based big data-based insulin pump individualized configuration optimization system including insulin pump, real-time dynamic blood glucose monitoring system (CGMS), smart phone and blood glucose monitoring application software installed in smart phones, large cloud Data server.
  • CGMS real-time dynamic blood glucose monitoring system
  • smart phone smart phone
  • blood glucose monitoring application software installed in smart phones
  • large cloud Data server large cloud Data server
  • the insulin pump includes a syringe pump body with a control module and a wireless transmission module, a replaceable drug reservoir and a subcutaneous indwelling needle; a wireless transmission module of the insulin pump and a smartphone are wirelessly coupled via a wireless communication such as Bluetooth communication and with a blood glucose monitoring application Transfer data to each other.
  • the insulin pump can only transmit the insulin injection record and time information to the smartphone blood glucose monitoring application software and execute the command of the high dose and the basic infusion rate issued by the blood glucose monitoring application software.
  • the storage and cloud data synchronization and update of related parameters IS, CR, TA, GR are implemented in the mobile phone blood glucose monitoring application software, that is, the mobile phone blood glucose monitoring application software replaces the insulin pump. Control module to process data.
  • the real-time dynamic blood glucose monitoring system includes a replaceable implanted glucose sensor probe, a reusable signal collector and a signal transmitter; the signal transmitter of the real-time dynamic blood glucose monitoring system is wirelessly coupled to the smartphone via a wireless communication such as Bluetooth communication.
  • the blood glucose monitoring application transmits data to each other.
  • the smartphone and the blood glucose monitoring application installed in the smartphone have data transmission through wireless transmission technology such as Bluetooth and real-time dynamic blood glucose monitoring system and insulin pump, and data uploading and downloading through the mobile data network or wireless network and the cloud big data server.
  • wireless transmission technology such as Bluetooth and real-time dynamic blood glucose monitoring system and insulin pump
  • the function. Smartphones can also be other smart devices.
  • the Cloud Big Data Server has the functions of storing, updating, calculating and transmitting user personal information and historical data.
  • User personal information and historical data stored by the Cloud Big Data Server include, but are not limited to, user name, gender, age, contact number, insulin pump product serial number used, insulin pump infusion dose, time and infusion rate record, blood glucose output value BG and corresponding data measure time and date Ts, the individual's recorded carbohydrate intake, sleep and exercise.
  • the cloud big data server calculates the individualized parameters related to diabetes according to the stored user historical data, and automatically corrects and outputs the parameter output data of the insulin pump and the implanted glucose sensor to the smart phone, and the parameters include Not limited to the unit carbohydrate amount of 1 unit of insulin conversion CR, insulin sensitivity index IS, insulin retention time TA, the rate at which the user releases glucose into the blood through metabolism during fasting, a single large dose injection BOLUS, basic loss Note rate BASAL and so on.
  • the insulin pump can download the latest user parameters from the cloud big data server through the smartphone, and then calculate the recommended insulin high-dose injection plan based on the user's input of carbohydrate intake, and recommend the user according to the user's time segmentation of the basic infusion rate. Updated basic infusion rate plan.
  • the cloud big data server can archive the individualized parameters related to diabetes and blood glucose historical data, insulin infusion historical data and form an analysis report for the doctor to refer to in order to plan the patient to continue the treatment after insulin pump therapy.
  • GR Glucose Release Rate, the rate at which the body releases glucose into the blood through metabolism during fasting.
  • BASAL Basic infusion rate, usually counted in insulin units per hour (U/h)
  • BGcurrent is the blood glucose value before the high-dose injection of CGMS
  • BGtarget is the target blood glucose level
  • BOLUSprev is the last large-dose injection
  • CARBS is the current carbohydrate intake entered by the user
  • TI is the largest The distance from the midpoint of the last high-dose injection, min (TI, TA) takes the smaller value in TI, TA, so that when TI is greater than or equal to TA, the last large dose of residual Is 0;
  • the BASAL infusion rate is calculated during the fasting period of time t:
  • BGstart is the average blood glucose for a period of time at the beginning of the fasting period of the CGMS reading
  • TI is the time from the midpoint of the last high-dose injection at the beginning of the period.
  • the cloud big data server can optimize its physiological parameters CR, IS, TA by real-time collecting real-time data obtained by users using CGMS and insulin pump for high-dose injection.
  • the specific steps are as follows:
  • Step A establish a regression equation
  • BGbefore is the blood glucose level before high-dose injection, the same as BGcurrent in the formula
  • BGafter is the measured blood glucose value after a large dose injection for a period of 2 hours after a large dose injection;
  • step B the following data near the high-injection injection time Tstart is obtained from the insulin pump and the CGMS through the smartphone:
  • the blood glucose level BGafter measured by the implanted dynamic glucose sensor after Tend (for example, 2 hours after Tstart)
  • the data in the last three to six months is used for regression, and the subscript number n of the historical data variable is arranged in reverse order according to Tstart, that is, the closer to the current historical data, the smaller the serial number is;
  • Step C build a sample matrix
  • TAu is the upper limit allowed by the TA
  • TAl is the lower limit allowed by the TA
  • Step F reject the abnormal data: calculate the residual: The data items whose residuals are greater than the threshold are eliminated, and then the regression algorithms A to F are repeated until there is no data item whose residual is greater than the threshold;
  • Step G calculating the updated physiological parameters IS, CR, TA according to the regression algorithm result:
  • Step H finally, get with Correcting the currently set IS, CR, and TA with a certain correction ratio ⁇ , the value range of ⁇ is 0 ⁇ 1,
  • TAl: TA ⁇ %, where 0 ⁇ 100;
  • TAu: TA ⁇ %, where 100 ⁇ 150;
  • the cloud big data server can optimize the value of its physiological parameter GR in different time periods and the corresponding basic infusion rate BASAL by collecting real-time data obtained by users using CGMS and insulin pump in real time.
  • the specific steps are as follows:
  • Step A firstly segment the 24 hours a day according to the basic infusion rate set by the user with reference to the doctor's suggestion and his own situation, and the GR and BASAL values in each time period need to be independently set and calculated, for each time period and time period.
  • the data of 2 hours after the occurrence of eating or high-dose injection needs to be excluded from this time period.
  • the data of this time period is updated to only Contains data for a longer continuous time after 2 hours of removal of the fed/high dose injection in the time period; for example, if the time period is set to: (1) 6:00-13:00; (2) 13:00-20: 00; (3) 20:00 - 6:00 the next day, the user eats and injects insulin at 7:00, 12:00, 18:00, then the time period (1) is adjusted to 9:00-12:00; 2) Adjusted to 14:00-18:00; time period (3) 20:00 - next day 6:00 remains unchanged;
  • Step B collecting sample data in each valid time period:
  • Tstart the start time of the period
  • BGstart the average blood glucose level in the previous short period of time (for example, 15 minutes)
  • BGend Average blood glucose in the last short period of time (eg 15 minutes)
  • the data of the same period in the last three to six months is used for regression, and the subscript number n of the historical data variable is arranged in reverse order according to Tstart, that is, the closer to the current historical data, the smaller the serial number is;
  • Step C for each effective time period, consider the effect of ingesting insulin, during which the body releases the total amount of glucose ⁇ BG into the blood through metabolism:
  • Step D using the regression method to calculate the updated value of the GR for each valid time period
  • T' n Tcurrent-Tstart n and Tcurrent is the current time, that is, the time Tstart 1 of the most recent historical data;
  • Step E will get Correct the currently set GR with a certain correction ratio ⁇
  • ⁇ ranges from 0 ⁇ 1;
  • Step F using the modified GR and the historical sample packet of the simultaneous segment
  • Step G all calculated Perform time weighting to calculate the current BASAL correction value
  • w'(T') is a time-dependent weight, and the closer the sample is to the current time, the greater the weight
  • Step H if calculated If the difference from the current BASAL value exceeds the threshold, it will be obtained Correct the currently set BASAL with a certain correction ratio ⁇ :
  • ⁇ ranges from 0 ⁇ 1;
  • BASAL as a setting parameter for the insulin pump base injection rate, and store it with the physiological parameter GR to the cloud big data server and push it to the mobile phone application and insulin pump.
  • the cloud big data server can optimize the basic infusion rate BASAL for different time periods by collecting real-time data obtained by users using CGMS and insulin pump in real time.
  • the specific steps are as follows:
  • Step A firstly segment the 24 hours a day according to the basic infusion rate set by the user with reference to the doctor's suggestion and his own situation, and the BASAL value in each time period needs to be independently set and calculated, for each time period and before the time period.
  • the data for 2 hours after the occurrence of a meal or high-dose injection needs to be excluded from this time period, and the data of this time period is updated to include only time. Data for the longer continuous time remaining after 2 hours of eating/dilution injection in the segment;
  • Step B collecting sample data in each valid time period:
  • Tstart the start time of the period
  • BGstart the average blood glucose level in the previous period of the time period
  • BGend Average blood glucose in the last short period of time
  • Step C for the nth time period, using the historical sample data packet of the time period to calculate the corrected time period according to the formula value:
  • Step D all calculated Perform time weighting to calculate the current BASAL correction value
  • w'(T') is a time-dependent weight, and the closer the sample is to the current time, the greater the weight
  • Step E if calculated If the difference from the current BASAL value exceeds the threshold, it will be obtained Correct the currently set BASAL with a certain correction ratio ⁇ :
  • ⁇ ranges from 0 ⁇ 1.
  • An optimization method for individualized configuration of insulin pump based on cloud big data includes the following steps:
  • Step 1 After the system is started, the smart phone application obtains the cloud big data server data to determine whether the user is using the insulin injection system for the first time, and if so, prompts the user to set the parameters according to the doctor's order, IS, CR, TA, GR, time division Segment and base injection rate or continue with the default settings; if not, download the updated above parameters from the cloud big data server;
  • Step 2 if the user manually inputs a large dose injection command, enter the high dose mode, otherwise the insulin pump is in the basic injection mode;
  • Step 3 In the basic injection mode, the insulin injection is performed according to the preset current time period basis rate, and the blood glucose data monitored by the CGMS is periodically uploaded to the cloud server; after the time period is finished or the user performs the operation of the insulin pump Whether there is an update of the cloud big data server GR and the base rate, if yes, update the local storage parameters, and then repeat step two, if not, repeat step two directly;
  • Step 4 In the high-dose mode, the insulin pump prompts the user to manually input the amount of carbohydrates to be ingested by the smartphone application and confirms the target blood glucose level to be reached, and obtains the current blood glucose value BGcurrent measured by the CGMS;
  • Step 5 Calculate the required high-dose injection volume using the previously set or obtained parameter values:
  • BOLUS CARBS/CR+(BGcurrent-BGtarget)/IS-BOLUSprev[1-min(TI,TA)/TA];
  • Step 6 prompting the patient to confirm the infusion volume and the large dose infusion time, calculate the injection stop time
  • T BOLUS infusion volume / bolus-rate, bolus-rate is user-defined insulin high-dose infusion rate
  • Step 7 Upload the insulin injection information Tstart, Tend, BOLUS, CARBS and CGMS blood glucose monitoring data to the cloud big data server;
  • Step 8 Perform a high dose injection until the Tend time is reached
  • Step IX detecting whether there is an update of the physiological parameter in the cloud, if yes, updating the local storage parameter, and then repeating step two, if not, repeating step two directly.

Abstract

一种基于云端大数据的胰岛素泵个体化配置优化系统及方法，该系统包括胰岛素泵、实时动态血糖监测系统、智能手机及安装在智能手机中的血糖监测应用软件、云端大数据服务器。胰岛素泵个体化配置优化系统可以通过在云端储存的用户个人的血糖测量历史数据有效地计算每个用户的个体最优胰岛素注射量和注射速率，辅助医生和患者制定更有效的糖尿病治疗方案。

Description

基于云端大数据的胰岛素泵个体化配置优化系统和方法 技术领域
本发明涉及智能医疗器械技术领域，具体涉及一种基于云端大数据的胰岛素泵个体化配置优化系统和方法。
背景技术
糖尿病是由于患者自身胰岛素缺失(一型糖尿病)或对胰岛素产生抗性或者胰岛素分泌速率降低(二型糖尿病)而造成的血液中糖分过高从而造成各种健康问题的疾病。所有的一型糖尿病患者和大部分中晚期二型糖尿病患者都需要从体外注射胰岛素来控制血糖。连续皮下胰岛素注射装置(CSII)，又称皮下胰岛素注射泵，是采用人工智能控制的胰岛素输入装置，通过持续皮下输注胰岛素的方式，模拟胰岛素的生理性分泌模式从而控制高血糖的一种胰岛素治疗设备。生理状态下胰岛素分泌按与进餐的关系可大致分为两部分：一是不依赖于进餐的持续微量分泌，即基础胰岛素分泌；二是由进餐后高血糖刺激引起的大量胰岛素分泌。为模拟生理性胰岛素分泌，胰岛素泵通过人工智能控制，以可调节的脉冲式皮下输注方式进行基础率胰岛素注射，同时在进餐时，由患者自身根据食物种类和总量来设定餐前大剂量胰岛素及输注模式以控制餐后血糖。临床研究证实，与多次胰岛素注射相比，胰岛素泵可以更有效地控制糖化血红蛋白的水平，同时还改善了患者的生活质量。
胰岛素注射泵具有如下特点：
(1)更有利于精确、平稳地控制血糖，减少血糖波动、明显减少低血糖发生的风险、减少胰岛素吸收的变异。
(2)提高患者生活质量。胰岛素泵的使用可提高患者对治疗的依从性，减少多次皮下注射胰岛素给糖尿病患者带来的痛苦和不便；增加糖尿病患者进食、运动的自由度；提高患者自我血糖管理能力；减轻糖尿病患者心理负担。
不同于使用长效胰岛素注射针和速效胰岛素注射针的每次定量注射，CSII胰岛素注射泵的剂量和输注速率可以随时由患者进行调整。在生理状态下，人体内的血糖与胰岛素水平处于不断变化的动态平衡过程中，且会受多种因素的影响，如何确定胰岛素注射泵的剂量一直是糖尿病治疗的研究重点之一。2009版 中国胰岛素泵治疗指南推荐的输注方案可以概括为：首先应根据患者糖尿病分型、血糖水平以及体重情况确定初始剂量并分配到基础输注量和餐前大剂量中，再根据病人实际情况确定补充大剂量和校正大剂量以综合控制患者血糖。
在胰岛素注射泵具体实施过程中，基础输注量，餐前、补充及校正大剂量、以及基础输注率所采取的时间段多根据规范或经验来设定。其中，基础输注量是指维持机体基础血糖代谢所需的胰岛素量，可以通过调节基础输注率及相应的时间段来调节基础输注量对于患者的作用。基础输注率是指胰岛素泵提供基础胰岛素的速度，一般以胰岛素用量单位(U)/h表示，其设定模式较多，可根据血糖控制的需要设置为一个或多个时间段，临床大多分为3～6个时间段。相对T2DM患者，一般T1DM患者需要采用更多分段。在T2DM患者中，血糖波动值较大的所谓脆性DM患者的分段方法往往与普通患者有差异。餐前、补充及校正大剂量输注量是由患者在餐前餐后根据当前的碳水化合物摄入量和自身的生理参数，例如胰岛素-碳水化合物代谢比例和胰岛素敏感度等因素自行设定。
由于患者之间存在着巨大的个体差异并且随着每个人的病程发展不同，患者生理参数会持续发生改变，因此临床上往往难以确定并及时调整最适合患者的胰岛素输注量，从而实现精准、个体化的血糖调控。目前在糖尿病治疗领域一般认为，当医生具有丰富而专业的临床应用经验时，由实时动态血糖监测系统(CGMS)配合CSII构成的体外开环系统可以帮助医生通过CGMS动态血糖图谱指导为患者胰岛素泵剂量的细致调节，达到完美血糖控制。
实时动态血糖监测系统，一般称为CGMS，是通过皮下埋入的葡萄糖传感器全程连续地记录细胞间液的葡萄糖水平，从而反映的血糖变化。从CGMS获取的连续血糖信息可以作出血糖变化图供医生进行临床分析和诊断，全面了解病人血糖波动类型，这对血糖控制及糖尿病治疗有重大意义。
目前，相对于数量巨大的糖尿病患者，中国目前没有足够的有丰富经验的糖尿病专家医生为每位患者及时制定最佳疗法，因此使用人工智能算法支持的CGMS和CSII胰岛素泵系统可以辅助医生和患者完成一些简单的医疗决策是有必要的。例如，中国专利CN101254322A公开了一种基于模型预测控制的大剂量胰岛素全自动智能输注方法和装置，在实时动态血糖监测系统(CGMS)支持下，通过强跟踪滤波器在线检测饮食并输注初始大剂量胰岛素，故其主要关注点在于 餐前大剂量。然而由于考虑到可能会引起的低血糖风险，自动计算得出的大剂量胰岛素剂量对于用户而言很可能是不安全的。再如，中国专利CN103418053B公开了一种通过实时动态血糖监测系统(CGMS)的数据进行建模和仿真来辅助优化基础输注率的个体化胰岛素注射泵系统，但是其并不会对大剂量胰岛素注射有临床指导意义。又如，中国专利CN102500013A公开了一种便携式智能胰岛素泵及其控制模型，其通过动态监控用户的血糖水平，跟踪血糖变化量输注胰岛素治疗糖尿病。这一闭环的输注泵控制模型并未考虑到患者在餐后可能有的剧烈血糖波动，有着使用上的安全隐患。此外，上述这些发明都有参考数据量有限的不足，而且均采用的是当前时刻的配置调控方法，与历史数据无关，所以其调控结果无法反映患者本身的特性和病程发展，无法用于指导拆除胰岛素泵之后的用药。
发明内容
本发明目的是提供一种基于云端大数据的胰岛素泵个体化配置优化系统和方法，以解决现有技术的不足。
本发明采用以下技术方案：
一种基于云端大数据的胰岛素泵个体化配置优化系统，包括胰岛素泵、实时动态血糖监测系统、智能手机及安装在智能手机中的血糖监测应用软件、云端大数据服务器；
胰岛素泵包括带有控制模块和无线传输模块的注射泵本体、可更换的储药器和皮下留置针；胰岛素泵的无线传输模块与智能手机通过无线方式联接并与血糖监测应用软件相互传输数据；
实时动态血糖监测系统包括可更换的植入式葡萄糖传感器探头、可重复使用的信号采集器和信号发射器；实时动态血糖监测系统的信号发射器与智能手机通过无线方式联接并与血糖监测应用软件相互传输数据；
智能手机和安装在智能手机中的血糖监测应用软件具有通过无线传输技术与实时动态血糖监测系统和胰岛素泵进行数据传输，以及通过手机数据网络或无线网络与云端大数据服务器进行数据上传下载的功能；
云端大数据服务器具有用户个人信息和历史数据存储，更新，计算和传输的功能；
云端大数据服务器根据存储的用户历史数据计算用户与糖尿病相关的个体化参数，并对胰岛素泵和植入式葡萄糖传感器的参数输出数据进行自动修正计算并推送至智能手机中，所述参数包括1单位胰岛素转化的单位碳水化合物数量CR、胰岛素敏感指数IS、胰岛素存留时间TA、用户在禁食期间身体通过新陈代谢向血液中释放葡萄糖的速率GR、单次大剂量注射量BOLUS、基础输注率BASAL；
胰岛素泵可通过智能手机从云端大数据服务器下载最新的用户参数，然后根据用户输入的碳水化合物摄入量计算建议胰岛素大剂量注射方案，并根据用户对基础输注率的时间分段向用户建议更新过的基础输注率方案。
进一步地，云端大数据服务器储存的用户个人信息和历史数据包括用户姓名，性别，年龄，联系号码，所使用胰岛素泵产品序列号，胰岛素泵输注剂量、时间及输注速率记录，血糖输出值BG与相对应的数据测量时间日期Ts，患者个人所记录的碳水化合物摄入量、睡眠及运动情况。
进一步地，在云端大数据服务器中用户生理参数CR，IS，TA，GR以及胰岛素输注量的定义和计算方式如下：
CR：1单位胰岛素转化的单位碳水化合物数量
IS：胰岛素敏感指数
TA：胰岛素存留时间
GR：用户在禁食期间身体通过新陈代谢向血液中释放葡萄糖的速率
BOLUS：单次大剂量注射量
BASAL：基础输注率，通常以胰岛素单位/小时(U/h)进行计数
根据给定的CR，IS，TA，则BOLUS注射剂量的计算公式为：
Figure PCTCN2018109282-appb-000001
其中，BGcurrent是CGMS读取的大剂量注射前的血糖值；BGtarget是目标血糖值；BOLUSprev为上一次大剂量的注射量；CARBS为用户所输入的当前碳水化合物摄入量；TI为本次大剂量距离上次大剂量注射过程中点的时间，min(TI，TA)取TI，TA中的较小值，使得当TI大于或等于TA时，上一次大剂量的残留量
Figure PCTCN2018109282-appb-000002
为0；
根据给定的GR，则在时长为t的禁食期间BASAL输注率的计算方式为：
Figure PCTCN2018109282-appb-000003
其中BGstart是CGMS读取的禁食时间段开始时一段时间的平均血糖，TI为时段开始时距离上次大剂量注射过程中点的时间。
进一步地，云端大数据服务器可通过实时收集用户使用CGMS和胰岛素泵进行大剂量注射所获得的实时数据来优化其生理参数CR，IS，TA，具体步骤如下：
步骤A，建立回归方程
Figure PCTCN2018109282-appb-000004
其中，BGbefore是大剂量注射前的血糖值，同计算公式中的BGcurrent；BGafter是大剂量注射一段时间后的实测血糖值；
步骤B，通过智能手机从胰岛素泵和CGMS获取提取每次大剂量注射时间Tstart附近的以下数据：
注射开始时间Tstart：胰岛素泵数据
注射结束时间Tend：胰岛素泵数据
Tstart时刻开始的大剂量注射量BOLUS：胰岛素泵数据
Tstart时刻植入式动态葡萄糖传感器测得的血糖值BGbefore
Tend一段时间后植入式动态葡萄糖传感器测得的血糖值BGafter
Tstart时刻附近的由用户输入的糖水化合物摄入量CARBS
形成用于计算的样本记录包
[Tstart n Tend n BOLUS n CARBS n BGbefore n BGafter n]
将最近三至六个月内的数据用于回归，历史数据变量的下标序号n根据Tstart逆序排列，即越接近当前的历史数据，序号越小；
步骤C，构建样本矩阵：
Figure PCTCN2018109282-appb-000005
其中，
ΔBG n＝BGafter n-BGbefore n，
当TI n＜TAl时，BOLUS′ n＝BOLUS n+BOLUS n+1，TI′ n＝TI n，TI n＝Tstart n-(Tstart n+1+Tend n+1)/2；
当TI n＞TAu时，BOLUS′ n＝BOLUS n，TI′ n＝0；
当TAl≤TI n≤TAu时，样本舍弃；
TAu是TA允许设置的上限，TAl是TA允许设置的下限；
步骤D，若对于所有n，X n，3＝0，则：
Figure PCTCN2018109282-appb-000006
否则样本矩阵保持不变；
步骤E，求解超定方程G＝XC，
用加权最小二乘法求解：
Figure PCTCN2018109282-appb-000007
步骤F，剔除异常数据：计算残差：
Figure PCTCN2018109282-appb-000008
剔除残差大于阈值的数据项，然后重复回归算法A～F，直至不存在残差大于阈值的数据项；
步骤G，根据回归算法结果计算更新的生理参数IS，CR，TA：
Figure PCTCN2018109282-appb-000009
Figure PCTCN2018109282-appb-000010
若存在
Figure PCTCN2018109282-appb-000011
则
Figure PCTCN2018109282-appb-000012
否则
Figure PCTCN2018109282-appb-000013
步骤H，最终，得到的
Figure PCTCN2018109282-appb-000014
和
Figure PCTCN2018109282-appb-000015
以一定的校正比例γ修正当前设定的IS、CR和TA，γ的取值范围为0＜γ＜1，
Figure PCTCN2018109282-appb-000016
Figure PCTCN2018109282-appb-000017
Figure PCTCN2018109282-appb-000018
作为现在起胰岛素泵大剂量注射的设置参数；
同时修订TAl和TAu：
TAl：＝TA×τ％，其中0＜τ＜100；
TAu：＝TA×υ％，其中100＜υ＜150；
储存并更新生理参数IS、CR、TA、TAl和TAu至云端大数据服务器并推送至手机应用和胰岛素泵。
进一步地，云端大数据服务器可通过实时收集用户使用CGMS和胰岛素泵所获得的实时数据来优化其生理参数GR在不同时间段的值，以及相应的基础输注率BASAL，具体步骤如下：
步骤A，首先根据用户参考医生建议和自身情况所制定的基础输注率对一天24小时进行分段，每个时间段中的GR与BASAL值需要独立设置和计算，对于每个时间段和时段之前的2小时内，如果出现了进食，伴随进食或未伴随进食的大剂量注射，进食或大剂量注射发生后的2个小时的数据需要从此时间段中排除，本时间段的数据更新为仅包含时间段中去除进食/大剂量注射2小时后剩余较长的连续时间的数据；
步骤B，在每一个有效的时间段中采集样本数据：
Tstart：该时段开始时间
BGstart：该时间段中前一小段时间的血糖平均值
BGend：最后一小段时间的血糖平均值
BASAL：该时间段基础输注率
t：该时间段的时长
IS：胰岛素敏感指数
TA：胰岛素存留时间
Figure PCTCN2018109282-appb-000019
时间段开始时的体内残留胰岛素
SNR：动态血糖数据信噪比
形成用于计算的样本记录包
[Tstart n BGstart n BGend n BASAL n t n RESIDUAL n SNR n]及系统参数IS和TA；
将最近三至六个月内同时间段的数据用于回归，历史数据变量的下标序号n根据Tstart逆序排列，即越接近当前的历史数据，序号越小；
步骤C，对每一个有效时间段，考虑摄入胰岛素的效用，该期间身体通过新陈代谢向血液中释放葡萄糖总量ΔBG：
ΔBG n＝BGend n-BGstart n+[BASAL n×t n+RESIDUAL n]×IS
建立回归方程ΔBG＝GR×t；
步骤D，对每一个有效时间段，使用回归法计算GR的更新值
Figure PCTCN2018109282-appb-000020
Figure PCTCN2018109282-appb-000021
其中，w(T′)为时间相关权重，其中，T′ n＝Tcurrent-Tstart n，Tcurrent为当前时间，即最近一个历史数据的时间Tstart 1；
距离最近一个样本越近，权重越大；
步骤E，将得到的
Figure PCTCN2018109282-appb-000022
以一定的校正比例γ修正当前设定的GR
Figure PCTCN2018109282-appb-000023
γ的取值范围为0＜γ＜1；
步骤F，使用修正后的GR和同时间段的历史样本数据包
[BGstart n t n RESIDUAL n]根据公式计算修正后的该时间段应设的
Figure PCTCN2018109282-appb-000024
值：
Figure PCTCN2018109282-appb-000025
步骤G，将计算得出的所有
Figure PCTCN2018109282-appb-000026
进行时间加权计算出当前的BASAL校正值
Figure PCTCN2018109282-appb-000027
Figure PCTCN2018109282-appb-000028
其中，w′(T′)为时间相关权重，样本距离时间当前越近，权重越大；
步骤H，若计算得出的
Figure PCTCN2018109282-appb-000029
与当前BASAL值的差超过阈值，则将得到的
Figure PCTCN2018109282-appb-000030
以一定的校正比例γ修正当前设定的BASAL：
Figure PCTCN2018109282-appb-000031
γ的取值范围为0＜γ＜1；
储存并更新BASAL作为胰岛素泵基础注射率的设置参数，并和生理参数GR一并储存至云端大数据服务器并推送至手机应用和胰岛素泵。
进一步地，云端大数据服务器可通过实时收集用户使用CGMS和胰岛素泵所获得的实时数据来优化不同时间段的的基础输注率BASAL，另一种方法具体步骤如下：
步骤A，首先根据用户参考医生建议和自身情况所制定的基础输注率对一天24小时进行分段，每个时间段中的BASAL值需要独立设置和计算，对于每个时间段和时段之前的2小时内，如果出现了进食，伴随进食或未伴随进食的大剂量注射，进食或大剂量注射发生后的2个小时的数据需要从此时间段中排除，本时间段的数据更新为仅包含时间段中去除进食/大剂量注射2小时后剩余较长的连续时间的数据；
步骤B，在每一个有效的时间段中采集样本数据：
Tstart：该时段开始时间
BGstart：该时间段中前一小段时间的血糖平均值
BGend：最后一小段时间的血糖平均值
BASAL：该时间段基础输注率
t：该时间段的时长
IS：胰岛素敏感指数
TA：胰岛素存留时间
Figure PCTCN2018109282-appb-000032
时间段开始时的体内残留胰岛素
形成用于计算的样本记录包
[Tstart n BGstart n BGend n BASAL n t n RESIDUAL n]及系统参数IS和TA；将最近三至六个月内同时间段的数据用于回归，历史数据变量的下标序号n根据Tstart逆序排列，即越接近当前的历史数据，序号越小；
步骤C，对于第n个时间段，使用该时间段的历史样本数据包根据公式计算修正后的该时间段应设的
Figure PCTCN2018109282-appb-000033
值：
Figure PCTCN2018109282-appb-000034
步骤D，将计算得出的所有
Figure PCTCN2018109282-appb-000035
进行时间加权计算出当前的BASAL校正值
Figure PCTCN2018109282-appb-000036
Figure PCTCN2018109282-appb-000037
其中，w′(T′)为时间相关权重，样本距离时间当前越近，权重越大；
步骤E，若计算得出的
Figure PCTCN2018109282-appb-000038
与当前BASAL值的差超过阈值，则将得到的
Figure PCTCN2018109282-appb-000039
以一定的校正比例γ修正当前设定的BASAL：
Figure PCTCN2018109282-appb-000040
γ的取值范围为0＜γ＜1。
一种基于云端大数据的胰岛素泵个体化配置优化方法，包括如下步骤：
步骤一，当系统启动后，由智能手机应用获取云端大数据服务器数据来判断用户是否是初次使用此胰岛素注射系统，如果是，则提示用户按医嘱设置参数IS、CR、TA、GR、时间分段以及基础注射率或使用默认设置继续；如果否，则从云端大数据服务器下载更新过的以上参数；
步骤二，如果用户手动输入大剂量注射命令则进入大剂量模式，否则胰岛素 泵处于基础注射模式；
步骤三，在基础注射模式中，按照预设的当前时间段基础率进行胰岛素注射，并定期将CGMS监测到的血糖数据上传至云端服务器；该时间段结束后或用户进行胰岛素泵的操作后检查是否存在云端大数据服务器GR和基础率的更新，如果是，则更新本地储存参数，然后重复步骤二，如果否，则直接重复步骤二；
步骤四，在大剂量模式中，胰岛素泵通过智能手机应用提示用户手动输入将摄入的碳水化合物量CARBS并确认需要达到的目标血糖值，同时获取CGMS所测得的当前血糖值BGcurrent；
步骤五，使用先前设置或获取的参数值计算所需大剂量注射量：
BOLUS＝CARBS/CR+(BGcurrent-BGtarget)/IS-BOLUSprev[1-min(TI，TA)/TA]；
步骤六，提示患者确认输注量和大剂量输注时间，计算注射停止时间
Tend＝Tstart+T BOLUS，其中T BOLUS＝输注量/bolus-rate，bolus-rate为用户定义的胰岛素大剂量输注速度；
步骤七，将胰岛素注射信息Tstart、Tend、BOLUS、CARBS和CGMS血糖监测数据上传至云端大数据服务器；
步骤八，执行大剂量注射直至到达Tend时间；
步骤九，检测是否存在生理参数在云端有更新，如果是，则更新本地储存参数，然后重复步骤二，如果否，则直接重复步骤二。
本发明的有益效果：
1、本发明提出了包含智能手机、云端大数据服务器、实时动态血糖监测系统和胰岛素泵的胰岛素泵个体化配置优化系统。本发明同时建立了一套算法，通过建立血糖波动，碳水化合物摄入量和胰岛素注射记录的历史数据之间的回归方程并计算出使用者与糖尿病相关的生理参数，并且根据数据的更新实时修正这些参数并重新计算所需要的胰岛素注射率。由于每个糖尿病患者的生理特征和病程发展不同，个体差异巨大，因此每个人所需要的胰岛素注射方式也有很大不同。本发明所构建的系统可以通过在云端储存的用户个人的血糖测量历史数据有效地计算每个用户的个体最优胰岛素注射量和注射速率，辅助医生和患者制定更有效的糖尿病治疗方案。
2、本发明实现了自动根据用户历史数据实时更新胰岛素泵的设定，可以使患者用户及时了解自身的病情，在无需挂号就医的情况下即可完成糖尿病治疗方案的调整并提供实时的反馈信息；由此本发明减轻了患者对病情控制不好的心理焦虑和需要及时管理泵设置的生活负担，同时节约了患者需要频繁就诊的时间和费用。
3、本发明云端大数据服务器可以对用户与糖尿病相关的个体化参数和血糖历史数据，胰岛素输注历史数据进行存档并形成分析报告供医生参考，以便计划患者脱离胰岛素泵疗法后的继续治疗方案。
附图说明
图1为本发明系统结构示意图。
图2为本发明方法流程示意图。
具体实施方式
下面结合实施例和附图对本发明做更进一步地解释。下列实施例仅用于说明本发明，但并不用来限定本发明的实施范围。
一种基于云端大数据的胰岛素泵个体化配置优化系统，如图1所示，包括胰岛素泵、实时动态血糖监测系统(CGMS)、智能手机及安装在智能手机中的血糖监测应用软件、云端大数据服务器。
胰岛素泵包括带有控制模块和无线传输模块的注射泵本体、可更换的储药器和皮下留置针；胰岛素泵的无线传输模块与智能手机通过诸如蓝牙通信等无线方式联接并与血糖监测应用软件相互传输数据。在手机与胰岛素泵的联接和数据传输时，也可以是胰岛素泵仅传输胰岛素注射记录和时间信息至智能手机血糖监测应用软件并执行血糖监测应用软件所发出的大剂量和基础输注率的命令；对BOLUS和BASA按下述算法的计算，与相关参数IS、CR、TA、GR的存储和云数据同步及更新均在手机血糖监测应用软件中实现，即手机血糖监测应用软件替代胰岛素泵的控制模块来进行数据的处理工作。
实时动态血糖监测系统包括可更换的植入式葡萄糖传感器探头、可重复使用的信号采集器和信号发射器；实时动态血糖监测系统的信号发射器与智能手机通过诸如蓝牙通信等无线方式联接并与血糖监测应用软件相互传输数据。
智能手机和安装在智能手机中的血糖监测应用软件具有通过蓝牙等无线传 输技术与实时动态血糖监测系统和胰岛素泵进行数据传输，以及通过手机数据网络或无线网络与云端大数据服务器进行数据上传下载的功能。智能手机也可以是其它智能设备。
云端大数据服务器具有用户个人信息和历史数据存储，更新，计算和传输的功能。云端大数据服务器储存的用户个人信息和历史数据包括但不限于用户姓名，性别，年龄，联系号码，所使用胰岛素泵产品序列号，胰岛素泵输注剂量、时间及输注速率记录，血糖输出值BG与相对应的数据测量时间日期Ts，患者个人所记录的碳水化合物摄入量、睡眠及运动情况等。
云端大数据服务器根据存储的用户历史数据计算用户与糖尿病相关的个体化参数，并对胰岛素泵和植入式葡萄糖传感器的参数输出数据进行自动修正计算并推送至智能手机中，所述参数包括但不限于1单位胰岛素转化的单位碳水化合物数量CR、胰岛素敏感指数IS、胰岛素存留时间TA、用户在禁食期间身体通过新陈代谢向血液中释放葡萄糖的速率GR、单次大剂量注射量BOLUS、基础输注率BASAL等。
胰岛素泵可通过智能手机从云端大数据服务器下载最新的用户参数，然后根据用户输入的碳水化合物摄入量计算建议胰岛素大剂量注射方案，并根据用户对基础输注率的时间分段向用户建议更新过的基础输注率方案。
云端大数据服务器可以对用户与糖尿病相关的个体化参数和血糖历史数据，胰岛素输注历史数据进行存档并形成分析报告供医生参考，以便计划患者脱离胰岛素泵疗法后的继续治疗方案。
在云端大数据服务器中用户生理参数CR，IS，TA，GR以及胰岛素输注量的定义和计算方式如下：
CR：Carbohydrate Ratio，1单位胰岛素转化的单位碳水化合物数量
IS：insulin sensitivity，胰岛素敏感指数
TA：active insulin time，胰岛素存留时间
GR：Glucose Release Rate，用户在禁食期间身体通过新陈代谢向血液中释放葡萄糖的速率
BOLUS：单次大剂量注射量
BASAL：基础输注率，通常以胰岛素单位/小时(U/h)进行计数
根据给定的CR，IS，TA，则BOLUS注射剂量的计算公式为：
Figure PCTCN2018109282-appb-000041
其中，BGcurrent是CGMS读取的大剂量注射前的血糖值；BGtarget是目标血糖值；BOLUSprev为上一次大剂量的注射量；CARBS为用户所输入的当前碳水化合物摄入量；TI为本次大剂量距离上次大剂量注射过程中点的时间，min(TI，TA)取TI，TA中的较小值，使得当TI大于或等于TA时，上一次大剂量的残留量
Figure PCTCN2018109282-appb-000042
为0；
根据给定的GR，则在时长为t的禁食期间BASAL输注率的计算方式为：
Figure PCTCN2018109282-appb-000043
其中BGstart是CGMS读取的禁食时间段开始时一段时间的平均血糖，TI为时段开始时距离上次大剂量注射过程中点的时间。
云端大数据服务器可通过实时收集用户使用CGMS和胰岛素泵进行大剂量注射所获得的实时数据来优化其生理参数CR，IS，TA，具体步骤如下：
步骤A，建立回归方程
Figure PCTCN2018109282-appb-000044
其中，BGbefore是大剂量注射前的血糖值，同计算公式中的BGcurrent；BGafter是大剂量注射一段时间后如大剂量注后射2小时的实测血糖值；
步骤B，通过智能手机从胰岛素泵和CGMS获取提取每次大剂量注射时间Tstart附近的以下数据：
注射开始时间Tstart：胰岛素泵数据
注射结束时间Tend：胰岛素泵数据
Tstart时刻开始的大剂量注射量BOLUS：胰岛素泵数据
Tstart时刻植入式动态葡萄糖传感器测得的血糖值BGbefore
Tend一段时间后植入式动态葡萄糖传感器测得的血糖值BGafter(如Tstart后2小时)
Tstart时刻附近的由用户输入的糖水化合物摄入量CARBS
形成用于计算的样本记录包
[Tstart n Tend n BOLUS n CARBS n BGbefore n BGafter n]
将最近三至六个月内的数据用于回归，历史数据变量的下标序号n根据Tstart逆序排列，即越接近当前的历史数据，序号越小；
步骤C，构建样本矩阵：
Figure PCTCN2018109282-appb-000045
其中，
ΔBG n＝BGafter n-BGbefore n，
当TI n＜TAl时，BOLUS′ n＝BOLUS n+BOLUS n+1，TI′ n＝TI n，TI n＝Tstart n-(Tstart n+1+Tend n+1)/2；
当TI n＞TAu时，BOLUS′ n＝BOLUS n，TI′ n＝0；
当TAl≤TI n≤TAu时，样本舍弃；
TAu是TA允许设置的上限，TAl是TA允许设置的下限；
步骤D，若对于所有n，X n，3＝0，则：
Figure PCTCN2018109282-appb-000046
否则样本矩阵保持不变；
步骤E，求解超定方程G＝XC，
用加权最小二乘法求解：
步骤F，剔除异常数据：计算残差：
Figure PCTCN2018109282-appb-000048
剔除残差大于阈值的数据项，然后重复回归算法A～F，直至不存在残差大于阈值的数据项；
步骤G，根据回归算法结果计算更新的生理参数IS，CR，TA：
Figure PCTCN2018109282-appb-000049
Figure PCTCN2018109282-appb-000050
若存在
Figure PCTCN2018109282-appb-000051
则
Figure PCTCN2018109282-appb-000052
否则
Figure PCTCN2018109282-appb-000053
步骤H，最终，得到的
Figure PCTCN2018109282-appb-000054
和
Figure PCTCN2018109282-appb-000055
以一定的校正比例γ修正当前设定的IS、CR和TA，γ的取值范围为0＜γ＜1，
Figure PCTCN2018109282-appb-000056
Figure PCTCN2018109282-appb-000057
Figure PCTCN2018109282-appb-000058
作为现在起胰岛素泵大剂量注射的设置参数；
同时修订TAl和TAu：
TAl：＝TA×τ％，其中0＜τ＜100；
TAu：＝TA×υ％，其中100＜υ＜150；
储存并更新生理参数IS、CR、TA、TAl和TAu至云端大数据服务器并推送至手机应用和胰岛素泵。
云端大数据服务器可通过实时收集用户使用CGMS和胰岛素泵所获得的实时数据来优化其生理参数GR在不同时间段的值，以及相应的基础输注率BASAL，具体步骤如下：
步骤A，首先根据用户参考医生建议和自身情况所制定的基础输注率对一天24小时进行分段，每个时间段中的GR与BASAL值需要独立设置和计算，对于每个时间段和时段之前的2小时内，如果出现了进食，伴随进食或未伴随进食的大剂量注射，进食或大剂量注射发生后的2个小时的数据需要从此时间段中排除， 本时间段的数据更新为仅包含时间段中去除进食/大剂量注射2小时后剩余较长的连续时间的数据；例如，若时间段设置为：(1)6：00-13：00；(2)13：00-20：00；(3)20：00-次日6：00，用户于7：00，12：00，18：00进食并注射胰岛素，则时段(1)调整为9：00-12：00；时段(2)调整为14：00-18：00；时段(3)20：00-次日6：00保持不变；
步骤B，在每一个有效的时间段中采集样本数据：
Tstart：该时段开始时间
BGstart：该时间段中前一小段时间(例如15分钟)的血糖平均值
BGend：最后一小段时间(例如15分钟)的血糖平均值
BASAL：该时间段基础输注率
t：该时间段的时长
IS：insulin sensitivity，胰岛素敏感指数
TA：active insulin time，胰岛素存留时间
Figure PCTCN2018109282-appb-000059
时间段开始时的体内残留胰岛素
SNR：动态血糖数据信噪比
形成用于计算的样本记录包
[Tstart n BGstart n BGend n BASAL n t n RESIDUAL n SNR n]及系统参数IS和TA；
将最近三至六个月内同时间段的数据用于回归，历史数据变量的下标序号n根据Tstart逆序排列，即越接近当前的历史数据，序号越小；
步骤C，对每一个有效时间段，考虑摄入胰岛素的效用，该期间身体通过新陈代谢向血液中释放葡萄糖总量ΔBG：
ΔBG n＝BGend n-BGstart n+[BASAL n×t n+RESIDUAL n]×IS
建立回归方程ΔBG＝GR×t；
步骤D，对每一个有效时间段，使用回归法计算GR的更新值
Figure PCTCN2018109282-appb-000060
Figure PCTCN2018109282-appb-000061
其中，w(T′)为时间相关权重，其中，T′ n＝Tcurrent-Tstart n，Tcurrent为当前时间，即最近一个历史数据的时间Tstart 1；
距离最近一个样本越近，权重越大；例如：
Figure PCTCN2018109282-appb-000062
步骤E，将得到的
Figure PCTCN2018109282-appb-000063
以一定的校正比例γ修正当前设定的GR
Figure PCTCN2018109282-appb-000064
γ的取值范围为0＜γ＜1；
步骤F，使用修正后的GR和同时间段的历史样本数据包
[BGstart n t n RESIDUAL n]根据公式计算修正后的该时间段应设的
Figure PCTCN2018109282-appb-000065
值：
Figure PCTCN2018109282-appb-000066
步骤G，将计算得出的所有
Figure PCTCN2018109282-appb-000067
进行时间加权计算出当前的BASAL校正值
Figure PCTCN2018109282-appb-000068
Figure PCTCN2018109282-appb-000069
其中，w′(T′)为时间相关权重，样本距离时间当前越近，权重越大；
例如：
Figure PCTCN2018109282-appb-000070
步骤H，若计算得出的
Figure PCTCN2018109282-appb-000071
与当前BASAL值的差超过阈值，则将得到的
Figure PCTCN2018109282-appb-000072
以一定的校正比例γ修正当前设定的BASAL：
Figure PCTCN2018109282-appb-000073
γ的取值范围为0＜γ＜1；
储存并更新BASAL作为胰岛素泵基础注射率的设置参数，并和生理参数GR一并储存至云端大数据服务器并推送至手机应用和胰岛素泵。
云端大数据服务器可通过实时收集用户使用CGMS和胰岛素泵所获得的实时数据来优化不同时间段的的基础输注率BASAL，另一种方法具体步骤如下：
步骤A，首先根据用户参考医生建议和自身情况所制定的基础输注率对一天24小时进行分段，每个时间段中的BASAL值需要独立设置和计算，对于每个时间段和时段之前的2小时内，如果出现了进食，伴随进食或未伴随进食的大剂量注射，进食或大剂量注射发生后的2个小时的数据需要从此时间段中排除，本时间段的数据更新为仅包含时间段中去除进食/大剂量注射2小时后剩余较长的连续时间的数据；
步骤B，在每一个有效的时间段中采集样本数据：
Tstart：该时段开始时间
BGstart：该时间段中前一小段时间的血糖平均值
BGend：最后一小段时间的血糖平均值
BASAL：该时间段基础输注率
t：该时间段的时长
IS：胰岛素敏感指数
TA：胰岛素存留时间
Figure PCTCN2018109282-appb-000074
时间段开始时的体内残留胰岛素
形成用于计算的样本记录包
[Tstart n BGstart n BGend n BASAL n t n RESIDUAL n]及系统参数IS和TA；将最近三至六个月内同时间段的数据用于回归，历史数据变量的下标序号n根据Tstart逆序排列，即越接近当前的历史数据，序号越小；
步骤C，对于第n个时间段，使用该时间段的历史样本数据包根据公式计算修正后的该时间段应设的
Figure PCTCN2018109282-appb-000075
值：
Figure PCTCN2018109282-appb-000076
步骤D，将计算得出的所有
Figure PCTCN2018109282-appb-000077
进行时间加权计算出当前的BASAL校正 值
Figure PCTCN2018109282-appb-000078
Figure PCTCN2018109282-appb-000079
其中，w′(T′)为时间相关权重，样本距离时间当前越近，权重越大；
步骤E，若计算得出的
Figure PCTCN2018109282-appb-000080
与当前BASAL值的差超过阈值，则将得到的
Figure PCTCN2018109282-appb-000081
以一定的校正比例γ修正当前设定的BASAL：
Figure PCTCN2018109282-appb-000082
γ的取值范围为0＜γ＜1。
一种基于云端大数据的胰岛素泵个体化配置优化方法，如图2所示，包括如下步骤：
步骤一，当系统启动后，由智能手机应用获取云端大数据服务器数据来判断用户是否是初次使用此胰岛素注射系统，如果是，则提示用户按医嘱设置参数IS、CR、TA、GR、时间分段以及基础注射率或使用默认设置继续；如果否，则从云端大数据服务器下载更新过的以上参数；
步骤二，如果用户手动输入大剂量注射命令则进入大剂量模式，否则胰岛素泵处于基础注射模式；
步骤三，在基础注射模式中，按照预设的当前时间段基础率进行胰岛素注射，并定期将CGMS监测到的血糖数据上传至云端服务器；该时间段结束后或用户进行胰岛素泵的操作后检查是否存在云端大数据服务器GR和基础率的更新，如果是，则更新本地储存参数，然后重复步骤二，如果否，则直接重复步骤二；
步骤四，在大剂量模式中，胰岛素泵通过智能手机应用提示用户手动输入将摄入的碳水化合物量CARBS并确认需要达到的目标血糖值，同时获取CGMS所测得的当前血糖值BGcurrent；
步骤五，使用先前设置或获取的参数值计算所需大剂量注射量：
BOLUS＝CARBS/CR+(BGcurrent-BGtarget)/IS-BOLUSprev[1-min(TI，TA)/TA]；
步骤六，提示患者确认输注量和大剂量输注时间，计算注射停止时间
Tend＝Tstart+T BOLUS，其中T BOLUS＝输注量/bolus-rate，bolus-rate为用户定义的 胰岛素大剂量输注速度；
步骤七，将胰岛素注射信息Tstart、Tend、BOLUS、CARBS和CGMS血糖监测数据上传至云端大数据服务器；
步骤八，执行大剂量注射直至到达Tend时间；
步骤九，检测是否存在生理参数在云端有更新，如果是，则更新本地储存参数，然后重复步骤二，如果否，则直接重复步骤二。

Claims (7)

  1. 一种基于云端大数据的胰岛素泵个体化配置优化系统，其特征在于，包括胰岛素泵、实时动态血糖监测系统、智能手机及安装在智能手机中的血糖监测应用软件、云端大数据服务器；
    胰岛素泵包括带有控制模块和无线传输模块的注射泵本体、可更换的储药器和皮下留置针；胰岛素泵的无线传输模块与智能手机通过无线方式联接并与血糖监测应用软件相互传输数据；
    实时动态血糖监测系统包括可更换的植入式葡萄糖传感器探头、可重复使用的信号采集器和信号发射器；实时动态血糖监测系统的信号发射器与智能手机通过无线方式联接并与血糖监测应用软件相互传输数据；
    智能手机和安装在智能手机中的血糖监测应用软件具有通过无线传输技术与实时动态血糖监测系统和胰岛素泵进行数据传输，以及通过手机数据网络或无线网络与云端大数据服务器进行数据上传下载的功能；
    云端大数据服务器具有用户个人信息和历史数据存储，更新，计算和传输的功能；
    云端大数据服务器根据存储的用户历史数据计算用户与糖尿病相关的个体化参数，并对胰岛素泵和植入式葡萄糖传感器的参数输出数据进行自动修正计算并推送至智能手机中，所述参数包括1单位胰岛素转化的单位碳水化合物数量CR、胰岛素敏感指数IS、胰岛素存留时间TA、用户在禁食期间身体通过新陈代谢向血液中释放葡萄糖的速率GR、单次大剂量注射量BOLUS、基础输注率BASAL；
    胰岛素泵可通过智能手机从云端大数据服务器下载最新的用户参数，然后根据用户输入的碳水化合物摄入量计算建议胰岛素大剂量注射方案，并根据用户对基础输注率的时间分段向用户建议更新过的基础输注率方案。
  2. 根据权利要求1所述的基于云端大数据的胰岛素泵个体化配置优化系统，其特征在于，云端大数据服务器储存的用户个人信息和历史数据包括用户姓名，性别，年龄，联系号码，所使用胰岛素泵产品序列号，胰岛素泵输注剂量、时间及输注速率记录，血糖输出值BG与相对应的数据测量时间日期Ts，患者个人所记录的碳水化合物摄入量、睡眠及运动情况。
  3. 根据权利要求1所述的基于云端大数据的胰岛素泵个体化配置优化系统， 其特征在于，在云端大数据服务器中用户生理参数CR，IS，TA，GR以及胰岛素输注量的定义和计算方式如下：
    CR：1单位胰岛素转化的单位碳水化合物数量
    IS：胰岛素敏感指数
    TA：胰岛素存留时间
    GR：用户在禁食期间身体通过新陈代谢向血液中释放葡萄糖的速率
    BOLUS：单次大剂量注射量
    BASAL：基础输注率，通常以胰岛素单位/小时(U/h)进行计数
    根据给定的CR，IS，TA，则BOLUS注射剂量的计算公式为：
    Figure PCTCN2018109282-appb-100001
    其中，BGcurrent是CGMS读取的大剂量注射前的血糖值；BGtarget是目标血糖值；BOLUSprev为上一次大剂量的注射量；CARBS为用户所输入的当前碳水化合物摄入量；TI为本次大剂量距离上次大剂量注射过程中点的时间，min(TI，TA)取TI，TA中的较小值，使得当TI大于或等于TA时，上一次大剂量的残留量
    Figure PCTCN2018109282-appb-100002
    为0；
    根据给定的GR，则在时长为t的禁食期间BASAL输注率的计算方式为：
    Figure PCTCN2018109282-appb-100003
    其中BGstart是CGMS读取的禁食时间段开始时一段时间的平均血糖，TI为时段开始时距离上次大剂量注射过程中点的时间。
  4. 根据权利要求3所述的基于云端大数据的胰岛素泵个体化配置优化系统，其特征在于，云端大数据服务器可通过实时收集用户使用CGMS和胰岛素泵进行大剂量注射所获得的实时数据来优化其生理参数CR，IS，TA，具体步骤如下：
    步骤A，建立回归方程
    Figure PCTCN2018109282-appb-100004
    其中，BGbefore是大剂量注射前的血糖值，同计算公式中的BGcurrent；BGafter是大剂量注射一段时间后的实测血糖值；
    步骤B，通过智能手机从胰岛素泵和CGMS获取提取每次大剂量注射时间Tstart附近的以下数据：
    注射开始时间Tstart：胰岛素泵数据
    注射结束时间Tend：胰岛素泵数据
    Tstart时刻开始的大剂量注射量BOLUS：胰岛素泵数据
    Tstart时刻植入式动态葡萄糖传感器测得的血糖值BGbefore
    Tend一段时间后植入式动态葡萄糖传感器测得的血糖值BGafter
    Tstart时刻附近的由用户输入的糖水化合物摄入量CARBS
    形成用于计算的样本记录包
    [Tstart n Tend n BOLUS n CARBS n BGbefore n BGafter n]
    将最近三至六个月内的数据用于回归，历史数据变量的下标序号n根据Tstart逆序排列，即越接近当前的历史数据，序号越小；
    步骤C，构建样本矩阵：
    Figure PCTCN2018109282-appb-100005
    其中，
    ΔBG n＝BGafter n-BGbefore n，
    当TI n＜TAl时，BOLUS′ n＝BOLUS n+BOLUS n+1，TI′ n＝TI n，TI n＝Tstart n-(Tstart n+1+Tend n+1)/2；
    当TI n＞TAu时，BOLUS′ n＝BOLUS n，TI′ n＝0；
    当TAl≤TI n≤TAu时，样本舍弃；
    TAu是TA允许设置的上限，TAl是TA允许设置的下限；
    步骤D，若对于所有n，X n，3＝0，则：
    Figure PCTCN2018109282-appb-100006
    否则样本矩阵保持不变；
    步骤E，求解超定方程G＝XC，
    用加权最小二乘法求解：
    Figure PCTCN2018109282-appb-100007
    步骤F，剔除异常数据：计算残差：
    Figure PCTCN2018109282-appb-100008
    剔除残差大于阈值的数据项，然后重复回归算法A～F，直至不存在残差大于阈值的数据项；
    步骤G，根据回归算法结果计算更新的生理参数IS，CR，TA：
    Figure PCTCN2018109282-appb-100009
    Figure PCTCN2018109282-appb-100010
    若存在
    Figure PCTCN2018109282-appb-100011
    则
    Figure PCTCN2018109282-appb-100012
    否则
    Figure PCTCN2018109282-appb-100013
    步骤H，最终，得到的
    Figure PCTCN2018109282-appb-100014
    和
    Figure PCTCN2018109282-appb-100015
    以一定的校正比例γ修正当前设定的IS、CR和TA，γ的取值范围为0＜γ＜1，
    Figure PCTCN2018109282-appb-100016
    Figure PCTCN2018109282-appb-100017
    Figure PCTCN2018109282-appb-100018
    作为现在起胰岛素泵大剂量注射的设置参数；
    同时修订TAl和TAu：
    TAl：＝TA×τ％，其中0＜τ＜100；
    TAu：＝TA×υ％，其中100＜υ＜150；
    储存并更新生理参数IS、CR、TA、TAl和TAu至云端大数据服务器并推送至手机应用和胰岛素泵。
  5. 根据权利要求3所述的基于云端大数据的胰岛素泵个体化配置优化系统，其特征在于，云端大数据服务器可通过实时收集用户使用CGMS和胰岛素泵所获得的实时数据来优化其生理参数GR在不同时间段的值，以及相应的基础输注率BASAL，具体步骤如下：
    步骤A，首先根据用户参考医生建议和自身情况所制定的基础输注率对一天24小时进行分段，每个时间段中的GR与BASAL值需要独立设置和计算，对于每个时间段和时段之前的2小时内，如果出现了进食，伴随进食或未伴随进食的大剂量注射，进食或大剂量注射发生后的2个小时的数据需要从此时间段中排除，本时间段的数据更新为仅包含时间段中去除进食/大剂量注射2小时后剩余较长的连续时间的数据；
    步骤B，在每一个有效的时间段中采集样本数据：
    Tstart：该时段开始时间
    BGstart：该时间段中前一小段时间的血糖平均值
    BGend：最后一小段时间的血糖平均值
    BASAL：该时间段基础输注率
    t：该时间段的时长
    IS：胰岛素敏感指数
    TA：胰岛素存留时间
    Figure PCTCN2018109282-appb-100019
    时间段开始时的体内残留胰岛素
    SNR：动态血糖数据信噪比
    形成用于计算的样本记录包
    [Tstart n BGstart n BGend n BASAL n t n RESIDUAL n SNR n]及系统参数IS和TA；
    将最近三至六个月内同时间段的数据用于回归，历史数据变量的下标序号n根据Tstart逆序排列，即越接近当前的历史数据，序号越小；
    步骤C，对每一个有效时间段，考虑摄入胰岛素的效用，该期间身体通过新陈代谢向血液中释放葡萄糖总量ΔBG：
    ΔBG n＝BGend n-BGstart n+[BASAL n×t n+RESIDUAL n]×IS
    建立回归方程ΔBG＝GR×t；
    步骤D，对每一个有效时间段，使用回归法计算GR的更新值
    Figure PCTCN2018109282-appb-100020
    Figure PCTCN2018109282-appb-100021
    其中，w(T′)为时间相关权重，其中，T′ n＝Tcurrent-Tstart n，Tcurrent为当前时间，即最近一个历史数据的时间Tstart 1；
    距离最近一个样本越近，权重越大；
    步骤E，将得到的
    Figure PCTCN2018109282-appb-100022
    以一定的校正比例γ修正当前设定的GR
    Figure PCTCN2018109282-appb-100023
    γ的取值范围为0＜γ＜1；
    步骤F，使用修正后的GR和同时间段的历史样本数据包
    [BGstart n t n RESIDUAL n]根据公式计算修正后的该时间段应设的
    Figure PCTCN2018109282-appb-100024
    值：
    Figure PCTCN2018109282-appb-100025
    步骤G，将计算得出的所有
    Figure PCTCN2018109282-appb-100026
    进行时间加权计算出当前的BASAL校正值
    Figure PCTCN2018109282-appb-100027
    Figure PCTCN2018109282-appb-100028
    其中，w′(T′)为时间相关权重，样本距离时间当前越近，权重越大；
    步骤H，若计算得出的
    Figure PCTCN2018109282-appb-100029
    与当前BASAL值的差超过阈值，则将得到的
    Figure PCTCN2018109282-appb-100030
    以一定的校正比例γ修正当前设定的BASAL：
    Figure PCTCN2018109282-appb-100031
    γ的取值范围为0＜γ＜1；
    储存并更新BASAL作为胰岛素泵基础注射率的设置参数，并和生理参数GR一并储存至云端大数据服务器并推送至手机应用和胰岛素泵。
  6. 根据权利要求3所述的基于云端大数据的胰岛素泵个体化配置优化系统，其特征在于，云端大数据服务器可通过实时收集用户使用CGMS和胰岛素泵所获得的实时数据来优化不同时间段的的基础输注率BASAL，另一种方法具体步骤如下：
    步骤A，首先根据用户参考医生建议和自身情况所制定的基础输注率对一天24小时进行分段，每个时间段中的BASAL值需要独立设置和计算，对于每个时间段和时段之前的2小时内，如果出现了进食，伴随进食或未伴随进食的大剂量注射，进食或大剂量注射发生后的2个小时的数据需要从此时间段中排除，本时间段的数据更新为仅包含时间段中去除进食/大剂量注射2小时后剩余较长的连续时间的数据；
    步骤B，在每一个有效的时间段中采集样本数据：
    Tstart：该时段开始时间
    BGstart：该时间段中前一小段时间的血糖平均值
    BGend：最后一小段时间的血糖平均值
    BASAL：该时间段基础输注率
    t：该时间段的时长
    IS：胰岛素敏感指数
    TA：胰岛素存留时间
    Figure PCTCN2018109282-appb-100032
    时间段开始时的体内残留胰岛素
    形成用于计算的样本记录包
    [Tstart n BGstart n BGend n BASAL n t n RESIDUAL n]及系统参数IS和TA；将最近三至六个月内同时间段的数据用于回归，历史数据变量的下标序号n根据Tstart逆序排列，即越接近当前的历史数据，序号越小；
    步骤C，对于第n个时间段，使用该时间段的历史样本数据包根据公式计算修正后的该时间段应设的
    Figure PCTCN2018109282-appb-100033
    值：
    Figure PCTCN2018109282-appb-100034
    步骤D，将计算得出的所有
    Figure PCTCN2018109282-appb-100035
    进行时间加权计算出当前的BASAL校正值
    Figure PCTCN2018109282-appb-100036
    Figure PCTCN2018109282-appb-100037
    其中，w′(T′)为时间相关权重，样本距离时间当前越近，权重越大；
    步骤E，若计算得出的
    Figure PCTCN2018109282-appb-100038
    与当前BASAL值的差超过阈值，则将得到的
    Figure PCTCN2018109282-appb-100039
    以一定的校正比例γ修正当前设定的BASAL：
    Figure PCTCN2018109282-appb-100040
    γ的取值范围为0＜γ＜1。
  7. 一种基于云端大数据的胰岛素泵个体化配置优化方法，其特征在于，包括如下步骤：
    步骤一，当系统启动后，由智能手机应用获取云端大数据服务器数据来判断用户是否是初次使用此胰岛素注射系统，如果是，则提示用户按医嘱设置参数IS、CR、TA、GR、时间分段以及基础注射率或使用默认设置继续；如果否，则从云端大数据服务器下载更新过的以上参数；
    步骤二，如果用户手动输入大剂量注射命令则进入大剂量模式，否则胰岛素泵处于基础注射模式；
    步骤三，在基础注射模式中，按照预设的当前时间段基础率进行胰岛素注射，并定期将CGMS监测到的血糖数据上传至云端服务器；该时间段结束后或用户进行胰岛素泵的操作后检查是否存在云端大数据服务器GR和基础率的更新，如果是，则更新本地储存参数，然后重复步骤二，如果否，则直接重复步骤二；
    步骤四，在大剂量模式中，胰岛素泵通过智能手机应用提示用户手动输入将摄入的碳水化合物量CARBS并确认需要达到的目标血糖值，同时获取CGMS所测得的当前血糖值BGcurrent；
    步骤五，使用先前设置或获取的参数值计算所需大剂量注射量：
    BOLUS＝CARBS/CR+(BGcurrent-BGtarget)/IS-BOLUSprev[1-min(TI，TA)/TA]；
    步骤六，提示患者确认输注量和大剂量输注时间，计算注射停止时间Tend＝Tstart+T BOLUS，其中T BOLUS＝输注量/bolus-rate，bolus-rate为用户定义的胰岛素大剂量输注速度；
    步骤七，将胰岛素注射信息Tstart、Tend、BOLUS、CARBS和CGMS血糖监测数据上传至云端大数据服务器；
    步骤八，执行大剂量注射直至到达Tend时间；
    步骤九，检测是否存在生理参数在云端有更新，如果是，则更新本地储存参数，然后重复步骤二，如果否，则直接重复步骤二。
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