CN117672531A - Diabetes risk evaluation system based on multiple parameters - Google Patents
Diabetes risk evaluation system based on multiple parameters Download PDFInfo
- Publication number
- CN117672531A CN117672531A CN202410041748.XA CN202410041748A CN117672531A CN 117672531 A CN117672531 A CN 117672531A CN 202410041748 A CN202410041748 A CN 202410041748A CN 117672531 A CN117672531 A CN 117672531A
- Authority
- CN
- China
- Prior art keywords
- user
- unit
- risk
- health
- trend
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000011156 evaluation Methods 0.000 title claims abstract description 59
- 206010012601 diabetes mellitus Diseases 0.000 title claims abstract description 50
- 230000036541 health Effects 0.000 claims abstract description 68
- 238000013480 data collection Methods 0.000 claims abstract description 20
- 238000004458 analytical method Methods 0.000 claims abstract description 19
- 238000012545 processing Methods 0.000 claims abstract description 15
- NOESYZHRGYRDHS-UHFFFAOYSA-N insulin Chemical compound N1C(=O)C(NC(=O)C(CCC(N)=O)NC(=O)C(CCC(O)=O)NC(=O)C(C(C)C)NC(=O)C(NC(=O)CN)C(C)CC)CSSCC(C(NC(CO)C(=O)NC(CC(C)C)C(=O)NC(CC=2C=CC(O)=CC=2)C(=O)NC(CCC(N)=O)C(=O)NC(CC(C)C)C(=O)NC(CCC(O)=O)C(=O)NC(CC(N)=O)C(=O)NC(CC=2C=CC(O)=CC=2)C(=O)NC(CSSCC(NC(=O)C(C(C)C)NC(=O)C(CC(C)C)NC(=O)C(CC=2C=CC(O)=CC=2)NC(=O)C(CC(C)C)NC(=O)C(C)NC(=O)C(CCC(O)=O)NC(=O)C(C(C)C)NC(=O)C(CC(C)C)NC(=O)C(CC=2NC=NC=2)NC(=O)C(CO)NC(=O)CNC2=O)C(=O)NCC(=O)NC(CCC(O)=O)C(=O)NC(CCCNC(N)=N)C(=O)NCC(=O)NC(CC=3C=CC=CC=3)C(=O)NC(CC=3C=CC=CC=3)C(=O)NC(CC=3C=CC(O)=CC=3)C(=O)NC(C(C)O)C(=O)N3C(CCC3)C(=O)NC(CCCCN)C(=O)NC(C)C(O)=O)C(=O)NC(CC(N)=O)C(O)=O)=O)NC(=O)C(C(C)CC)NC(=O)C(CO)NC(=O)C(C(C)O)NC(=O)C1CSSCC2NC(=O)C(CC(C)C)NC(=O)C(NC(=O)C(CCC(N)=O)NC(=O)C(CC(N)=O)NC(=O)C(NC(=O)C(N)CC=1C=CC=CC=1)C(C)C)CC1=CN=CN1 NOESYZHRGYRDHS-UHFFFAOYSA-N 0.000 claims description 44
- 238000012544 monitoring process Methods 0.000 claims description 35
- 238000012502 risk assessment Methods 0.000 claims description 23
- 102000004877 Insulin Human genes 0.000 claims description 22
- 108090001061 Insulin Proteins 0.000 claims description 22
- 210000000577 adipose tissue Anatomy 0.000 claims description 22
- 239000008280 blood Substances 0.000 claims description 22
- 210000004369 blood Anatomy 0.000 claims description 22
- 230000036772 blood pressure Effects 0.000 claims description 22
- 229940125396 insulin Drugs 0.000 claims description 22
- WQZGKKKJIJFFOK-GASJEMHNSA-N Glucose Natural products OC[C@H]1OC(O)[C@H](O)[C@@H](O)[C@@H]1O WQZGKKKJIJFFOK-GASJEMHNSA-N 0.000 claims description 19
- 239000008103 glucose Substances 0.000 claims description 19
- 238000000034 method Methods 0.000 claims description 13
- 235000005911 diet Nutrition 0.000 claims description 10
- 230000037213 diet Effects 0.000 claims description 10
- 230000002452 interceptive effect Effects 0.000 claims description 8
- 238000007781 pre-processing Methods 0.000 claims description 8
- 238000012854 evaluation process Methods 0.000 claims description 4
- 238000011160 research Methods 0.000 claims description 4
- 238000011527 multiparameter analysis Methods 0.000 abstract 1
- 235000013305 food Nutrition 0.000 description 3
- 238000007689 inspection Methods 0.000 description 3
- 238000013459 approach Methods 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 2
- 235000006694 eating habits Nutrition 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000002068 genetic effect Effects 0.000 description 2
- 230000035764 nutrition Effects 0.000 description 2
- 235000016709 nutrition Nutrition 0.000 description 2
- 230000002265 prevention Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000019771 cognition Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 201000010099 disease Diseases 0.000 description 1
- 239000003814 drug Substances 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000008676 import Effects 0.000 description 1
- 230000002085 persistent effect Effects 0.000 description 1
- 238000004393 prognosis Methods 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 230000001360 synchronised effect Effects 0.000 description 1
- 235000013311 vegetables Nutrition 0.000 description 1
- 235000020985 whole grains Nutrition 0.000 description 1
Landscapes
- Measuring And Recording Apparatus For Diagnosis (AREA)
- Medical Treatment And Welfare Office Work (AREA)
Abstract
The invention provides a diabetes risk evaluation system based on multiple parameters, which comprises a data collection module, an analysis processing module and a result feedback module; the data collection module is used for collecting personal health data of a user, the analysis processing module is used for evaluating diabetes risk of the user and generating health advice, and the result feedback module is used for feeding back a diabetes risk evaluation result and the health advice to the user; the proposal provides comprehensive diabetes risk and health trend assessment by combining the physiological indexes of the users and the multi-parameter analysis of life style.
Description
Technical Field
The invention relates to the technical field of medical auxiliary systems, in particular to a diabetes risk evaluation system based on multiple parameters.
Background
Diabetes is a global common disorder, and its management and prevention has become an important challenge in the healthcare field. With the rapid development of digital and personalized medicine, the need for an efficient, accurate diabetes risk assessment and management system has increased significantly; in particular, conventional approaches tend to be inefficient and inflexible when dealing with large amounts of patient health data, making complex risk predictions, and developing personalized treatment plans.
Referring to the related published technical scheme, the technology with publication number of CN110021437A provides a method and a system for managing diabetes, wherein the method comprises the following steps: receiving corresponding inspection parameters according to the working mode selected by the user; comparing and analyzing the inspection parameters with preset standards to obtain an evaluation result; providing a monitoring plan and a management scheme of the diabetes inspection parameters according to the evaluation result; according to the scheme, the risk, the curative effect, the prognosis and the like of the illness are evaluated according to the checking result input by the user, corresponding monitoring plans and daily management suggestions are given, specialized whole-course guidance and self-management are provided, and screening and early intervention prevention and guiding treatment schemes of diabetes high-risk groups and early intervention and treatment of diseases can be better realized. This approach is relatively inadequate in facing dynamic health trend analysis and personalized health advice because it relies primarily on single-examination medical parameters, without adequately combining persistent health data and lifestyle factors.
Disclosure of Invention
The invention aims to provide a diabetes risk evaluation system based on multiple parameters, aiming at the defects in the prior art.
The invention adopts the following technical scheme:
a diabetes risk evaluation system based on multiple parameters, which comprises a data collection module, an analysis processing module and a result feedback module;
the data collection module is used for collecting personal health data of a user, the analysis processing module is used for evaluating diabetes risk of the user and generating health advice, and the result feedback module is used for feeding back a diabetes risk evaluation result and the health advice to the user;
the data collection module comprises a physiological index collection unit and a life style recording unit; the physiological index acquisition unit is used for acquiring physiological index information of a user; the life style recording unit is used for acquiring life style information of a user; the personal health data includes physiological index information and lifestyle information;
the analysis processing module comprises a preprocessing unit, a risk assessment unit, a trend assessment unit and a suggestion generation unit; the preprocessing unit is used for normalizing and integrating the data collected by the data collection module; the risk assessment unit is used for assessing the current diabetes mellitus risk of the user; the trend evaluation unit is used for evaluating the health trend of the user; the advice generation unit provides personalized health advice for the user based on the diabetes mellitus risk and health trend of the user;
further, the physiological index acquisition unit specifically comprises a blood sugar monitoring unit, a blood pressure monitoring unit, a body fat monitoring unit and an insulin monitoring unit; the blood glucose monitoring unit is used for regularly acquiring blood glucose level data of a user, the blood pressure monitoring unit is used for regularly acquiring blood pressure level data of the user, the body fat monitoring unit is used for regularly acquiring body fat level data of the user, and the insulin monitoring unit is used for regularly acquiring insulin level data of the user;
furthermore, the life style recording unit provides an interactive window for the user, so that the user can periodically fill in life style information of the user and corresponding time information through the interactive window, and the life style information comprises diet information of the user;
further, the risk assessment unit analyzes the physiological index information and life style information in a set acquisition period to complete assessment of the current diabetes risk of the user; the evaluation process of the risk evaluation unit satisfies the following formula:
；
wherein,for the risk assessment of illness, < > for>For the total acquisition number of acquisition of user data in an acquisition period,/->Is->Blood glucose level of the user acquired a second time,/->Is->Blood pressure level of the user acquired a second time,/->Is->The next acquired level of body fat of the user,is->Secondary acquired user insulin level,/->Is->The sugar intake level of the user obtained by the time is obtained through the diet information of the user; />For blood glucose risk value->For blood pressure risk value->For body fat risk value,/->For insulin risk value, < >>For standard sugar intake level, said +.>、/>、/>、/>And->Pre-acquired according to medical research and clinical guidelines; />、/>、/>、/>Andis a weight coefficient, satisfy->The method comprises the steps of carrying out a first treatment on the surface of the The larger the risk assessment value is, the higher the risk of diabetes mellitus is;
further, the trend evaluation unit calculates the health trend evaluation valueCompleting evaluation of health trends of users; the health trend evaluation value satisfies:
；
the higher the health trend evaluation value, the worse the health trend representing the user, i.e., the more obvious the worsening trend of the user's health condition, the higher the possibility of future illness.
The beneficial effects obtained by the invention are as follows:
the invention comprehensively collects the physiological index information and life style information of the user through the data collection module, and provides a rich and multidimensional data basis for subsequent analysis; the analysis processing module is combined with the user data collected by the data collection module to comprehensively evaluate the diabetes risk and the health trend of the user, so that the current illness risk state of the user can be obtained, the dynamic change trend of the health state of the user can be captured, the deep analysis enables the system to combine the illness risk and the health trend of the user, and highly personalized health suggestions are provided for the user, so that the cognition and management of the user on the health condition of the user can be enhanced.
Drawings
The invention will be further understood from the following description taken in conjunction with the accompanying drawings. The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the embodiments. Like reference numerals designate corresponding parts throughout the different views.
FIG. 1 is a schematic diagram of the overall module of the present invention.
Fig. 2 is a flow chart of a multi-parameter-based diabetes risk assessment method according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the following examples thereof; it should be understood that the detailed description and specific examples, while indicating the invention, are intended for purposes of illustration only and are not intended to limit the invention; other systems, methods, and/or features of the present embodiments will be or become apparent to one with skill in the art upon examination of the following detailed description; it is intended that all such additional systems, methods, features and advantages be included within this description; included within the scope of the invention and protected by the accompanying claims; additional features of the disclosed embodiments are described in, and will be apparent from, the following detailed description.
Embodiment one: as shown in fig. 1, the present embodiment provides a diabetes risk evaluation system based on multiple parameters, which includes a data collection module, an analysis processing module, and a result feedback module.
The data collection module is used for collecting personal health data of the user, the analysis processing module is used for evaluating diabetes risk of the user and generating health advice, and the result feedback module is used for feeding back the diabetes risk evaluation result and the health advice to the user.
The data collection module comprises a physiological index collection unit and a life style recording unit; the physiological index acquisition unit is used for acquiring physiological index information of a user; the life style recording unit is used for acquiring life style information of a user; the personal health data includes physiological index information and lifestyle information.
The analysis processing module comprises a preprocessing unit, a risk assessment unit, a trend assessment unit and a suggestion generation unit; the preprocessing unit is used for normalizing and integrating the data collected by the data collection module; the risk assessment unit is used for assessing the current diabetes mellitus risk of the user; the trend evaluation unit is used for evaluating the health trend of the user; the advice generation unit provides personalized wellness advice to the user based on the diabetes risk and wellness trend of the user.
Further, the physiological index acquisition unit specifically comprises a blood sugar monitoring unit, a blood pressure monitoring unit, a body fat monitoring unit and an insulin monitoring unit; the blood glucose monitoring unit is used for regularly acquiring blood glucose level data of a user, the blood pressure monitoring unit is used for regularly acquiring blood pressure level data of the user, the body fat monitoring unit is used for regularly acquiring body fat level data of the user, and the insulin monitoring unit is used for regularly acquiring insulin level data of the user.
Further, the life style recording unit provides an interactive window for the user, so that the user can periodically fill in life style information of the user and corresponding time information through the interactive window, and the life style information comprises diet information of the user.
Further, the risk assessment unit analyzes the physiological index information and life style information in a set acquisition period to complete assessment of the current diabetes risk of the user; the evaluation process of the risk evaluation unit satisfies the following formula:
；
wherein,for the risk assessment of illness, < > for>For the total acquisition number of acquisition of user data in an acquisition period,/->Is->Blood glucose level of the user acquired a second time,/->Is->Blood pressure level of the user acquired a second time,/->Is->The next acquired level of body fat of the user,is->Secondary acquired user insulin level,/->Is->The sugar intake level of the user obtained by the time is obtained through the diet information of the user; />For blood glucose risk value->For blood pressure risk value->For body fat risk value,/->For insulin risk value, < >>For standard sugar intake level, said +.>、/>、/>、/>And->Pre-acquired according to medical research and clinical guidelines; />、/>、/>、/>Andis a weight coefficient, satisfy->The method comprises the steps of carrying out a first treatment on the surface of the The larger the risk of developing an illness evaluation value, the higher the risk of developing diabetes on behalf of the user.
Further, the trend evaluation unit calculates the health trend evaluation valueCompleting evaluation of health trends of users; the health trend evaluation value satisfies:
；
the higher the health trend evaluation value, the worse the health trend representing the user, i.e., the more obvious the worsening trend of the user's health condition, the higher the possibility of future illness.
Embodiment two: this embodiment should be understood to include at least all of the features of any one of the foregoing embodiments, and be further modified based thereon.
The embodiment provides a diabetes risk evaluation system based on multiple parameters, which comprises a data collection module, an analysis processing module and a result feedback module.
The data collection module is used for collecting personal health data of the user, the analysis processing module is used for evaluating diabetes risk of the user and generating health advice, and the result feedback module is used for feeding back the diabetes risk evaluation result and the health advice to the user.
The data collection module comprises a physiological index collection unit and a life style recording unit; the physiological index acquisition unit is used for acquiring physiological index information of a user; the life style recording unit is used for acquiring life style information of a user; the personal health data includes physiological index information and lifestyle information.
The analysis processing module comprises a preprocessing unit, a risk assessment unit, a trend assessment unit and a suggestion generation unit; the preprocessing unit is used for normalizing and integrating the data collected by the data collection module; the risk assessment unit is used for assessing the current diabetes mellitus risk of the user; the trend evaluation unit is used for evaluating the health trend of the user; the advice generation unit provides personalized wellness advice to the user based on the diabetes risk and wellness trend of the user.
Further, the physiological index acquisition unit specifically comprises a blood sugar monitoring unit, a blood pressure monitoring unit, a body fat monitoring unit and an insulin monitoring unit; the blood glucose monitoring unit is used for regularly acquiring blood glucose level data of a user, the blood pressure monitoring unit is used for regularly acquiring blood pressure level data of the user, the body fat monitoring unit is used for regularly acquiring body fat level data of the user, and the insulin monitoring unit is used for regularly acquiring insulin level data of the user.
Specifically, in a set collection time interval, if the collection time interval is set to be one day, the physiological index collection unit collects personal health data of a user by using a blood glucose meter, a blood pressure meter, a body fat scale and an insulin tester; the personal health data includes blood glucose level, blood pressure level, body fat ratio, and insulin level; the collected personal health data may be manually entered into the system or synchronized to the system by means of an automated device import for subsequent analysis and evaluation.
Further, the life style recording unit provides an interactive window for the user, so that the user can periodically fill in life style information of the user and corresponding time information through the interactive window, and the life style information comprises diet information of the user.
Further, the risk assessment unit analyzes the physiological index information and life style information in a set acquisition period to complete assessment of the current diabetes risk of the user; the evaluation process of the risk evaluation unit satisfies the following formula:
；
wherein,for the risk assessment of illness, < > for>For the total acquisition number of acquisition of user data in an acquisition period,/->Is->Blood glucose level of the user acquired a second time,/->Is->Blood pressure level of the user acquired a second time,/->Is->The next acquired level of body fat of the user,is->Secondary acquired user insulin level,/->Is->The sugar intake level of the user obtained by the time is obtained through the diet information of the user; />For blood glucose risk value->For blood pressure risk value->For body fat risk value,/->For insulin risk value, < >>For standard sugar intake level, said +.>、/>、/>、/>And->Pre-acquired according to medical research and clinical guidelines; />、/>、/>、/>Andis a weight coefficient, satisfy->The method comprises the steps of carrying out a first treatment on the surface of the The larger the risk of developing an illness evaluation value, the higher the risk of developing diabetes on behalf of the user.
Specifically, a database containing rich food nutrition information is provided in the lifestyle recording unit; the lifestyle recording unit obtains the sugar intake level of the user by matching the diet information filled in by the user with the nutrition information in the database.
Further, the trend evaluation unit calculates the health trend evaluation valueCompleting evaluation of health trends of users; the health trend evaluation value satisfies:
；
the higher the health trend evaluation value, the worse the health trend representing the user, i.e., the more obvious the worsening trend of the user's health condition, the higher the possibility of future illness.
Further, the suggestion generation unit comprises a file acquisition unit, a comprehensive evaluation unit and a suggestion matching unit; the archive acquisition unit is used for acquiring personal medical archive information of a user; the comprehensive evaluation unit provides comprehensive evaluation for the user based on personal medical profile information, illness risks and health trends of the user, and the suggestion matching unit provides corresponding health suggestions for the user according to comprehensive evaluation results of the user.
The comprehensive evaluation unit performs comprehensive evaluation on the user to meet the following conditions:
；
wherein,for comprehensive evaluation value, ->Is a genetic influencing factor, meta-L>For medical history influencing factors, ++>Is an age-affecting factor;
further, for genetic influencing factorsThe method meets the following conditions:
；
further, for medical history influencing factorsThe method meets the following conditions:
；
further, for age-related factorsThe method meets the following conditions:
；
wherein,age for the user;
the suggestion matching unit sets a plurality of comprehensive evaluation threshold intervals, and provides health suggestions of corresponding intervals for the user according to the intervals where the comprehensive evaluation values of the user are located; the following is an example of the suggestion matching unit execution:
；
wherein,is the upper limit of the low risk interval,/->Is the upper limit of the middle risk interval>Is the upper limit of the high risk interval, meets the following conditions；
Suggesting that the user maintain a current lifestyle when the user is in a low risk interval;
when the user is in the middle risk interval, the user dietary habit is recommended to be adjusted, the intake of high-sugar food is reduced, the intake of vegetable and whole grain food is increased, and medium-intensity exercise is performed regularly in the diet information filled by the user;
when the user is in a high risk interval, advice to the user includes medical and physical monitoring on a regular basis, in addition to adjusting the user's eating habits and increasing exercise.
As shown in fig. 2, the present embodiment provides a diabetes risk evaluation method based on multiple parameters, which is applied to a diabetes risk evaluation system based on multiple parameters, and the method includes:
s1: collecting physiological index information and life style information of a user;
s2: assessing a user's risk of diabetes;
s3: evaluating the change trend of the health condition of the user;
s4: calculating a comprehensive evaluation value by combining personal medical file information, illness risks and health trends of the user;
s5: classifying the users into different risk intervals according to the comprehensive evaluation values of the users, and providing corresponding personalized health suggestions for the users in each risk interval.
The foregoing disclosure is only a preferred embodiment of the present invention and is not intended to limit the scope of the invention, so that all equivalent technical changes made by applying the description of the present invention and the accompanying drawings are included in the scope of the present invention, and in addition, elements in the present invention can be updated as the technology develops.
Claims (5)
1. The diabetes risk evaluation system based on the multiple parameters is characterized by comprising a data collection module, an analysis processing module and a result feedback module;
the data collection module is used for collecting personal health data of a user, the analysis processing module is used for evaluating diabetes risk of the user and generating health advice, and the result feedback module is used for feeding back a diabetes risk evaluation result and the health advice to the user;
the data collection module comprises a physiological index collection unit and a life style recording unit; the physiological index acquisition unit is used for acquiring physiological index information of a user; the life style recording unit is used for acquiring life style information of a user; the personal health data includes physiological index information and lifestyle information;
the analysis processing module comprises a preprocessing unit, a risk assessment unit, a trend assessment unit and a suggestion generation unit; the preprocessing unit is used for normalizing and integrating the data collected by the data collection module; the risk assessment unit is used for assessing the current diabetes mellitus risk of the user; the trend evaluation unit is used for evaluating the health trend of the user; the advice generation unit provides personalized wellness advice to the user based on the diabetes risk and wellness trend of the user.
2. The multi-parameter based diabetes risk assessment system according to claim 1, wherein the physiological index acquisition unit specifically comprises a blood glucose monitoring unit, a blood pressure monitoring unit, a body fat monitoring unit and an insulin monitoring unit; the blood glucose monitoring unit is used for regularly acquiring blood glucose level data of a user, the blood pressure monitoring unit is used for regularly acquiring blood pressure level data of the user, the body fat monitoring unit is used for regularly acquiring body fat level data of the user, and the insulin monitoring unit is used for regularly acquiring insulin level data of the user.
3. The multi-parameter based diabetes risk assessment system according to claim 2, wherein the lifestyle recording unit provides the user with an interactive window such that the user can periodically fill in the user's own lifestyle information including the user's diet information and corresponding time information through the interactive window.
4. A multi-parameter based diabetes risk assessment system according to claim 3, wherein said risk assessment unit performs assessment of a user's current diabetes risk by analyzing physiological index information and lifestyle information over a set acquisition period; the evaluation process of the risk evaluation unit satisfies the following formula:
；
wherein,for the risk assessment of illness, < > for>For the total acquisition number of acquisition of user data in an acquisition period,/->Is->Blood glucose level of the user acquired a second time,/->Is->Blood pressure level of the user acquired a second time,/->Is->Secondary acquired user body fat level, +.>Is->Secondary acquired user insulin level,/->Is->The sugar intake level of the user obtained by the time is obtained through the diet information of the user; />For blood glucose risk value->For blood pressure risk value->For body fat risk value,/->For insulin risk value, < >>For standard sugar intake level, said +.>、/>、/>、/>And->Pre-acquired according to medical research and clinical guidelines; />、/>、/>、/>And->Is a weight coefficient, satisfy->The method comprises the steps of carrying out a first treatment on the surface of the The larger the risk of developing an illness evaluation value, the higher the risk of developing diabetes on behalf of the user.
5. The multi-parameter based diabetes risk assessment system according to claim 4, wherein the trend assessment unit calculates the health trend assessment valueCompleting evaluation of health trends of users; the health trend evaluation value satisfies:
；
the higher the health trend evaluation value, the worse the health trend representing the user, i.e., the more obvious the worsening trend of the user's health condition, the higher the possibility of future illness.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410041748.XA CN117672531A (en) | 2024-01-11 | 2024-01-11 | Diabetes risk evaluation system based on multiple parameters |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410041748.XA CN117672531A (en) | 2024-01-11 | 2024-01-11 | Diabetes risk evaluation system based on multiple parameters |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117672531A true CN117672531A (en) | 2024-03-08 |
Family
ID=90075258
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410041748.XA Pending CN117672531A (en) | 2024-01-11 | 2024-01-11 | Diabetes risk evaluation system based on multiple parameters |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117672531A (en) |
-
2024
- 2024-01-11 CN CN202410041748.XA patent/CN117672531A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| AU2022200642B2 (en) | Analysis of glucose median, variability, and hypoglycemia risk for therapy guidance | |
| US20210100515A1 (en) | Analysis of glucose median, variability, and hypoglycemia risk for therapy guidance | |
| Chakradar et al. | A non-invasive approach to identify insulin resistance with triglycerides and HDL-c ratio using machine learning | |
| CN108648827B (en) | Cardiovascular and cerebrovascular disease risk prediction method and device | |
| CN103959291B (en) | Glucose predictions device based on the regularization network with adaptively selected core and regularization parameter | |
| US20040162678A1 (en) | Method of screening for disorders of glucose metabolism | |
| KR20090024808A (en) | Assessing dementia and dementia-type disorders | |
| US20210098099A1 (en) | Systems and methods for selecting a treatment schema based on user willingness | |
| Ho et al. | Imputation-enhanced prediction of septic shock in ICU patients | |
| CN113838577B (en) | Convenient layered old people MODS early death risk assessment model, device and establishment method | |
| CN114023441A (en) | Severe AKI early risk assessment model and device based on interpretable machine learning model and development method thereof | |
| KR20190063954A (en) | Method for predicting change of nutrient metabolism by artificial intelligence cloud and method for measuring nutrient metabolism by artificial intelligence cloud and method managing diease by using it | |
| CN115602325A (en) | Chronic disease risk assessment method and system based on multi-model algorithm | |
| CN115101199A (en) | Interpretable fair early death risk assessment model and device for critically ill elderly patients and establishment method thereof | |
| US20070161868A1 (en) | Method and system for determining whether additional laboratory tests will yield values beyond a threshold level | |
| Huo et al. | Predicting the meal macronutrient composition from continuous glucose monitors | |
| Roy et al. | OBESEYE: Interpretable Diet Recommender for Obesity Management using Machine Learning and Explainable AI | |
| RU2699284C2 (en) | System and method of interpreting data and providing recommendations to user based on genetic data thereof and data on composition of intestinal microbiota | |
| CN117672531A (en) | Diabetes risk evaluation system based on multiple parameters | |
| Doina et al. | Nutrition software for clinical dietitians: Patient management and nutrition care process guidance | |
| CN114093518B (en) | Diabetes risk assessment system and method based on obesity degree | |
| CN117153416B (en) | Diabetes screening data processing and management system | |
| US20220230751A1 (en) | Decision support system, and method in relation thereto | |
| Yamaguchi et al. | Trend estimation of blood glucose level fluctuations based on data mining | |
| Karamichalis | Using Machine Learning techniques to understand glucose fluctuation in response to breathing signals |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication |