CN106408481A - Abnormal card consumption personnel information automatic extraction system and method - Google Patents
Abnormal card consumption personnel information automatic extraction system and method Download PDFInfo
- Publication number
- CN106408481A CN106408481A CN201510448453.5A CN201510448453A CN106408481A CN 106408481 A CN106408481 A CN 106408481A CN 201510448453 A CN201510448453 A CN 201510448453A CN 106408481 A CN106408481 A CN 106408481A
- Authority
- CN
- China
- Prior art keywords
- card
- social security
- personal information
- exception
- abnormal
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 230000002159 abnormal effect Effects 0.000 title claims abstract description 29
- 238000000605 extraction Methods 0.000 title claims abstract description 15
- 238000000034 method Methods 0.000 title claims description 21
- 238000012216 screening Methods 0.000 claims abstract description 20
- 238000007405 data analysis Methods 0.000 claims description 7
- 238000004458 analytical method Methods 0.000 claims description 6
- 238000010276 construction Methods 0.000 claims description 6
- 239000000284 extract Substances 0.000 claims description 6
- 230000008569 process Effects 0.000 claims description 6
- 238000001914 filtration Methods 0.000 abstract description 9
- 230000007246 mechanism Effects 0.000 abstract description 5
- 238000012545 processing Methods 0.000 description 4
- 238000005065 mining Methods 0.000 description 3
- 230000006835 compression Effects 0.000 description 2
- 238000007906 compression Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 239000003814 drug Substances 0.000 description 2
- 238000012546 transfer Methods 0.000 description 2
- 206010000117 Abnormal behaviour Diseases 0.000 description 1
- 239000008186 active pharmaceutical agent Substances 0.000 description 1
- 238000009412 basement excavation Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000007418 data mining Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000000151 deposition Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000003032 molecular docking Methods 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000003825 pressing Methods 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
An abnormal card consumption personnel information automatic extraction method comprises the steps of receiving the social insurance transaction data from various levels of medical mechanisms, and selecting all social insurance transaction data within a preset time quantum from the above received social insurance transaction data; receiving the filtering conditions set by users, and according to the above filtering conditions, screening all social insurance card numbers according with the filtering conditions from the selected social insurance transaction data and the corresponding transaction recordings; analyzing the above screened social insurance card numbers and the corresponding transaction recordings to determine the abnormal social insurance card numbers and the corresponding transaction recordings, and extracting the personal information of the patients corresponding to the abnormal social insurance card numbers; and outputting the personal information of the patients. The present invention also provides an abnormal card consumption personnel information automatic extraction system. According to the present invention, by detecting the abnormal card consumption, the abnormal card consumption crowds are detected and monitored.
Description
Technical field
The present invention relates to data regulation technique field, particularly a kind of personal information that medical insurance card is swiped the card extremely
Automatic extracting system and method.
Background technology
Medical insurance has had become as the one of state revenue and expenditure and has born greatly at present, but have illegal person to brush using robber,
The illegal means such as string brush medical insurance card are usurped the medical insurance amount of money and are carried out peddling making profit by the medicine of illegal acquirement, cause
The waste of medical insurance resource.
For example, insurance fraud people uses multiple from the social security card of other normal patient there collections in same doctor
Institute is repeatedly, carry out between different hospital social security and swipe the card, by abnormal method profit continually or in batches
Carry out the swipe of social security card with the in full or vast scale public expense amount in social security card, to gain fast-selling medicine by cheating,
Again peddled after collecting, therefrom tryed to gain price difference sudden huge profits.
Generally, the insurance fraud behavior of insurance fraud people includes following two modes：
String brush:It is relatively fixed a collection of insured people between different institutions, regular swipe the card；And
Close brush:Insured people swipes the card in agglomerating frequently " coincidence " the same Time Continuous of same mechanism knot group.
Currently also lack the effective monitoring that medical insurance card is swiped the card extremely it is impossible to effectively to the abnormal behavior swiped the card
Carry out management and control.
Content of the invention
In view of the foregoing it is necessary to provide a kind of abnormal swipe the card personal information automatic extracting system and method,
It can be detected to the abnormal crowd of swiping the card to the abnormal mode being detected of swiping the card and be monitored.
A kind of abnormal personal information automatic extracting system of swiping the card, including：Interface module, for from medical treatment at different levels
Authorities social security transaction data；Data screening module, the filter condition setting for receive user, and root
According to above-mentioned filter condition, filter out all social securities meeting filter condition from selected social security transaction data
Card number and corresponding transaction record；Data analysis module, for analyze the above-mentioned social security card number screening and
Corresponding transaction record, the abnormal social security card number of determination and corresponding transaction record, and the exception extracting
The personal information of the corresponding patient of social security card number；And display module, for exporting described patient's
Personal information.
Preferably, described filter condition includes:
Minimum total value of swiping the card within preset time for the patient is filtered；Or
Minimum number of times of swiping the card within preset time for the patient is filtered.
Preferably, described filter condition also includes:
Primary dcreening operation condition and secondary screening condition.
Preferably, described interface module is USB interface, serial ports, infrared interface or blue tooth interface.
Preferably, described display module is a display screen or printer.
A kind of abnormal personal information extraction method of swiping the card, including：Receive social security from medical institutions at different levels
Transaction data；The filter condition that receive user sets, and according to above-mentioned filter condition, from selected social security
The all social security card numbers meeting filter condition and corresponding transaction record is filtered out in transaction data；Analysis is above-mentioned
The social security card number screening and corresponding transaction record, determine abnormal social security card number and corresponding transaction note
Record, and the personal information of the corresponding patient of social security card number of the exception extracting；And output is described medical
The personal information of personnel.
Preferably, described filter condition includes:
Minimum total value of swiping the card within preset time for the patient is filtered；Or
Minimum number of times of swiping the card within preset time for the patient is filtered.
Preferably, described filter condition also includes:
Primary dcreening operation condition and secondary screening condition.
Preferably, the above-mentioned social security card number screening of described analysis and corresponding transaction record, determines abnormal
Social security card number and the step of corresponding transaction record include：
Build special construction tree T；And
Built according to said structure tree T and increase tree.
Preferably, the step of described structure structure tree T includes：
Input transaction database D and minimum support threshold value min_sup；
Scanning transaction database D, obtains the support number of frequent item set F and frequent item set F；
By the support number descending sort of F, obtain frequent episode table L；
Build the root node of Tree, be labeled as " null "；
Scan database D again, to the following operation of every affairs execution in D:
Extract the frequent episode in D the order sequence press in L, after order is sorted, frequent episode table is [p | P], wherein,
P refers to header element, and P is remaining element list；
Call insert_tree ([p | P], T), the execution of this process is as follows:If T has child, N makes
N.item_name=p.item_name, then the counting increase by 1 of N；Otherwise generate a new node N, put
It is counted as 1, is linked to its father node T, and is linked to have identical by node chain structure
The node of item_name, if P non-NULL, recursive call insert_tree (P, N).
Swipe the card personal information automatic extracting system using exception of the present invention and method introduces and is based on
The mode being detected that exception is swiped the card of FP-Growth algorithm being detected to the abnormal crowd of swiping the card and being monitored.
Brief description
Fig. 1 is that the present invention swipes the card the business framework figure of personal information automatic extracting system preferred embodiment extremely.
Fig. 2 is that the present invention swipes the card the functional block diagram of personal information automatic extracting system preferred embodiment extremely.
Fig. 3 is that the present invention swipes the card the implementing procedure figure of personal information extraction method preferred embodiment extremely.
Fig. 4 be using the present invention extremely swipe the card personal information extraction method structure frequent pattern tree (fp tree) T
Example.
Specific embodiment
Refering to shown in Fig. 1, being that the present invention swipes the card the industry of personal information automatic extracting system preferred embodiment extremely
Business frame diagram.In the present embodiment, extremely the swipe the card basic service flow process of detection of described medical insurance card is：Medical people
The diagnosis information of member 3 generates social security transaction data by the settlement system of medical institutions 2.Described social security transaction
Data periodically or is real-time transmitted to Medical Insurance Organizations 1.Described social security can be concluded the business by Medical Insurance Organizations 1
Data storage is in database 3, and swipes the card personal information automatic extracting system 10 to described social security using abnormal
Transaction data such as termly carries out filtering, analyzes at the operation, filters out rapidly from the social security transaction data of magnanimity
A small amount of abnormal data.
Described Medical Insurance Organizations 1 pass through network, as LAN or internet are led to described medical institutions 2
News connect.
Described Medical Insurance Organizations 1 can be various places social security office or other any mechanisms, described medical institutions 2
It can be the hospital of each rank of various places.
The exception of described Medical Insurance Organizations 1 swipe the card personal information automatic extracting system 10 can be one service
Device system.This server system, as a hardware system, has higher computing capability.This server system
The main hardware composition of system comprises several major parts as follows：Central processing unit, internal memory, chipset, I/O are total
Line, I/O equipment, power supply, cabinet and related software.
In other embodiments of the invention, the exception of described Medical Insurance Organizations 1 personal information of swiping the card is automatic
Extraction system 10 can also be the software systems being made up of program code, and it can be installed and run on
Arbitrarily have in server or any personal electric product of higher computational power, in described server or
Under the processor of person's electronic product, the such as execution of central processing unit (CPU, Central Processing Unit),
Realize certain default function, such as social security transaction data termly carried out filtering, analyze etc. with operation, from sea
A small amount of abnormal data is filtered out rapidly in the social security transaction data of amount.
Refering to described in Fig. 2, being that the present invention swipes the card the work(of personal information automatic extracting system preferred embodiment extremely
Can module map.Exception of the present invention is swiped the card, and personal information automatic extracting system 10 is bottom-up to be divided into interface
Layer, operation layer and boundary layer.
Described interface layer externally provides various interface mode, and it includes interface module 100.Described interface module
100 include data-interface, service interface and/or other access API etc..Described data-interface can be USB
Interface, serial ports, infrared interface and blue tooth interface etc., are by the interface of data transfer, e.g., from medical treatment
Mechanism 2 receives social security transaction data.
Described operation layer is the core of entirely abnormal personal information automatic extracting system 10 of swiping the card, and it includes data
Screening module 102 and data analysis module 103, the social security transaction data receiving for docking port module 100
The operation such as termly carry out filtering, analyze, filtering out rapidly a small amount of different from the social security transaction data of magnanimity
Regular data.
Described boundary layer is responsible for representing and man-machine interaction of interface, and it includes display module 104 and rule system
Cover half block 106, for showing the data processing rule of the above-mentioned abnormal data screening and input user's setting
Then etc..It is defeated that described display module 104 can be that a display screen or printer etc. are used for visualizing data
The equipment going out.Described Rulemaking module 106 can be a user interface.
Hereinafter, in conjunction with Fig. 3, describe above-mentioned each module in detail.
Refering to shown in Fig. 3, being that the present invention swipes the card the reality of personal information extraction method preferred embodiment extremely
Apply flow chart.
Described in the present embodiment, abnormal personal information extraction method of swiping the card is not limited to step shown in flow chart
Suddenly, in addition in step shown in flow chart, some steps can be omitted, the order between step can change.
Step S10, interface module 100 receives social security transaction data from medical institutions 2 at different levels.Described recipient
Formula can update for automatic race batch, full dose.The social security transaction data of above-mentioned reception can be stored in be protected with medical treatment
In the database 3 that dangerous mechanism 1 connects.Further, interface module 100 can be from the social security of above-mentioned reception
The all social security transaction data in a preset time period, the such as society of some calendar month are chosen in transaction data
Protect transaction data.
Step S11, the filter condition that data screening module 102 receive user sets.Described filter condition is permissible
Including as (1), to patient, the minimum total value of swiping the card within preset time filters；Or (2)
Minimum number of times of swiping the card within preset time for the patient is filtered.Further, described filter condition
Primary dcreening operation condition and secondary screening condition can also be included.For example, described primary dcreening operation condition can be selected calendar month
Social security transaction data in accumulative number of times of swiping the card be more than first default value, the patient of such as 19 times
As primary dcreening operation personnel.Described secondary screening condition can be to transfer certain time scope to primary dcreening operation personnel, such as of that month and
The first trimester social security transaction data of 4 totally months, according to default condition, such as accumulative number of times of swiping the card be more than
One the second preset value, such as 50 times, is screened further.
In the present embodiment, the described filtering rod that user can set through described Rulemaking module 106
Part.
Step S12, data screening module 102 according to above-mentioned filter condition, from selected social security transaction data
In filter out all social security card numbers meeting filter condition and corresponding transaction record.
Step S13, data analysis module 103 analyzes the above-mentioned social security card number screening and corresponding transaction note
The data such as record, determine abnormal social security card number and corresponding transaction record.In the present embodiment, described data is divided
The data such as social security card number and corresponding transaction record is carried out the spy of the Information Compression based on symbol by analysis module 103
Different structuring is processed, and extracts the frequency exceeding threshold value from the structuring compressed data structure being treated
Numerous Item Sets (combination of social security card number) form, and Frequent Item Sets form is exported according to specified format.
In detail, described data analysis module 103 carries out data based on FP-Growth algorithm structure tree construction
Analysis, extracts Frequent Item Sets.
The committed step of Mining Association Rules is to find fuzzy frequent itemsets, and referred to as frequency collects.From large database
Mining Frequent Patterns are most important for many data mining tasks.
FP-Growth algorithm of the present invention increases the whole Frequent Item Sets of excavation by frequent mode.It is adopted
With the thought divided and rule:The transaction database of implicit frequent mode is compressed into a frequent pattern tree (fp tree),
But still remain the related information of item collection；Then the compressed data storehouse generating is divided into one group of condition database,
Wherein each associates a frequent item, and the condition pattern tree that it constructs is excavated.
The described method building tree construction includes：
1) build special construction tree T.
As shown in the table, it is transaction database D data sample.
| Things ID | Element entry in affairs | Affairs after filtering and reordering |
| 001 | R,Z,H,J,P | Z,R |
| 002 | Z,Y,X,W,V,U,T,S | Z,X,Y,S,T |
| 003 | Z | Z |
| 004 | R,X,N,O,S | X,S,R |
| 005 | Y,R,X,Z,Q,T,P | Z,X,Y,R,T |
| 006 | Y,Z,X,E,Q,S,T,M | Z,X,Y,S,T |
Input:Transaction database D and minimum support threshold value min_sup.
Output:Corresponding frequent pattern tree (fp tree) T.
Method：Mainly in two steps:
(1) scan transaction database D, obtain frequent item set F and their support number.Support number by F
Descending sort, obtains frequent episode table L.
(2) create the root node of Tree, be labeled as " null ".
Scan database again, to the following operation of every affairs execution in D:
Extract the frequent episode in D the order sequence pressing in L.After order sequence, frequent episode table is [p | P], here
P refers to header element, and P is remaining element list.Call insert_tree ([p | P], T). this process executes
As follows:If T has child N to make N.item_name=p.item_name, the counting of N increases by 1；No
Then generate a new node N, put it and be counted as 1, be linked to its father node T, and by section
Point chain structure is linked to the node with identical item_name.If P non-NULL, recursive call
insert_tree(P,N).
Table 1 data builds after completing and should obtain Fig. 4 example.Fig. 4 left part is a dictionary structure,
To preserve head pointer table.In addition to depositing pointer, head pointer table is also used for preserving the sum of every dvielement in number.
Analysis:The building process of T needs transaction database is scanned twice, finally stores into database compressing
One tree, this tree contains the full detail of Frequent Pattern Mining.T is generally for long pattern and intensive number
According to storehouse there is very high compression ratio, but for the database with short pattern in a large number general it may be necessary to its
His method is optimized to meet the rate request of data analysis.
2) build and increase tree.
Input:The T tree being generated by algorithm 1；Minimum support threshold value min_sup.
Output:The complete set of frequent mode.
Method:With growth (T, null).This process is realized as follows:
Procedure growth(Tree,α)
{
If Tree comprises individual paths P, then
For path P interior joint each combination (being denoted as β) do
Generation pattern β ∪ α, its support is set to the support of the minimum of β interior joint；}
Else for each α i Tree item head table do
Generation pattern β=α i ∪ α, its support support=α i support；
The conditional pattern base of construction β, then constructs the condition tree Tree β of β；
if Treeβ≠then
Recursive call growth (Tree β, β)；}
end
}
This method will be seen that the problem of long frequent mode to be converted into recursively and finds short pattern, then connect thereafter
Sew.It will least frequently item as suffix.The method significantly reduces search expense.
Step S14, the corresponding patient's of social security card number of the exception that data analysis module 103 extracts is individual
People's information.
Step states S15, and display module 104 exports the personal information of described patient.
It should be noted last that, above example only in order to technical scheme to be described and unrestricted,
Although being described in detail to the present invention with reference to preferred embodiment, those of ordinary skill in the art should manage
Solution, can modify to technical scheme or equivalent, without deviating from technical solution of the present invention
Spirit and scope.
Claims (10)
1. it is characterised in that this exception is swiped the card, personnel believe a kind of abnormal personal information automatic extracting system of swiping the card
Breath automatic extracting system includes：
Interface module, for receiving social security transaction data from medical institutions at different levels, and hands over from the social security of above-mentioned reception
Easily choose all social security transaction data in a preset time period in data；
Data screening module, the filter condition setting for receive user, and according to above-mentioned filter condition, from institute
The all social security card numbers meeting filter condition and corresponding transaction note is filtered out in the social security transaction data chosen
Record；
Data analysis module, for analyzing the above-mentioned social security card number screening and corresponding transaction record, determines
Abnormal social security card number and corresponding transaction record, and the corresponding medical people of social security card number of the exception extracting
The personal information of member；And
Display module, for exporting the personal information of described patient.
2. exception as claimed in claim 1 swipes the card personal information automatic extracting system it is characterised in that institute
State filter condition to include:
Minimum total value of swiping the card within preset time for the patient is filtered；Or
Minimum number of times of swiping the card within preset time for the patient is filtered.
3. exception as claimed in claim 2 swipes the card personal information automatic extracting system it is characterised in that institute
State filter condition also to include:
Primary dcreening operation condition and secondary screening condition.
4. exception as claimed in claim 1 swipes the card personal information automatic extracting system it is characterised in that institute
Stating interface module is USB interface, serial ports, infrared interface or blue tooth interface.
5. exception as claimed in claim 1 swipes the card personal information automatic extracting system it is characterised in that institute
Stating display module is a display screen or printer.
6. it is characterised in that this exception is swiped the card, personnel believe a kind of abnormal personal information extraction method of swiping the card
Breath extraction method includes：
Receive social security transaction data from medical institutions at different levels, and choose one from the social security transaction data of above-mentioned reception
All social security transaction data in individual preset time period；
The filter condition that receive user sets, and according to above-mentioned filter condition, from selected social security transaction data
In filter out all social security card numbers meeting filter condition and corresponding transaction record；
Analyze the above-mentioned social security card number screening and corresponding transaction record, determine abnormal social security card number and phase
The transaction record answered, and the personal information of the corresponding patient of social security card number of the exception extracting；And
Export the personal information of described patient.
7. exception as claimed in claim 6 swipes the card personal information extraction method it is characterised in that institute
State filter condition to include:
Minimum total value of swiping the card within preset time for the patient is filtered；Or
Minimum number of times of swiping the card within preset time for the patient is filtered.
8. exception as claimed in claim 7 swipes the card personal information extraction method it is characterised in that institute
State filter condition also to include:
Primary dcreening operation condition and secondary screening condition.
9. exception as claimed in claim 6 swipes the card personal information extraction method it is characterised in that institute
State the above-mentioned social security card number screening of analysis and corresponding transaction record, determine abnormal social security card number and phase
The step of the transaction record answered includes：
Build special construction tree T；And
Built according to said structure tree T and increase tree.
10. as exception that claim 9 is stated swipes the card personal information extraction method it is characterised in that institute
The step stating structure structure tree T includes：
Input transaction database D and minimum support threshold value min_sup；
Scanning transaction database D, obtains the support number of frequent item set F and frequent item set F；
By the support number descending sort of F, obtain frequent episode table L；
Build the root node of Tree, be labeled as " null "；
Scan database D again, to the following operation of every affairs execution in D:
Extract the frequent episode in D the order sequence press in L, after order is sorted, frequent episode table is [p | P], wherein,
P refers to header element, and P is remaining element list；
Call insert_tree ([p | P], T), the execution of this process is as follows:If T has child, N makes
N.item_name=p.item_name, then the counting increase by 1 of N；Otherwise generate a new node N, put
It is counted as 1, is linked to its father node T, and is linked to have identical by node chain structure
The node of item_name, if P non-NULL, recursive call insert_tree (P, N).
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510448453.5A CN106408481A (en) | 2015-07-28 | 2015-07-28 | Abnormal card consumption personnel information automatic extraction system and method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510448453.5A CN106408481A (en) | 2015-07-28 | 2015-07-28 | Abnormal card consumption personnel information automatic extraction system and method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN106408481A true CN106408481A (en) | 2017-02-15 |
Family
ID=58008913
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510448453.5A Pending CN106408481A (en) | 2015-07-28 | 2015-07-28 | Abnormal card consumption personnel information automatic extraction system and method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106408481A (en) |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107133437A (en) * | 2017-03-03 | 2017-09-05 | 平安医疗健康管理股份有限公司 | The method and device that monitoring medicine is used |
| CN108573430A (en) * | 2018-03-14 | 2018-09-25 | 北京经纬信息技术公司 | A kind of data processing method, device and computer readable storage medium |
| CN108962342A (en) * | 2018-06-14 | 2018-12-07 | 四川久远银海软件股份有限公司 | A kind of homogeneity crowd screening technique and device |
| CN109544360A (en) * | 2018-10-27 | 2019-03-29 | 平安医疗健康管理股份有限公司 | A kind of medical insurance card based on data processing is swiped the card processing method and relevant device |
| CN109658109A (en) * | 2018-10-29 | 2019-04-19 | 平安医疗健康管理股份有限公司 | Detection method, device, terminal and the storage medium that medical insurance is swiped the card extremely |
| CN111028088A (en) * | 2019-11-11 | 2020-04-17 | 太平洋医疗健康管理有限公司 | Group cheating and insurance behavior identification method and system based on frequent set mining |
| WO2020082796A1 (en) * | 2018-10-27 | 2020-04-30 | 平安医疗健康管理股份有限公司 | Method, device and apparatus for processing medical visit information based on data analysis, and medium |
| WO2020119131A1 (en) * | 2018-12-13 | 2020-06-18 | 平安医疗健康管理股份有限公司 | Medication scheme abnormality identification method and device, terminal, and readable storage medium |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2003071388A3 (en) * | 2002-02-15 | 2003-12-18 | Fair Isaac Corp | Consistency modeling of healthcare claims to detect fraud and abuse |
| CN104537764A (en) * | 2014-12-31 | 2015-04-22 | 浙江大学 | Method and system for detecting abnormal usage of medical insurance cards |
-
2015
- 2015-07-28 CN CN201510448453.5A patent/CN106408481A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2003071388A3 (en) * | 2002-02-15 | 2003-12-18 | Fair Isaac Corp | Consistency modeling of healthcare claims to detect fraud and abuse |
| CN104537764A (en) * | 2014-12-31 | 2015-04-22 | 浙江大学 | Method and system for detecting abnormal usage of medical insurance cards |
Non-Patent Citations (1)
| Title |
|---|
| 孙自广: "FP-growth频繁集挖掘算法的分析与实现", 《广西工学院学报》 * |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107133437A (en) * | 2017-03-03 | 2017-09-05 | 平安医疗健康管理股份有限公司 | The method and device that monitoring medicine is used |
| CN107133437B (en) * | 2017-03-03 | 2018-09-14 | 平安医疗健康管理股份有限公司 | The method and device that monitoring drug uses |
| CN108573430A (en) * | 2018-03-14 | 2018-09-25 | 北京经纬信息技术公司 | A kind of data processing method, device and computer readable storage medium |
| CN108962342A (en) * | 2018-06-14 | 2018-12-07 | 四川久远银海软件股份有限公司 | A kind of homogeneity crowd screening technique and device |
| CN109544360A (en) * | 2018-10-27 | 2019-03-29 | 平安医疗健康管理股份有限公司 | A kind of medical insurance card based on data processing is swiped the card processing method and relevant device |
| WO2020082796A1 (en) * | 2018-10-27 | 2020-04-30 | 平安医疗健康管理股份有限公司 | Method, device and apparatus for processing medical visit information based on data analysis, and medium |
| CN109658109A (en) * | 2018-10-29 | 2019-04-19 | 平安医疗健康管理股份有限公司 | Detection method, device, terminal and the storage medium that medical insurance is swiped the card extremely |
| WO2020119131A1 (en) * | 2018-12-13 | 2020-06-18 | 平安医疗健康管理股份有限公司 | Medication scheme abnormality identification method and device, terminal, and readable storage medium |
| CN111028088A (en) * | 2019-11-11 | 2020-04-17 | 太平洋医疗健康管理有限公司 | Group cheating and insurance behavior identification method and system based on frequent set mining |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106408481A (en) | Abnormal card consumption personnel information automatic extraction system and method | |
| Waghade et al. | A comprehensive study of healthcare fraud detection based on machine learning | |
| CN107145587A (en) | A kind of anti-fake system of medical insurance excavated based on big data | |
| US20120136676A1 (en) | Method and apparatus for multi-domain anomaly pattern definition and detection | |
| Jay et al. | Analysis of social communities with iceberg and stability-based concept lattices | |
| Huang et al. | An investigation of Zipf's Law for fraud detection (DSS# 06-10-1826R (2)) | |
| Haque et al. | Identifying health insurance claim frauds using mixture of clinical concepts | |
| CN113657548A (en) | Medical insurance abnormality detection method, device, computer equipment and storage medium | |
| Hassan et al. | An apriori algorithm-based association rule analysis to detect human suicidal behaviour | |
| CN109698016A (en) | Disease automatic coding and device | |
| Kieseberg et al. | Protecting anonymity in data-driven biomedical science | |
| Duman et al. | Heath care fraud detection methods and new approaches | |
| CN107038669A (en) | Abnormal settlement data warning system and method | |
| CN106170787A (en) | Find adverse health event via behavioral data | |
| Kumar et al. | Analysis of Business Intelligence in Healthcare Using Machine Learning | |
| CN120449214A (en) | Medical data privacy protection method and computer device | |
| CN113642672A (en) | Feature processing method and device of medical insurance data, computer equipment and storage medium | |
| CN101853428A (en) | Remote characteristic query system and method | |
| Andry et al. | Analysis of the Omicron virus cases using data mining methods in rapid miner applications | |
| CN113642669A (en) | Fraud prevention detection method, device and equipment based on feature analysis and storage medium | |
| Mathew et al. | Distributed privacy-preserving decision support system for highly imbalanced clinical data | |
| Heslop et al. | An analysis of high-cost users at an Australian public health service organization | |
| Kim et al. | Anomaly and signature filtering improve classifier performance for detection of suspicious access to EHRs | |
| Hryhoruk et al. | Mining sequential patterns with timelines from digital health data | |
| Kieseberg et al. | Protecting anonymity in the data-driven medical sciences |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20170215 |