CN109659035A - Medical data exception recognition methods, equipment and storage medium based on machine learning - Google Patents
Medical data exception recognition methods, equipment and storage medium based on machine learning Download PDFInfo
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- CN109659035A CN109659035A CN201811530502.XA CN201811530502A CN109659035A CN 109659035 A CN109659035 A CN 109659035A CN 201811530502 A CN201811530502 A CN 201811530502A CN 109659035 A CN109659035 A CN 109659035A
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- 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
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
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- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
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Abstract
The medical data exception recognition methods based on machine learning that the invention discloses a kind of, it include: the medical data of the history of insured people for receiving medical institutions and acquire and uploading, and using preset Chinese physical therapy project as keyword, the medical data containing the Chinese physical therapy project are filtered out from the medical data of the history；The medical Exception Model of Chinese medicine is established using machine learning mode as training sample using the medical data filtered out；Medical data to be detected are obtained, and the medical data to be detected are directed into the medical Exception Model of the Chinese medicine and carry out anomalous identification, obtain recognition result；If the recognition result of the medical data to be detected is abnormal, it is determined that the medical data to be detected are Chinese medicine project over-treatment.The invention also discloses a kind of equipment and storage mediums.The present invention realizes the intellectual analysis of data by the machine learning in artificial intelligence technology, to precisely efficiently identify out suspicious medical data, and effectively controls the medical care precess of violation.
Description
Technical field
The present invention relates to technical field of data processing more particularly to a kind of medical data exception identifications based on machine learning
Method, equipment and storage medium.
Background technique
Social medical insurance is state and society according to certain laws and regulations, is provided for the labourer into scope of insurance coverage
Basic medical demand guarantee when illness and the social security system established.Currently, medical service organ is widely distributed, it is insured people
Member provides more convenience-for-people medical services, however under the driving of interests, some mechanisms occur making unwarranted inspections, disorderly write a prescription, repeat just
It examines repetition the violation operations such as write a prescription, collect fees arbitrarily, wastes originally limited medical resource, while also compromising insurant
Legitimate interests, and then it is unfavorable for the sustainable development of quality of medical care and medical treatment & health.
Summary of the invention
The medical data exception recognition methods that the main purpose of the present invention is to provide a kind of based on machine learning, equipment and
Storage medium, it is intended to solve how precisely to efficiently identify out suspicious medical data, to effectively control the medical treatment behaviour of violation
The technical issues of making.
To achieve the above object, a kind of medical data exception recognition methods based on machine learning provided by the invention, institute
State medical data exception recognition methods the following steps are included:
The medical data of the history of insured people that medical institutions acquires and upload are received, and are with preset Chinese physical therapy project
Keyword filters out the medical data containing the Chinese physical therapy project from the medical data of the history；
The medical Exception Model of Chinese medicine is established using machine learning mode as training sample using the medical data filtered out；
Obtain medical data to be detected, and by the medical data to be detected be directed into the Chinese medicine go to a doctor Exception Model into
Row anomalous identification, obtains recognition result；
If the recognition result of the medical data to be detected is abnormal, it is determined that the medical data to be detected are Chinese medicine item
Mesh over-treatment.
Optionally, described using preset Chinese physical therapy project as keyword, it filters out and contains from the medical data of the history
There are the medical data of the Chinese physical therapy project, comprising:
According to the data content of the medical data of the history, and using Chinese physical therapy project as keyword, reject without
State the medical data of history of Chinese physical therapy project, wherein the data content includes at least consultation time, visit symptom, physical therapy
Project and physical therapy expense；
According to the consultation time and Chinese physical therapy project in the medical data of remaining history, counts and obtain odd-numbered day Chinese physical therapy
Number and moon Chinese physical therapy number；
It is pre- to judge whether the odd-numbered day Chinese physical therapy number of the medical data of history and/or moon Chinese physical therapy number are greater than or equal to
If threshold value；
If history is gone to a doctor, the odd-numbered day Chinese physical therapy number and/or moon Chinese physical therapy number of data are greater than or equal to default threshold
History data of going to a doctor then are labeled as abnormal medical data by value；
If history is gone to a doctor, the odd-numbered day Chinese physical therapy number and/or moon Chinese physical therapy number of data are less than preset threshold, will
The medical data of history are labeled as data of normally going to a doctor；
The medical data of the exception of mark and the normally medical data are merged, medical data are obtained.
Optionally, described that Chinese medicine is established just using machine learning mode as training sample using the medical data filtered out
Examining Exception Model includes:
Processing is extracted to the medical data filtered out, obtains characteristic parameter and timing information；
It is gone to a doctor using the history of insured people corresponding to the medical data that filter out data of going to a doctor as input quantity, with this data
Obtained characteristic parameter and timing information are handled as output quantity, using recurrent neural network to the input quantity and the output
The value sample of amount is trained, and obtains the medical Exception Model of Chinese medicine.
Optionally, the described pair of medical data filtered out extract processing, obtain characteristic parameter and timing information includes:
Data cleansing, participle slice are successively carried out to the medical data filtered out and remove unrelated word, obtains entry, and
Feature extraction is carried out to the entry, obtains characteristic parameter；
According to time location of the characteristic parameter in medical data, the corresponding timing information of characteristic parameter is determined.
Optionally, after the operation for obtaining the medical Exception Model of Chinese medicine, the medical data exception recognition methods
Further include:
Cross validation or random intersection Holdout verifying are rolled over using K to verify the medical Exception Model of the Chinese medicine, are obtained
To verification result, so that maintenance personnel safeguards or improves to the medical Exception Model of the Chinese medicine according to the verification result.
Optionally, described that the medical data to be detected are directed into the medical abnormal knowledge of Exception Model progress of the Chinese medicine
Not, obtaining recognition result includes:
The medical data to be detected are directed into the medical Exception Model of the Chinese medicine and carry out anomalous identification, it is described to obtain
The index value of medical data to be detected；
Judge whether the index value is greater than or equal to preset standard value；
If the index value is greater than or equal to preset standard value, it is determined that the recognition result of the medical data to be detected is
It is abnormal；
If the index value is less than preset standard value, it is determined that the recognition result of the medical data to be detected is normal.
Optionally, medical data to be detected are obtained described, and the medical data to be detected is directed into the Chinese medicine
Medical Exception Model carries out anomalous identification, and after obtaining the operation of recognition result, the medical data exception recognition methods is also wrapped
It includes:
The medical data to be detected are merged with the medical data filtered out, to update the Chinese medicine go to a doctor it is different
The training sample of norm type；
According to the training sample of update, the medical Exception Model of the Chinese medicine is further trained, to obtain
The medical Exception Model of the Chinese medicine of update.
Optionally, if the recognition result in the medical data to be detected is abnormal, it is determined that it is described it is to be detected just
After examining the operation that data are Chinese medicine project over-treatment, the medical data exception recognition methods further include:
Obtain the corresponding insured people's information of the abnormal medical data to be detected and medical institutions' information；
According to insured people's information, right-safeguarding notice is issued to insured people；
According to medical institutions' information, the notice that notifies and/or deduct fees in violation of rules and regulations is issued to medical institutions.
In addition, to achieve the above object, the present invention also provides a kind of medical data exceptions to identify equipment, the medical data
Anomalous identification equipment includes: memory, processor and is stored in the number that can be run on the memory and on the processor
According to anomalous identification program, realized as described in any one of above-mentioned when the data exception recognizer is executed by the processor
The step of medical data exception recognition methods based on machine learning.
In addition, to achieve the above object, it is described computer-readable the present invention also provides a kind of computer readable storage medium
It is stored with data exception recognizer on storage medium, realizes when the data exception recognizer is executed by processor as above-mentioned
Any one of described in the medical data exception recognition methods based on machine learning the step of.
The present invention passes through first receives the medical data of the history of insured people that medical institutions acquires and upload, and with preset
Chinese physical therapy project is keyword, filters out the medical number containing the Chinese physical therapy project from the medical data of the history
According to；The medical Exception Model of Chinese medicine is established using machine learning mode as training sample using the medical data filtered out.Then
Medical data to be detected are obtained, and medical data to be detected are directed into the medical Exception Model of Chinese medicine and carry out anomalous identification, are obtained
Recognition result, and then identify medical data with the presence or absence of abnormal conditions.If recognition result is abnormal, it is determined that be detected medical
Data are Chinese medicine project over-treatment.The present invention realizes the intellectual analysis of data by the machine learning in artificial intelligence technology,
To precisely efficiently identify out suspicious medical data, and effectively control the medical care precess of violation, and then improves supervision medical treatment
The dynamics of mechanism, Perfecting Supervision mechanism, and then promote the sustainable development of quality of medical care and medical treatment & health.
Detailed description of the invention
Fig. 1 is the structural schematic diagram that the medical data exception that the embodiment of the present invention is related to identifies equipment operating environment；
Fig. 2 is that the present invention is based on the flow diagrams of medical one embodiment of data exception recognition methods of machine learning；
Fig. 3 is the refinement flow diagram of mono- embodiment of Fig. 2 step S20；
Fig. 4 is that medical data to be detected are directed into the medical Exception Model of Chinese medicine in Fig. 2 step S30 to carry out anomalous identification,
Obtain the refinement flow diagram of one embodiment of recognition result；
Fig. 5 is that the present invention is based on the flow diagrams of medical another embodiment of data exception recognition methods of machine learning.
The embodiments will be further described with reference to the accompanying drawings for the realization, the function and the advantages of the object of the present invention.
Specific embodiment
It should be appreciated that described herein, specific examples are only used to explain the present invention, is not intended to limit the present invention.
Referring to Fig.1, Fig. 1 is that the medical data exception that the embodiment of the present invention is related to identifies the structure of equipment operating environment
Schematic diagram.
The medical data exception identification equipment of the present embodiment may include such as mobile phone, tablet computer, laptop, palm
The computer installations such as computer, server.
As shown in Figure 1, the medical data exception identification equipment may include: processor 1001, such as CPU, communication bus
1002, user interface 1003, network interface 1004, memory 1005.Wherein, communication bus 1002 for realizing these components it
Between connection communication.User interface 1003 may include display screen (Display), input unit such as keyboard (Keyboard),
Network interface 1004 optionally may include standard wireline interface and wireless interface (such as WI-FI interface).Memory 1005 can be with
It is high speed RAM memory, is also possible to stable memory (non-volatile memory), such as magnetic disk storage.Storage
Device 1005 optionally can also be the storage device independently of aforementioned processor 1001.
It will be understood by those skilled in the art that the hardware configuration for data exception identification equipment of going to a doctor shown in Fig. 1 is not
The restriction to medical data exception identification equipment is constituted, may include components more more or fewer than diagram, or combine certain
Component or different component layouts.
As shown in Figure 1, as may include operating system, net in a kind of memory 1005 of computer readable storage medium
Network communication module, Subscriber Interface Module SIM and computer program.Wherein, operating system is to manage and control medical data exception to know
The program of other equipment and software resource supports the operation of data exception recognizer and other softwares and/or program.
In the hardware configuration of medical data exception identification equipment shown in Fig. 1, network interface 1004 is mainly used for accessing
Network；User interface 1003 is mainly used for detecting confirmation Command And Edit instruction etc..And processor 1001 can be used for calling and deposit
The data exception recognizer stored in reservoir 1005, and execute the medical data exception recognition methods below based on machine learning
Each embodiment in operation.
Device hardware structure is identified based on above-mentioned medical data exception, proposes that the present invention is based on the medical data of machine learning
Each embodiment of abnormality recognition method.
It is that the present invention is based on the processes of medical one embodiment of data exception recognition methods of machine learning to show referring to Fig. 2, Fig. 2
It is intended to.
The embodiment of the invention provides the embodiments of medical data exception recognition methods, it should be noted that although flowing
Logical order is shown in blit, but in some cases, it can be to be different from shown or described by sequence execution herein
The step of.
In the present embodiment, the medical data exception recognition methods based on machine learning includes:
Step S10 receives the medical data of the history of insured people that medical institutions acquires and upload, and with preset middle medical knowledge
Treatment project is keyword, goes to a doctor from history and filters out the medical data containing Chinese physical therapy project in data；
In the present embodiment, social medical insurance is state and society according to certain laws and regulations, for into scope of insurance coverage
The labourer guarantee of basic medical demand and social security system for establishing when illness is provided.And insured is exactly to participate in social insurance
Abbreviation, insured people namely refer to participate in social insurance insurant.Medical institutions refer to that sets up in a manner prescribed by law is engaged in
Medical diagnosis on disease, the general name for treating movable health organ, including hospital, sanatorium, health care institute, clinic, clinic, disease prevention and cure
It stands, Health Center, clinic and first-aid station etc., more convenience-for-people medical services is provided for insurant.
In the present embodiment, patient be also simultaneously insured people seen a doctor in medical institutions it is medical, will generate it is a large amount of it is relevant just
Data are examined, personal information (age, gender, date of birth, identification card number etc.), consultation time, physician office visits including insured people,
Visit symptom and its corresponding physical therapy project and physical therapy expense or the contents such as corresponding medical medication and drug cost.Insured people
All diagnosis informations on the day of medical institutions go to a doctor and pass through brush social security card and can display real-time.History go to a doctor data be insured people from
It goes to a doctor for the first time to medical institutions to backward every time in the medical generated data of same mechanism.Various regions medical institutions respectively with
This medical data exception identification equipment establishes communication connection, realizes data interaction.Medical institutions acquire the history of all insured people
Medical data are simultaneously aggregated into local data base, and the medical data of all history of acquisition are then uploaded to this medical data exception and are known
Other equipment carries out relevant treatment, so each insured people the medical information of each medical institutions, different periods can a mesh
It is clear.It is understood that history is gone to a doctor, data are with a complete data of insured artificial independent individual；And various regions medical treatment
Mechanism can independent operation, can data under independent acquisition oneself mechanism, without by other mechanisms or insured people etc.
It is premeditated to intervene.
In the present embodiment, data volume is quickly accumulated under big data environment, to analyze the valence that mass data is contained
Value, it is particularly significant to filter out valuable data.And data screening is in vitally in entire flow chart of data processing
Position.The increase of data volume and the complication of structure, this makes the data screening towards big data that will necessarily expend more resource, because
This selects suitable algorithm very necessary to quick and precisely filter out valuable data.And each algorithm suffers from oneself and makes
With required environment, the increase of data complexity improves the difficulty of selection appropriate algorithm under big data environment；Secondly big number
According to huge data volume but also to analyze valuable data by single algorithm more and more difficult.Thus the screening of data exists
The first step for occupying critically important status and mathematical modeling in mathematical modeling, when and the only data got well just can guarantee
The authenticity and accuracy of the result obtained.It is filtered out from the medical data of each history based on default screening rule and meets modeling
Medical data are exactly the screening in magnanimity initial data (the medical data of the history for multiple insured people that more medical institutions upload)
The medical data of representational history are provided, and then are prepared for data mining, mathematical modeling.Specific data screening process is under
It is described in detail in literary embodiment.
In the present embodiment, preset Chinese physical therapy project include acupuncture, moxa-moxibustion, cupping, massage, scraping, dipping, it is stifling,
Massage, qigong etc..Due to the medical data of history for the insured people that each medical institutions upload, some includes the correlation of Chinese physical therapy
Data, some do not have, and are the accuracy and reliability for improving model identification, go to a doctor and count to the history of original all insured people
According to being screened, specifically, the medical data of the history without containing Chinese physical therapy project are rejected, and retains and contain Chinese physical therapy project
History go to a doctor data, and then reduce operation, avoid data omission, avoid influencing Chinese medicine and go to a doctor the training of Exception Model.
It is medical to establish Chinese medicine using machine learning mode as training sample using the medical data filtered out by step S20
Exception Model；
In the present embodiment, model is the mathematical model constructed with mathematical logic method and mathematical linguistics, and machine learning
Computer is exactly allowed to learn knowledge new out from existing data, that is, according to the medical data filtered out as training number
According to the study of the system of progress, for example how to identify data exception etc..Briefly, developer only needs mass data that is, will
The medical data filtered out input to computer, then sum up Data analysis logic therein by computer oneself, summarize
Corresponding logical code, to obtain the medical Exception Model of a Chinese medicine.Trained process is exactly to utilize training sample and combine
Sample label corresponding to data, sample characteristics, i.e., existing data determine the process of model parameter.Chinese medicine is gone to a doctor Exception Model
Can be local disposition and be also possible to cloud and dispose online, preferably cloud is disposed online, can real-time update data, optimize Chinese medicine
The accuracy and reliability of medical Exception Model, while distributed can also extend, further satisfaction practical ginseng complicated and changeable
Guarantor goes to a doctor situation.
It should be added that Chinese medicine is gone to a doctor, Exception Model is that the ginseng for receiving Chinese traditional treatment is issued by doctor for this kind of
The medical data of guarantor and the anomalous identification model established, and then more acurrate can reliably identify that medical data to be detected are
No presence such as repeats to go to a doctor, repeats Chinese physical therapy, this kind of abnormal medical situation of excess Chinese physical therapy.
Step S30, obtains medical data to be detected, and by medical data to be detected be directed into Chinese medicine go to a doctor Exception Model into
Row anomalous identification, obtains recognition result；
In the present embodiment, also insured people is to the medical generated data of medical institutions for medical data to be detected, only
Unlike one, which is not intended to model and be used to detect whether that there are abnormal conditions, and the data can be ginseng
The medical data or the medical data of nearly half a year of guarantor closely several times are also possible to the medical data of history, do not do any restriction herein.
It is gone to a doctor Exception Model by Chinese medicine, so that it may quickly recognize medical data to be detected with the presence or absence of such as repeating to go to a doctor, repeat
Physical therapy, this kind of abnormal medical situation of excess physical therapy, to contain medical institutions' violation operation, and have ensured the conjunction of insurant
Method interests.It should be understood that it includes the data of Chinese physical therapy project that medical data to be detected, which are, and then medical by Chinese medicine
Exception Model can targetedly identify existing exception.
Step S40, if the recognition result of medical data to be detected is abnormal, it is determined that medical data to be detected are Chinese medicine item
Mesh over-treatment.
In the present embodiment, the recognition result of medical data to be detected is exception, illustrates that insured people is medical by unreasonable
Chinese traditional treatment, at the same time, medical institutions must violation operations.Chinese medicine project over-treatment i.e. insured people's odd-numbered day or Dan Yue
Issued by doctor receive Chinese physical therapy project number it is excessive.Further, the medical treatment for abnormal medical data, to violation
Mechanism is accordingly punished, such as the prescription power, the fine that reduce the credibility of medical institutions, cancel doctor, serious or even can
To revoke the practicing requirements etc. of medical institutions, it is configured with specific reference to actual conditions.It should be understood that if to be detected medical
The recognition result of data be it is normal, then illustrate that insured people is medical by the reasonable treatment for closing rule, violation operation be not present.
In the present embodiment, the medical data of the history for the insured people for acquiring and uploading by reception medical institutions first, and with
Preset Chinese physical therapy project is keyword, goes to a doctor from history and filters out the medical data containing Chinese physical therapy project in data；
The medical Exception Model of Chinese medicine is established using machine learning mode as training sample using the medical data filtered out.Then it obtains
Medical data to be detected, and medical data to be detected are directed into the medical Exception Model of Chinese medicine and carry out anomalous identification, it is identified
As a result, identifying medical data with the presence or absence of abnormal conditions in turn.If recognition result is abnormal, it is determined that medical data to be detected
For Chinese medicine project over-treatment.The present invention realizes the intellectual analysis of data by the machine learning in artificial intelligence technology, thus
Suspicious medical data precisely are efficiently identified out, and effectively control the medical care precess of violation, and then improve supervision medical institutions
Dynamics, Perfecting Supervision mechanism, so promote quality of medical care and medical treatment & health sustainable development.
Further, based on the above embodiment, in the present embodiment, step S10 is key with preset Chinese physical therapy project
Word goes to a doctor from history and filters out the medical data containing Chinese physical therapy project in data, comprising:
Step a, according to the data content of the medical data of each history, and using preset Chinese physical therapy project as keyword,
Reject without containing Chinese physical therapy project history go to a doctor data, wherein data content include at least consultation time, physical therapy project and
Physical therapy expense；
In the present embodiment, data of going to a doctor, personal information (age, gender, date of birth, identification card number including insured people
Deng), consultation time, physician office visits, visit symptom and its corresponding physical therapy project and physical therapy expense or corresponding medical medication and
The contents such as drug cost.Visit symptom, such as flu, headache, fracture, diabetes, acute myopericarditis, enterogastritis, brain tumor
Etc..Currently, the treatment type of medical institutions' setting can simply be divided into two classes, one kind is doctor trained in Western medicine, and one kind is Chinese medicine.Wherein,
Chinese medicine (Traditional Chinese Medicine) refers generally to based on the traditional medicine created with Chinese Han nationality working people
Medicine so also referred to as Chinese medicine be a Men Xueke of diagnosis and prevention and treatment for studying Human Physiology, pathology and disease etc..Chinese medicine
It learns using yin-yang and five elements as theoretical basis, human body is regarded as to the entity of gas, shape, mind, closed and joined by " four methods of diagnosis " four methods of diagnosis
Method, seek the cause of disease, characteristic of disease, sick position, the vital organs of the human body in the analysis interpretation of the cause, onset and process of an illness and human body, channels and collaterals joint, qi-blood-body fluid variation, sentence
Disconnected pathogenic and vital contention, and then obtain name of disease, conclusion are come to testify type, with diagnosis and treatment principle, formulate " sweat, spit, under and temperature, it is clear, mend,
Disappear " etc. therapies so that human body is reached yin-yang using a variety for the treatment of means such as Chinese medicine, acupuncture, massage, massage, cupping, the qigong, dietotherapy
It reconciles and rehabilitation.Correspondingly, physical therapy project is just divided into doctor trained in Western medicine physical therapy project and Chinese physical therapy project.Chinese physical therapy project includes needle
Moxibustion, moxa-moxibustion, cupping, massage, scraping, dipping, stifling, massage, qigong etc..Medical medication can equally be divided into Western medicine and Chinese medicine,
In, Chinese medicine is with the acquisition of Chinese traditional medicine theoretical direction, processes, preparation, and illustration mechanism instructs the medicine of clinical application
Object.Simultaneously it is understood that different physical therapy projects are corresponding to collect different physical therapy expenses, the medical medication of difference equally correspondence is collected
Different drug costs are arranged with specific reference to actual conditions.
Scheme to further understand the present invention, with the medical data instance of the history of insured people, the medical number of the history of insured people A
According to such as table 1, the medical data such as table 2 of the history of insured people B, the medical data such as table 3 of the history of insured people C；
Table 1
| Consultation time | Visit symptom | Physical therapy project | Physical therapy expense | Medical medication | Drug cost |
| 2017-12-3 | Cough | Nothing | Nothing | Cough syrup | 15 |
| 2018-4-9 | Hemorrhoid complicated by anal fistula | Acupuncture | 100 | Nothing | Nothing |
| 2018-4-12 | Hemorrhoid complicated by anal fistula | Acupuncture | 100 | Nothing | Nothing |
| 2018-4-12 | Hemorrhoid complicated by anal fistula | Acupuncture | 100 | Nothing | Nothing |
| 2018-4-12 | Hemorrhoid complicated by anal fistula | Acupuncture | 100 | Nothing | Nothing |
| 2018-5-7 | Hemorrhoid complicated by anal fistula | Moxa-moxibustion | 60 | Nothing | Nothing |
| 2018-9-7 | Hemorrhoid complicated by anal fistula | Nothing | Nothing | Hemorrhoid complicated by anal fistula is relaxed ball | 40 |
Table 2
| Consultation time | Visit symptom | Physical therapy project | Physical therapy expense | Medical medication | Drug cost |
| 2015-6-3 | Fever | Nothing | Nothing | Antipyretic | 50 |
| 2018-4-9 | Hemorrhoid complicated by anal fistula | Moxa-moxibustion | 60 | Nothing | Nothing |
| 2018-8-3 | Flu | Nothing | Nothing | Cold drug | 20 |
Table 3
By table 1-3 it is found that the diagnosis records of insured people A do not have a Chinese physical therapy project, and the diagnosis records of insured people B and C
There is Chinese physical therapy project, thus insured people A is rejected, retains the medical data of history of insured people B and C.
Step b is counted and is obtained in the odd-numbered day according to the consultation time and Chinese physical therapy project in the medical data of remaining history
Medical knowledge treats number and moon Chinese physical therapy number；
In the present embodiment, further to help computer learning, the medical data of history after screening is classified, that is, are divided
For this abnormal and normal two major classes data, specifically, consultation time and the middle medical knowledge gone to a doctor in data according to remaining each history
Treatment project, statistics obtain odd-numbered day Chinese physical therapy number and moon Chinese physical therapy number.Since remaining history data of going to a doctor are that have
Medical knowledge treats project, and by taking insured people B and C as an example, statistics obtains insured people B and receives 1 acupuncture and moxibustion therapy in 2018-4-9 respectively,
2018-4-12 receives 3 acupuncture and moxibustion therapy, receives 1 moxibustion treatment in 2018-5-7, and then count and obtain the moon acupuncture in April
Treatment number of times are 4 times, and the moon moxibustion treatment number in May is 1 time；Insured people C receives 1 Chinese physical therapy, April in 2018-4-9
The moon moxibustion treatment number of part is 1 time.
Step c, judge history go to a doctor data odd-numbered day Chinese physical therapy number and/or moon Chinese physical therapy number whether be greater than or
Equal to preset threshold；
Step d, if history is gone to a doctor, the odd-numbered day Chinese physical therapy number and/or moon Chinese physical therapy number of data are greater than or equal to pre-
If threshold value, then history data of going to a doctor are labeled as abnormal medical data；
Step e, if history is gone to a doctor, the odd-numbered day Chinese physical therapy number and/or moon Chinese physical therapy number of data are less than default threshold
History data of going to a doctor then are labeled as data of normally going to a doctor by value；
The medical data of exception of mark and normally medical data merging are obtained medical data by step f.
In the present embodiment, since the quantity of the medical data of remaining history is big, with each insured artificial individual, gone through to each
The medical data of history are judged specifically, whether the odd-numbered day Chinese physical therapy number for judging that statistics obtains is greater than or equal to default threshold
Value judges whether the obtained moon Chinese physical therapy number of statistics is greater than or equal to preset threshold, and then obtains history data of going to a doctor and be
It is abnormal or normal.While to help computer learning, analysis is obtained into the abnormal or normal medical data of history and stamps mark
Label, that is, do mark and distinguish, and then computer can data be classified as which kind by marking simple and clear knowing, into
It and is that training pattern is ready.Further, be each Chinese physical therapy item setup threshold value, for example, the acupuncture odd-numbered day be more than 2 times,
It is more than 20 time, month moxa-moxibustions is more than 30 inferior that the moxa-moxibustion odd-numbered day, which is more than 5 time, month acupuncture, so by odd-numbered day acupuncture be more than 2 history just
Examining data markers is abnormal data, and the history by odd-numbered day acupuncture no more than 2 times goes to a doctor data markers as normal data.With insured people
For B and C, insured people B receives 3 acupuncture and moxibustion therapy in the 2018-4-12 odd-numbered day, is more than preset threshold, is labeled as abnormal medical
Data.Insured people C odd-numbered day/moon Chinese physical therapy number is less than preset threshold, is labeled as data of normally going to a doctor.
In the present embodiment, either abnormal or normal data, as long as the going through containing Chinese physical therapy project retained
The medical data of history are to meet the medical data of modeling, and it is exactly abnormal that these data, which are not normal, thus just by the exception of mark
It examines data and normally medical data merges, obtain medical data.
It is the refinement flow diagram of mono- embodiment of Fig. 2 step S20 referring to Fig. 3, Fig. 3.
Based on the above embodiment, in the present embodiment, step S20, the medical data to filter out are used as training sample
Machine learning mode establishes the medical Exception Model of Chinese medicine, comprising:
Step S21 extracts processing to the medical data filtered out, obtains characteristic parameter and timing information；
Specifically, step S21 includes:
1, data cleansing, participle slice are successively carried out to the medical data filtered out and remove unrelated word, obtain entry,
And feature extraction is carried out to entry, obtain characteristic parameter；
2, the time location according to characteristic parameter in medical data, determines the corresponding timing information of characteristic parameter.
In the present embodiment, although the medical data filtered out are the medical data of two kinds of different types (abnormal and normal),
But it requires to extract it processing, including data cleansing, participle are sliced and remove unrelated word: a. data cleansing, it is such as clear
Except the missing value of data, more dividing value, inconsistent code, repeated data etc..B. participle slice, as python stammerer participle, in
ICTCLAS tool of institute, section etc., can also condition random field (CRF) algorithm etc. based on statistics, network new word identification can pass through
New dictionary is manually added, or the solidified inside degree by calculating N-Grams is segmented come statistic frequency to design new word discovery and calculate
Method etc..C. unrelated word, such as removal punctuation mark, messy code, null, blank character are removed.If medical data are split into
The dry entry with meaning, entry includes that duodenal ulcer, chronic gastritis, chronic colitis, scapulohumeral periarthritis, lumbar vertebra disease etc. are this kind of
Symptom word；Including this kind of physical therapy word such as moxa-moxibustion, cupping, massage, scraping；Including words such as expense, timing nodes.
In the present embodiment, characteristic parameter is the parameter information for characterizing substance or phenomenon characteristic, and feature extraction is
Entry is further processed, such as induction and conclusion, screening, during obtained characteristic parameter includes: visit symptom and its is corresponding
Medical knowledge treats project, physical therapy expense, consultation time, physician office visits, medical frequency；By data of going to a doctor, it is medical to obtain insurant
Symptom is tumour, the physical therapy expense that corresponding Chinese physical therapy project is acupuncture, the number of acupuncture, the frequency of continued treatment, acupuncture
Zhan always treats expenditure ratio of project etc..
In the present embodiment, time series databases be mainly used for refer to processing band time tag (according to the time sequence variation,
I.e. the time serializes) data, the data with time tag are also referred to as time series data.The medical data in terms of time dimension,
Known to medical data refer to same target (insured people) acquired by different consultation times (changing according to the sequence of time)
Data.Chronologically-based feature, go to a doctor each time generated Chinese physical therapy project, physical therapy expense etc. must correspond to one
The time location of time point, i.e. characteristic parameter in medical data, and then determine the corresponding timing information of characteristic parameter.
Step S22, using the history of insured people corresponding to the medical data that filter out go to a doctor data as input quantity, with this
The characteristic parameter and timing information that medical data processing obtains are as output quantity, using recurrent neural network to input quantity and output
The value sample of amount is trained, and obtains the medical Exception Model of Chinese medicine.
Basic number in the present embodiment, by two distinct types of medical data as the medical Exception Model of training Chinese medicine
According to can make computer deep learning, to cope with the medical data of different users, and then whether distinguish medical data to be detected
Belong to Exception Type.Filter out that meet the medical data of modeling be to be labeled as abnormal history and go to a doctor data and to be labeled as normal
History is gone to a doctor data, and each history is gone to a doctor the corresponding insured people of data.Each medical data is extracted to obtain characteristic parameter
And timing information.
In the present embodiment, recurrent neural network (Recurrent Neural Network, abbreviation RNN) be by addition across
The more connection hidden layer certainly at time point, and modeled in chronological order.Modeling is exactly to filter out the medical number for meeting modeling
It goes to a doctor as input quantity, with this characteristic parameter and timing that data processing obtains according to the history of corresponding insured people data of going to a doctor
Information uses following algorithm as output quantity, and in modeling process: 1. regard each object (insured people) as one kind, calculate two-by-two
Between minimum range；2. a new class will be merged into apart from the smallest two classes；3. recalculating between new class and all classes
Distance；4. repeating 2,3, until all classes are finally merged into one kind, training obtains the medical Exception Model of Chinese medicine.It should be understood that
It is that the medical data of each insured people can regard a point as in a model, by model, the discrete of each insured people can be analyzed
Degree.
Further, it after obtaining the medical Exception Model of Chinese medicine, also needs to verify the effect of model, specifically,
Cross validation is rolled over using K or Chinese medicine Exception Model of going to a doctor is verified in Holdout verifying, is verified as a result, verifying in turn
The recognition effect of the medical Exception Model of the Chinese medicine trained.K rolls over cross validation, and initial samples are divided into K subsample, a list
Only subsample is kept as the data of verifying model, other K-1 sample is used to train.Cross validation repetition K times, each
Subsample verifying is primary, and average K result or the other combinations of use finally obtain a single estimation.Holdout
Verifying is not a kind of cross validation, because data there is no cross-reference, are to select part from initial sample at random, is formed
Cross-validation data, and it is remaining just as training data.In general, the data less than script sample one third are chosen as
Verify data.By the way that verification result is fed back to related maintenance personnel, and then personnel are maintained easily according to verification result to Chinese medicine
Medical Exception Model is safeguarded or is improved.For example the overall accuracy of verification result is when being not up to preset threshold such as 90%, dimension
Shield personnel can be gone to a doctor according to Chinese medicine Exception Model input quantity and output quantity analysis data, optimize operation method, and then improve in
Cure medical Exception Model；For example the overall accuracy of verification result can put into production use when reaching preset threshold.
Referring to Fig. 4, Fig. 4 is different for medical data to be detected are directed into the medical Exception Model progress of Chinese medicine in Fig. 2 step S30
Common sense is other, obtains the refinement flow diagram of one embodiment of recognition result.
Based on the above embodiment, in the present embodiment, it is medical abnormal to be directed into Chinese medicine by step S30 for medical data to be detected
Model carries out anomalous identification, obtains recognition result, comprising:
Medical data to be detected are directed into the medical Exception Model of Chinese medicine and carry out anomalous identification by step S31, to be checked to obtain
Survey the index value of medical data；
Whether step S32, judge index value are greater than or equal to preset standard value；
Step S33, if index value is greater than or equal to preset standard value, it is determined that the recognition result of medical data to be detected is
It is abnormal；
Step S34, if index value is less than preset standard value, it is determined that the recognition result of medical data to be detected is normal.
In the present embodiment, identification obtains index value, and index value can be consistent with the prior art herein, has very poor finger as used
Mark, quartile spacing index, eight quartile spacing indexs, equal poor index, standard deviation requirement etc., are set with specific reference to actual conditions
It sets.The bigger expression variation of index value is big, it is wide to spread, and the small expression deviation of value is small, comparatively dense.If medical data to be detected identify to obtain
Index value it is big, it is abnormal for illustrating dispersion degree greatly.If the index value that medical data to be detected identify is small, illustrate discrete
Small degree is normal.To determine whether medical data to be detected are abnormal, preset a standard value, preset standard convenient for machine
Value is the critical value of a normal medical data after the completion of model training, is determined as exception more than this critical value.
It is further alternative, in an alternative embodiment of the invention, after step S30, data exception recognition methods of going to a doctor
Further include:
Step A merges medical data to be detected with the medical data filtered out, medical abnormal to update Chinese medicine
The training sample of model, with the medical Exception Model of the Chinese medicine updated；
Step B further trains Chinese medicine Exception Model of going to a doctor according to the training sample of update.
In the present embodiment, medical data to be detected are merged with the medical data filtered out, is using constantly newly-increased
Training sample, Chinese medicine Exception Model of going to a doctor constantly is adjusted and is optimized, the analysis data of medical data is increased, helps
When analyzing medical data to be detected, more typical and high accuracy analysis foundations are improved, and then improve anomalous identification
Accuracy.It should be clear that medical data to be detected have been detected and finished, by the way that the medical data detected are added, realize real
When more new data, while updated training sample, it is more to be used for trained data, can advanced optimize and improve Chinese medicine
Medical Exception Model.
It is that the present invention is based on the processes of medical another embodiment of data exception recognition methods of machine learning referring to Fig. 5, Fig. 5
Schematic diagram.
Based on the above embodiment, after the step s 40, medical data exception recognition methods further include:
Step S50 obtains the corresponding insured people's information of abnormal medical data to be detected and medical institutions' information；
Step S60 issues right-safeguarding notice to insured people according to insured people's information；
Step S70 issues the notice that notifies and/or deduct fees in violation of rules and regulations to medical institutions according to medical institutions' information.
In the present embodiment, medical data to be detected be insured people to medical institutions it is medical caused by data, including it is insured
People's information, while it being uploaded to server by medical institutions, the information such as the identity of medical institutions are carried, when to be detected medical
When data analysis is determined as abnormal, insured people and medical institutions can be determined by insured people's information, medical institutions' information, in turn
Correspondence is issued a notice.To insured people issue right-safeguarding notice, for inform user by unreasonable Chinese traditional treatment, inform user how
The lawful right of oneself is safeguarded in accordance with the law.Violation and/or notice of deducting fees are issued to medical institutions, is disobeyed for informing that medical institutions exist
Rule operation, imposes a fine it or is alerted, and then contains violations of rules and regulations, and improves the dynamics of supervision medical institutions, Perfecting Supervision
Mechanism, and then promote the sustainable development of quality of medical care and medical treatment & health.Right-safeguarding is notified, is notified in violation of rules and regulations, the content for notice of deducting fees
It can write according to the actual situation, not do any restriction herein.
It should be added that the sequencing of step S60 and step S70 can be step S70 after first step S60,
It is also possible to step S60 after first step S70, is also possible to step S60 and step S70 while carrying out, do not do any restriction herein.
In addition, being stored on the computer readable storage medium the present invention also provides a kind of computer readable storage medium
There is data exception recognizer, is realized as described in any one of above-mentioned when the data exception recognizer is executed by processor
The step of medical data exception recognition methods based on machine learning.
Computer readable storage medium specific embodiment of the present invention and the above-mentioned medical data exception based on machine learning are known
Each embodiment of other method is essentially identical, and in this not go into detail.
It should be noted that, in this document, the terms "include", "comprise" or its any other variant are intended to non-row
His property includes, so that the process, method, article or the device that include a series of elements not only include those elements, and
And further include other elements that are not explicitly listed, or further include for this process, method, article or device institute it is intrinsic
Element.In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that including being somebody's turn to do
There is also other identical elements in the process, method of element, article or device.
The serial number of the above embodiments of the invention is only for description, does not represent the advantages or disadvantages of the embodiments.
Through the above description of the embodiments, those skilled in the art can be understood that above-described embodiment side
Method can be realized by means of software and necessary general hardware platform, naturally it is also possible to by hardware, but in many cases
The former is more preferably embodiment.Based on this understanding, technical solution of the present invention substantially in other words does the prior art
The part contributed out can be embodied in the form of software products, which is stored in a readable storage medium
In matter (such as ROM/RAM, magnetic disk, CD), including some instructions are used so that a terminal (can be mobile phone, computer, service
Device, air conditioner or network equipment etc.) method that executes each embodiment of the present invention.
The embodiment of the present invention is described with above attached drawing, but the invention is not limited to above-mentioned specific
Embodiment, the above mentioned embodiment is only schematical, rather than restrictive, those skilled in the art
Under the inspiration of the present invention, without breaking away from the scope protected by the purposes and claims of the present invention, it can also make very much
Form, it is all using equivalent structure or equivalent flow shift made by description of the invention and accompanying drawing content, directly or indirectly
Other related technical areas are used in, all of these belong to the protection of the present invention.
The above is only a preferred embodiment of the present invention, is not intended to limit the scope of the invention, all to utilize this hair
Equivalent structure or equivalent flow shift made by bright specification and accompanying drawing content is applied directly or indirectly in other relevant skills
Art field, is included within the scope of the present invention.
Claims (10)
1. a kind of medical data exception recognition methods based on machine learning, which is characterized in that the medical data exception identification
Method the following steps are included:
It receives that medical institutions acquires and the history of insured people that uploads is gone to a doctor data, and is crucial with preset Chinese physical therapy project
Word filters out the medical data containing the Chinese physical therapy project from the medical data of the history；
The medical Exception Model of Chinese medicine is established using machine learning mode as training sample using the medical data filtered out；
Obtain medical data to be detected, and by the medical data to be detected be directed into the Chinese medicine go to a doctor Exception Model carry out it is different
Common sense is other, obtains recognition result；
If the recognition result of the medical data to be detected is abnormal, it is determined that the medical data to be detected are Chinese medicine project mistake
Degree medical treatment.
2. medical data exception recognition methods as described in claim 1, which is characterized in that described with Chinese physical therapy project is to close
Key word filters out the medical data containing the Chinese physical therapy project from the medical data of the history, comprising:
According to the data content of the medical data of the history, and using preset Chinese physical therapy project as keyword, rejecting is not contained
The medical data of the history of the Chinese physical therapy project, wherein the data content includes at least consultation time, visit symptom, reason
Treatment project and physical therapy expense；
According to the consultation time and Chinese physical therapy project in the medical data of remaining history, counts and obtain odd-numbered day Chinese physical therapy number
With moon Chinese physical therapy number；
Whether the odd-numbered day Chinese physical therapy number and/or moon Chinese physical therapy number for judging the medical data of history are greater than or equal to default threshold
Value；
If history is gone to a doctor, the odd-numbered day Chinese physical therapy number and/or moon Chinese physical therapy number of data are greater than or equal to preset threshold,
History data of going to a doctor are labeled as abnormal medical data；
If history is gone to a doctor, the odd-numbered day Chinese physical therapy number and/or moon Chinese physical therapy number of data are less than preset threshold, by history
Medical data are labeled as data of normally going to a doctor；
The medical data of the exception of mark and the normally medical data are merged, medical data are obtained.
3. medical data exception recognition methods as described in claim 1, which is characterized in that the medical data to filter out
As training sample, using machine learning mode, establishing the medical Exception Model of Chinese medicine includes:
Processing is extracted to the medical data filtered out, obtains characteristic parameter and timing information；
It is gone to a doctor using the history of insured people corresponding to the medical data that filter out data of going to a doctor as input quantity, with this data processing
Obtained characteristic parameter and timing information is as output quantity, using recurrent neural network to the input quantity and the output quantity
Value sample is trained, and obtains the medical Exception Model of Chinese medicine.
4. medical data exception recognition methods as claimed in claim 3, which is characterized in that the described pair of medical data filtered out
Processing is extracted, characteristic parameter is obtained and timing information includes:
Data cleansing, participle slice are successively carried out to the medical data filtered out and remove unrelated word, obtains entry, and to institute
Predicate item carries out feature extraction, obtains characteristic parameter；
According to time location of the characteristic parameter in medical data, the corresponding timing information of characteristic parameter is determined.
5. medical data exception recognition methods as claimed in claim 3, which is characterized in that obtain the medical exception of Chinese medicine described
After the operation of model, the medical data exception recognition methods further include:
Cross validation or random intersection Holdout verifying are rolled over using K to verify the medical Exception Model of the Chinese medicine, are tested
Card is as a result, so that maintenance personnel safeguards or improves to the medical Exception Model of the Chinese medicine according to the verification result.
6. medical data exception recognition methods as described in claim 1, which is characterized in that described by the medical number to be detected
Anomalous identification is carried out according to the medical Exception Model of the Chinese medicine is directed into, obtaining recognition result includes:
The medical data to be detected are directed into the medical Exception Model of the Chinese medicine and carry out anomalous identification, it is described to be checked to obtain
Survey the index value of medical data；
Judge whether the index value is greater than or equal to preset standard value；
If the index value is greater than or equal to preset standard value, it is determined that the recognition result of the medical data to be detected is different
Often；
If the index value is less than preset standard value, it is determined that the recognition result of the medical data to be detected is normal.
7. medical data exception recognition methods as described in claim 1, which is characterized in that obtain medical number to be detected described
According to, and the medical data to be detected are directed into the medical Exception Model of the Chinese medicine and carry out anomalous identification, obtain recognition result
Operation after, the medical data exception recognition methods further include:
The medical data to be detected are merged with the medical data filtered out, to update the medical abnormal mould of the Chinese medicine
The training sample of type；
According to the training sample of update, the medical Exception Model of the Chinese medicine is further trained, to be updated
Chinese medicine go to a doctor Exception Model.
8. such as medical data exception recognition methods of any of claims 1-7, which is characterized in that if described
The recognition result of medical data to be detected is abnormal, it is determined that the medical data to be detected are the behaviour of Chinese medicine project over-treatment
After work, the medical data exception recognition methods further include:
Obtain the corresponding insured people's information of the abnormal medical data to be detected and medical institutions' information；
According to insured people's information, right-safeguarding notice is issued to insured people；
According to medical institutions' information, the notice that notifies and/or deduct fees in violation of rules and regulations is issued to medical institutions.
9. a kind of medical data exception identifies equipment, which is characterized in that the medical data exception identification equipment includes: storage
Device, processor and it is stored in the data exception recognizer that can be run on the memory and on the processor, the number
It is realized when being executed according to anomalous identification program by the processor and is based on machine learning as described in any item of the claim 1 to 8
Medical data exception recognition methods the step of.
10. a kind of computer readable storage medium, which is characterized in that it is different to be stored with data on the computer readable storage medium
Normal recognizer is realized when the data exception recognizer is executed by processor as described in any item of the claim 1 to 8
The step of medical data exception recognition methods based on machine learning.
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