CN111063435A - Diabetes typing diagnosis system - Google Patents

Diabetes typing diagnosis system Download PDF

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CN111063435A
CN111063435A CN201911039832.3A CN201911039832A CN111063435A CN 111063435 A CN111063435 A CN 111063435A CN 201911039832 A CN201911039832 A CN 201911039832A CN 111063435 A CN111063435 A CN 111063435A
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纪立农
史大威
刘蔚
陈婧
何璐西
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Peking University Peoples Hospital
Beijing Institute of Technology BIT
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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
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Abstract

The invention provides a diabetes typing diagnosis system, which collects the glucose measurement values of participants and carries out sectional processing to obtain a trend fluctuation function, and completes the diagnosis of diabetes according to the trend fluctuation function. The system can overcome the current situation that the measured instantaneous blood sugar fluctuation data information is complicated caused by various interference factors, utilizes the data analysis technology to disclose the fluctuation rule of the dynamic blood sugar change process of different types of diabetes, and initially establishes a new index which is based on instantaneous blood sugar monitoring data and is used for evaluating the internal insulin generation of patients and assisting diabetes typing, namely a trend-removing fluctuation function Fd(l) To aid in the diagnosis of diabetes and to reveal the nature of diabetes.

Description

Diabetes typing diagnosis system
Technical Field
The invention relates to a diabetes typing diagnosis system, and belongs to the technical field of medical signal processing.
Background
Diabetes is a metabolic disease characterized by hyperglycemia, chronic hyperglycemia will cause microvascular complications such as diabetic retinopathy, diabetic nephropathy, diabetic neuropathy, and the like, which are collectively referred to as four chronic non-infectious diseases together with respiratory diseases, cardiovascular diseases, and tumors.
The diabetes typing diagnosis at present divides the diabetes into type 1 diabetes and type 2 diabetes according to the main function of pancreatic β cells, the etiology of the type 1 diabetes emphasizes that the autoimmune system destroys pancreatic β cells, so that the insulin secretion is absolutely deficient, and the type 2 diabetes emphasizes that the insulin is relatively insufficient on the basis of insulin resistance.
The current Diabetes typing method is limited to clinical manifestations and dynamic changes of islet β cell function according to the course of disease, depends on individual experience found by clinicians according to practice, lacks widely recognized differential diagnosis procedures, and therefore, it is clinically difficult to homogenize Diabetes typing, generally, β cell function is achieved by serum C Peptide assay, and C Peptide production is the same as insulin amount, and is considered the best embodiment of endogenous insulin secretion. many studies report the diagnostic performance of C Peptide on type 1 and type 2 Diabetes mellitus [1] (Ton C L-OM, Schersten B.predictiveness of C-Peptide for Diabetes mellitus in both types of Diabetes mellitus and Diabetes mellitus [ 18:83-88 ], 2. tissue of C-Peptide for Diabetes mellitus in both tissues and tissues of Diabetes mellitus ] this method is related to carbohydrate C1, protein, vitamin D.12, protein, vitamin C, vitamin D.12, protein, vitamin C.7, vitamin D.7, vitamin C.7. and D.7. the relevant test results of Diabetes mellitus [1, protein, vitamin J.13, vitamin J.7, protein, vitamin J.A.7, C.7, vitamin D.7, protein, vitamin J.7, vitamin D.7, vitamin D.A.7, vitamin J.7. A.7. A.A.1. A.A.A.7. A.1. A.7. A.1. A.A.A.A.A.1. A. 1. A.1. A.A.A.A.A.A.A.A.A.A.A.A.A.A.A.A.A.A.A.A.A.A.A.A.A.A.A.A.A.A.A.A.A.A.A.A.A.A.A.A.1. A.A.A.A.A.A.A.A.A.A.A.A.A.A.A.
The rise of the blood sugar level is the most intuitive expression of the diabetes, and the fluctuation rule of the dynamic blood sugar change process of different types of diabetes is researched, thereby being beneficial to disclosing the essence of the diabetes. Thanks to the development of blood glucose Monitoring devices, the use of Continuous Glucose Monitoring (CGM) and Flash Glucose Monitoring (FGM) has increased rapidly over the past few years. With the application of machine learning, data-driven classification of blood glucose patterns and anomaly detection have also been attempted in type 1 diabetes and insulin pump closed loop development. Document [5] (Hall H, Perelman D, Breschi A, Limcaoco P, Kellogg R, McLaughlin T, Snyder M. glucopyranoses derived newatterns of glucose dyregulation. PLoS biol.2018 Jul 24; 16(7): e 2005143.).
FGM data shows that the dynamic changes in blood glucose contain a large amount of implicit information. It is crucial to establish a link between endogenous etiology and blood glucose excursion information. To date, there has been no study using indices based on blood glucose dynamic curves for diabetes classification.
In the invention, the characteristic of blood sugar fluctuation is described by utilizing a trend-removing fluctuation function, and the internal relation between the calculation index and the pancreatic islet function is established while the classification of diabetes is further guided. This type of function is derived from Detrended Fluctuation Analysis (DFA), a method that is commonly used to analyze long-range correlations of time series. Document [6] (Peng C K, Mietus J, Hausdorff J M, et al, Long-range associations and non-Gaussian behavor the heart beat. Phys Rev Lett,1993,70(9):1343-1346.), document [7] (Peng C K, BuldyrevS V, Havlin S, et al, molar association of DNA nucleotides. Phys Rev E,1994,49(2):1685-1689.), document [8] (Peng CK, Havlin S, Stanley HE, Godberger AL. Quanentification of reactions and amplification reactions in nucleic acid sequences and copolymers series 1995, 1995-82).
Here, we have established new indices for predicting Beta cell function and diabetes classification based on monitoring instantaneous glucose data, using detrending fluctuation functions for analysis. This will likely pave the way to the deep utilization of the vast amount of data provided by blood glucose monitoring devices and the study of digitally accurate drug delivery in diabetes studies.
Disclosure of Invention
In view of the above, the present invention provides a diabetes typing diagnosis system, which can overcome the current situation that the measured instantaneous blood sugar fluctuation data information is complicated due to various interference factors, reveal the fluctuation rules of the dynamic blood sugar variation process of different types of diabetes by using a data analysis technology, and initially establish a new index, namely a trend-removing fluctuation function F, for evaluating the internal insulin production of patients and assisting diabetes typing based on the instantaneous blood sugar monitoring datad(l) To aid in the diagnosis of diabetes and to reveal the nature of diabetes.
The invention is realized by the following technical scheme:
a diabetes typing diagnosis system collects glucose measurement values of participants and carries out segmentation processing to obtain a trend fluctuation function, and typing diagnosis of diabetes is completed according to the trend fluctuation function.
Furthermore, the diagnosis system comprises a data acquisition and preprocessing module, a full data processing module, a segmented data processing module, a trend fluctuation function calculation module and a diabetes diagnosis module;
data acquisition and preprocessing moduleBlock, collecting glucose measurements of participants to obtain a length of N1The glucose time sequence G (k) of (1), the entire sequence being divided into n segments of length l
Figure BDA0002252529440000041
A full data processing module for calculating the mean value of the sequence G (k)
Figure BDA0002252529440000042
Recalculate the mean-removed sequence
Figure BDA0002252529440000043
The summation sequence y (k) is then calculated as:
Figure BDA0002252529440000044
Figure BDA0002252529440000045
a segment data processing module for calculating data value of each segment
Figure BDA0002252529440000046
Figure BDA0002252529440000047
Wherein, i ═ 1, 2., (i-1) l +1, (i-1) l + 2., (ii);
ωi＝[ω0,i,ω1,i]T
ωi＝(Xi TXi)-1Xi TYi
Figure BDA0002252529440000048
wherein the coefficient kj,i＝(i-1)l+j，j＝1,2......l，YiIs composed of Y (k)) A column vector of data point information in the ith segment;
a trend fluctuation function calculation module for calculating the data value of each segment
Figure BDA0002252529440000049
Integration into Yt(k) Calculating a detrending sequence Yl(k)；
Figure BDA0002252529440000051
Yl(k)＝Y(k)-Yt(k)
Defining a detrending fluctuation function Fd(l) Comprises the following steps:
Figure BDA0002252529440000052
diabetes diagnostic Module, will Fd(l) And comparing with a set threshold, judging as type 2 diabetes when the value is less than or equal to the set threshold, and judging as type 1 diabetes when the value is greater than the set threshold.
Further, l is more than or equal to 10, preferably l is 34.
Further, the threshold value of the present invention is 0.7.
Advantageous effects
The invention provides a diagnosis system for the state of an illness of a diabetic patient by utilizing a data analysis technology and combining a trend fluctuation analysis thought, and the diagnosis system has the following specific effects:
(1) the system has the most prominent advantages that the influence of various external factors on the data can be reduced, the internal fluctuation characteristics of the data can be conveniently disclosed, the effective information of blood sugar fluctuation is mined, and the endogenous causes of diabetes mellitus are reflected.
(2) The system obtains quantitative indexes through a data analysis method through a trend-removing fluctuation function, provides ideas for deeply utilizing a large amount of data provided by blood sugar monitoring equipment and researching digital accurate administration in diabetes research, and can assist in diagnosis of diabetes to a certain extent.
Drawings
FIG. 1 is a social demographic and clinical profile of participants in an example of the invention;
FIG. 2 is a graph showing the change in Spanish correlation coefficient with respect to the fasting C-peptide at different values of l for the fluctuation function in the example of the present invention;
FIG. 3 is a histogram of the waviness function distribution calculated from FGM information of all participants in an example of the present invention, wherein the red curve is obtained by fitting a bimodal Gaussian mixture model;
FIG. 4 is a comparison table of the spearman correlation coefficients for the detrending fluctuation function, MAGE, SD, Mean BG, TIR and fasting C-peptide in the examples of the invention;
FIG. 5 is a graph of the fluctuation function distribution of the type 1 diabetes group and the type 2 diabetes group in the example of the present invention;
FIG. 6 is a graph of the performance of a subject evaluating the effect of a waviness function typing in an example of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention.
The diagnostic system of the embodiment of the present invention requires diagnosis based on glucose measurements of participants, and therefore the measurement and collection of data in conjunction with the diagnostic system is accomplished by a FGM device which determines the blood glucose of each participant, in practice providing an instantaneous monitoring device for each participant, comprising an electrochemical sensor based on glucose oxidase and a receiver. The sensor was placed subcutaneously and the participants were asked to wear daily, the device transmitting glucose measurements at that time to the receiver every 15 minutes and replaced for 14 days.
According to the diabetes typing diagnosis system provided by the embodiment of the invention, the diagnosis system acquires the glucose measurement values of participants and performs sectional processing to obtain the trend fluctuation function, and the diabetes diagnosis is completed according to the trend fluctuation function.
The diagnosis system comprises a data acquisition and preprocessing module, a full data processing module, a segmented data processing module, a trend fluctuation function calculation module and a diabetes diagnosis module;
the data acquisition and pretreatment module is used for acquiring and pretreating glucose measurement values of participants to obtain a glucose measurement value with the length of N1The glucose time series G (k) of (a), dividing the entire sequence into n segments of length l,
Figure BDA0002252529440000071
the method specifically comprises the following steps: after data acquisition is carried out by the module, the length N is obtained1The glucose time sequence G (k) of (1), the sampling period of the sequence being T: ═ 15[ min ]](ii) a Then, the whole sequence is divided equally into a plurality of segments with the length of l by pretreatment, namely, each segment contains l data points, and the actual sequence length adopted in the analysis is
Figure BDA00022525294400000710
Is a rounded-down symbol.
A full data processing module for calculating the mean value of the sequence G (k)
Figure BDA0002252529440000073
Calculating a sequence after mean value removal, and then calculating a summation sequence Y (k) as follows:
Figure BDA0002252529440000074
Figure BDA0002252529440000075
mean removal sequence satisfies
Figure BDA0002252529440000076
A segmentation data processing module that equally divides y (k) into N-N/l segments, each segment containing l glucose data points.
Calculating trend data values for each segment
Figure BDA0002252529440000077
Figure BDA0002252529440000078
Wherein, i ═ 1, 2., (i-1) l +1, (i-1) l + 2., (ii);
definition of ωi＝[ω0,i,ω1,i]T，ωiIs obtained by least squares calculation, thereby omegai＝(Xi TXi)-1Xi TYi
Figure BDA0002252529440000079
Wherein, XiIs a l x 2 matrix, coefficient kj,i＝(i-1)l+j，j＝1,2......l，YiIs a column vector containing information on the data points in the ith segment of y (k).
A trend fluctuation function calculation module for calculating the data value of each segment
Figure BDA0002252529440000081
Integration into Yt(k) Calculating a detrending sequence Yl(k) Detrending sequence Yl(k) N is defined as Y (k) and Y (1, 2)t(k) The difference of (a):
Figure BDA0002252529440000082
Yl(k)＝Y(k)-Yt(k)
wherein, Yl(k) And Yt(k) The value of (c) depends on the value of l;
defining a detrending fluctuation function Fd(l) Comprises the following steps:
Figure BDA0002252529440000083
diabetes diagnostic Module, will Fd(l) And comparing with a set threshold, judging as type 2 diabetes when the value is less than or equal to the set threshold, and judging as type 1 diabetes when the value is greater than the set threshold.
From a system biological point of view, the fluctuations in diabetic blood glucose are the net result of a complex metabolic system activity, both disturbed by behaviours such as physical exercise and food intake and work to reduce postprandial glucose fluctuations, reduce the risk of hypoglycemia and ensure stable fasting blood glucose levels; and is regulated by the intrinsic hormone network, including the pancreas, liver, gut, adipose tissue, kidney, and brain. Under normal physiological conditions, behavioral effects can also cause blood glucose fluctuations, but can be controlled to a small extent. In the diabetic state, however, the behavioral effects of blood glucose fluctuations are amplified due to deregulation of the hormone network. The blood glucose fluctuations under the action of the user are regarded as trend fluctuations, and the trend fluctuation function index enables the hormone system to automatically converge under the condition of eliminating external influences from the internal regulation capacity of the hormone system. The system has the most prominent advantage that the influence of external factors on the blood sugar fluctuation is reduced by filtering the trend components in the glucose time series, so that the intrinsic fluctuation characteristics of the data can be conveniently revealed. The glucose time series G (k) is first obtained by removing the mean and adding the sums to obtain Y (k), thereby eliminating the overall bias of the time series. Then, the summation sequence Y (k) is divided into equal-length segments, and the trend component Y is obtained by a linear regression method according to the data point information contained in each segmentt(k) In that respect Finally, the detrended fluctuation component Y is calculatedl(k) Variance of (i), i.e. Fd(l) So as to reflect the fluctuation situation of the system after removing the trend item, thereby revealing the endogenous cause of the diabetes.
The system obtains the trend-removing fluctuation function which is a quantitative index obtained by a data analysis method, and provides an idea for deeply utilizing a large amount of data provided by blood sugar monitoring equipment and researching digital accurate administration in diabetes research. Currently, the classification of diabetes still depends on clinical judgment, lacks widely accepted differential diagnosis processes, and is difficult to homogenize. Albeit bloodClear C peptide is commonly used to assess the intrinsic secretion of insulin, guiding the classification of diabetes, but there are also external interference factors such as non-normative measurement methods. Also, medically, Beta cell function is a primary consideration in determining the staging diagnosis of diabetes, usually reflected by serum C-peptide levels. Since C-peptide levels interfere with blood glucose levels, kidney function and individual insulin resistance, quantitative evaluation is difficult. The use of a detrending fluctuation function may be used in a quantitative manner to assist in the diagnosis of diabetes to some extent. In the embodiment of the invention, after basic data of blood glucose fluctuation of a patient is obtained by FGM equipment, a diagnosis system can be realized by MATLAB 2016bfor MAC programming, and the diagnosis system cleans, processes and subsequently calculates the data obtained by testing the blood glucose testing equipment to obtain a trend-removing fluctuation function Fd(l) Can pass through the trend fluctuation function Fd(l) A diagnosis of the type of diabetes is made.
At the same time, the detrending fluctuation function Fd(l) The values of (a) can also be combined with Mean blood glucose excursion (MAGE), blood glucose Standard Deviation (SD), blood glucose Mean (Mean BG) and 70-180mg/dL time range percentage (TIR) indicators, and in subsequent experiments, optimized selection, comparative evaluation and validity evaluation of the indicators were performed by SPSS software version 16.0(SPSS inc.
The following discussion makes use of the detrending fluctuation function Fd(l) Validation of diabetes diagnosis:
to study the present invention, 113 hospitalized diabetic patients were co-enrolled in the endocrine metabolism department of the people hospital of Beijing university, from month 1 in 2018 to month 6 in 2019, all in the age range of 18-75 years, and were diagnosed for diabetes type according to the 1999 World Health Organization (WHO) criteria. The diagnosis and classification of diabetes are clear and are carried out by a specialist in endocrine and independently confirmed by another specialist. Exclusion criteria for these patients included: another CGM system is currently in use, does not accept this new blood glucose monitoring method, medical conditions change greatly, FGM systems cannot be used, and patients are known to be allergic to medical adhesives and pregnant. The study was approved by the institutional review board of the people hospital, Beijing university, and informed written consent was obtained from all participants.
The research invention comprises the following steps:
first, as shown in figure 1, all participants received physical examination including height, weight and blood pressure measurements prior to the study. BMI is calculated as weight (kilograms) divided by height in square meters. Blood pressure was measured three times using a standard mercury sphygmomanometer and the measurements were averaged. Prior to the study, Triglyceride (TG), Total Cholesterol (TC), high density lipoprotein cholesterol (HDL-C), low density lipoprotein cholesterol (LDL-C), fasting C-peptide, fasting insulin, and Fasting Plasma Glucose (FPG) were assayed by an enzyme immunoassay using a biochemical analyzer (7600-120; Hitachi, Tokyo, Japan). Hemoglobin A1c (HbA1c) levels in whole blood were measured using automated high performance liquid chromatography (primus ultra 2, three biotechnologies, brene, kowilcoxo, ireland) and standard procedures. In addition, participants were required to wear a free blood glucose monitoring (yapeh, usa) device every day to continuously monitor subcutaneous interstitial glucose. The device comprises an electrochemical sensor based on glucose oxidase and a receiver. The sensors were placed subcutaneously, replaced every 14 days, and the receiver wirelessly transmitted and stored interstitial glucose measurements every 15 minutes.
Secondly, processing and analyzing data according to blood sugar monitoring data, obtaining fluctuation functions of all participants by using the system, and referring to fig. 2, the correlation analysis result of dividing the blood sugar data into different segments l is shown, and when l is 34, the correlation coefficient is highest, and the trend-removing fluctuation function is used as FdAnd (4) showing.
And thirdly, calculating an index which is commonly used for reflecting blood sugar fluctuation, comprising the following steps: mean blood glucose excursion (MAGE), blood glucose Standard Deviation (SD), Mean blood glucose (Mean BG), and percent time range of 70-180mg/dL (TIR) for comparison with the derived excursion function performance. FIG. 3 shows the results of spearman correlation analysis of the above five indices, showing that there is a significant correlation between the fluctuation function and the fasting C-peptide (r ═ 0.751; p)<0.01)，FdThe higher the patients the lower the fasting C-peptide. Compared with other four performance indexes, the trend-removing fluctuation functionFd(r＝-0.751p<0.01) is most strongly correlated with fasting C-peptide, with MAGE having the lowest correlation coefficient (r-0.334; p is a radical of<0.01), even though it also represents the degree of variation in blood glucose. The definition of TIR determines that it can reflect certain blood sugar fluctuation information, and the relation of the TIR to fasting C peptide is higher than that of average blood sugar (r is 0.620, P<0.01). SD describes the degree of dispersion between a group of individuals (r ═ 0.678; p<0.01), performs better than Mean BG and TIR, but is lower than the fluctuation function Fd。
And fourthly, evaluating the typing effect of the provided indexes. FIG. 4 is a histogram of the fluctuation function of all participants, the fluctuation function F of different subjectsdThe value of (a) is between 3.380 and 15.361. Average 8.188, interquartile Range (IQR) 4.400, F for all participantsdThe distribution histogram shows a bimodal distribution. In addition, detrending fluctuation function F for patients with type 1 diabetes and type 2 diabetesdThe performance of (c) was evaluated separately. Wherein, in the type 1 diabetes group, the median and the IQR are respectively 10.007 and 2.578; the median in type 2 diabetes group was 5.844 and IQR was 1.883, as shown in figure 5. FIG. 6 is a graph of the performance characteristics of subjects evaluating the effect of a waviness function typing. The area under the curve (AUC) reached 0.866, significance p ═ 0.000. The Yoden index (Yi) was 0.693. These statistics show that FdCan be used for guiding diabetes classification, and the cut point is preferably selected to be 0.7 (sensitivity 88.5%, specificity 81.8%).
The embodiment of the invention is described in detail in the above with reference to the accompanying drawings, and the new indexes for predicting Beta cell function and diabetes classification are established by analyzing the trend-removing fluctuation function based on the instantaneous blood glucose monitoring data, and the effectiveness of the indexes is evaluated by using data analysis and evaluation means. This will suggest an in-depth utilization of the large amount of data provided by blood glucose monitoring devices and the digital accurate dosing in research diabetes studies.
In summary, the above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (5)

1. The diabetes typing diagnosis system is characterized in that the diagnosis system collects glucose measurement values of participants and carries out sectional processing to obtain a trend fluctuation function, and the diabetes diagnosis is completed according to the trend fluctuation function.
2. The diabetes typing diagnosis system according to claim 1, wherein the diagnosis system comprises a data acquisition and preprocessing module, a full data processing module, a segmented data processing module, a trend fluctuation function calculation module and a diabetes diagnosis module;
a data acquisition and preprocessing module for acquiring glucose measurement values of participants to obtain a length N1The glucose time sequence G (k) of (1), the entire sequence being divided into n segments of length l
Figure FDA0002252529430000011
A full data processing module for calculating the mean value of the sequence G (k)
Figure FDA0002252529430000012
Recalculate the mean-removed sequence
Figure FDA0002252529430000013
The summation sequence y (k) is then calculated as:
Figure FDA0002252529430000014
Figure FDA0002252529430000015
a segment data processing module for calculating data value of each segment
Figure FDA0002252529430000016
Figure FDA0002252529430000017
Wherein, i ═ 1, 2., (i-1) l +1, (i-1) l + 2., (ii);
ωi＝[ω0,i,ω1,i]T
ωi＝(Xi TXi)-1Xi TYi
Figure FDA0002252529430000018
wherein the coefficient kj,i＝(i-1)l+j，j＝1,2......l，YiIs a column vector containing the information of data points in the ith segment of Y (k);
a trend fluctuation function calculation module for calculating the data value of each segment
Figure FDA0002252529430000021
Integration into Yt(k) Calculating a detrending sequence Yl(k)；
Figure FDA0002252529430000022
Yl(k)＝Y(k)-Yt(k)
Defining a detrending fluctuation function Fd(l) Comprises the following steps:
Figure FDA0002252529430000023
diabetes diagnostic Module, will Fd(l) And comparing with a set threshold, judging as type 2 diabetes when the value is less than or equal to the set threshold, and judging as type 1 diabetes when the value is greater than the set threshold.
3. The diabetes typing diagnosis system according to claim 2, wherein l.gtoreq.10.
4. The diabetes typing diagnostic system according to claim 2, wherein l-34.
5. The diabetes typing diagnostic system according to claim 2, wherein the threshold value is 0.7.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111755125A (en) * 2020-07-07 2020-10-09 医渡云（北京）技术有限公司 Method, device, medium and electronic device for analyzing patient measurement index
CN115804593A (en) * 2021-09-15 2023-03-17 深圳硅基仿生科技股份有限公司 Glucose monitoring system for glucose concentration levels before and after meal

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110264378A1 (en) * 2008-11-26 2011-10-27 University Of Virginia Patent Foundation Method, System, and Computer Program Product For Tracking of Blood Glucose Variability in Diabetes
CN105160199A (en) * 2015-09-30 2015-12-16 刘毅 Continuous blood sugar monitoring based method for processing and displaying diabetes management information with intervention information
CN107122610A (en) * 2017-04-28 2017-09-01 广州普麦健康咨询有限公司 A kind of diabetes classifying method and device
CN108766576A (en) * 2018-07-03 2018-11-06 深圳迪美泰数字医学技术有限公司 A kind of health deposit appraisal procedure, device and its application

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110264378A1 (en) * 2008-11-26 2011-10-27 University Of Virginia Patent Foundation Method, System, and Computer Program Product For Tracking of Blood Glucose Variability in Diabetes
CN105160199A (en) * 2015-09-30 2015-12-16 刘毅 Continuous blood sugar monitoring based method for processing and displaying diabetes management information with intervention information
CN107122610A (en) * 2017-04-28 2017-09-01 广州普麦健康咨询有限公司 A kind of diabetes classifying method and device
CN108766576A (en) * 2018-07-03 2018-11-06 深圳迪美泰数字医学技术有限公司 A kind of health deposit appraisal procedure, device and its application

Non-Patent Citations (5)

* Cited by examiner, † Cited by third party
Title
COLÁS ANA等: "Detrended Fluctuation Analysis in the prediction of type 2 diabetes mellitus in patients at risk: Model optimization and comparison with other metrics", 《PLOS ONE》 *
周翔海等: "2型糖尿病及糖尿病前期简易决策树模型外部验证的研究", 《中国糖尿病杂志》 *
应长江: "血糖波动加重糖尿病大鼠肾损伤的机制研究", 《中国博士学位论文全文数据库 医药卫生科技辑》 *
李慧: "2型糖尿病患者血糖波动特征与血管并发症的关系", 《中国优秀硕士学位论文全文数据库 医药卫生科技辑》 *
来云云等: "动态血糖序列的精细复合多尺度熵分析", 《生物医学工程学杂志》 *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111755125A (en) * 2020-07-07 2020-10-09 医渡云（北京）技术有限公司 Method, device, medium and electronic device for analyzing patient measurement index
CN111755125B (en) * 2020-07-07 2024-04-23 医渡云（北京）技术有限公司 Method, device, medium and electronic equipment for analyzing patient measurement index
CN115804593A (en) * 2021-09-15 2023-03-17 深圳硅基仿生科技股份有限公司 Glucose monitoring system for glucose concentration levels before and after meal

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