CN107358047A - Diabetic assesses and management system - Google Patents
Diabetic assesses and management system Download PDFInfo
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- CN107358047A CN107358047A CN201710571698.6A CN201710571698A CN107358047A CN 107358047 A CN107358047 A CN 107358047A CN 201710571698 A CN201710571698 A CN 201710571698A CN 107358047 A CN107358047 A CN 107358047A
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Abstract
The invention discloses a kind of diabetic assessment and management system, including patient information collection module, regression model to establish module, data processing module, regression coefficient computing module, risk class evaluation module and follow-up setting module；The present invention passes through Logistic regression analyses, find out the risk factors that contribution be present to complication for patients, and according to its size to assigning corresponding score value, pass through the accumulative of risk factors, objective scoring is carried out to patient symptom, add up situation according to score to judge the size of complication risk, while this scoring changes and dynamic change with conditions of patients again.Meanwhile because all data are stored within Internet Server, therefore, as long as there is the computer that can connect networking, anywhere doctor can realize above-mentioned function by access software website.
Description
Technical field
The present invention relates to the technical field of chronic disease management software, assesses and manages more particularly, to a kind of diabetic
System.
Background technology
With the offer of living standards of the people, diabetes also turn into one of principal disease for influenceing health of people, existing skill
Diabetes-management system in art, based on the IT of diabetic's case history management, be conceived to diagnosis and treatment auxiliary, scientific research support with
And the application of community network, effectively help doctor to be observed patient's long-term treatment situation, help doctor to be diagnosed,
By long-term Data Collection, research institution is helped to carry out related scientific research, and the diagnosis and treatment by can be applied to community medicine are believed
Platform is ceased, hospital's optimization medical resource is helped, exports consulting services, the system need to be installed on a server in hospital network
Reach patient's two-way flow, the shared effect of medical record information.
But diabetes-management system now in the art there is no complication risk forecast function, while the system belongs to
Business development software, use the higher expense of needs.The reason for causing disadvantages mentioned above is probably：1. the software is opened by commercial company
Hair, it develops in purpose the composition with profit, therefore inevitably produces higher cost of use；2. software is conceived to
Case control, it have ignored the judgement of complication risk.
The content of the invention
The shortcomings that it is a primary object of the present invention to overcome prior art and deficiency, there is provided a kind of diabetic assess and
Management system, the present invention are conceived to diagnosis and treatment auxiliary, scientific research support and community based on the IT management of diabetic's case history
The application of network, effectively help doctor to be observed patient's long-term treatment situation, help doctor to be diagnosed, also by length
The Data Collection of phase, research institution is helped to carry out related scientific research, and by can be applied to the medical information platform of community medicine,
Hospital's optimization medical resource is helped, exports consulting services.
In order to achieve the above object, the present invention uses following technical scheme：
The invention provides a kind of diabetic assessment and management system, including patient information collection module, recurrence mould
Type establishes module, data processing module, regression coefficient computing module, risk class evaluation module and follow-up setting module；
The patient information collection module, for collecting the essential information and inspection result of patient, and by essential information
Database is stored in inspection result, establishes the data archival of the patient；
The data processing module, for made a definite diagnosis between diabetes, prediabetes and Healthy People it is dangerous because
Plain relation, using each variable of above-mentioned Baseline as independent variable, using the incidence of diabetes as dependent variable, build
Vertical Multivariate Logistic Regression model, analyzes influence of each independent variable to onset diabetes situation, and then find out and cause glycosuria
Relevant risk factors occur for disease, and the Baseline refers to：(1) ordinary circumstance of patient, including age, sex and religion
Educate situation；(2) physical examination outcomes, including height, body weight, blood pressure and waist hip circumference；(3) laboratory examination results, including blood glucose,
Blood fat and insulin；
Regression coefficient computing module, the meaning for calculating the contribution of each variable and obtain regression coefficient β, β are
Independent variable often increases a grade, and patient is to increase a relative risk for suffering from diabetes, selects the variable of significance to build
Formwork erection type, the variable of the significantly meaning refer to the variable that regression coefficient value is more than 1；
The risk class evaluation module, for scoring complication for patients risk, so as to mark off risk class；
The follow-up setting module, for doing follow-up periodically or non-periodically to the state of an illness of patient, and by the record of follow-up
Data entry system.
As preferable technical scheme, after the patient information collection module collects data, by patient's different time
Medical content is uploaded to the webserver by internet and stored, and the data of each patient is stored with different tabs, and
And the customizing messages that can identify patient identity is provided with tab, the customizing messages name, date of birth, go to a doctor
Number and ID, doctor patient assessment's record can be scanned for by the customizing messages of the patient, transfer and stored on server
Data consultation, look back the details of medical situation in the past.
As preferable technical scheme, in addition to enquiry module, the content of inquiry includes：The identity information of patient, certain
Medical specific time, reason, monitoring project content and numerical value, whether carry out therapeutic scheme adjustment；When doctor needs, as long as
Selection is corresponding to consult project, it is possible to transfers and stores on the server the previously record result of all projects, passes through curve
Mode, shown by transverse axis of consultation time, to consult the variation tendency of a certain index.
As preferable technical scheme, in the data processing module, in addition to assignment module, for trouble can be caused
The size that the hazards that complication occurs in person are contributed according to the factor carries out assignment, and the assignment of each factor is used for risk class
The foundation of risk class division is carried out in module.
As preferable technical scheme, in the risk class grading module, patient establish first part of medical archives with
And each time during further consultation, the identity information of patient and medical data can be all inputted, the danger if meeting risk contribution
Factor inputs, then gets a risk score according to these hazards to be medical every time, the risk score is with risk factors
Increase and accumulate, scoring it is higher, prompt Morbidity control it is not good enough, while there is complication risk may more, follow-up patient's specification
Treatment and effective control of the state of an illness, the risk score then gradually reduce.
As preferable technical scheme, in the patient information collection module, the essential information of patient includes age, property
Not, medical history, body weight and result of clinical detection.
As preferable technical scheme, the items inputted when the risk class evaluation module is by analyzing patient assessment refer to
Mark, each index is compared with the risk factors preselected, once there is the risk factors phase one filtered out with early stage
The content of cause, corresponding score value will be provided, and the score value of all risk factors is tired out according to pre-designed score value
Add, calculate the final score of patient, objectively react the stage residing for the current disease of patient.
The present invention compared with prior art, has the following advantages that and beneficial effect：
1st, the object of the invention is overcoming deficiency of the prior art, by the essential information and coherence check knot of collecting patient
Fruit, such as：The indexs such as body weight, blood pressure, blood glucose, saccharification, blood fat, the above results are stored in database, establish archives for patient, compare
The hazards relation between diabetes, prediabetes and Healthy People is made a definite diagnosis, by each change of above-mentioned Baseline
Amount is used as independent variable, using the incidence of diabetes as dependent variable, establish Multivariate Logistic Regression model, calculates each change
The contribution of amount simultaneously obtains regression coefficient β, β meaning and for independent variable often increases a grade individual and increase to suffer from diabetes
Relative risk, the variable of significance is selected to establish model, so as to realize that " diabetic assesses and management system
(SEAD) " to the forecast function of onset diabetes.
2nd, the present invention can score complication for patients risk, so as to mark off risk class.The function passes through quantization
Scoring function, user can be allowed there are following complications and clearly prejudged, judged result is more directly perceived accurate, from
And intervene ahead of time, its forecast function is not available for current software.
Brief description of the drawings
Fig. 1 is the structural representation of present system.
Embodiment
With reference to embodiment and accompanying drawing, the present invention is described in further detail, but embodiments of the present invention are unlimited
In this.
Embodiment
As shown in figure 1, the present embodiment diabetic assesses and management system, by being registered to patient assessment's information
Achieve, establish database of patient information, by the database, can clearly check the change of patient assessment's record and indices
Change trend, while disease risks degree can be predicted, provide specific scoring；The system can also be according to patient characteristic
Scan for, risk score is provided according to the basic health status of patient, the function such as further consultation and follow-up prompting is set.Specifically,
The system of the present embodiment includes：
A kind of diabetic assesses and management system, it is characterised in that including patient information collection module, regression model
Establish module, data processing module, regression coefficient computing module, risk class evaluation module and follow-up setting module.
The patient information collection module, for collecting the essential information and inspection result of patient, and by essential information
Database is stored in inspection result, establishes the data archival of the patient.
The data processing module, for made a definite diagnosis between diabetes, prediabetes and Healthy People it is dangerous because
Plain relation, using each variable of above-mentioned Baseline as independent variable, using the incidence of diabetes as dependent variable, build
Vertical Multivariate Logistic Regression model, analyzes influence of each independent variable to onset diabetes situation, and then find out and cause glycosuria
Relevant risk factors occur for disease, and the Baseline refers to：(1) ordinary circumstance of patient, including age, sex and religion
Educate situation；(2) physical examination outcomes, including height, body weight, blood pressure and waist hip circumference；(3) laboratory examination results, including blood glucose,
Blood fat and insulin.
Regression coefficient computing module, the meaning for calculating the contribution of each variable and obtain regression coefficient β, β are
Independent variable often increases a grade, and patient is to increase a relative risk for suffering from diabetes, selects the variable of significance to build
Formwork erection type, the variable of the significantly meaning refer to the variable that regression coefficient value is more than 1.
The risk class evaluation module, for scoring complication for patients risk, so as to mark off risk class；
The follow-up setting module, for doing follow-up periodically or non-periodically to the state of an illness of patient, and by the record of follow-up
Data entry system.
The system is developed using PHP+MYSQL+APACHE programming languages, and its running environment is as follows：
Hardware：CPU：Intel double-core@2.50GHz or more；
Hard disk：More than 40G；
Internal memory：More than 1G；
Display：Resolution ratio 1024*768 or more；
Network bandwidth：It is required that more than 2M；
Operating system：Support Windows 2000/2003/XP/Vista/Windows7, including 32 and 64 versions.
Browser：Recommend FirFox 46.0.1 above versions, more than IE8.0 versions.
In the present embodiment, after the patient information collection module collects data, by patient's different time it is medical in
To hold and webserver storage is uploaded to by internet, the data of each patient is stored with different tabs, and in option
Card, which is provided with, can identify the customizing messages of patient identity, the customizing messages name, date of birth, medical number and ID,
Doctor can be scanned for by the customizing messages of the patient to patient assessment's record, transferred the data stored on server and looked into
Read, look back the details of medical situation in the past.
The content that the patient information collection module also includes enquiry module inquiry includes：The identity information of patient, certain
Medical specific time, reason, monitoring project content and numerical value, whether carry out therapeutic scheme adjustment；When doctor needs, as long as
Selection is corresponding to consult project, it is possible to transfers and stores on the server the previously record result of all projects, passes through curve
Mode, shown by transverse axis of consultation time, to consult the variation tendency of a certain index.The essential information of patient includes year
Age, sex, medical history, body weight and result of clinical detection.
In the data processing module, in addition to assignment module, for patient can be caused the danger of complication occur
The size that factor is contributed according to the factor carries out assignment, and the assignment of each factor is used to carry out risk class in risk class module
The foundation of division.
In the risk class grading module, first part of medical archives is established in patient and each time during further consultation,
The identity information of patient and medical data will be inputted, if meeting that the hazards of risk contribution input, then according to these
Hazards get a risk score to be medical every time, and the risk score is accumulated with the increase of risk factors, and scoring is got over
Height, prompt Morbidity control it is not good enough, while there is complication risk may more, effective control of follow-up patient Canonical management and the state of an illness
System, the risk score then gradually reduce.The indices inputted when the risk class evaluation module is by analyzing patient assessment,
Each index is compared with the risk factors preselected, once have consistent with the risk factors that early stage filters out
Content, corresponding score value will be provided according to pre-designed score value, and the score value of all risk factors is added up, counted
The final score of patient is calculated, objectively reacts the stage residing for the current disease of patient.
The risk class grading module includes the function of data input, data processing and data output：
Data input refers to include age, sex, medical history, body weight and the result of clinical detection etc. provided during patient assessment and counted
According to gathering input system by medical personnel, system is compared early stage by mass data, including Healthy People, diabetic and is had
The diabetic of complication, risk analysis is carried out to features described above with Logistic regression analyses, sugar may be had influence on by finding out
Occur the hazards of complication after urine disease morbidity, and selected for different hazards there is the contribution of complication
Go out the hazards being had a great influence to it as Appreciation gist, assign corresponding score value.In the information of patient assessment, by medical personnel
Computer is inputted, by the Internet transmission to server, stores and waits and analyzed in next step.
The indices inputted when data processing is by analyzing patient assessment, by each index and preselect
Risk factors compare, once there is the content consistent with the risk factors that early stage filters out, software will be according to being pre-designed
Good score value, provides corresponding score value, and the score value of all risk factors is added up, and calculates the final score of patient, with
The objectively stage residing for the reaction current disease of patient.
Data output refers in a manner of score value, arrives medical personnel's computer by internet outflow, shows, for medical care people
Member inquiry, the data of all inputs, by the interrogation of doctor, have a medical check-up and coherent detection obtain.Software systems are by the items of input
Index is converted into unified numerical score, goes out specific fraction with the form calculus of score, all evaluation indexes correspond to score value progress
It is cumulative to obtain overall score.Score value is higher, and the risk that complication currently occurs in representative is bigger, otherwise person's risk is smaller.Medical personnel
The size of complication possibility occurrence can be then estimated according to overall score.Score value is analyzed by doctor again and then instructs clinic
Diagnosis and treatment.
Meanwhile the score value of the score is dynamic.I.e. as patient is to the positive or passive treatment of disease, when some evaluations
After target improvement or exacerbation, the scoring of patient can also produce change therewith.Therefore, for patient, believe after going to a doctor each time
The input of breath, timely assessed equivalent to having been made again to patient's current body state.
In order to further verify the feasibility of the present invention, 200 parts of volunteer's blood samples and their essential information, root are acquired
According to inspection result by they be divided into healthy group, diabetes without complication group and diabetic complication group, by comparing between each group
Difference between baseline characteristic and assay, using Logistic regression analyses, finding out can cause patient complication occur
Hazards, and according to each hazards contribute size, assign score value.Patient establish first part of medical archives with
And each time during further consultation, these essential informations and medical data, such as body weight, waist hip circumference, blood glucose, blood fat can be all inputted,
As long as there is the information input for meeting risk contribution, the system can be got a risk and comment according to these data to be medical every time
Point, this scoring is accumulated with the increase of risk factors, and scoring is higher, prompts Morbidity control not good enough, while complication occur
Risk may be bigger.Effective control of follow-up patient Canonical management and the state of an illness, the scoring may be reduced gradually.Thus, this is
The appraisal result of system is dynamic evolution.
After patient per is medical, when doctor can set next reexamination time according to actual conditions and carry out follow-up to patient
Between, software can be recorded the time on the server by internet, when reaching the time, when doctor logs in the system,
System can be reminded doctor to carry out corresponding operating, do not operated such as, then the prompting will not disappear with automatic spring.
By above-mentioned technical scheme, the present invention can be predicted scoring to complication for patients risk, pass through
Logistic regression analyses, the risk factors that contribution be present to complication for patients are found out, and it is corresponding to assigning according to its size
Score value, by the accumulative of risk factors, objective scoring is carried out to patient symptom, adds up situation according to score to judge complication wind
The size of danger, while this scoring changes and dynamic change with conditions of patients again, this point is that current existing software does not possess.
Meanwhile because all data are stored within Internet Server, therefore, as long as having the computer that can connect networking, doctor
Anywhere above-mentioned function can be realized by access software website.
Above-described embodiment is the preferable embodiment of the present invention, but embodiments of the present invention are not by above-described embodiment
Limitation, other any Spirit Essences without departing from the present invention with made under principle change, modification, replacement, combine, simplification,
Equivalent substitute mode is should be, is included within protection scope of the present invention.
Claims (7)
1. a kind of diabetic assesses and management system, it is characterised in that is built including patient information collection module, regression model
Formwork erection block, data processing module, regression coefficient computing module, risk class evaluation module and follow-up setting module；
The patient information collection module, for collecting the essential information and inspection result of patient, and by essential information and inspection
The fruit that comes to an end is stored in database, establishes the data archival of the patient；
The data processing module, closed for having made a definite diagnosis the hazards between diabetes, prediabetes and Healthy People
System, using each variable of above-mentioned Baseline as independent variable, using the incidence of diabetes as dependent variable, establish more
First Logistic regression models, influence of each independent variable to onset diabetes situation is analyzed, and then find out and cause diabetes to be sent out
Raw relevant risk factors, the Baseline refer to：(1) ordinary circumstance of patient, including age, sex and education feelings
Condition；(2) physical examination outcomes, including height, body weight, blood pressure and waist hip circumference；(3) laboratory examination results, including blood glucose, blood fat
And insulin；
Regression coefficient computing module, the meaning for calculating the contribution of each variable and obtain regression coefficient β, β is from change
Amount often increases a grade, and patient is to increase a relative risk for suffering from diabetes, selects the variable of significance to establish mould
Type, the variable of the significantly meaning refer to the variable that regression coefficient value is more than 1；
The risk class evaluation module, for scoring complication for patients risk, so as to mark off risk class；
The follow-up setting module, for doing follow-up periodically or non-periodically to the state of an illness of patient, and by the record data of follow-up
Input system.
2. diabetic according to claim 1 assesses and management system, it is characterised in that the patient information is collected
After module collects data, the content that patient's different time is gone to a doctor is uploaded to the webserver by internet and stored, often
The data of individual patient is stored with different tabs, and the specific letter that can identify patient identity is provided with tab
Breath, the customizing messages name, date of birth, medical number and ID, doctor can be by the customizing messages of the patient to patient
Diagnosis records scan for, and transfer the data consultation stored on server, look back the details of medical situation in the past.
3. diabetic according to claim 2 assesses and management system, it is characterised in that also including enquiry module,
The content of inquiry includes：The identity information of patient, certain medical specific time, reason, monitoring project content and numerical value, whether
Carry out therapeutic scheme adjustment；When doctor needs, as long as selection is corresponding to consult project, it is possible to transfer storage on the server
The previously record result of all projects, by way of curve, shows by transverse axis of consultation time, to consult a certain finger
Target variation tendency.
4. diabetic according to claim 1 assesses and management system, it is characterised in that the data processing module
In, in addition to assignment module, for patient can be caused the size that the hazards of complication are contributed according to the factor occur
Assignment is carried out, the assignment of each factor is used in risk class module the foundation for carrying out risk class division.
5. diabetic according to claim 4 assesses and management system, it is characterised in that the risk class scoring
In module, establish first part of medical archives in patient and each time during further consultation, can all input patient identity information and
Medical data, if meeting that the hazards of risk contribution input, then get one according to these hazards to be medical every time
Individual risk score, the risk score are accumulated with the increase of risk factors, and scoring is higher, prompt Morbidity control not good enough, simultaneously
Complication risk occur may more, and effective control of follow-up patient Canonical management and the state of an illness, the risk score then gradually reduces.
6. diabetic's assessment and management system according to claim 1, it is characterised in that the patient information collects mould
In block, the essential information of patient includes age, sex, medical history, body weight and result of clinical detection.
7. diabetic's assessment and management system according to claim 1, it is characterised in that the risk class assesses mould
The indices inputted when block is by analyzing patient assessment, each index is compared with the risk factors preselected,
Once there is the content consistent with the risk factors that early stage filters out, will be provided corresponding according to pre-designed score value
Score value, and the score value of all risk factors is added up, the final score of patient is calculated, objectively reacts the current disease of patient
The residing stage.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1922200A (en) * | 2003-12-31 | 2007-02-28 | 拉帕波特家族医学研究院 | Methods of predicting a benefit of antioxidant therapy for prevention of cardiovascular disease in hyperglycemic patients |
| WO2009064901A2 (en) * | 2007-11-13 | 2009-05-22 | Veridex, Llc | Diagnostic biomarkers of diabetes |
| CN102930163A (en) * | 2012-11-01 | 2013-02-13 | 北京理工大学 | Method for judging 2 type diabetes mellitus risk state |
| CN104881591A (en) * | 2015-06-25 | 2015-09-02 | 贾建平 | Cognitive disorder data processing method and system |
| CN105930674A (en) * | 2016-03-04 | 2016-09-07 | 江苏鹿得医疗电子股份有限公司 | Medical health data collection based intelligent health forecasting and decision-making system |
-
2017
- 2017-07-13 CN CN201710571698.6A patent/CN107358047A/en not_active Withdrawn
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1922200A (en) * | 2003-12-31 | 2007-02-28 | 拉帕波特家族医学研究院 | Methods of predicting a benefit of antioxidant therapy for prevention of cardiovascular disease in hyperglycemic patients |
| WO2009064901A2 (en) * | 2007-11-13 | 2009-05-22 | Veridex, Llc | Diagnostic biomarkers of diabetes |
| CN102930163A (en) * | 2012-11-01 | 2013-02-13 | 北京理工大学 | Method for judging 2 type diabetes mellitus risk state |
| CN104881591A (en) * | 2015-06-25 | 2015-09-02 | 贾建平 | Cognitive disorder data processing method and system |
| CN105930674A (en) * | 2016-03-04 | 2016-09-07 | 江苏鹿得医疗电子股份有限公司 | Medical health data collection based intelligent health forecasting and decision-making system |
Non-Patent Citations (3)
| Title |
|---|
| 张静 等: "糖尿病发病危险因素的Logistic回归分析", 《医学与社会》 * |
| 段智娟: "2型糖尿病患者合并抑郁及相关因素的临床研究", 《中国优秀硕士学位论文全文数据库医药卫生科技辑》 * |
| 郑乐群 等: "2型糖尿病的发病危险因素非条件Logistic回归分析", 《临床研究》 * |
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