CN107423560A - Based on Rating Model type-II diabetes are carried out with the method and device of risk score - Google Patents
Based on Rating Model type-II diabetes are carried out with the method and device of risk score Download PDFInfo
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Abstract
The present invention proposes a kind of method and device that based on Rating Model type-II diabetes are carried out with risk score, wherein, based on Rating Model type-II diabetes should be carried out with the method for risk score is included：Obtain the genetic test result of user and outside pathogenetic feature data；According to the Rating Model of genetic test result, outside pathogenetic feature data and training in advance, the risk score of the type-II diabetes of user is determined；The advisory information according to corresponding to obtaining risk score, and risk score and corresponding advisory information are supplied to user.The method that based on Rating Model type-II diabetes are carried out with risk score of the present invention, facilitate the risk score that user obtains type-II diabetes, and user is facilitated to be known from suffering from the risk of disease according to risk score, relative to conventional method, reduce time and the cost of the risk score of user's acquisition type-II diabetes, improve the user experience of user.
Description
Technical field
The present invention relates to medical data process field, more particularly to one kind enters sector-style based on Rating Model to type-II diabetes
The method and device nearly to score.
Background technology
Type-II diabetes original name, more in the sequela of 35~40 years old, accounts for diabetic Adult Onset's patients with type Ⅰ DM
More than 90%.The ability that type-II diabetes patient's body produces insulin not completely loses, and some patient's body insulin is very
It is excessive to producing, but the action effect of insulin is poor, therefore the insulin of patient's body is a kind of relative shortage, can be passed through
Some oral drugs stimulate the secretion of internal insulin.But still there are some patients to need to use insulin therapy to the later stage.
In correlation technique, sugared endurance test, fasting blood-glucose or HbA1c and the complication such as PVR of hospital can be passed through
(Retinopathy) mode such as relation of result detect the detection of diabetes, and the mode of above-mentioned detection diabetes is typically
Determine whether user suffers from diabetes according to physical features such as blood glucose etc., diabetes are only suffered from the result obtained or are not had
There are to obtain diabetes, the risk of user can not be provided, so as to which user can not be prevented according to risk, also, go
The consumed time is detected by hospital and cost is higher, not convenient enough for user.Because type-II diabetes are by inherent cause
Caused by environmental factor collective effect, belong to multigenic disease, therefore, how with reference to inherent cause and environmental factor, be
User provide it is a kind of can the accurate evaluation type-II diabetes that go out user risk it is particularly significant for a user.
The content of the invention
It is contemplated that at least solves one of technical problem in correlation technique to a certain extent.
Therefore, it is an object of the present invention to propose that a kind of Rating Model that is based on carries out risk score to type-II diabetes
Method, this method provide a kind of method of the risk score of the type-II diabetes by Rating Model accurate evaluation user,
Facilitate user and obtain the risk score of type-II diabetes, and facilitate user to be known from suffering from the wind of disease according to risk score
Danger, relative to conventional method, reduce time and the cost of the risk score of user's acquisition type-II diabetes, improve user's
User experience.
Second object of the present invention is to propose a kind of to carry out risk score to type-II diabetes based on Rating Model
Device.
Third object of the present invention is to propose a kind of to carry out risk score to type-II diabetes based on Rating Model
Device.
Fourth object of the present invention is to propose a kind of nonvolatile computer storage media.
The 5th purpose of the present invention is to propose a kind of computer program product.
For the above-mentioned purpose, first aspect present invention embodiment proposes a kind of is entered based on Rating Model to type-II diabetes
The method of row risk score, including：Obtain the genetic test result of user and outside pathogenetic feature data；Examined according to the gene
The Rating Model of result, outside pathogenetic feature data and training in advance is surveyed, determines that the risk of the type-II diabetes of the user is commented
Point；The advisory information according to corresponding to obtaining the risk score, and the risk score and corresponding advisory information are provided
To the user.
The method that based on Rating Model type-II diabetes are carried out with risk score of the embodiment of the present invention, by by acquired in
User genetic test result and outside pathogenetic feature data input into the Rating Model of training in advance, pass through Rating Model
The risk score of the type-II diabetes of user, and the advisory information according to corresponding to obtaining risk score are determined, and user is suffered from
The risk score of type-II diabetes and corresponding advisory information are supplied to user, thus, facilitate user and obtain two type glycosurias
The risk score of disease, and facilitate user to be known from suffering from the risk of disease according to risk score, relative to conventional method, reduce
User obtains time and the cost of the risk score of type-II diabetes, improves the user experience of user.
For the above-mentioned purpose, second aspect of the present invention embodiment proposes one kind and made based on Rating Model to type-II diabetes
The device of risk score is carried out, including：Acquisition module, for the genetic test result for obtaining user and outside pathogenetic feature number
According to；Determining module, for the Rating Model according to the genetic test result, outside pathogenetic feature data and training in advance, really
The risk score of the type-II diabetes of the fixed user；Processing module is used for the recommendation letter according to corresponding to obtaining the risk score
Breath, and the risk score and corresponding advisory information are supplied to the user.
The device that based on Rating Model type-II diabetes are carried out with risk score of the embodiment of the present invention, by by acquired in
User genetic test result and outside pathogenetic feature data input into the Rating Model of training in advance, pass through Rating Model
The risk score of the type-II diabetes of user, and the advisory information according to corresponding to obtaining risk score are determined, and user is suffered from
The risk score of type-II diabetes and corresponding advisory information are supplied to user, thus, facilitate user and obtain two type glycosurias
The risk score of disease, and facilitate user to be known from suffering from the risk of disease according to risk score, relative to conventional method, reduce
User obtains time and the cost of the risk score of type-II diabetes, improves the user experience of user.
Third aspect present invention embodiment proposes a kind of carries out risk score based on Rating Model to type-II diabetes
Device, it is characterised in that including：Processor；For storing the memory of processor-executable instruction；Wherein, the processor
It is configured as：Obtain the genetic test result of user and outside pathogenetic feature data；According to the genetic test result, outside cause
The Rating Model of sick characteristic and training in advance, determine the risk score of the type-II diabetes of the user；According to the wind
Advisory information corresponding to the scoring acquisition of danger, and the risk score and corresponding advisory information are supplied to the user.
Fourth aspect present invention embodiment provides a kind of nonvolatile computer storage media, and the computer storage is situated between
Matter is stored with one or more program, when one or more of programs are performed by an equipment so that the equipment
Perform the method that based on Rating Model type-II diabetes are carried out with risk score with first aspect present invention embodiment.
Fifth aspect present invention embodiment provides a kind of computer program product, when in the computer program product
When instruction processing unit performs, a kind of method that based on Rating Model type-II diabetes are carried out with risk score, methods described are performed
Including：Obtain the genetic test result of user and outside pathogenetic feature data；According to the genetic test result, outside spy of causing a disease
The Rating Model of data and training in advance is levied, determines the risk score of the type-II diabetes of the user；Commented according to the risk
Separately win and take corresponding advisory information, and the risk score and corresponding advisory information are supplied to the user.
The additional aspect of the present invention and advantage will be set forth in part in the description, and will partly become from the following description
Obtain substantially, or recognized by the practice of the present invention.
Brief description of the drawings
The above-mentioned and/or additional aspect and advantage of the present invention will become in the description from combination accompanying drawings below to embodiment
Substantially and it is readily appreciated that, wherein：
Fig. 1 is the method that based on Rating Model type-II diabetes are carried out with risk score according to one embodiment of the invention
Flow chart；
Fig. 2 is the refined flow chart of training Rating Model；
Fig. 3 is the side that based on Rating Model type-II diabetes are carried out with risk score according to another embodiment of the present invention
The flow chart of method；
Fig. 4 is the device that based on Rating Model type-II diabetes are carried out with risk score according to one embodiment of the invention
Structural representation；
Fig. 5 is the dress that based on Rating Model type-II diabetes are carried out with risk score according to another embodiment of the present invention
The structural representation put；
Fig. 6 is the dress that based on Rating Model type-II diabetes are carried out with risk score according to another embodiment of the invention
The structural representation put.
Embodiment
Embodiments of the invention are described below in detail, the example of the embodiment is shown in the drawings, wherein from beginning to end
Same or similar label represents same or similar element or the element with same or like function.Below with reference to attached
The embodiment of figure description is exemplary, is only used for explaining the present invention, and is not considered as limiting the invention.
In the description of the invention, it is to be understood that term " multiple " refers to two or more；Term " first ",
" second " is only used for describing purpose, and it is not intended that instruction or hint relative importance.
Because the symptom of type-II diabetes may be similar to a patients with type Ⅰ DM, and symptom is often unreal apparent.So
It may be just diagnosed to be with diabetes after morbidity for many years, complication now occurred.Because type-II diabetes are by heredity
Caused by factor and environmental factor collective effect, belong to multigenic disease, therefore, the embodiment combination inherent cause and environment
A kind of factor, it is proposed that method and device that based on Rating Model type-II diabetes are carried out with risk score.
Below with reference to the accompanying drawings description is according to embodiments of the present invention is commented type-II diabetes progress risk based on Rating Model
The method and device divided.
Fig. 1 is the method that based on Rating Model type-II diabetes are carried out with risk score according to one embodiment of the invention
Flow chart.
As shown in figure 1, the side that based on Rating Model type-II diabetes are carried out with risk score according to embodiments of the present invention
Method, comprise the following steps.
S11, obtain the genetic test result of user and outside pathogenetic feature data.
The genetic test result and outside uploaded as a kind of exemplary embodiment, reception user by terminal is caused a disease
Characteristic.
Wherein, terminal can be the hardware device that computer, tablet personal computer, smart mobile phone etc. have various operating systems.
For example, during smart mobile phone, user can by smart mobile phone by the genetic test result of itself and
Outside pathogenetic feature data upload onto the server.
Wherein, comprising the gene data for having substantial connection with type-II diabetes in genetic test result, for example, base
Because TCF7L2 genes can be included in testing result.
, wherein it is desired to explanation, most of all relevant with β cell functions with the related gene of diabetes.
Wherein, outside pathogenetic feature data refer to outside other relevant with causing type-II diabetes in addition to genetic factors
Portion's factor.
Wherein, outside pathogenetic feature data can include but is not limited to lifestyle data, family's medical history data, pharmacohistory
Data and physical trait data.
Wherein, the data in terms of lifestyle data can include but is not limited to mean motion amount, diet are such as whether preference
The data such as rich food dish, coffee.
Data can include the disease condition of lineal relative's type-II diabetes within three generations during h disease.
Pharmacohistory data can include but is not limited to glucocorticoid, thiazide diuretic (Thiazide), beta receptor retardance
The use feelings of agent (β-blockers), atypical antipsychotic (Atypical antipsychotic) and statins
Condition., wherein it is desired to explanation, the use of said medicine can improve the risk of diabetes.
Physical trait data can include but is not limited to personal body-mass index BMI (Body Mass Index), year
The data such as discipline, sex, the length of one's sleep.
As a kind of exemplary embodiment, can provide the user a kind of including life style, family's medical history, pharmacohistory
The survey of option, and the data acquisition life style number filled according to user in survey must be filled out with physical trait etc.
According to, outside pathogenetic feature data such as family's medical history data, pharmacohistory data and physical trait data.
For example, when user opens the product for assessing type-II diabetes risk by terminal, type-II diabetes are assessed
The product of risk can prompt the genetic test result that user uploads itself, and provide comprising life style, family's medical history, pharmacohistory
The user interface of option must be filled out with physical trait etc., and receives user and is directed to the related data that respective selection is filled in, and will be used
The related data that family is filled in uploads onto the server so that server obtain the lifestyle data of user, family's medical history data,
The outside pathogenetic feature data such as pharmacohistory data and physical trait data.
As another exemplary embodiment, the genetic test result that user is uploaded by terminal, Yi Jicong are received
Pre-save the outside pathogenetic feature data that corresponding user is obtained in the database of the outside pathogenetic feature data of user.
, wherein it is desired to illustrate, the outside pathogenetic feature data of the user preserved in above-mentioned database can be by more
Kind of mode obtains, for example, in user's registration, could fill out said external pathogenetic feature data, or, server is from other medical cares
The outside pathogenetic feature data of user are obtained in system.
S12, according to the Rating Model of genetic test result, outside pathogenetic feature data and training in advance, determine user's
The risk score of type-II diabetes.
In one embodiment of the invention, after the genetic test result of user and outside pathogenetic feature data are obtained,
Can be by genetic test result and outside pathogenetic feature data input to the Rating Model of training in advance, Rating Model is by analyzing base
Because testing result and outside pathogenetic feature data determine the risk score of the type-II diabetes of user.
, wherein it is desired to understand, above-mentioned Rating Model is obtained by training in advance.
In one embodiment of the invention, the detailed process of Rating Model is trained, as shown in Fig. 2 can include：
S21, obtain the sample genetic test result and sample foreign pathogenetic feature data of sample of users.
As a kind of exemplary embodiment, caused in the sample foreign that sample of users is obtained by way of survey
During sick characteristic, after the Questionnaire results of sample of users are obtained, it can determine whether that the sample foreign in Questionnaire results causes
Whether sick characteristic is complete, when the sample foreign pathogenetic feature data in determining Questionnaire results are imperfect, can incite somebody to action
Corresponding Questionnaire results are deleted, to reduce incomplete sample foreign pathogenetic feature data to follow-up training Rating Model
Influence.
As another exemplary embodiment, the sample foreign of sample of users is being obtained by way of survey
During pathogenetic feature data, after the Questionnaire results of sample of users are obtained, the sample foreign in Questionnaire results can determine whether
Whether pathogenetic feature data are complete, can when the sample foreign pathogenetic feature data in determining Questionnaire results are imperfect
Determine whether incomplete ratio exceedes predetermined threshold value in sample foreign pathogenetic feature data, if it exceeds default threshold
Value, corresponding Questionnaire results are deleted, to reduce incomplete sample foreign pathogenetic feature data to follow-up training scoring
The influence of model.
If it is determined that incomplete ratio is not less than predetermined threshold value in sample foreign pathogenetic feature data, then according to other samples
Pathogenetic feature data complete questionnaire adjustment result in this outside adjusts incomplete part in result to the questionnaire and handled, with
Make the sample foreign pathogenetic feature data in questionnaire adjustment result complete.
Wherein, predetermined threshold value is pre-set.
For example, it is assumed that not comprising age information in current questionnaire adjustment result, and determine current questionnaire adjustment
As a result the incomplete ratio of sample foreign pathogenetic feature data is not less than predetermined threshold value, now, if according to other sample foreigns
The complete questionnaire adjustment result of pathogenetic feature data determines that the average value at age is 30 years old, then is adjusted 30 years old as current questionnaire
As a result the age information in.
S22, obtain scoring labeled data corresponding with sample genetic test result and sample foreign pathogenetic feature data.
S23, sample genetic test result, sample foreign pathogenetic feature data and scoring labeled data are trained, with
Obtain Rating Model.
As a kind of exemplary embodiment, server can utilize sample of the machine learning method to great amount of samples user
Genetic test result, sample foreign pathogenetic feature data and scoring labeled data are trained, to determine sample genetic test
As a result, the corresponding relation between sample foreign pathogenetic feature data and scoring labeled data, and according to the corresponding relation obtained
Establish Rating Model.
That is, the embodiment is entered by machine learning method to a large amount of medical datas related on type-II diabetes
Row analysis, by the method for machine learning and data mining, have found the potential rule of type-II diabetes, has trained high accuracy
The high performance model for assessing risk.
Wherein, scoring labeled data is the sample genetic test result and sample foreign pathogenetic feature number according to sample of users
According to the risk score of the type-II diabetes marked in advance.
, wherein it is desired to understand, the height of risk score is relevant with following factor：The sample gene inspection of sample of users
Survey result and determine whether some crucial protein related to type-II diabetes undergo mutation, and determine sample genetic test
As a result whether the gene related to type-II diabetes undergos mutation in, and is undergone mutation in the gene related to type-II diabetes
When, the type (such as missing, termination in advance) of gene mutation is determined, and determine that the gene related to type-II diabetes occurs prominent
Whether change influences β cell functions, and the source of mutation.
Wherein, the source of mutation may be from paternal or maternal.
Wherein, the source of mutation can be determined by the paternal or maternal genetic test result of sample of users.
In one embodiment of the invention, in order to accurately establish Rating Model, random forests algorithm can be based on, to sample
Genetic test result, sample foreign pathogenetic feature data and scoring labeled data are trained, to obtain Rating Model.
Specifically, after sample genetic test result, sample foreign pathogenetic feature data and scoring labeled data is obtained,
Random forests algorithm can be first passed through to carry out sample genetic test result, sample foreign pathogenetic feature data and scoring labeled data
Training, to obtain the Rating Model trained.
After Rating Model is trained, test data set can be obtained, wherein, test data set includes test cdna
Testing result, the outside pathogenetic feature data of test and corresponding scoring labeled data.Then, by test cdna testing result, survey
The outside pathogenetic feature data input of examination is into Rating Model, to obtain the risk score result exported in Rating Model, afterwards, leads to
Cross corresponding scoring labeled data in the risk score result exported in Rating Model and test data set and determine Rating Model
The degree of accuracy whether exceed the degree of accuracy threshold value that pre-sets, if not less than the degree of accuracy threshold value pre-set, to scoring
The model parameter of model carries out tuning processing, to improve the degree of accuracy of Rating Model by adjusting model parameter.
, wherein it is desired to explanation, test data set obtains in advance, for example, can press the data collected in advance
Ratio is randomly divided into training dataset and test data set, to obtain test data set by this way.
, wherein it is desired to understand, during using Rating Model, in order to improve constantly the accurate of Rating Model
Property, Rating Model is updated based on the training data set after renewal after preset time can be spaced.
S13, the advisory information according to corresponding to obtaining risk score, and risk score and corresponding advisory information are provided
To user.
As a kind of exemplary embodiment, determining that the risk that user suffers from type-II diabetes is commented by Rating Model
After point, it can be thought according to the risk score for suffering from type-II diabetes pre-saved with the corresponding pass of advisory information, obtained and corresponding wind
Advisory information corresponding to the scoring of danger, and risk score and corresponding advisory information are supplied to user.
, wherein it is desired to understand, the risk that risk score is smaller to show that user suffers from type-II diabetes is lower, risk score
It is higher show that user suffers from type-II diabetes risk it is higher.
The method that based on Rating Model type-II diabetes are carried out with risk score of the embodiment of the present invention, by by acquired in
User genetic test result and outside pathogenetic feature data input into the Rating Model of training in advance, pass through Rating Model
The risk score of the type-II diabetes of user, and the advisory information according to corresponding to obtaining risk score are determined, and user is suffered from
The risk score of type-II diabetes and corresponding advisory information are supplied to user, thus, there is provided one kind passes through Rating Model
The method of the risk score of the type-II diabetes of accurate evaluation user, the risk score that user obtains type-II diabetes is facilitated,
And facilitate user to be known from suffering from the risk of disease according to risk score, relative to conventional method, reduce user and obtain two types
The time of the risk score of diabetes and cost, improve the user experience of user.
Fig. 3 is the side that based on Rating Model type-II diabetes are carried out with risk score according to another embodiment of the present invention
The flow chart of method.
As shown in figure 3, the side that based on Rating Model type-II diabetes are carried out with risk score according to embodiments of the present invention
Method, comprise the following steps.
S31, obtain the genetic test result of user and outside pathogenetic feature data.
S32, according to the Rating Model of genetic test result, outside pathogenetic feature data and training in advance, determine user's
The risk score of type-II diabetes.
, wherein it is desired to explanation, the explanation to step S11-S12 are also applied for step S31-S32, not gone to live in the household of one's in-laws on getting married herein
State.
S33, judges whether risk score exceedes predetermined threshold value, if so, then performing step S34, otherwise performs step S35.
Wherein, predetermined threshold value is the threshold value of the risk score pre-set.
S34, the advisory information for providing a user risk score and further checking.
That is, when judging that the risk score of type-II diabetes of user exceedes predetermined threshold value, it may be determined that user
Type-II diabetes are very likely suffered from, in order to find and treat in time, now, can suggest that user carries out a step inspection to hospital.
S35, provide a user risk score and prevent the advisory information of type-II diabetes.
Wherein, dietary recommendation and exercise suggestion can be included but is not limited to by preventing the advisory information of type-II diabetes.
The method that based on Rating Model type-II diabetes are carried out with risk score of the embodiment of the present invention, by by acquired in
User genetic test result and outside pathogenetic feature data input into the Rating Model of training in advance, pass through Rating Model
The risk score of the type-II diabetes of user is determined, and gives user rational advisory information according to risk score, facilitates use
Family further checks or understood the information relevant with prevention type-II diabetes according to advisory information.
In order to realize above-described embodiment, the invention also provides one kind based on Rating Model to type-II diabetes progress risk
The device of scoring.
Fig. 4 is the device that based on Rating Model type-II diabetes are carried out with risk score according to one embodiment of the invention
Structural representation.
As shown in figure 4, based on Rating Model type-II diabetes should be carried out with the device of risk score includes acquisition module
110th, determining module 120 and processing module 130, wherein：
Acquisition module 110 is used to obtain the genetic test result of user and outside pathogenetic feature data.
Wherein, outside pathogenetic feature data can include but is not limited to lifestyle data, family's medical history data, pharmacohistory
Data and physical trait data.
Wherein, outside pathogenetic feature data refer to outside other relevant with causing type-II diabetes in addition to genetic factors
Portion's factor.
Wherein, outside pathogenetic feature data can include but is not limited to lifestyle data, family's medical history data, pharmacohistory
Data and physical trait data.
Wherein, the data in terms of lifestyle data can include but is not limited to mean motion amount, diet are such as whether preference
The data such as rich food dish, coffee.
Data can include the disease condition of lineal relative's type-II diabetes within three generations during h disease.
Pharmacohistory data can include but is not limited to glucocorticoid, thiazide diuretic (Thiazide), beta receptor retardance
The use feelings of agent (β-blockers), atypical antipsychotic (Atypical antipsychotic) and statins
Condition., wherein it is desired to explanation, the use of said medicine can improve the risk of diabetes.
Physical trait data can include but is not limited to personal body-mass index BMI (Body Mass Index), year
The data such as discipline, sex, the length of one's sleep.
Determining module 120 is used for the Rating Model according to genetic test result, outside pathogenetic feature data and training in advance,
Determine the risk score of the type-II diabetes of user.
Processing module 130 is used for the advisory information according to corresponding to obtaining risk score, and by risk score and corresponding
Advisory information is supplied to user.
In one embodiment of the invention, in order to pass through the risk of the type-II diabetes of Rating Model accurate evaluation user
Scoring, on the basis of the embodiment shown in Fig. 4, as shown in figure 5, the device can also include training module 140, wherein, instruction
Practice sample genetic test result and sample foreign pathogenetic feature data that module 140 is used to obtain sample of users, and acquisition and sample
Scoring labeled data corresponding to this genetic test result and sample foreign pathogenetic feature data, and according to sample genetic test knot
Fruit, sample foreign pathogenetic feature data and scoring labeled data are trained, to obtain Rating Model.
In one embodiment of the invention, in order to train to obtain accurate Rating Model, training module 140 is specifically used
In：Based on random forests algorithm, entered according to sample genetic test result, sample foreign pathogenetic feature data and scoring labeled data
Row training, to obtain Rating Model.
, wherein it is desired to understand, after Rating Model is trained by training module 140, the device can also include
Test module (not shown), test module are used to obtain test data set, wherein, test data set includes test
Genetic test result, the outside pathogenetic feature data of test and corresponding scoring labeled data.Then, test cdna is detected and tied
Fruit, the outside pathogenetic feature data input of test are into Rating Model, to obtain the risk score result exported in Rating Model, it
Afterwards, scoring is determined by corresponding scoring labeled data in the risk score result exported in Rating Model and test data set
Whether the degree of accuracy of model exceedes the degree of accuracy threshold value pre-set, if not less than the degree of accuracy threshold value pre-set, it is right
The model parameter of Rating Model carries out tuning processing, to improve the degree of accuracy of Rating Model by adjusting model parameter.
, wherein it is desired to understand, during using Rating Model, in order to improve constantly the accurate of Rating Model
Property, Rating Model is updated based on the training data set after renewal after preset time can be spaced.
In one embodiment of the invention, in order to reasonably suggest to user, on the basis of the embodiment shown in Fig. 5
On, as shown in fig. 6, the processing module 130, which can include judging unit 131, first, provides the offer unit of unit 132 and second
133, wherein：
Judging unit 131 is used to judge whether risk score exceedes predetermined threshold value.
First provide unit 132 be used for when judging that risk score exceedes predetermined threshold value, provide a user risk score with
The advisory information further checked.
Second, which provides unit 133, is used for when judging risk score not less than predetermined threshold value, provides a user risk score
With the advisory information of prevention type-II diabetes.
, wherein it is desired to explanation, the foregoing method to based on Rating Model type-II diabetes are carried out with risk score are real
The explanation for applying example is also applied for the device that based on Rating Model type-II diabetes are carried out with risk score of the embodiment, this
Place repeats no more.
The device that based on Rating Model type-II diabetes are carried out with risk score of the embodiment of the present invention, by by acquired in
User genetic test result and outside pathogenetic feature data input into the Rating Model of training in advance, pass through Rating Model
The risk score of the type-II diabetes of user, and the advisory information according to corresponding to obtaining risk score are determined, and user is suffered from
The risk score of type-II diabetes and corresponding advisory information are supplied to user, thus, facilitate user and obtain two type glycosurias
The risk score of disease, and facilitate user to be known from suffering from the risk of disease according to risk score, relative to conventional method, reduce
User obtains time and the cost of the risk score of type-II diabetes, improves the user experience of user.
In the description of this specification, reference term " one embodiment ", " some embodiments ", " example ", " specifically show
The description of example " or " some examples " etc. means specific features, structure, material or the spy for combining the embodiment or example description
Point is contained at least one embodiment or example of the present invention.In this manual, to the schematic representation of above-mentioned term not
Identical embodiment or example must be directed to.Moreover, specific features, structure, material or the feature of description can be with office
Combined in an appropriate manner in one or more embodiments or example.In addition, in the case of not conflicting, the skill of this area
Art personnel can be tied the different embodiments or example and the feature of different embodiments or example described in this specification
Close and combine.
In addition, term " first ", " second " are only used for describing purpose, and it is not intended that instruction or hint relative importance
Or the implicit quantity for indicating indicated technical characteristic.Thus, define " first ", the feature of " second " can be expressed or
Implicitly include at least one this feature.In the description of the invention, " multiple " are meant that two or more, unless separately
There is clearly specific limit.
Any process or method described otherwise above description in flow chart or herein is construed as, and represents to include
Module, fragment or the portion of the code of the executable instruction of one or more the step of being used to realize specific logical function or process
Point, and the scope of the preferred embodiment of the present invention includes other realization, wherein can not press shown or discuss suitable
Sequence, including according to involved function by it is basic simultaneously in the way of or in the opposite order, carry out perform function, this should be of the invention
Embodiment person of ordinary skill in the field understood.
Expression or logic and/or step described otherwise above herein in flow charts, for example, being considered use
In the order list for the executable instruction for realizing logic function, may be embodied in any computer-readable medium, for
Instruction execution system, device or equipment (such as computer based system including the system of processor or other can be held from instruction
The system of row system, device or equipment instruction fetch and execute instruction) use, or combine these instruction execution systems, device or set
It is standby and use.For the purpose of this specification, " computer-readable medium " can any can be included, store, communicate, propagate or pass
Defeated program is for instruction execution system, device or equipment or the dress used with reference to these instruction execution systems, device or equipment
Put.The more specifically example (non-exhaustive list) of computer-readable medium includes following：Electricity with one or more wiring
Connecting portion (electronic installation), portable computer diskette box (magnetic device), random access memory (RAM), read-only storage
(ROM), erasable edit read-only storage (EPROM or flash memory), fiber device, and portable optic disk is read-only deposits
Reservoir (CDROM).In addition, computer-readable medium, which can even is that, to print the paper of described program thereon or other are suitable
Medium, because can then enter edlin, interpretation or if necessary with it for example by carrying out optical scanner to paper or other media
His suitable method is handled electronically to obtain described program, is then stored in computer storage.
It should be appreciated that each several part of the present invention can be realized with hardware, software, firmware or combinations thereof.Above-mentioned
In embodiment, software that multiple steps or method can be performed in memory and by suitable instruction execution system with storage
Or firmware is realized.If, and in another embodiment, can be with well known in the art for example, realized with hardware
Any one of row technology or their combination are realized：With the logic gates for realizing logic function to data-signal
Discrete logic, have suitable combinational logic gate circuit application specific integrated circuit, programmable gate array (PGA), scene
Programmable gate array (FPGA) etc..
Those skilled in the art are appreciated that to realize all or part of step that above-described embodiment method carries
Suddenly it is that by program the hardware of correlation can be instructed to complete, described program can be stored in a kind of computer-readable storage medium
In matter, the program upon execution, including one or a combination set of the step of embodiment of the method.
In addition, each functional unit in each embodiment of the present invention can be integrated in a processing module, can also
That unit is individually physically present, can also two or more units be integrated in a module.Above-mentioned integrated mould
Block can both be realized in the form of hardware, can also be realized in the form of software function module.The integrated module is such as
Fruit is realized in the form of software function module and as independent production marketing or in use, can also be stored in a computer
In read/write memory medium.
Storage medium mentioned above can be read-only storage, disk or CD etc..Although have been shown and retouch above
Embodiments of the invention are stated, it is to be understood that above-described embodiment is exemplary, it is impossible to be interpreted as the limit to the present invention
System, one of ordinary skill in the art can be changed to above-described embodiment, change, replace and become within the scope of the invention
Type.
Claims (10)
- A kind of 1. method that based on Rating Model type-II diabetes are carried out with risk score, it is characterised in that comprise the following steps：Obtain the genetic test result of user and outside pathogenetic feature data；According to the Rating Model of the genetic test result, outside pathogenetic feature data and training in advance, determine the user's The risk score of type-II diabetes；The advisory information according to corresponding to obtaining the risk score, and the risk score and corresponding advisory information are provided To the user.
- 2. the method as described in claim 1, it is characterised in that the training Rating Model, including：Obtain the sample genetic test result and sample foreign pathogenetic feature data of sample of users；Obtain scoring labeled data corresponding with the sample genetic test result and sample foreign pathogenetic feature data；The sample genetic test result, sample foreign pathogenetic feature data and the scoring labeled data are trained, with Obtain the Rating Model.
- 3. method as claimed in claim 2, it is characterised in that described to be caused to the sample genetic test result, sample foreign Sick characteristic and the scoring labeled data are trained, to obtain the Rating Model, including：Based on random forests algorithm, the sample genetic test result, sample foreign pathogenetic feature data and the scoring are marked Note data are trained, to obtain the Rating Model.
- 4. the method as described in claim 1, it is characterised in that the recommendation letter according to corresponding to obtaining the risk score Breath, including：Judge whether the risk score exceedes predetermined threshold value；If judging, the risk score exceedes predetermined threshold value, to the user provides the risk score and further checks Advisory information；If judging, not less than predetermined threshold value, the risk score and prevention two types sugar are provided to the user for the risk score Urinate the advisory information of disease.
- 5. the method as described in claim any one of 1-4, it is characterised in that the outside pathogenetic feature data include life side Formula data, family's medical history data, pharmacohistory data and physical trait data.
- A kind of 6. device that based on Rating Model type-II diabetes are carried out with risk score, it is characterised in that including：Acquisition module, for the genetic test result for obtaining user and outside pathogenetic feature data；Determining module, for the Rating Model according to the genetic test result, outside pathogenetic feature data and training in advance, really The risk score of the type-II diabetes of the fixed user；Processing module, for the advisory information according to corresponding to risk score acquisition, and by the risk score and correspondingly Advisory information be supplied to the user.
- 7. device as claimed in claim 6, it is characterised in that described device also includes：Training module, for obtaining the sample genetic test result and sample foreign pathogenetic feature data of sample of users, and obtain The labeled data that scores corresponding with the sample genetic test result and sample foreign pathogenetic feature data, and to the sample Genetic test result, sample foreign pathogenetic feature data and the scoring labeled data are trained, to obtain the scoring mould Type.
- 8. device as claimed in claim 6, it is characterised in that the training module, be specifically used for：Based on random forests algorithm, the sample genetic test result, sample foreign pathogenetic feature data and the scoring are marked Note data are trained, to obtain the Rating Model.
- 9. device as claimed in claim 6, it is characterised in that the processing module includes：Judging unit, for judging whether the risk score exceedes predetermined threshold value；First provides unit, for when judging that the risk score exceedes predetermined threshold value, the risk to be provided to the user Scoring and the advisory information further checked；Second provides unit, for when judging the risk score not less than predetermined threshold value, the wind to be provided to the user Danger scoring and the advisory information of prevention type-II diabetes.
- 10. the device as described in claim any one of 6-9, it is characterised in that the outside pathogenetic feature data include life Mode data, family's medical history data, pharmacohistory data and physical trait data.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108766569A (en) * | 2018-05-07 | 2018-11-06 | 苏州超云生命智能产业研究院有限公司 | Health data processing method and health data processing system |
| CN109616168A (en) * | 2018-12-14 | 2019-04-12 | 北京工业大学 | A kind of medical field Intelligent management model construction method based on electronic health record |
| CN110037710A (en) * | 2018-01-16 | 2019-07-23 | 中央研究院 | The System and method for of non-intrusion type estimation HBA1C and blood glucose value |
| CN110838366A (en) * | 2019-10-15 | 2020-02-25 | 平安科技（深圳）有限公司 | Method and device for predicting disease risk |
| CN113658704A (en) * | 2021-09-17 | 2021-11-16 | 平安国际智慧城市科技股份有限公司 | Diabetes risk prediction device, apparatus and storage medium |
| CN115602328A (en) * | 2022-11-16 | 2023-01-13 | 深圳技术大学（Cn） | Early warning method and device for acute leukemia |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104087658A (en) * | 2014-06-03 | 2014-10-08 | 南京医科大学 | SNPs primers for detecting type 2 diabetes risk and application thereof |
| CN106295241A (en) * | 2015-06-25 | 2017-01-04 | 杭州圣庭生物技术有限公司 | Breast carcinoma risk assessment algorithm based on BRCA1 and BRCA2 sudden change |
| CN106326651A (en) * | 2011-08-26 | 2017-01-11 | 弗吉尼亚大学专利基金会 | Method and system for adaptive advisory control of diabetes |
-
2017
- 2017-06-27 CN CN201710501487.5A patent/CN107423560A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106326651A (en) * | 2011-08-26 | 2017-01-11 | 弗吉尼亚大学专利基金会 | Method and system for adaptive advisory control of diabetes |
| CN104087658A (en) * | 2014-06-03 | 2014-10-08 | 南京医科大学 | SNPs primers for detecting type 2 diabetes risk and application thereof |
| CN106295241A (en) * | 2015-06-25 | 2017-01-04 | 杭州圣庭生物技术有限公司 | Breast carcinoma risk assessment algorithm based on BRCA1 and BRCA2 sudden change |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110037710A (en) * | 2018-01-16 | 2019-07-23 | 中央研究院 | The System and method for of non-intrusion type estimation HBA1C and blood glucose value |
| CN108766569A (en) * | 2018-05-07 | 2018-11-06 | 苏州超云生命智能产业研究院有限公司 | Health data processing method and health data processing system |
| CN109616168A (en) * | 2018-12-14 | 2019-04-12 | 北京工业大学 | A kind of medical field Intelligent management model construction method based on electronic health record |
| CN110838366A (en) * | 2019-10-15 | 2020-02-25 | 平安科技（深圳）有限公司 | Method and device for predicting disease risk |
| CN113658704A (en) * | 2021-09-17 | 2021-11-16 | 平安国际智慧城市科技股份有限公司 | Diabetes risk prediction device, apparatus and storage medium |
| CN115602328A (en) * | 2022-11-16 | 2023-01-13 | 深圳技术大学（Cn） | Early warning method and device for acute leukemia |
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