CN106845098A - A kind of implementation method of the medical diagnosis on disease packet based on decision Tree algorithms - Google Patents
A kind of implementation method of the medical diagnosis on disease packet based on decision Tree algorithms Download PDFInfo
- Publication number
- CN106845098A CN106845098A CN201710031118.4A CN201710031118A CN106845098A CN 106845098 A CN106845098 A CN 106845098A CN 201710031118 A CN201710031118 A CN 201710031118A CN 106845098 A CN106845098 A CN 106845098A
- Authority
- CN
- China
- Prior art keywords
- disease
- medical diagnosis
- decision tree
- diagnosis
- medical
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Withdrawn
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/243—Classification techniques relating to the number of classes
- G06F18/24323—Tree-organised classifiers
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
- G16H40/20—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
Abstract
The present invention proposes a kind of implementation method of the medical diagnosis on disease packet based on decision Tree algorithms, including：The basic data of kinds of Diseases is obtained, the basic data of kinds of Diseases is divided into multiple medical diagnosis on disease packets according to human dissection system dissects major class；According to default type of surgery and the complexity data of operation, major class is dissected into each medical diagnosis on disease packet and is further divided into multiple medical diagnosis on disease operation subclasses；Subclass is operated for each medical diagnosis on disease, is chosen with decision Tree algorithms and is met pre-conditioned factor, case sample is subdivided into diagnosis relevant group；In the cutting characteristic variable that decision tree is chosen, cutting posterior nodal point expense average is defined as general complication less than the complication of predetermined threshold value, cutting posterior nodal point average expense is defined as important complication higher than the complication of predetermined threshold value, diagnosis relevant group is divided with this.The present invention, with medical diagnosis and decision Tree algorithms, realizes case automation packet based on clinical case data.
Description
Technical field
The present invention relates to Computer Applied Technology field, more particularly to a kind of medical diagnosis on disease packet based on decision Tree algorithms
Implementation method.
Background technology
The cost of medical insurance pays the important step that calculating is health security system, is related to medical insurance benefits of different parties.
The growth rate of current medical expense is fast, in order to the situation for preventing medical insurance unable to make ends meet is, it is necessary to do medical insurance control expense.
The mode of traditional medical insurance control expense has total value to prepay and by methods such as project payings." being paid by project " is according to patient
Receive the spent expense of service in hospital to be submitted an expense account by charge document.Defect is cannot to constrain the medical act of hospital, is easily made
It is excessive into service, and hospital lacks cost control consciousness, tendency introduces sophisticated diagnostic equipment and promotes high price medicine, causes expense
With significantly rising." total astragalan " be by medical insurance department with the total expenditure of early stage hospital be according to calculate per capita health care's expense,
Hospital expenses total value is appropriated per year by this expense standard, cost overruns such as occur, then undertaken by hospital.Although controlling on the whole
The excessively rapid growth of medical expenses is made, defect is to have easily caused the medical waste of hospital and asked for great treatment only minor illness, and causes medical treatment cost
Control is difficult to implement.
How according to known disease basic data, the classification to medical diagnosis on disease is realized with related algorithm, so that after being
The application such as medical insurance control expense of phase lays the first stone, and is the technical problem for being currently needed for solving.
The content of the invention
The purpose of the present invention is intended at least solve one of described technological deficiency.
Therefore, it is an object of the invention to propose a kind of implementation method of the medical diagnosis on disease packet based on decision Tree algorithms.
To achieve these goals, embodiments of the invention provide a kind of medical diagnosis on disease packet based on decision Tree algorithms
Implementation method, including：
Step S1, obtains the basic data of kinds of Diseases, by the basic data of the kinds of Diseases according to human dissection system
System is divided into multiple medical diagnosis on disease packets and dissects major class；
Step S2, according to default type of surgery and the complexity data of operation, each medical diagnosis on disease is grouped and is dissected
Major class is further divided into multiple medical diagnosis on disease operation subclasses；
Step S3, for medical diagnosis on disease operation subclass each described, sets dependent variable and independent variable, with decision Tree algorithms
Selection meets pre-conditioned factor, and case sample is subdivided into diagnosis relevant group, including：Using the decision Tree algorithms to every
The variate-value of the individual medical diagnosis on disease operation subclass carries out cutting, and calculates error sum of squares SSE1 and cutting before cutting respectively
Error sum of squares SSE2, chooses SSE2 variable's attributes corresponding with the maximum of SSE1 differences and is selected as optimal cutting afterwards, weight
Multiple above-mentioned steps, until it is identical or reach the cutting number of plies of regulation by for the characteristics of variables each numerical value of posterior nodal point is divided, then stop
Only algorithm iteration；
Step S4, in the cutting characteristic variable that decision tree is chosen, predetermined threshold value is less than by cutting posterior nodal point expense average
Complication be defined as general complication, by cutting posterior nodal point average expense higher than predetermined threshold value complication be defined as it is important simultaneously
Hair disease, diagnosis relevant group is divided with this.
Further, major class is dissected in the multiple medical diagnosis on disease packet, including：The nervous system disease group, disease of digestive system
Group, orthopaedic system diseases group.
Further, in the step S3, the dependent variable is the corresponding treatment total cost of medical diagnosis on disease operation subclass,
Whether independent variable is complication, patient age and dead, the pre-conditioned factor to influence medical expense.
Further, in the step S3, the decision Tree algorithms are using classification regression tree algorithm.
Further,
Wherein, vi is the occurrence before variable X i cuttings, and vi' is the occurrence after variable X i cuttings, i=1,2 ... n.
Further, after the step S4, also comprise the following steps：According to the diagnosis relevant group for dividing, insured
Payment, Insurance Fraud anomalous identification, medical insurance control expense and medical performance evaluation operation.
The implementation method of the packet of the medical diagnosis on disease based on decision Tree algorithms according to embodiments of the present invention, with clinical case number
Based on, dissection major class is grouped by marking off medical diagnosis on disease, and then segments out medical diagnosis on disease operation subclass, examined with medical science
Disconnected and decision Tree algorithms, a class is classified as by the case that CC is close, resource consumption is close, realizes case automation packet,
For medical insurance payment, medical services performance appraisal etc. provide support, science, the stability of medical resource management can be improved
And accuracy.
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 practice of the invention.
Brief description of the drawings
Of the invention above-mentioned and/or additional aspect and advantage will become from description of the accompanying drawings below to embodiment is combined
Substantially and be readily appreciated that, wherein：
Fig. 1 is the flow of the implementation method being grouped according to the medical diagnosis on disease based on decision Tree algorithms of the embodiment of the present invention
Figure；
Fig. 2 is the schematic diagram of the formation diagnosis relevant group according to the embodiment of the present invention.
Specific embodiment
Embodiments of the invention are described below in detail, the example of embodiment is shown in the drawings, wherein identical from start to finish
Or similar label represents same or similar element or the element with same or like function.Retouched below with reference to accompanying drawing
The embodiment stated is exemplary, it is intended to for explaining the present invention, and be not considered as limiting the invention.
As shown in figure 1, the implementation method of the packet of the medical diagnosis on disease based on decision Tree algorithms of the embodiment of the present invention, including such as
Lower step：
Step S1, obtains the basic data of kinds of Diseases, by the basic data of kinds of Diseases according to human dissection system point
For major class is dissected in multiple medicals diagnosis on disease packet.Wherein, the basic data of kinds of Diseases includes the diagnostic data to disease, i.e. basis
The Main Diagnosis result of disease, 26 major classes are divided into by disease according to the analyzing system of human body.
In one embodiment of the invention, major class is dissected in multiple medical diagnosis on disease packets, including：The nervous system disease group,
Disease of digestive system group, orthopaedic system diseases group.It should be noted that it is according to human dissection that major class is dissected in medical diagnosis on disease packet
System is divided, and other classifications are also included in addition to the example above, be will not be repeated here.
Step S2, according to default type of surgery and the complexity data of operation, each medical diagnosis on disease is grouped and is dissected
Major class is further divided into multiple medical diagnosis on disease operation subclasses.
For example, orthopaedic system diseases are sorted out according to type of surgery and the complexity of operation, by large joint
Displacement technique, large joint overhaul technology, large joint are overhauled with outer operation, Minor articulus displacement overhaul technology, Minor articulus except displacement is turned over except displacement
Art, spinal fusion surgery, the operation of backbone non-fused, pelvis acetabular bone art, upper limbs long bone art, limb art, muscle flesh beyond repairing
Tendon art, peripheral nerve operation, debridement surgical and other bones and muscle operation etc. are partitioned into same ' medical diagnosis on disease operation subclass ', with
Ensure that disease packet meets medical science and clinical practice.
Step S3, subclass is operated for each medical diagnosis on disease, sets dependent variable and independent variable, is chosen with decision Tree algorithms
Meet pre-conditioned factor, case sample is subdivided into diagnosis relevant group.
In one embodiment of the invention, dependent variable is the corresponding treatment total cost of medical diagnosis on disease operation subclass, from
Whether variable is complication, patient age and dead, the pre-conditioned factor to influence medical expense.That is, treating total expense
It is used as dependent variable, complication, patient age and whether death etc. is as independent variable, with decision Tree algorithms, select influence
The key factor of expense, diagnosis relevant group is further subdivided into by case sample.
Specifically, cutting is carried out to the variate-value of each medical diagnosis on disease operation subclass using decision Tree algorithms, to variate-value
Cutting using evaluation type dependent variable square error and (per data value to the difference of average square summation),
Error sum of squares SSE2, choosing after error sum of squares SSE1 (Sum of Square Error) and cutting are calculated before cutting respectively
Take SSE2 variable's attributes corresponding with the maximum of SSE1 differences to be selected as optimal cutting, repeat the above steps, until pressing pin
Each numerical value of posterior nodal point is divided to the characteristics of variables identical or reach the cutting number of plies of regulation, then stop algorithm iteration.
Wherein, vi is the occurrence before variable X i cuttings, and vi' is the occurrence after variable X i cuttings, i=1,2 ... n.
Preferably, according to data type of a variable, decision Tree algorithms can using classification regression tree algorithm (CART,
Classification And Regression Trees) algorithm carries out feature extraction.
Step S4, in the cutting characteristic variable that decision tree is chosen, predetermined threshold value is less than by cutting posterior nodal point expense average
Complication be defined as general complication, by cutting posterior nodal point average expense higher than predetermined threshold value complication be defined as it is important simultaneously
Hair disease, diagnosis relevant group is divided with this.
Additionally, after step s4, also comprising the following steps：According to the diagnosis relevant group for dividing, carry out insurance payment, protect
Danger fraud anomalous identification, medical insurance control expense and medical performance evaluation operation.
Specifically, medical diagnosis on disease group result can be applied to following field：
1. insurance pays：Inpatient data are divided into various disease diagnostic bank according to diagnosis, to each group unbundling,
Medical Insurance Organizations are disposable in diagnosis and treatment overall process to pay medical expense to hospital.
2. Insurance Fraud anomalous identification：Medical resource situation, the abnormal case of investigation, identification are consumed by analyzing similar medical record
Excessive risk medical insurance fraud.
3. medical insurance control expense：Each department basic medical insurance is determined according to medical diagnosis on disease group expense by medical insurance department of government
Total value is prepay.
4. medical performance evaluation：Multi-class classification is realized by medical record data, from dimensions such as production capacity, efficiency, securities to doctor
Treatment behavior carries out performance evaluation, improves medical services efficiency.
The medical diagnosis on disease packet based on decision Tree algorithms of the invention is illustrated with reference to specific embodiment.Table 1
Show the basic data of medical diagnosis on disease.
Table 1
(1) analyzing system classification：According to Main Diagnosis, following case sample analyzing system classification is circulation system disease.
Setting block code is B.
(2) activity classification：According to case Main Diagnosis and operation technique, sample is subdivided into two medical diagnosis on disease operations
Class：Percutaneous cardiovascular procedure and coronal artery medicine elution bracket implantation group (patient's identification number：1st, 2,3), setting block code is
BB1；Percutaneous cardiovascular procedure and coronary artery non-drug eluting stenter to implant group (patient's identification number：4th, 5), setting block code is
BB2。
(3) diagnosis relevant group is divided using decision Tree algorithms
(3.1) cutting variable and characteristic value are selected
In to BB1 group dicing process, the forward and backward overall error quadratic sum of each characteristic value cutting of each variable is calculated, chosen
" 17 years old age ", after cutting, the case composition left side less than or equal to 17 years old was set, the disease more than 17 years old as optimal cutting characteristic value
Example composition right side tree.
(3.2) algorithm iteration
Set up decision-tree model to left side tree, choose complication as optimal cutting variable, relatively low one group of expense average
It is general complication, higher one group of expense average is important complication.
(3.3) algorithm stop condition
As all cost values of certain node are equal, node data collection very little, or error is reduced less, then algorithm is in the node
Stop iteration.
(3.4) " diagnosis relevant group " is formed
With reference to Fig. 2, case data set is divided into 3 groups according to decision-tree model：(age is less than 17 years old BB14, with one
As complication), BB12 (age be less than 17 years old, with important complication), BB19 (age is more than 17 years old).Each group case is calculated to put down
Equal expense, BB14 groups average cost is 124567.9 yuan, and BB12 groups average cost is 174448.6 yuan, and BB19 average costs are
280663.4 yuan, can be used as prediction medical services expense reference value.
All possible group result is as shown in table 2：
Table 2
The implementation method of the packet of the medical diagnosis on disease based on decision Tree algorithms according to embodiments of the present invention, with clinical case number
Based on, dissection major class is grouped by marking off medical diagnosis on disease, and then segments out medical diagnosis on disease operation subclass, examined with medical science
Disconnected and decision Tree algorithms, a class is classified as by the case that CC is close, resource consumption is close, realizes case automation packet,
For medical insurance payment, medical services performance appraisal etc. provide support, science, the stability of medical resource management can be improved
And accuracy.
In the description of this specification, reference term " one embodiment ", " some embodiments ", " example ", " specifically show
The description of example " or " some examples " etc. means to combine specific features, structure, material or spy that the embodiment or example are described
Point is contained at least one embodiment of the invention or example.In this manual, to the schematic representation of above-mentioned term not
Necessarily refer to identical embodiment or example.And, the specific features of description, structure, material or feature can be any
One or more embodiments or example in combine in an appropriate manner.
Although embodiments of the invention have been shown and described above, it is to be understood that above-described embodiment is example
Property, it is impossible to limitation of the present invention is interpreted as, one of ordinary skill in the art is not departing from principle of the invention and objective
In the case of above-described embodiment can be changed within the scope of the invention, change, replace and modification.The scope of the present invention
By appended claims and its equivalent limit.
Claims (6)
1. the implementation method that a kind of medical diagnosis on disease based on decision Tree algorithms is grouped, it is characterised in that comprise the following steps：
Step S1, obtains the basic data of kinds of Diseases, by the basic data of the kinds of Diseases according to human dissection system point
For major class is dissected in multiple medicals diagnosis on disease packet；
Step S2, according to default type of surgery and the complexity data of operation, major class is dissected by each medical diagnosis on disease packet
It is further divided into multiple medical diagnosis on disease operation subclasses；
Step S3, for medical diagnosis on disease operation subclass each described, sets dependent variable and independent variable, is chosen with decision Tree algorithms
Meet pre-conditioned factor, case sample is subdivided into diagnosis relevant group, including：Using the decision Tree algorithms to each institute
The variate-value for stating medical diagnosis on disease operation subclass carries out cutting, and calculates respectively before cutting after error sum of squares SSE1 and cutting by mistake
Difference quadratic sum SSE2, chooses SSE2 variable's attributes corresponding with the maximum of SSE1 differences and is selected as optimal cutting, in repetition
Step is stated, until it is identical or reach the cutting number of plies of regulation by for the characteristics of variables each numerical value of posterior nodal point is divided, then stop calculating
Method iteration；
Step S4, decision tree choose cutting characteristic variable in, by cutting posterior nodal point expense average less than predetermined threshold value and
Hair disease is defined as general complication, cutting posterior nodal point average expense is defined as higher than the complication of predetermined threshold value important concurrent
Disease, diagnosis relevant group is divided with this.
2. the implementation method that the medical diagnosis on disease based on decision Tree algorithms as claimed in claim 1 is grouped, it is characterised in that described
Major class is dissected in multiple medical diagnosis on disease packets, including：The nervous system disease group, disease of digestive system group, orthopaedic system diseases group.
3. the implementation method that the medical diagnosis on disease based on decision Tree algorithms as claimed in claim 1 is grouped, it is characterised in that in institute
State in step S3, the dependent variable is the corresponding treatment total cost of medical diagnosis on disease operation subclass, and independent variable is complication, patient
Age and whether dead, the pre-conditioned factor to influence medical expense.
4. the implementation method that the medical diagnosis on disease based on decision Tree algorithms as claimed in claim 1 is grouped, it is characterised in that in institute
State in step S3, the decision Tree algorithms are using classification regression tree algorithm.
5. the implementation method that the medical diagnosis on disease based on decision Tree algorithms as claimed in claim 1 is grouped, it is characterised in that
Wherein, vi is the occurrence before variable X i cuttings, and vi' is the occurrence after variable X i cuttings, i=1,2 ... n.
6. the implementation method that the medical diagnosis on disease based on decision Tree algorithms as claimed in claim 1 is grouped, it is characterised in that in institute
State after step S4, also comprise the following steps：According to the diagnosis relevant group for dividing, carry out insurance payment, Insurance Fraud and know extremely
Not, medical insurance control expense and medical performance evaluation is operated.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710031118.4A CN106845098A (en) | 2017-01-17 | 2017-01-17 | A kind of implementation method of the medical diagnosis on disease packet based on decision Tree algorithms |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710031118.4A CN106845098A (en) | 2017-01-17 | 2017-01-17 | A kind of implementation method of the medical diagnosis on disease packet based on decision Tree algorithms |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN106845098A true CN106845098A (en) | 2017-06-13 |
Family
ID=59124761
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710031118.4A Withdrawn CN106845098A (en) | 2017-01-17 | 2017-01-17 | A kind of implementation method of the medical diagnosis on disease packet based on decision Tree algorithms |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106845098A (en) |
Cited By (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107463772A (en) * | 2017-07-20 | 2017-12-12 | 广州慧扬健康科技有限公司 | The constructing system of multi-C vector spectrum of disease |
| CN107463771A (en) * | 2017-07-14 | 2017-12-12 | 北京华搏信息技术有限公司 | A kind of method and system of case packet |
| CN108287958A (en) * | 2018-01-15 | 2018-07-17 | 北京航空航天大学 | Artificial intelligence program person writes the limited decision Method of digital aircraft source code |
| CN108376564A (en) * | 2018-02-06 | 2018-08-07 | 天津艾登科技有限公司 | Medical diagnosis on disease complication recognition methods based on random forests algorithm and system |
| CN108968951A (en) * | 2018-08-15 | 2018-12-11 | 武汉中旗生物医疗电子有限公司 | Electrocardiogram detecting method, apparatus and system |
| CN109255013A (en) * | 2018-08-14 | 2019-01-22 | 平安医疗健康管理股份有限公司 | Claims Resolution decision-making technique, device, computer equipment and storage medium |
| CN109545298A (en) * | 2018-10-30 | 2019-03-29 | 平安医疗健康管理股份有限公司 | A kind of disease score value calculation method and calculating equipment based on data analysis |
| CN109801693A (en) * | 2018-12-14 | 2019-05-24 | 平安医疗健康管理股份有限公司 | Medical record group technology and device, terminal and computer readable storage medium |
| CN110245703A (en) * | 2019-06-13 | 2019-09-17 | 泰康保险集团股份有限公司 | Medical information processing method, device, medium and electronic equipment |
| CN110729054A (en) * | 2019-10-14 | 2020-01-24 | 平安医疗健康管理股份有限公司 | Abnormal diagnosis behavior detection method and device, computer equipment and storage medium |
| CN111048190A (en) * | 2019-11-29 | 2020-04-21 | 挂号网（杭州）科技有限公司 | DRG grouping method based on artificial intelligence |
| CN111898797A (en) * | 2020-06-22 | 2020-11-06 | 青岛国新健康产业科技有限公司 | Medical expense prediction method and device, electronic equipment and storage medium |
| CN112133395A (en) * | 2020-07-10 | 2020-12-25 | 青岛国新健康产业科技有限公司 | Case grouping method and device, electronic equipment and storage medium |
| CN113658711A (en) * | 2021-08-31 | 2021-11-16 | 平安医疗健康管理股份有限公司 | Medical data localization method and device, computer equipment and storage medium |
| CN113707281A (en) * | 2020-05-22 | 2021-11-26 | 四川大学华西医院 | Dynamic disease group grouping realization method based on multi-dimensional disease characteristics |
| CN116646038A (en) * | 2023-04-06 | 2023-08-25 | 北京百度网讯科技有限公司 | Method, apparatus, electronic device and storage medium for determining medical data packet |
-
2017
- 2017-01-17 CN CN201710031118.4A patent/CN106845098A/en not_active Withdrawn
Cited By (26)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107463771A (en) * | 2017-07-14 | 2017-12-12 | 北京华搏信息技术有限公司 | A kind of method and system of case packet |
| CN107463771B (en) * | 2017-07-14 | 2021-01-12 | 北京华搏信息技术有限公司 | Case grouping method and system |
| CN107463772B (en) * | 2017-07-20 | 2020-12-18 | 广州慧扬健康科技有限公司 | Multi-dimensional vector disease spectrum construction system |
| CN107463772A (en) * | 2017-07-20 | 2017-12-12 | 广州慧扬健康科技有限公司 | The constructing system of multi-C vector spectrum of disease |
| CN108287958A (en) * | 2018-01-15 | 2018-07-17 | 北京航空航天大学 | Artificial intelligence program person writes the limited decision Method of digital aircraft source code |
| CN108287958B (en) * | 2018-01-15 | 2021-05-11 | 北京航空航天大学 | Artificial intelligence programmer written digital aircraft source code limited selection decision method |
| CN108376564A (en) * | 2018-02-06 | 2018-08-07 | 天津艾登科技有限公司 | Medical diagnosis on disease complication recognition methods based on random forests algorithm and system |
| CN109255013A (en) * | 2018-08-14 | 2019-01-22 | 平安医疗健康管理股份有限公司 | Claims Resolution decision-making technique, device, computer equipment and storage medium |
| CN108968951A (en) * | 2018-08-15 | 2018-12-11 | 武汉中旗生物医疗电子有限公司 | Electrocardiogram detecting method, apparatus and system |
| CN108968951B (en) * | 2018-08-15 | 2021-06-22 | 武汉中旗生物医疗电子有限公司 | Electrocardiogram detection method, device and system |
| CN109545298A (en) * | 2018-10-30 | 2019-03-29 | 平安医疗健康管理股份有限公司 | A kind of disease score value calculation method and calculating equipment based on data analysis |
| CN109801693B (en) * | 2018-12-14 | 2023-07-14 | 平安医疗健康管理股份有限公司 | Medical records grouping method and device, terminal and computer readable storage medium |
| CN109801693A (en) * | 2018-12-14 | 2019-05-24 | 平安医疗健康管理股份有限公司 | Medical record group technology and device, terminal and computer readable storage medium |
| CN110245703A (en) * | 2019-06-13 | 2019-09-17 | 泰康保险集团股份有限公司 | Medical information processing method, device, medium and electronic equipment |
| CN110729054A (en) * | 2019-10-14 | 2020-01-24 | 平安医疗健康管理股份有限公司 | Abnormal diagnosis behavior detection method and device, computer equipment and storage medium |
| CN110729054B (en) * | 2019-10-14 | 2023-04-11 | 深圳平安医疗健康科技服务有限公司 | Abnormal diagnosis behavior detection method and device, computer equipment and storage medium |
| CN111048190A (en) * | 2019-11-29 | 2020-04-21 | 挂号网（杭州）科技有限公司 | DRG grouping method based on artificial intelligence |
| CN113707281A (en) * | 2020-05-22 | 2021-11-26 | 四川大学华西医院 | Dynamic disease group grouping realization method based on multi-dimensional disease characteristics |
| CN111898797A (en) * | 2020-06-22 | 2020-11-06 | 青岛国新健康产业科技有限公司 | Medical expense prediction method and device, electronic equipment and storage medium |
| CN112331285A (en) * | 2020-07-10 | 2021-02-05 | 青岛国新健康产业科技有限公司 | Case grouping method, case grouping device, electronic equipment and storage medium |
| CN112133395A (en) * | 2020-07-10 | 2020-12-25 | 青岛国新健康产业科技有限公司 | Case grouping method and device, electronic equipment and storage medium |
| CN112331285B (en) * | 2020-07-10 | 2023-01-10 | 青岛国新健康产业科技有限公司 | Case grouping method, case grouping device, electronic equipment and storage medium |
| CN113658711A (en) * | 2021-08-31 | 2021-11-16 | 平安医疗健康管理股份有限公司 | Medical data localization method and device, computer equipment and storage medium |
| CN113658711B (en) * | 2021-08-31 | 2024-04-05 | 深圳平安医疗健康科技服务有限公司 | Medical data localization method, device, computer equipment and storage medium |
| CN116646038A (en) * | 2023-04-06 | 2023-08-25 | 北京百度网讯科技有限公司 | Method, apparatus, electronic device and storage medium for determining medical data packet |
| CN116646038B (en) * | 2023-04-06 | 2024-04-16 | 北京百度网讯科技有限公司 | Method, apparatus, electronic device and storage medium for determining medical data packet |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106845098A (en) | A kind of implementation method of the medical diagnosis on disease packet based on decision Tree algorithms | |
| Leite et al. | Radiomics and machine learning in oral healthcare | |
| Amasya et al. | Cervical vertebral maturation assessment on lateral cephalometric radiographs using artificial intelligence: comparison of machine learning classifier models | |
| CN103501731B (en) | A computer-implemented method, a computing device and a computer readable storage medium for providing alignment information data for the alignment of an orthopaedic implant for a joint of a patient | |
| Yang et al. | A systematic review of nerve transfer and nerve repair for the treatment of adult upper brachial plexus injury | |
| Turchetti et al. | Health technology assessment | |
| Henschke et al. | Taxonomy of medical devices in the logic of health technology assessment | |
| Eksteen et al. | Incremental sheet forming (ISF) in the manufacturing of titanium based plate implants in the bio-medical sector | |
| Pibouleau et al. | Bayesian statistical method was underused despite its advantages in the assessment of implantable medical devices | |
| Radensky et al. | The estimated cost of managing focal spasticity: a physician practice patterns survey | |
| CN117524465B (en) | Artificial intelligence-based spinal surgery scheme decision-making and judging system and method | |
| Zahras et al. | Soft tissue tumor classification using stochastic support vector machine | |
| Briggs et al. | The costs and benefits of primary total hip replacement: how likely are new prostheses to be cost-effective? | |
| Lam et al. | Introduction to biomedical devices | |
| CN111067669B (en) | Method and device for determining acetabular bone defect treatment mode | |
| Tjardes et al. | Less than full circumferential fusion of a tibial nonunion is sufficient to achieve mechanically valid fusion-Proof of concept using a finite element modeling approach | |
| DE102013109057A1 (en) | A method of planning and preparing an operative procedure in the human or animal body, device for carrying out such an intervention and use of the device | |
| Gladkova et al. | Adaptation of CAD-system creo for development of individual spinal implant | |
| Gillies et al. | Periprosthetic bone remodelling of a collum femoris preserving cementless titanium femoral hip replacement | |
| Dazley et al. | Closing the loop between evidence-based medicine and care delivery: a possible role for clinical audits in spinal surgery | |
| Yahiro | Review of the “Historical cohort study of pedicle screw fixation in thoracic, lumbar, and sacral spinal fusions” report | |
| Horvath | Empirically assessing 510 (k) device safety | |
| Le Hanneur et al. | Surgical strategy in extensive proximal brachial plexus palsies | |
| Uttam et al. | The Cost of Operating: Analysis of Single-Use Instrument Costs for Craniotomies | |
| Suganthe et al. | Convolutional Neural Network Based Multi Class Classification Model for Brain Tumor Diagnosis |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WW01 | Invention patent application withdrawn after publication | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20170613 |