CN109741826A - Anaesthetize evaluation decision tree constructing method and equipment - Google Patents
Anaesthetize evaluation decision tree constructing method and equipment Download PDFInfo
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Abstract
The embodiment of the invention provides a kind of anesthesia evaluation decision tree constructing method and equipment.Wherein, which comprises the training sample for obtaining anesthesia evaluation decision tree determines the branch variable of the anesthesia evaluation decision tree according to the information gain-ratio of the training sample；The verifying sample for obtaining anesthesia evaluation decision tree, according to the verifying sample, beta pruning after carrying out to the branch variable obtains final anesthesia evaluation decision tree；Wherein, the final anesthesia evaluation decision tree is for exporting anesthesia grade output variable.Anesthesia evaluation decision tree constructing method provided in an embodiment of the present invention and equipment, by using the method for model training, and subtract branch after combining confidence interval method to carry out model, it is available for carrying out the anesthesia evaluation decision tree-model of anesthesia assessment, the workload of preoperative anesthesia assessment is reduced, and then improves the efficiency of anesthesia preoperative evaluation.
Description
Technical field
The present embodiments relate to medical big data technical field more particularly to a kind of anesthesia evaluation decision tree constructing methods
And equipment.
Background technique
Currently, the generally existing crew shortage's phenomenon of China Anesthetist, hospital of the daily amount for surgical at 100, every Rhizoma Gastrodiae
Liquor-saturated Number of Outpatients must be more than 100, the working strength and pressure of outpatient service Anesthetist and the waiting of patient with operation and medical treatment body
It tests and will be by great challenge.Many solutions are also used in order to solve this problem, and traditional solution is to be based on
The anesthesia of machine learning is assessed, and is all to take part vital signs to carry out ASA classification, is understood the vital signs information of patient
Not comprehensive enough, assessment ASA staging error rate is higher, and lacks perfect anesthesia plan.Therefore, for it is existing now these
Problem, finds a kind of patient data's assessment result by algorithm model output, and doctor can be using assessment result as reference, so
Doctor's opinion is proposed for unreasonable assessment afterwards, and then improves the assessment accuracy rate of algorithm model and anaesthetizes the efficiency of assessment
Method just becomes industry technical problem urgently to be resolved.
Summary of the invention
In view of the above-mentioned problems existing in the prior art, the embodiment of the invention provides a kind of anesthesia evaluation decision tree building sides
Method and equipment.
In a first aspect, the embodiment provides a kind of anesthesia evaluation decision tree constructing methods, comprising: obtain anesthesia
The training sample of evaluation decision tree determines point of the anesthesia evaluation decision tree according to the information gain-ratio of the training sample
Principal deformation amount；The verifying sample for obtaining anesthesia evaluation decision tree is cut after carrying out to the branch variable according to the verifying sample
Branch, obtains final anesthesia evaluation decision tree；Wherein, the final anesthesia evaluation decision tree is for exporting anesthesia grade output
Variable.
Further, the training sample for obtaining anesthesia evaluation decision tree, comprising: extract in anesthesia assessment big data
70% data, the training sample as anesthesia evaluation decision tree.
Further, the information gain-ratio according to the training sample determines point of the anesthesia evaluation decision tree
Principal deformation amount, correspondingly, the information gain-ratio of the training sample includes:
Wherein, a is life characteristic attribute；Gain_ratio is to select vital signs attribute a as the training of Split Attribute
The information gain-ratio of sample；D is the training sample for anaesthetizing evaluation decision tree；Gain is to select vital signs attribute a as division
The information gain of attribute；IV is the comentropy of a；Ent is the comentropy of D；DiTo be divided according to vital signs attribute a to D,
V branch node is generated, wherein it is a that i-th of branch node, which contains all values on a in D,iAnesthesia evaluation decision tree
Training sample number；Pk is ratio shared by kth class sample in D；Y is the species number of sample in D.
Further, the verifying sample for obtaining anesthesia evaluation decision tree, comprising: extract in anesthesia assessment big data
30% data, the verifying sample as anesthesia evaluation decision tree.
Further, described according to the verifying sample, beta pruning after carrying out to the branch variable obtains final anesthesia
Evaluation decision tree, comprising: use confidence interval method, by just too distribution table obtain individual node anesthesia assessment result error,
For the father node of the individual node, the anesthesia assessment result error of all child nodes of father node subordinate is obtained, into one
Step obtains the weighted value of the anesthesia assessment result error of all child nodes, if the weighted value is greater than the fiber crops of the father node
Liquor-saturated assessment result error, and the anesthesia assessment result error of the individual node is minimum value, then by the father node subordinate institute
There is child node trimming to reject.
Further, it is described use confidence interval method, by just too distribution table obtain individual node anesthesia assessment result
Error, comprising:
Er=Br/Ar
Wherein, 1- α is confidence level；ArFor the anesthesia assessment result number of individual node；BrFor ArThe anesthesia of middle mistake is commented
Estimate result number；ErFor error rate；μrFor the anesthesia assessment result error of individual node, μrConfidence interval beZα/2The quantile being positive in too distribution；P is that confidence level is 1- α
Probability distribution.
Further, the weighted value of the anesthesia assessment result error for further obtaining all child nodes, comprising:
Wherein,For the weighted value of the anesthesia assessment result error of all child nodes；I is i-th of child node；K is all
The number of child node；θiThe ratio occupied under the father node for i-th of child node；μiAnesthesia for i-th of individual node is commented
Estimate resultant error, and is minimized.
Second aspect, the embodiment provides a kind of anesthesia evaluation decision tree construction devices, comprising:
Branch variant determination module, for obtaining the training sample of anesthesia evaluation decision tree, according to the training sample
Information gain-ratio determines the branch variable of the anesthesia evaluation decision tree；
It anaesthetizes evaluation decision tree and obtains module, for obtaining the verifying sample of anesthesia evaluation decision tree, according to the verifying
Sample, beta pruning after carrying out to the branch variable, obtains final anesthesia evaluation decision tree；
Wherein, the final anesthesia evaluation decision tree is for exporting anesthesia grade output variable.
The third aspect, the embodiment provides a kind of electronic equipment, comprising:
At least one processor；And
At least one processor being connect with processor communication, in which:
Memory is stored with the program instruction that can be executed by processor, and the instruction of processor caller is able to carry out first party
Evaluation decision tree constructing method is anaesthetized in the various possible implementations in face provided by any possible implementation.
Fourth aspect, the embodiment provides a kind of non-transient computer readable storage medium, non-transient calculating
Machine readable storage medium storing program for executing stores computer instruction, and computer instruction makes the various possible realization sides of computer execution first aspect
Evaluation decision tree constructing method is anaesthetized in formula provided by any possible implementation.
Anesthesia evaluation decision tree constructing method provided in an embodiment of the present invention and equipment, by using the side of model training
Method, and subtract branch after combining confidence interval method to carry out model, the anesthesia assessment for being available for carrying out anesthesia assessment is determined
Plan tree-model, reduces the workload of preoperative anesthesia assessment, and then improves the efficiency of anesthesia preoperative evaluation.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to do a simple introduction, it should be apparent that, the accompanying drawings in the following description is this hair
Bright some embodiments for those of ordinary skill in the art without creative efforts, can be with root
Other attached drawings are obtained according to these attached drawings.
Fig. 1 is anesthesia evaluation decision tree constructing method flow chart provided in an embodiment of the present invention；
Fig. 2 is preoperative anesthesia assessment system structural schematic diagram provided in an embodiment of the present invention；
Fig. 3 is anesthesia evaluation decision tree construction device structural schematic diagram provided in an embodiment of the present invention；
Fig. 4 is the entity structure schematic diagram of electronic equipment provided in an embodiment of the present invention.
Specific embodiment
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with the embodiment of the present invention
In attached drawing, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described embodiment is
A part of the embodiment of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art
Every other embodiment obtained without making creative work, shall fall within the protection scope of the present invention.In addition,
Technical characteristic in each embodiment or single embodiment provided by the invention can mutual any combination, to form feasible skill
Art scheme, but must be based on can be realized by those of ordinary skill in the art, when the combination of technical solution occur it is mutual
Contradiction or when cannot achieve, it will be understood that the combination of this technical solution is not present, also not the present invention claims protection scope
Within.
The embodiment of the invention provides a kind of anesthesia evaluation decision tree constructing methods, referring to Fig. 1, this method comprises:
101, the training sample for obtaining anesthesia evaluation decision tree determines institute according to the information gain-ratio of the training sample
State the branch variable of anesthesia evaluation decision tree；
102, the verifying sample for obtaining anesthesia evaluation decision tree carries out the branch variable according to the verifying sample
Beta pruning afterwards obtains final anesthesia evaluation decision tree.
Wherein, the final anesthesia evaluation decision tree is for exporting anesthesia grade output variable.Specifically, final fiber crops
The variable of liquor-saturated evaluation decision tree output is to anaesthetize the decision tree output variable that grade is I, II, III, IV, V, VI diagnostic result.
On the basis of the above embodiments, the anesthesia evaluation decision tree constructing method provided in the embodiment of the present invention, it is described
Obtain the training sample of anesthesia evaluation decision tree, comprising: the data for extracting in anesthesia assessment big data 70% are assessed as anesthesia
The training sample of decision tree.Specifically, vital signs attribute a is obtained from the training sample of anesthesia evaluation decision tree, and a has
V value { a1,a2,…,av, eigenmatrix is as shown in table 1:
Table 1
On the basis of the above embodiments, the anesthesia evaluation decision tree constructing method provided in the embodiment of the present invention, it is described
According to the information gain-ratio of the training sample, the branch variable of the anesthesia evaluation decision tree, correspondingly, the training are determined
The information gain-ratio of sample includes:
Wherein, a is life characteristic attribute；Gain_ratio is to select vital signs attribute a as the training of Split Attribute
The information gain-ratio of sample；D is the training sample for anaesthetizing evaluation decision tree；Gain is to select vital signs attribute a as division
The information gain of attribute；IV is the comentropy of a；Ent is the comentropy of D；DiTo be divided according to vital signs attribute a to D,
V branch node is generated, wherein it is a that i-th of branch node, which contains all values on a in D,iAnesthesia evaluation decision tree
Training sample number；Pk is ratio shared by kth class sample in D；Y is the species number of sample in D.
On the basis of the above embodiments, the anesthesia evaluation decision tree constructing method provided in the embodiment of the present invention, it is described
Obtain the verifying sample of anesthesia evaluation decision tree, comprising: the data for extracting in anesthesia assessment big data 30% are assessed as anesthesia
The verifying sample of decision tree.
On the basis of the above embodiments, the anesthesia evaluation decision tree constructing method provided in the embodiment of the present invention, it is described
According to the verifying sample, beta pruning after carrying out to the branch variable obtains final anesthesia evaluation decision tree, comprising: uses
Confidence interval method, by just too distribution table obtain individual node anesthesia assessment result error, for the father of the individual node
Node obtains the anesthesia assessment result error of all child nodes of father node subordinate, further obtains all child nodes
Anesthesia assessment result error weighted value, if the weighted value is greater than the anesthesia assessment result error of the father node, and institute
The anesthesia assessment result error for stating individual node is minimum value, then rejects all child node trimmings of the father node subordinate.
On the basis of the above embodiments, the anesthesia evaluation decision tree constructing method provided in the embodiment of the present invention, it is described
Using confidence interval method, by just too distribution table obtain individual node anesthesia assessment result error, comprising:
Er=Br/Ar
Wherein, 1- α is confidence level；ArFor the anesthesia assessment result number of individual node；BrFor ArThe anesthesia of middle mistake is commented
Estimate result number；ErFor error rate；μrFor the anesthesia assessment result error of individual node, μrConfidence interval beZα/2The quantile being positive in too distribution；P is that confidence level is 1- α
Probability distribution.
On the basis of the above embodiments, the anesthesia evaluation decision tree constructing method provided in the embodiment of the present invention, it is described
Further obtain the weighted value of the anesthesia assessment result error of all child nodes, comprising:
Wherein,For the weighted value of the anesthesia assessment result error of all child nodes；I is i-th of child node；K is all
The number of child node；θiThe ratio occupied under the father node for i-th of child node；μiAnesthesia for i-th of individual node is commented
Estimate resultant error, and is minimized.
As the preferred embodiment in above-mentioned each embodiment, the decision tree mould that evaluation decision tree-model is selected from C4.5 is anaesthetized
Type.The anesthesia evaluation decision tree of the embodiment of the present invention below will in the application scenarios for pre-operative anesthesia recruitment evaluation
Corresponding introduction is made to the application in conjunction with Fig. 2.It should be noted that introduced below be intended to be merely illustrative of the present technical solution
Practical application value, be not a limitation on the technical scheme of the present invention.All technical solutions for meeting spirit of that invention essence,
Within the protection domain of this patent..Referring to fig. 2, include: in Fig. 2 pre-operative data input system, doctor's guide data acquisition,
History data, measured body weight, blood pressure measurement, measurement in time of feeling suffocated, blows out match measurement, blood glucose measurement, Yi Shengfu at height measurement
It helps and checks throat, checks defective tooth, allergies, smoking history, history of drinking history, metabolism amount, circulation system disease history, respiratory system disease
Medical history, disease of digestive system history, disease in the urological system history, endocrine system disease history, the nervous system disease history, psychiatric system disease
Medical history, disease in the blood system history, disease of skeletal system history, radiotherapy medical history, chemotherapy medical history, operation medical history, air flue assessment, history number
According to model training, algorithm model (i.e. anesthesia evaluation decision tree-model), personal data assessment, sign data processing, model iteration,
Anaesthetize comments, user and operation, patient data's input system, mobile device, ASA classification, anesthesia plan, doctor and behaviour
Make, doctor's suggestion feedback.As seen from Figure 2, by history of forming data in the acquisition of doctor's guide data and history data, by going through
The training of history data model is trained algorithm model (i.e. anesthesia evaluation decision tree-model), obtains available algorithm model.It
Afterwards user by operation by personal information typing patient data's input system, after sign data is handled, input algorithm model into
The assessment of row personal data.ASA classification, anesthesia plan are formed after assessment, and feed back to mobile device.Doctor is classified for ASA, fiber crops
Liquor-saturated to plan to propose doctor's opinion, then doctor's suggestion feedback forms new anesthesia comments, ASA classification, anesthesia plan.And it will
The retraining that the feedback opinion of doctor returns to algorithm model (i.e. anesthesia evaluation decision tree-model) progress algorithm model (is anaesthetized and is commented
Estimate the self study process of decision-tree model), the more accurate anesthesia evaluation decision tree-model of assessment result is obtained, under
Formulation and assessment of one wheel to anesthesia of patient plan.It should be noted that algorithm model (i.e. anesthesia evaluation decision tree-model) is set
It is placed among cloud server.
Anesthesia evaluation decision tree constructing method provided in an embodiment of the present invention, by using the method for model training, and is tied
It closes after confidence interval method carries out model and subtracts branch, be available for carrying out the anesthesia evaluation decision tree mould of anesthesia assessment
Type, reduces the workload of preoperative anesthesia assessment, and then improves the efficiency of anesthesia preoperative evaluation.
The optimized integration of each embodiment of the present invention is the processing that sequencing is carried out by the equipment with processor function
It realizes.Therefore engineering in practice, can be by the technical solution of each embodiment of the present invention and its function package at various moulds
Block.Based on this reality, on the basis of the various embodiments described above, the embodiment provides a kind of anesthesia assessments to determine
Plan tree construction device, the device are used to execute the anesthesia evaluation decision tree constructing method in above method embodiment.Referring to Fig. 3,
The device includes:
Branch variant determination module 301, for obtaining the training sample of anesthesia evaluation decision tree, according to the training sample
Information gain-ratio, determine it is described anesthesia evaluation decision tree branch variable；
It anaesthetizes evaluation decision tree and obtains module 302, for obtaining the verifying sample of anesthesia evaluation decision tree, tested according to described
Sample is demonstrate,proved, beta pruning after carrying out to the branch variable obtains final anesthesia evaluation decision tree；
Wherein, the final anesthesia evaluation decision tree is for exporting anesthesia grade output variable.
Anesthesia evaluation decision tree construction device provided in an embodiment of the present invention, is commented using branch variant determination module and anesthesia
Estimate decision tree and obtain module, subtracts branch by using the method for model training, and after combining confidence interval method to carry out model, it can be with
The anesthesia evaluation decision tree-model for carrying out anesthesia assessment is obtained, the workload of preoperative anesthesia assessment is reduced, into
And improve the efficiency of anesthesia preoperative evaluation.
The method of the embodiment of the present invention is to rely on electronic equipment to realize, therefore it is necessary to do one to relevant electronic equipment
Lower introduction.Based on this purpose, the embodiment provides a kind of electronic equipment, as shown in figure 4, the electronic equipment includes:
At least one processor (processor) 401, communication interface (Communications Interface) 404, at least one deposits
Reservoir (memory) 402 and communication bus 403, wherein at least one processor 401, communication interface 404, at least one storage
Device 402 completes mutual communication by communication bus 403.At least one processor 401 can call at least one processor
Logical order in 402, to execute following method: the training sample of anesthesia evaluation decision tree is obtained, according to the training sample
Information gain-ratio, determine it is described anesthesia evaluation decision tree branch variable；Obtain the verifying sample of anesthesia evaluation decision tree, root
According to the verifying sample, beta pruning after carrying out to the branch variable obtains final anesthesia evaluation decision tree；Wherein, it is described most
Whole anesthesia evaluation decision tree is for exporting anesthesia grade output variable.
In addition, the logical order in above-mentioned at least one processor 402 can be real by way of SFU software functional unit
Now and when sold or used as an independent product, it can store in a computer readable storage medium.Based in this way
Understanding, the technical solution of the present invention substantially portion of the part that contributes to existing technology or the technical solution in other words
Dividing can be embodied in the form of software products, which is stored in a storage medium, including several
Instruction is used so that a computer equipment (can be personal computer, server or the network equipment etc.) executes the present invention
The all or part of the steps of each embodiment the method.For example, the training sample for obtaining anesthesia evaluation decision tree, according to
The information gain-ratio of the training sample determines the branch variable of the anesthesia evaluation decision tree；Obtain anesthesia evaluation decision tree
Verifying sample, according to the verifying sample, beta pruning after carrying out to the branch variable obtains final anesthesia evaluation decision
Tree；Wherein, the final anesthesia evaluation decision tree is for exporting anesthesia grade output variable.And storage medium packet above-mentioned
It includes: USB flash disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random
Access Memory), the various media that can store program code such as magnetic or disk.
The apparatus embodiments described above are merely exemplary, wherein described, unit can as illustrated by the separation member
It is physically separated with being or may not be, component shown as a unit may or may not be physics list
Member, it can it is in one place, or may be distributed over multiple network units.It can be selected according to the actual needs
In some or all of the modules achieve the purpose of the solution of this embodiment.Those of ordinary skill in the art are not paying creativeness
Labour in the case where, it can understand and implement.
Through the above description of the embodiments, those skilled in the art can be understood that each embodiment can
It realizes by means of software and necessary general hardware platform, naturally it is also possible to pass through hardware.Based on this understanding, on
Stating technical solution, substantially the part that contributes to existing technology can be embodied in the form of software products in other words, should
Computer software product may be stored in a computer readable storage medium, such as ROM/RAM, magnetic disk, CD, including several fingers
It enables and using so that a computer equipment (can be personal computer, server or the network equipment etc.) executes each implementation
Method described in certain parts of example or embodiment.
Finally, it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；Although
Present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that: it still may be used
To modify the technical solutions described in the foregoing embodiments or equivalent replacement of some of the technical features；
And these are modified or replaceed, technical solution of various embodiments of the present invention that it does not separate the essence of the corresponding technical solution spirit and
Range.
Claims (10)
1. a kind of anesthesia evaluation decision tree constructing method characterized by comprising
The training sample for obtaining anesthesia evaluation decision tree determines that the anesthesia is commented according to the information gain-ratio of the training sample
Estimate the branch variable of decision tree；
The verifying sample for obtaining anesthesia evaluation decision tree, according to the verifying sample, beta pruning after carrying out to the branch variable is obtained
To final anesthesia evaluation decision tree；
Wherein, the final anesthesia evaluation decision tree is for exporting anesthesia grade output variable.
2. anesthesia evaluation decision tree constructing method according to claim 1, which is characterized in that the acquisition anesthesia assessment is determined
The training sample of plan tree, comprising:
Extract in anesthesia assessment big data 70% data, the training sample as anesthesia evaluation decision tree.
3. anesthesia evaluation decision tree constructing method according to claim 1, which is characterized in that described according to the trained sample
This information gain-ratio determines the branch variable of the anesthesia evaluation decision tree, correspondingly, the information gain of the training sample
Rate includes:
Wherein, a is life characteristic attribute；Gain_ratio is to select vital signs attribute a as the training sample of Split Attribute
Information gain-ratio；D is the training sample for anaesthetizing evaluation decision tree；Gain is to select vital signs attribute a as Split Attribute
Information gain；IV is the comentropy of a；Ent is the comentropy of D；DiTo be divided according to vital signs attribute a to D, generate
V branch node, wherein it is a that i-th of branch node, which contains all values on a in D,iAnesthesia evaluation decision tree training
Number of samples；Pk is ratio shared by kth class sample in D；Y is the species number of sample in D.
4. anesthesia evaluation decision tree constructing method according to claim 1, which is characterized in that the acquisition anesthesia assessment is determined
The verifying sample of plan tree, comprising:
Extract in anesthesia assessment big data 30% data, the verifying sample as anesthesia evaluation decision tree.
5. anesthesia evaluation decision tree constructing method according to claim 1, which is characterized in that described according to the verifying sample
This, beta pruning after carrying out to the branch variable obtains final anesthesia evaluation decision tree, comprising:
Using confidence interval method, by the way that just too distribution table obtains the anesthesia assessment result error of individual node, for described single
The father node of node obtains the anesthesia assessment result error of all child nodes of father node subordinate, further obtains the institute
There is the weighted value of the anesthesia assessment result error of child node, if the anesthesia assessment result that the weighted value is greater than the father node is missed
Difference, and the anesthesia assessment result error of the individual node is minimum value, then trims all child nodes of the father node subordinate
It rejects.
6. anesthesia evaluation decision tree constructing method according to claim 5, which is characterized in that described to use confidence interval
Method, by just too distribution table obtain individual node anesthesia assessment result error, comprising:
Er=Br/Ar
Wherein, 1- α is confidence level；ArFor the anesthesia assessment result number of individual node；BrFor ArKnot is assessed in the anesthesia of middle mistake
Fruit number；ErFor error rate；μrFor the anesthesia assessment result error of individual node, μrConfidence interval beZα/2The quantile being positive in too distribution；P is that confidence level is 1-
The probability distribution of α.
7. anesthesia evaluation decision tree constructing method according to claim 5, which is characterized in that described in the further acquisition
The weighted value of the anesthesia assessment result error of all child nodes, comprising:
Wherein,For the weighted value of the anesthesia assessment result error of all child nodes；I is i-th of child node；K is all child nodes
Number；θiThe ratio occupied under the father node for i-th of child node；μiFor the anesthesia assessment result of i-th of individual node
Error, and be minimized.
8. a kind of anesthesia evaluation decision tree construction device characterized by comprising
Branch variant determination module, for obtaining the training sample of anesthesia evaluation decision tree, according to the information of the training sample
Ratio of profit increase determines the branch variable of the anesthesia evaluation decision tree；
It anaesthetizes evaluation decision tree and obtains module, for obtaining the verifying sample for anaesthetizing evaluation decision tree, according to the verifying sample,
Beta pruning after carrying out to the branch variable, obtains final anesthesia evaluation decision tree；
Wherein, the final anesthesia evaluation decision tree is for exporting anesthesia grade output variable.
9. a kind of electronic equipment characterized by comprising
At least one processor, at least one processor, communication interface and bus；Wherein,
The processor, memory, communication interface complete mutual communication by the bus；
The memory is stored with the program instruction that can be executed by the processor, and the processor calls described program instruction,
To execute method as described in any one of claim 1 to 7.
10. a kind of non-transient computer readable storage medium, which is characterized in that the non-transient computer readable storage medium is deposited
Computer instruction is stored up, the computer instruction makes the computer execute the method as described in any one of claims 1 to 7.
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